cssi/SciSciGPT-SciSciNet huggingface dataset:
Enumeration of coalescent histories for caterpillar species trees and p-pseudocaterpillar gene trees.

Abstract For a fixed set X containing n taxon labels, an ordered pair consisting of a gene tree topology G and a species tree topology S bijectively labeled with the labels of X possesses a set of coalescent histories—mappings from the set of internal nodes of G to the set of edges of S describing possible lists of edges in S on which the coalescences in G take place. Enumerations of coalescent histories for gene trees and species trees have produced suggestive results regarding the pairs ( G , S ) that, for a fixed n, have the largest number of coalescent histories. We define a class of 2-cherry binary tree topologies that we term p-pseudocaterpillars, examining coalescent histories for non-matching pairs ( G , S ) in the case in which S has a caterpillar shape and G has a p-pseudocaterpillar shape. Using a construction that associates coalescent histories for ( G , S ) with a class of “roadblocked” monotonic paths, we identify the p-pseudocaterpillar labeled gene tree topology that, for a fixed caterpillar labeled species tree topology, gives rise to the largest number of coalescent histories. The shape that maximizes the number of coalescent histories places the “second” cherry of the p-pseudocaterpillar equidistantly from the root of the “first” cherry and from the tree root. A symmetry in the numbers of coalescent histories for p-pseudocaterpillar gene trees and caterpillar species trees is seen to exist around the maximizing value of the parameter p. The results provide insight into the factors that influence the number of coalescent histories possible for a given gene tree and species tree.
Thermal fluctuations and carrier localization induced by dynamic disorder in MAPbI3 described by a first-principles based tight-binding model

Author(s): Abramovitch, David J; Saidi, Wissam A; Tan, Liang Z | Abstract: Halide perovskites are strongly influenced by large amplitude anharmonic lattice fluctuations at room temperature. We develop a tight binding model for dynamically disordered MAPbI$_3$ based on density functional theory (DFT) calculations to calculate electronic structure for finite temperature crystal structures at the length scale of thermal disorder and carrier localization. The model predicts individual Hamiltonian matrix elements and band structures with high accuracy, owing to the inclusion of additional matrix elements and descriptors for non-Coulombic interactions. We apply this model to electronic structure at length and time scales inaccessible to first principles methods, finding an increase in band gap, carrier mass, and the sub-picosecond fluctuations in these quantities with increasing temperature as well as the onset of carrier localization in large supercells induced by thermal disorder at 300 K. We identify the length scale $L^*= 5$ nm as the onset of localization in the electronic structure, associated with associated with decreasing band edge fluctuations, increasing carrier mass, and Rashba splitting approaching zero.
How COVID-19 vaccine supply chains emerged in the midst of a pandemic.

Many months after COVID-19 vaccines were first authorized for public use, still limited supplies could only partially reduce the devastating loss of life and economic costs caused by the pandemic. Could additional vaccine doses have been manufactured more quickly some other way? Would alternative policy choices have made a difference? This paper provides a simple analytical framework through which to view the contours of the vaccine value chain. It then creates a new database that maps the COVID-19 vaccines of Pfizer/BioNTech, Moderna, AstraZeneca/Oxford, Johnson & Johnson, Novavax and CureVac to the product- and location-specific manufacturing supply chains that emerged in 2020 and 2021. It describes the choppy process through which dozens of other companies at nearly 100 geographically distributed facilities came together to scale up global manufacturing. The paper catalogues major pandemic policy initiatives-such as the United States' Operation Warp Speed-that are likely to have affected the timing and formation of those vaccine supply chains. Given the data, a final section identifies further questions for researchers and policymakers.
Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer 'grammar' in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.
First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions

Machine learning algorithms typically perform optimization over a class of non-convex functions. In this work, we provide bounds on the fundamental hardness of identifying the global minimizer of a non convex function. Specifically, we design a family of parametrized non-convex functions and employ statistical lower bounds for parameter estimation. We show that the parameter estimation problem is equivalent to the problem of function identification in the given family. We then claim that non convex optimization is at least as hard as function identification. Jointly, we prove that any first order method can take exponential time to converge to a global minimizer.
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization

Structured optimization problems arise in many applications. To efficiently solve these problems, it is important to leverage the structure information in the algorithmic design. This paper focuses...
Innate Organization of the Human Brain

The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We also quantified neonate individual variability, finding low variability for sensorimotor, but high for ventral attention networks. These neonate networks resembled adult networks (Yeo et al., 2011), but frontoparietal and limbic networks found in adults were indiscernible in neonates. Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks. Our results reveal the basic proto-organization of cortex at birth, but indicate a role for maturation and experience in developing adult-like functional brain organization.
Reduced Dopamine Signaling Impacts Pyramidal Neuron Excitability in Mouse Motor Cortex.

