cssi/SciSciGPT-SciSciNet huggingface dataset:
The lapse function in Friedmann—Lemaître–Robertson–Walker cosmologies

Abstract The Friedmann–Lemaitre–Robertson–Walker (FLRW) metric, the backbone of modern cosmology, is founded on the cosmological principle, which assumes homogeneity and isotropy throughout the cosmos. One of its simplifications is the choice of lapse function g t t = 1 , regardless of which stress–energy tensor T μ ν is used in Einstein’s field equations. It is sometimes argued that this selection is justified by gauge freedom, given that g t t in FLRW may be a function solely of t , not of the spatial coordinates, permitting a redefinition of the time. We show in this paper, however, that the comoving frame in the Hubble expansion is non inertial for all but a few special cases of the expansion factor a ( t ) . Changing the gauge changes the frame of reference and cannot alter this property of the expansion profile, since it would of necessity reformat the metric using the coordinates of a non-comoving observer. We therefore suggest that the pre-selection of g t t = 1 , independently of the equation of state in the cosmic fluid, incorrectly avoids the time dilation that ought to be present relative to the actual free-falling frame when a ≠ 0 .
HAQ: Hardware-Aware Automated Quantization With Mixed Precision

Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. There are plenty of specialized hardware for neural networks, but little research has been done for specialized neural network optimization for a particular hardware architecture. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals (latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design.
Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds

Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies.
Higher width moonshine

Abstract Weak moonshine for a finite group G is the phenomenon where an infinite dimensional graded G-module V G = ⨁ n ≫ − ∞ V G ( n ) has the property that its trace functions, known as McKay-Thompson series, are modular functions. Recent work by DeHority, Gonzalez, Vafa, and Van Peski established that weak moonshine holds for every finite group. Since weak moonshine only relies on character tables, which are not isomorphism class invariants, non-isomorphic groups can have the same McKay-Thompson series. We address this problem by extending weak moonshine to arbitrary width s ∈ Z + . For each 1 ≤ r ≤ s and each irreducible character χ i , we employ Frobenius' r-character extension χ i ( r ) : G ( r ) → C to define width r McKay-Thompson series for V G ( r ) : = V G × ⋯ × V G (r copies) for each r-tuple in G ( r ) : = G × ⋯ × G (r copies). These series are modular functions which then reflect differences between r-character values. Furthermore, we establish orthogonality relations for the Frobenius r-characters, which dictate the compatibility of the extension of weak moonshine for V G to width s weak moonshine.
Integral transform methods in goodness-of-fit testing, II: the Wishart distributions

We initiate the study of goodness-of-fit testing for data consisting of positive definite matrices. Motivated by the appearance of positive definite matrices in numerous applications, including factor analysis, diffusion tensor imaging, volatility models for financial time series, wireless communication systems, and polarimetric radar imaging, we apply the method of Hankel transforms of matrix argument to develop goodness-of-fit tests for Wishart distributions with given shape parameter and unknown scale matrix. We obtain the limiting null distribution of the test statistic and a corresponding covariance operator, show that the eigenvalues of the operator satisfy an interlacing property, and apply our test to some financial data. We establish the consistency of the test against a large class of alternative distributions and derive the asymptotic distribution of the test statistic under a sequence of contiguous alternatives. We obtain the Bahadur and Pitman efficiency properties of the test statistic and establish a modified version of Wieand’s condition.
Academic Stressors as Predictors of Achievement Goal Orientations of American and ESL International Students.

This study explores academic stressors and achievement goal orientations of college students, and the relationship between these variables using academic stressors as predictors. As the number of English as a Second Language (ESL) international students has increased rapidly in the US, students’ status (American or ESL international) was also examined. A total of 715 students participated in the study from two universities in the US. Results show that ESL international and American students have different achievement goal orientations and academic stressors. Additionally, student status and academic stressors predict college students’ various goal orientations. The implications provide useful suggestions to higher education professionals in order to better understand and assist diverse students to succeed in academia.
Increasing automation in policing

We know how artificial intelligence works in our lives: it helps in picking movies, choosing dates, and correcting misspellings. But what does it mean in policing? Is AI replacing traditional police tasks? Does the police use of AI present novel challenges? Should increasing police reliance on AI concern us? The answer to these questions is “Yes.” In the past decade the increasing reliance by police on artificial intelligence tools raises questions about how to strike the right balance between public safety and civil liberties.
Clonal reconstruction from time course genomic sequencing data