Dopaminergic modulation is essential for the control of voluntary movement, however the role of dopamine in regulating the neural excitability of the primary motor cortex (M1) is not well understood. Here, we investigated two modes by which dopamine influences the input/output function of M1 neurons. To test the direct regulation of M1 neurons by dopamine, we performed whole-cell recordings of excitatory neurons and measured excitability before and after local, acute dopamine receptor blockade. We then determined if chronic depletion of dopaminergic input to the entire motor circuit, via a mouse model of Parkinson’s disease, was sufficient to shift M1 neuron excitability. We show that D1 and D2 receptor (D1R, D2R) antagonism altered subthreshold and suprathreshold properties of M1 pyramidal neurons in a layer-specific fashion. The effects of D1R antagonism were primarily driven by changes to intrinsic properties, while the excitability shifts following D2R antagonism relied on synaptic transmission. In contrast, chronic depletion of dopamine to the motor circuit with 6-hydroxydopamine (6OHDA) induced layer-specific synaptic transmission-dependent shifts in M1 neuron excitability that only partially overlapped with the effects of acute D1R antagonism. These results suggest that while acute and chronic changes in dopamine modulate the input/output function of M1 neurons, the mechanisms engaged are distinct depending on the duration and origin of the manipulation. Our study highlights dopamine’s broad influence on M1 excitability by demonstrating the consequences of local and global dopamine depletion on neuronal input/output function. Significance statement Dopaminergic signaling is crucial for the control of voluntary movement, and loss of dopaminergic transmission in the motor circuit is thought to underlie motor symptoms in those with Parkinson’s disease (PD). Studies in animal models of PD highlight changes in M1 activity following dopamine depletion, however the mechanisms underlying this phenomenon remain poorly understood. Here we show that diminished dopamine signaling significantly alters the excitability and input/output function of M1 pyramidal neurons. The effects differed depending on the mode and location – local versus across the motor pathway - of the dopamine manipulation. Our results demonstrate how loss of dopamine can engage complex mechanisms to alter M1 neural activity.
LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms

Modern vehicles can be thought of as complex distributed embedded systems that run a variety of automotive applications with real-time constraints. Recent advances in the automotive industry towards greater autonomy are driving vehicles to be increasingly connected with various external systems (e.g., roadside beacons, other vehicles), which makes emerging vehicles highly vulnerable to cyber-attacks. Additionally, the increased complexity of automotive applications and the in-vehicle networks results in poor attack visibility, which makes detecting such attacks particularly challenging in automotive systems. In this work, we present a novel anomaly detection framework called LATTE to detect cyber-attacks in Controller Area Network (CAN) based networks within automotive platforms. Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with novel attention mechanisms to learn the normal operating behavior at design time. Subsequently, a novel detection scheme (also trained at design time) is used to detect various cyber-attacks (as anomalies) at runtime. We evaluate our proposed LATTE framework under different automotive attack scenarios and present a detailed comparison with the best-known prior works in this area, to demonstrate the potential of our approach.
Distribution of Panama’s narrow-range trees: are there hot-spots?

Tree species with narrow ranges are a conservation concern because heightened extinction risk accompanies their small populations. Assessing risks for these species is challenging, however, especially in tropical flora where their sparse populations seldom appear in traditional plots and inventories. Here, we utilize instead large scale databases that combine tree records from many sources to test whether the narrow-range tree species of Panama are concentrated at certain elevations or in certain provinces. Past investigations have suggested that the Choco region of eastern Panama and the high mountains of western Panama may be potential hotspots of narrow-range tree species. All individual records were collected from public databases, and the range size of each tree species found in Panama was estimated as a polygon enclosing all its locations. Species with ranges <20,000 km2 were defined as narrow endemics. We divided Panama into geographic regions and elevation zones and counted the number of individual records and the species richness in each, separating narrow-range species from all other species. The proportion of narrow endemics peaked at elevations above 2000 m, reaching 17.2% of the species recorded. At elevation <1500 m across the country, the proportion was 6-11%, except in the dry Pacific region, where it was 1.5%. Wet forests of the Caribbean coast had 8.4% narrow-range species, slightly higher than other regions. The total number of narrow endemics, however, peaked at mid-elevation, not high elevation, because total species richness was highest at mid-elevation. High elevation forests of west Panama had higher proportions of narrow endemic trees than low-elevation regions, supporting their hot-spot status, while dry lowland forests had the lowest proportion. This supports the notion that montane forests of Central America should be a conservation focus. However, given generally higher diversity at low- to mid-elevation, lowlands are also important habitats for narrow-range tree species, though conservation efforts here may not protect narrow-range tree species as efficiently.
Investigating the Relationship between (3200) Phaethon and (155140) 2005 UD through Telescopic and Laboratory Studies

The relationship between the Near-Earth Objects (3200) Phaethon and (155140) 2005 UD is unclear. While both are parents to Meteor Showers, (the Geminids and Daytime Sextantids, respectively), have similar visible-wavelength reflectance spectra and orbits, dynamical investigations have failed to find any likely method to link the two objects in the recent past. Here we present the first near-infrared reflectance spectrum of 2005 UD, which shows it to be consistently linear and red-sloped unlike Phaethon's very blue and concave spectrum. Searching for a process that could alter some common starting material to both of these end states, we hypothesized that the two objects had been heated to different extents, motivated by their near-Sun orbits, the composition of Geminid meteoroids, and previous models of Phaethon's surface. We thus set about building a new laboratory apparatus to acquire reflectance spectra of meteoritic samples after heating to higher temperatures than available in the literature to test this hypothesis and were loaned a sample of the CI Chondrite Orgueil from the Vatican Meteorite Collection for testing. We find that while Phaethon's spectrum shares many similarities with different CI Chondrites, 2005 UD's does not. We thus conclude that the most likely relationship between the two objects is that their similar properties are only by coincidence as opposed to a parent-fragment scenario, though the ultimate test will be when JAXA's DESTINY+ mission visits one or both objects later this decade. We also discuss possible paths forward to understanding Phaethon's properties from dynamical and compositional grounds.
Pandemic Panic: The Effect of Disaster-Related Stress on Negotiation Outcomes

Prior research often finds increased altruism following natural disasters. One explanation is the social heuristic hypothesis: humans are prosocial by nature but become self-interested when they have the opportunity to deliberate. As the stress of a disaster lowers people's ability to engage in effortful deliberation, their heuristic prosocial tendencies emerge. However, this link has often been explored with very simple tasks like the dictator game. Here we study the impact of COVID-related stress on outcomes in multi-issue negotiations with a computational virtual agent. These tasks are interesting because they share some of the characteristics of dictator games (some pot of resources must be divided) but they also involve presumably effortful perspective taking (that can grow the size of the pot). Furthermore, the interaction of humans with virtual agents allows us to explore the extent to which humans apply the CASA (computers as social actors) paradigm to negotiation when under considerable stress. In two experiments with a virtual negotiation partner, we provide evidence for two distinct pathways for how COVID-19 stress shapes prosocial behavior. Consistent with the social heuristic hypothesis, COVID-stress increases giving, mediated by heuristic thinking. But COVID-stress also seems to enhance information-exchange and perspective taking, which allowed participants to grow more value which they could give away. Our results give new insights into the relationship between stress, cognition, and prosocial behavior.
Matching Drivers to Riders: A Two-Stage Robust Approach

Matching demand (riders) to supply (drivers) efficiently is a fundamental problem for ride-hailing platforms who need to match the riders (almost) as soon as the request arrives with only partial knowledge about future ride requests. A myopic approach that computes an optimal matching for current requests ignoring future uncertainty can be highly sub-optimal. In this paper, we consider a two-stage robust optimization framework for this matching problem where future demand uncertainty is modeled using a set of demand scenarios (specified explicitly or implicitly). The goal is to match the current request to drivers (in the first stage) so that the cost of first stage matching and the worst-case cost over all scenarios for the second stage matching is minimized. We show that this two-stage robust matching is NP-hard under both explicit and implicit models of uncertainty. We present constant approximation algorithms for both models of uncertainty under different settings and show they improve significantly over standard greedy approaches.
Tune It the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density

Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achieving high accuracy and avoiding negative transfer. Supervised hyper-parameter validation is not possible without labeled target data, which raises the question: How can we validate unsupervised adaptation techniques in a realistic way? We first empirically analyze existing criteria and demonstrate that they are not very effective for tuning hyper-parameters. Intuitively, a well-trained source classifier should embed target samples of the same class nearby, forming dense neighborhoods in feature space. Based on this assumption, we propose a novel unsupervised validation criterion that measures the density of soft neighborhoods by computing the entropy of the similarity distribution between points. Our criterion is simpler than competing validation methods, yet more effective; it can tune hyper-parameters and the number of training iterations in both image classification and semantic segmentation models. The code used for the paper will be available at \url{this https URL}.
The shape of warm glow: Field experimental evidence from a fundraiser

Abstract Theory commonly posits agents who care both for the level of public good provision and the extent to which they personally contribute to the cause. Simply put, agents feel “warm glow” from the donations they make. I discuss a lottery fundraiser conducted in the field devised to exogenously vary the incentive to give and identify the structural parameters of warm glow. Estimates suggest that for participants claiming warm glow as their primary motivation, its shape is increasing and concave. Nevertheless, welfare analysis suggests that warm glow is unlikely to be the only important factor in the decision to give.
Proton mass decomposition and hadron cosmological constant

Lattice results on sigma terms and global analysis of parton momentum fractions are used to give the quark and glue fractions of the proton mass and rest energy. The mass decomposition in terms of the trace of the energy-momentum tensor is renormalization group invariant. The decomposition of the rest energy from the Hamiltonian and the gravitational form factors are scheme and scale dependent. The separation of the energy-momentum tensor into the traceless part, which is composed of the quark and glue parton momentum fractions, and the trace part has the minimum scheme dependence. We identify the glue part of the trace anomaly $⟨{H}_{\ensuremath{\beta}}⟩$ as the vacuum energy from the glue condensate in the vacuum. From the metric term of the gravitational form factors, which is the stress part of the stress-energy-momentum tensor, we find that the trace part of the rest energy, dominated by $⟨{H}_{\ensuremath{\beta}}⟩$, gives a constant restoring pressure that balances that from the traceless part of the Hamiltonian to confine the hadron, much like the cosmological constant Einstein introduced for a static universe. From a lattice calculation of $⟨{H}_{\ensuremath{\beta}}⟩$ in the charmonium, we deduce the associated string tension, which turns out to be in good agreement with that from a Cornell potential, which fits the charmonium spectrum.
More on change-making and related problems

Abstract Given a set of n integer-valued coin types and a target value t, the well-known change-making problem asks for the minimum number of coins that sum to t, assuming an unlimited number of coins in each type. In the more general all-targets version of the problem, we want the minimum number of coins summing to j, for every j = 0 , … , t . We obtain a number of new results on these problems, including (i) an algorithm for the all-targets problem with running time O ˜ ( t 4 / 3 ) (improving a previous O ˜ ( t 3 / 2 ) -time algorithm), (ii) a very simple O ˜ ( u 2 + t ) -time algorithm for the all-targets problem, where u denotes the maximum coin value, and (iii) an algorithm for the original (single-target) problem with running time O ˜ ( u ) (improving a previous O ˜ ( t ) -time algorithm by Chan and He (SOSA'20)). We also describe applications to the (single-capacity) unbounded knapsack problem and the minimum word break problem.
Automated evolution of feature logging statement levels using Git histories and degree of interest

Abstract Logging—used for system events and security breaches to describe more informational yet essential aspects of software features—is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, 1 transforms source code to revitalize feature log levels based on the “interestingness” of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with ∼3 million lines of code and ∼4K log statements. Our tool successfully analyzes 99.22% of logging statements, increases log level distributions by ∼ 20 % , and increases the focus of logs in bug fix contexts ∼ 83 % of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels.
Through the eyes of the founder: CEO characteristics and firms’ regulatory filings

We examine whether textual attributes of firms’ regulatory filings reflect CEO characteristics and whether investors consider this relation when assessing firm value. We build on prior research that shows founders have unique personality attributes, particularly overoptimism. We find that 10-K text for founder-led firms is characterized by “excess” optimism relative to current and future realized earnings and relative to non-founder-led firms. The effect is mitigated for firms with large auditors, high litigation risk and high analyst following. Based on stock price at the 10-K release, investors do not appear to appropriately discount the tone, resulting in predictable negative returns during the year subsequent to the 10-K release, particularly for the first two years after firms go public. Collectively, our findings contribute to the existing literature examining the effects of executives on firm disclosure by providing initial evidence about the conditions under which firms’ scripted narrative disclosures reflect CEO characteristics.
Kinesin-binding protein remodels the kinesin motor to prevent microtubule binding.