Bacterial cells during many replication cycles accumulate spontaneous mutations, which result in the birth of novel clones. As a result of this clonal expansion, an evolving bacterial population has different clonal composition over time, as revealed in the long-term evolution experiments (LTEEs). Accurately inferring the haplotypes of novel clones as well as the clonal frequencies and the clonal evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations. In this paper, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies observed from an evolving bacterial population at multiple time points. We formalize the problem using a maximum likelihood function, which is defined under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We develop a series of heuristic algorithms to address the maximum likelihood inference, and show through simulation experiments that the algorithms are fast and achieve near optimal accuracy that is practically plausible under the maximum likelihood framework. We also validate our method using experimental data obtained from a recent study on long-term evolution of Escherichia coli. We developed efficient algorithms to reconstruct the clonal evolution history from time course genomic sequencing data. Our algorithm can also incorporate clonal sequencing data to improve the reconstruction results when they are available. Based on the evaluation on both simulated and experimental sequencing data, our algorithms can achieve satisfactory results on the genome sequencing data from long-term evolution experiments. The program (ClonalTREE) is available as open-source software on GitHub at https://github.com/COL-IU/ClonalTREE.
Manganese Uptake, Mediated by SloABC and MntH, Is Essential for the Fitness of Streptococcus mutans.

ABSTRACT Early epidemiological studies implicated manganese (Mn) as a possible caries-promoting agent, while laboratory studies have indicated that manganese stimulates the expression of virulence-related factors in the dental pathogen Streptococcus mutans. To better understand the importance of manganese homeostasis to S. mutans pathophysiology, we first used RNA sequencing to obtain the global transcriptional profile of S. mutans UA159 grown under Mn-restricted conditions. Among the most highly expressed genes were those of the entire sloABC operon, encoding a dual iron/manganese transporter, and an uncharacterized gene, here mntH, that codes for a protein bearing strong similarity to Nramp-type transporters. While inactivation of sloC, which encodes the lipoprotein receptor of the SloABC system, or of mntH alone had no major consequence for the overall fitness of S. mutans, simultaneous inactivation of sloC and mntH (ΔsloC ΔmntH) impaired growth and survival under Mn-restricted conditions, including in human saliva or in the presence of calprotectin. Further, disruption of Mn transport resulted in diminished stress tolerance and reduced biofilm formation in the presence of sucrose. These phenotypes were markedly improved when cells were provided with excess Mn. Metal quantifications revealed that the single mutant strains contained intracellular levels of Mn similar to those seen with the parent strain, whereas Mn was nearly undetectable in the ΔsloC ΔmntH strain. Collectively, these results reveal that SloABC and MntH work independently and cooperatively to promote cell growth under Mn-restricted conditions and that maintenance of Mn homeostasis is essential for the expression of major virulence attributes in S. mutans. IMPORTANCE As transition biometals such as manganese (Mn) are essential for all forms of life, the ability to scavenge biometals in the metal-restricted host environment is an important trait of successful cariogenic pathobionts. Here, we showed that the caries pathogen Streptococcus mutans utilizes two Mn transport systems, namely, SloABC and MntH, to acquire Mn from the environment and that the ability to maintain the cellular levels of Mn is important for the manifestation of characteristics that associate S. mutans with dental caries. Our results indicate that the development of strategies to deprive S. mutans of Mn hold promise in the combat against this important bacterial pathogen.
A flexible procedure for mixture proportion estimation in positive‐unlabeled learning

Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] showed that MPE in PU learning is a generalization of the problem of estimating the proportion of true null hypotheses in multiple testing problems. Motivated by this idea, we propose reducing the problem to one dimension via construction of a probabilistic classifier trained on the P and U data sets followed by application of a one--dimensional mixture proportion method from the multiple testing literature to the observation class probabilities. The flexibility of this framework lies in the freedom to choose the classifier and the one--dimensional MPE method. We prove consistency of two mixture proportion estimators using bounds from empirical process theory, develop tuning parameter free implementations, and demonstrate that they have competitive performance on simulated waveform data and a protein signaling problem.
Round complexity of common randomness generation: the amortized setting

In this work we study the effect of rounds of interaction on the common randomness generation (CRG) problem. In the CRG problem, two parties, Alice and Bob, receive samples Xi and Yi, respectively, where (Xi, Yi) are drawn jointly from a source distribution μ. The two parties wish to agree on a common random key consisting of many bits of randomness, by exchanging messages that depend on each party's respective input and the previous messages. In this work we study the amortized version of the problem, i.e., the number of bits of communication needed per random bit output by Alice and Bob, in the limit as the number of bits generated tends to infinity. The amortized version of the CRG problem has been extensively studied in the information theory literature, though very little was known about the effect of interaction on this problem. Recently Bafna et al. (SODA 2019) considered the non-amortized version of the problem (so here the goal of the interaction is to generate a fixed number of random bits): they gave a family of sources μr,n parameterized by r, n ∈ N, such that with r + 2 rounds of communication one can generate n bits of common randomness with this source with O(r log n) communication, whereas with roughly r/2 rounds the communication complexity is Ω(n/ poly log n). Note in particular that their source is designed with the target number of bits in mind and hence the result does not apply to the amortized setting. In this work we strengthen the work of Bafna et al. in two ways: First we show that the results extend to the classical amortized setting. We also reduce the gap between the round complexity in the upper and lower bounds to an additive constant. Specifically we show that for every pair r, n ∈ N the (amortized) communication complexity to generate Ω(n) bits of common randomness from the source μr,n using r + 2 rounds of communication is O(r log n) whereas the amortized communication required to generate the same amount of randomness from r rounds is [MATH HERE]. Our techniques exploit known connections between information complexity and CRG, and the main novelty is our ability to analyze the information complexity of protocols getting inputs from the source μr,n.
Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development

Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional “shape-space” describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.
Rapid Numerical Approximation Method for Integrated Covariance Functions Over Irregular Data Regions

In many practical applications, spatial data are often collected at areal levels (i.e., block data) and the inferences and predictions about the variable at points or blocks different from those at which it has been observed typically depend on integrals of the underlying continuous spatial process. In this paper we describe a method based on Fourier transform by which multiple integrals of covariance functions over irregular data regions may be numerically approximated with the same level of accuracy to traditional methods, but at a greatly reduced computational expense.
The reconstruction of a wave equation from one side measurement

Abstract We are concerned with the reconstruction of a one dimensional wave equation, where the potential is known in a neighborhood of one of the end points of the boundary. We show then the sought potential can be determined by one single measurement of the solution at that end. We also show that the solution can be evaluated at the other end without the need of solving the wave equation.
Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Many studies have demonstrated covariation between muscle activations during behavior, suggesting that muscles are not controlled independently. According to one common proposal, this covariation reflects simplification of task performance by the nervous system so that muscles with similar contributions to task variables are controlled together. Alternatively, this covariation might reflect regulation of low-level aspects of movements that are common across tasks, such as stresses within joints. We examined these issues by analyzing covariation patterns in quadriceps muscle activity during locomotion in rats. The three monoarticular quadriceps muscles (vastus medialis [VM], vastus lateralis [VL], and vastus intermedius [VI]) produce knee extension and so have identical contributions to task performance; the biarticular rectus femoris (RF) produces an additional hip flexion. Consistent with the proposal that muscle covariation is related to similarity of muscle actions on task variables, we found that the covariation between VM and VL was stronger than their covariations with RF. However, covariation between VM and VL was also stronger than their covariations with VI. Since all vastii have identical actions on task variables, this finding suggests that covariation between muscle activity is not solely driven by simplification of overt task performance. Instead, the preferentially strong covariation between VM and VL is consistent with the control of internal joint stresses: Since VM and VL produce opposing mediolateral forces on the patella, the high positive correlation between their activation minimizes the net mediolateral patellar force. These results provide important insights into the interpretation of muscle covariations and their role in movement control.
Optimal Commissions and Subscriptions in Networked Markets

Problem definition: We consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions but does not directly control the transaction pric...
Exotic Spin-Dependent Interaction Searches at Indiana University

The axion is a hypothesized particle appearing in various theories beyond the Standard Model. It is a light spin-0 boson initially postulated to solve the strong CP problem and is also a strong candidate for dark matter. If the axion or an axion-like particle exists, it would mediate a P-odd and T-odd spin-dependent interaction. We describe two experiments under development at Indiana University-Bloomington to search for such an interaction.
The influence of wind-tunnel walls on discrete frequency noise

This paper describes an analytical model that can be used to examine the effects of wind-tunnel walls on discrete frequency noise. First, a complete physical model of an acoustic source in a wind tunnel is described, and a simplified version is then developed. This simplified model retains the important physical processes involved, yet it is more amenable to analysis. Second, the simplified physical model is formulated as a mathematical problem. An inhomogeneous partial differential equation with mixed boundary conditions is set up and then transformed into an integral equation. The integral equation has been solved with a panel program on a computer. Preliminary results from a simple model problem will be shown and compared with the approximate analytic solution.
A Deep Reinforcement Learning Approach to Multi-Component Job Scheduling in Edge Computing

We are interested in the optimal scheduling of a collection of multi-component application jobs in an edge computing system that consists of geo-distributed edge computing nodes connected through a wide area network. The scheduling and placement of application jobs in an edge system is challenging due to the interdependence of multiple components of each job, and the communication delays between the geographically distributed data sources and edge nodes and their dynamic availability. In this paper we explore the feasibility of applying Deep Reinforcement Learning (DRL) based design to address these challenges. We introduce a DRL actor-critic algorithm that aims to find an optimal scheduling policy to minimize average job slowdown in the edge system. We have demonstrated through simulations that our design outperforms a few existing algorithms, based on both synthetic data and a Google cloud data trace.
Staphylococcus aureus Evasion Proteins EapH1 and EapH2: Residue‐Level Investigation of an Alternative Binding Motif for Human Neutrophil Elastase