Kinesins are regulated in space and time to ensure activation only in the presence of cargo. Kinesin-binding protein (KIFBP), which is mutated in Goldberg-Shprintzen syndrome, binds to and inhibits...
Chern-Weil Global Symmetries and How Quantum Gravity Avoids Them

We draw attention to a class of generalized global symmetries, which we call “Chern-Weil global symmetries,” that arise ubiquitously in gauge theories. The Noether currents of these Chern-Weil global symmetries are given by wedge products of gauge field strengths, such as F2 ∧ H3 and tr( $$ {F}_2^2 $$ ), and their conservation follows from Bianchi identities. As a result, they are not easy to break. However, it is widely believed that exact global symmetries are not allowed in a consistent theory of quantum gravity. As a result, any Chern-Weil global symmetry in a low-energy effective field theory must be either broken or gauged when the theory is coupled to gravity. In this paper, we explore the processes by which Chern-Weil symmetries may be broken or gauged in effective field theory and string theory. We will see that many familiar phenomena in string theory, such as axions, Chern-Simons terms, worldvolume degrees of freedom, and branes ending on or dissolving in other branes, can be interpreted as consequences of the absence of Chern-Weil symmetries in quantum gravity, suggesting that they might be general features of quantum gravity. We further discuss implications of breaking and gauging Chern-Weil symmetries for particle phenomenology and for boundary CFTs of AdS bulk theories. Chern-Weil global symmetries thus offer a unified framework for understanding many familiar aspects of quantum field theory and quantum gravity.
Data Sharing and Compression for Cooperative Networked Control

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for mean prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are co-designed with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least $25\%$ while transmitting $80\%$ less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem.
A Recursive Algorithm for Solving the Generalized Velocities From the Momenta of Flexible Multibody Systems

Hamilton's equations can be used to define the dynamics of a tree configured flexible multibody system. Their states are the generalized coordinates and momenta (p, q). Numerical solution of these equations requires the time derivatives of the states be defined. Hamilton's equations have the benefit that the time derivative of the system momenta are easy to compute. However, the generalized velocities q need be solved from the system momenta as defined by p = J(q)q to support the computation of p and the propagation of q. Because of the size of J, the determination of q by linear equation solution schemes requires order ([N + Σ N i=1 n i ] 3 ) arithmetic operations, where N is the number of bodies and n i is the number of mode shape functions used to model the ith body deformations. It has been shown that q can be solved recursively from the momentum equations for rigid multibody systems (Naudet, Lefeber, and Terze, 2003, "Forward Dynamics of Open-Loop Multibody Mechanisms Using An Efficient Recursive Algorithm Based On Canonical Momenta," Multibody Syst. Dyn., 10, pp. 45―59). This paper extends that result to flexible multibody systems. The overall arithmetic operations to solve for q in this case is proportional to N if the effort to solve for the flexible coordinate rates for each body is weighted the same as that for the joint rate. However, each time the flexible coordinates rate of a body is solved an order (n 3 i ) operations is incurred. Thus, the total computational effort for flexible multibody systems includes an additional order (Σ N i=1 n 3 i ) operations.
Discovery of aryl aminothiazole γ-secretase modulators with novel effects on amyloid β-peptide production.

Abstract A series of analogs based on a prototype aryl aminothiazole γ-secretase modulator (GSM) were synthesized and tested for their effects on the profile of 37-to-42-residue amyloid β-peptides (Aβ), generated through processive proteolysis of precursor protein substrate by γ-secretase. Certain substitutions on the terminal aryl D ring resulted in an altered profile of Aβ production compared to that seen with the parent molecule. Small structural changes led to concentration-dependent increases in Aβ37 and Aβ38 production without parallel decreases in their precursors Aβ40 and Aβ42, respectively. The new compounds therefore apparently also stimulate carboxypeptidase trimming of Aβ peptides ≥ 43 residues, providing novel chemical tools for mechanistic studies of processive proteolysis by γ-secretase.
Calibration and Performance of the REgolith X-Ray Imaging Spectrometer (REXIS) Aboard NASA’s OSIRIS-REx Mission to Bennu

The REgolith X-ray Imaging Spectrometer (REXIS) instrument on board NASA’s OSIRIS-REx mission to the asteroid Bennu is a Class-D student collaboration experiment designed to detect fluoresced X-rays from the asteroid’s surface to measure elemental abundances. In July and November 2019 REXIS collected ∼615 hours of integrated exposure time of Bennu’s sun-illuminated surface from terminator orbits. As reported in Hoak et al. (Results from the REgolith X-ray Imaging Spectrometer (REXIS) at Bennu, 2021) the REXIS data do not contain a clear signal of X-ray fluorescence from the asteroid, in part due to the low incident solar X-ray flux during periods of observation. To support the evaluation of the upper limits on the detectable X-ray signal that may provide insights for the properties of Bennu’s regolith, we present an overview of the REXIS instrument, its operation, and details of its in-flight calibration on astrophysical X-ray sources. This calibration includes the serendipitous detection of the transient X-ray binary MAXI J0637-430 during Bennu observations, demonstrating the operational success of REXIS at the asteroid. We convey some lessons learned for future X-ray spectroscopy imaging investigations of asteroid surfaces.
Semi-implicit Hybrid Discrete $$\left( \text {H}^T_N\right) $$ Approximation of Thermal Radiative Transfer

The thermal radiative transfer (TRT) equations form an integro-differential system that describes the propagation and collisional interactions of photons. Computing numerical solutions of the TRT equations accurately and efficiently is challenging for several reasons, the first of which is that TRT is defined on a high-dimensional phase space that includes the independent variables of time, space, and velocity. In order to reduce the dimensionality of the phase space, classical approaches such as the P $$_N$$ (spherical harmonics) or the S $$_N$$ (discrete ordinates) ansatz are often used in the literature. In this work, we introduce a novel approach: the hybrid discrete (H $$^T_N$$ ) approximation to the radiative thermal transfer equations. This approach acquires desirable properties of both P $$_N$$ and S $$_N$$ , and indeed reduces to each of these approximations in various limits: H $$^1_N$$ $$\equiv $$ P $$_N$$ and H $$^T_0$$ $$\equiv $$ S $$_T$$ . We prove that H $$^T_N$$ results in a system of hyperbolic partial differential equations for all $$T\ge 1$$ and $$N\ge 0$$ . Another challenge in solving the TRT system is the inherent stiffness due to the large timescale separation between propagation and collisions, especially in the diffusive (i.e., highly collisional) regime. This stiffness challenge can be partially overcome via implicit time integration, although fully implicit methods may become computationally expensive due to the strong nonlinearity and system size. On the other hand, explicit time-stepping schemes that are not also asymptotic-preserving in the highly collisional limit require resolving the mean-free path between collisions, making such schemes prohibitively expensive. In this work we develop an asymptotic-preserving numerical method that is based on a nodal discontinuous Galerkin discretization in space, coupled with a semi-implicit discretization in time. In particular, we make use of a second order explicit Runge–Kutta scheme for the streaming term and an implicit Euler scheme for the material coupling term. Furthermore, in order to solve the material energy equation implicitly after each predictor and corrector step, we linearize the temperature term using a Taylor expansion; this avoids the need for an iterative procedure, and therefore improves efficiency. In order to reduce unphysical oscillation, we apply a slope limiter after each time step. Finally, we conduct several numerical experiments to verify the accuracy, efficiency, and robustness of the H $$^T_N$$ ansatz and the numerical discretizations.
Exercise-induced changes in climbing performance

Exercise is recommended to promote health and prevent a range of diseases. However, how exercise precipitates these benefits is unclear, nor do we understand why exercise responses differ so widely between individuals. We investigate how climbing ability in Drosophila melanogaster changes in response to an exercise treatment. We find extensive variation in baseline climbing ability and exercise-induced changes ranging from -13% to +20% in climbing ability. Climbing ability, and its exercise-induced change, is sex- and genotype-dependent. GWASs implicate 'cell-cell signalling' genes in the control of climbing ability. We also find that animal activity does not predict climbing ability and that the exercise-induced climbing ability change cannot be predicted from the activity level induced by the exercise treatment. These results provide promising new avenues for further research into the molecular pathways controlling climbing activity and illustrate the complexities involved in trying to predict individual responses to exercise.
Third-Degree Price Discrimination Versus Uniform Pricing

Abstract We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy. A uniform pricing policy offers the same price to all segments of the market. Our main result establishes that for a broad class of third-degree price discrimination problems with concave profit functions (in the price space) and common support, a uniform price is guaranteed to achieve one half of the optimal monopoly profits. This profit bound holds for any number of segments and prices that the seller might use under third-degree price discrimination. We establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor profit comparisons even for regular or monotone hazard rate distributions.
Hedgehog acts as a somatic stem cell factor in the Drosophila ovary

Secreted signalling molecules of the Hedgehog (Hh) family have many essential patterning roles during development of diverse organisms including Drosophila and humans1,2. Although Hedgehog proteins most commonly affect cell fate, they can also stimulate cell proliferation. In humans several distinctive cancers, including basal-cell carcinoma, result from mutations that aberrantly activate Hh signal transduction3. In Drosophila, Hh directly stimulates proliferation of ovarian somatic cells4,5,6. Here we show that Hh acts specifically on stem cells in the Drosophila ovary. These cells cannot proliferate as stem cells in the absence of Hh signalling, whereas excessive Hh signalling produces supernumerary stem cells. We deduce that Hh is a stem-cell factor and suggest that human cancers due to excessive Hh signalling might result from aberrant expansion of stem cell pools.
Health Services Research and Data Linkages: Issues, Methods, and Directions for the Future

Research on pressing health services and policy issues requires access to complete, accurate, and timely patient and organizational data. However, in the United States, health-related datasets are created and held by diverse—often unrelated—public and private organizations and individual researchers. To overcome incomplete data from a single source, skilled researchers take months or years to acquire, link, and extract meaningful information from a myriad of secondary datasets. Through data linkage it is possible to get more complete information without the time and cost burden of additional and often duplicate primary data collection. Typical core datasets that are often linked and the information they contain are described in Table 1. Table 1 Commonly Linked Databases We provide an overview of commonly linked files and describe a generic process for linking datasets. We categorize the major agents (i.e., who owns and controls data and who carries out the data linkage) into three areas: (1) individual investigators, (2) government-sponsored linked databases, and (3) public–private partnerships that facilitate use and linkage of data owned and controlled by private organizations. These different agents shape whether and how readily disparate datasets can be accessed, linked, and mined by researchers. We also describe challenges that may be encountered in the linkage process, and the benefits of combining secondary databases with primary qualitative and quantitative sources. Throughout the paper, we use cancer care research to illustrate our points. Cancer provides an excellent example because of its high prevalence and societal burden. As a result, several publicly and privately sponsored efforts have collaborated to create a data infrastructure that extends beyond traditional data systems. We conclude with recommendations to strengthen the existing data infrastructure. New strategies are needed to develop more accessible and comprehensive data systems to support the next generation of health services and policy research, including comparative effectiveness research.
The Contribution of Striations to the Eddy Energy Budget and Mixing: Diagnostic Frameworks and Results in a Quasigeostrophic Barotropic System with Mean Flow

AbstractLow-frequency oceanic motions have banded structures termed “striations.” Since these striations embedded in large-scale gyre flows can have large amplitudes, the authors investigated the effect of mean flow on their directions as well as their contribution to energetics and mixing using a β-plane, barotropic, quasigeostrophic ocean model. In spite of the model simplicity, striations are always found to exist regardless of the imposed barotropic mean flow. However, their properties are sensitive to the mean flow. Rhines jets move with the mean flow and are not necessarily striations. If the meridional component of the mean flow is large, Rhines jets become high-frequency motions; low-frequency striations still exist, but they are nonzonal, have small magnitudes, and contribute little to energetics and mixing. Otherwise, striations are zonal, dominated by Rhines jets, and contribute significantly to energetics and mixing. This study extends the theory of β-plane, barotropic turbulence, driven by wh...
Managing bleeding in cardiac surgery

Despite major advances in the management of perioperative blood conservation, transfusion rates in cardiac surgery remain very high, with large variations among individual centres. Significant differences in the approach to allogeneic red cell transfusion have been consistently observed among institutions in particular in cardiac surgery. The concept of blood conservation rests on three main pillars: (i) Increasing preoperative red blood cell mass, (ii) Decreasing peri-operative blood losses, and (iii) Optimizing transfusion practice. The acceptance of a lower perioperative haematocrit (in IJN the Hct on bypass is 20–25% and post bypass is >25%) could present an important element in current blood conservation practice. Other techniques include pharmacological (tranaxemic acid, aprotinin, desmopressin, rFVII) as well as non pharmacological techniques (PAD, ANH, red cell washing, tissue glue) as well as novel surgical and cardiology techniques (Off Pump surgery, minimal invasive surgery, stenting and other interventions).
BMP regulation of myogenesis in zebrafish

In amniotes, BMP signaling from lateral plate and dorsal neural tube inhibits differentiation of muscle precursors in the dermomyotome. Here, we show that BMPs are expressed adjacent to the dermomyotome during and after segmentation in zebrafish. In addition, downstream BMP pathway members are expressed within the somite during dermomyotome development. We also show that zebrafish dermomyotome is responsive to BMP throughout its development. Ectopic overexpression of Bmp2b increases expression of the muscle precursor marker pax3, and changes the time course of myoD expression. At later stages, overexpression increases the number of Pax7+ myogenic precursors, and delays muscle differentiation, as indicated by decreased numbers of MEF2+ nuclei, decreased number of multi-nucleated muscle fibers, and an increased myotome angle. In addition, we show that while BMP overexpression is sufficient to delay myogenic differentiation, inhibition of BMP does not detectably affect this process, suggesting that other factors redundantly inhibit myogenic differentiation.
Impact of lateral engineering on the logic performance of sub-50 nm InGaAs HEMTs

We have studied the combined impact of side-recess length and the gate length on the logic performance of sub-100 nm InGaAs HEMTs. We have found that increasing Lside significantly improves the short-channel effects and the logic performance of the device. Our experimental work confirms that lateral engineering at the drain side is essential for improving the electrostatic integrity of sub-100 nm gate length InGaAs HEMTs. The trade-off of widening Lside is the increase of Rs. With further device optimization in the form of reduced gate leakage current, self-aligned ohmic contacts and the development of a high performance p-channel device InGaAs-based FETs could well be the technology of choice when the Si CMOS roadmap comes to end.
Maintaining remission in ulcerative colitis – role of once daily extended-release mesalamine

The aminosalicylates (5-ASA; also referred to as mesalamine-based agents) are considered as first-line in the maintenance of remission of mild to moderate ulcerative colitis (UC). Traditionally these agents have required a large pill burden and multiple daily dosing regimens which may account for the low adherence rates, especially in patients in remission. Extended-release mesalamine is the first once daily mesalamine product approved by the Food and Drug Administration for the maintenance of UC remission. This review will examine the pharmacokinetics, dosing, efficacy, and safety data of extended-release mesalamine, and discuss the potential role of improving medication compliance and decreasing costs in UC maintenance.
Semiconductor Alloys: Local Bond Lengths, Mixing Enthalpies, and Microclusters

Several recent theoretical studies of the local structure of semiconductor alloys are summarized. First, dilute limit calculations of local bond lengths and mixing enthalpies are discussed. These calculations include effects due to both bond length and bond-angle distortions, as well as local chemical rearrangements. Then, a new statistical theory of concentrated alloys is described. Deviations from random alloy distributions (microclusters) are predicted.
Nutrition and health: patterns and policy perspectives in food-rich countries.

Nutrition and health are becoming legitimate policy issues in food-rich countries. Dietary patterns contribute to a large share of the dominant chronic disease profile in advanced industrial nations, burdening the public purse. Major issues facing governments concern decisions about the appropriate goals, scope, and instruments for effective policy, and the apparatus to promote it. Norway, using a long term, explicit and comprehensive public policy, and the U.S., using a market-oriented strategy, illustrate contrasting policy approaches to the issues. The experience of both have implications for current policy-making in other countries and for the kind of research needed to support it.
Oxymyoglobin and Lipid Oxidation in Yellowfin Tuna (Thunnus albacares) Loins

The present study characterized oxymyoglobin (OxyMb) and lipid oxidation in yellowfin tuna, and determined the influence of various aldehydes on tuna OxyMb stability in vitro. Surface metmyoglobin accumulation and lipid oxidation increased in tuna steaks with time (P < 0.05). In both tuna steaks and patties, a* values decreased (P < 0.05) and hue angle increased following 6 d storage (P < 0.05). Sensory evaluation demonstrated increased discoloration and decreased odor acceptability in refrigerated steaks and patties over time (P < 0.05). Hexanal, hexenal, and 4-hydroxynonenal enhanced OxyMb oxidation relative to controls in vitro (P < 0.05). These results indicate a relationship between myoglobin and lipid oxidation in tuna, and that aldehydes known to be produced during lipid oxidation can accelerate tuna OxyMb oxidation in vitro. Keywords: yellowfin tuna, oxymyoglobin, lipid oxidation, discoloration, aldehydes