Abstract The Staphylococcus aureus E xtracellular A dherence P rotein (Eap) and its homologs, EapH1 and EapH2, are a family of secreted proteins that potently inhibit the neutrophil serine proteases Neutrophil Elastase (hNE), Cathepsin G, and Proteinase 3. Similarly to EapH1, inhibition of hNE by EapH2 is characterized by a rapid association rate (2.9 × 105 M−1s−1) coupled with a very slow dissociation rate (5.9 × 10−4 s−1), yielding an apparent inhibition constant of 2.11 nM. As with EapH1, inhibition of hNE by EapH2 is also time-dependent in character. A phenylalanine in EapH2 replaces the leucine in EapH1 that sits over the hNE catalytic serine and creates a potential steric clash. Indeed, the EapH1 L59F mutant is severely decreased in its ability to inhibit hNE (~9500-fold). When compared to the EapH1:hNE co-crystal structure, a model of the EapH2:hNE complex predicts an alternative binding motif comprised of EapH2 residues 120–127. These putative interfacing residues were individually mutated and kinetically interrogated. The EapH2 N127A mutant resulted in the largest decrease in hNE inhibition (~200-fold) and loss of the time-dependent characteristic. Surprisingly, the time-dependent characteristic was still abolished in the EapH2 T125A mutant, even though it was less perturbed in hNE inhibition (~25-fold). T125 forms an intra-molecular hydrogen bond to the carbonyl oxygen of N127 in the EapH2 crystal structure. Given these observations, we conclude (i) that EapH2 has an altogether distinct hNE binding motif than EapH1, (ii) that N127 is the main functional determinant in EapH2, and (iii) that T125 serves an ancillary role aiding in the optimal orientation of N127.
7 – Linear Equations and Inequalities

Publisher Summary This chapter focuses on linear equations and inequalities in two variables. There are two main types of variation—direct variation and inverse variation. Variation problems are most common in the sciences, particularly in chemistry and physics. If two variables are related so that an increase in one produces a proportional decrease in the other, then the variables are said to vary inversely. To graph a linear inequality, first the boundary is graphed. Any point, not on the boundary, is chosen and its coordinates are substituted into the original inequality. If the resulting statement is true, the graph lies on the same side of the boundary as the test point. A false statement indicates that the solution set lies on the other side of the boundary. Many times relationships among different quantities are described in terms of more than two variables.
Chapter 12 – Communication Systems

The preceding chapters have described the systems of communication that exist between the cells and tissues within individual multicellular organisms. Communication also occurs frequently between two or more individual insects. Intra- and interspecific communications are absolutely essential for an organism’s survival and reproduction. In order to reproduce, an insect must find and accurately identify the opposite sex, so that time and energy are not wasted on mating attempts with stray objects in the environment that would fail to result in the production of offspring. Insects are frequently the prey of other animals, and there has been intense selective pressure for the evolution of defensive communications to minimize this loss. Also, because the competition for resources is most vigorous among the members of one’s own species that occupy an identical ecological niche, communication with others is necessary to recognize this competition so that individuals can be identified and cooperated with, avoided, or possibly forced away from an area.
The non-optimality of proposed monetary policy rules under timeless perspective commitment

Abstract In a near-canonical forward looking model, the ‘timeless perspective’ form of commitment typically provides outcomes that are on average superior to those resulting from discretion. The specific condition that appears in the literature is, however, not optimal from the relevant perspective.
Endowment Origin, Demographic Effects, and Individual Preferences in Contests

In modern firms the use of contests as an incentive device is ubiquitous. Nonetheless, experimental research shows that in the laboratory subjects routinely make suboptimal decisions in contests even to the extent of making negative returns. The purpose of this study is to investigate how earning the endowment, demographic differences and individual preferences impact behavior in contests. To this end, we conduct a laboratory experiment in which subjects expend costly resources (bids) to attain an award (prize). In line with other laboratory studies of contests, our results show that subjects overbid relative to theoretical predictions and incur substantial losses as a result. Making subjects earn their initial resource endowments mitigates the amount of overbidding and thus increases efficiency. Overbidding is linked to gender, with women bidding higher than men and having lower average earnings. Other demographic information, such as religiosity, and individual preferences, such as preferences towards winning and risk, also influence behavior in contests.
Using Low-Calorie Orange Juice as a Dietary Alternative to Alkali Therapy.

Purpose: The pursuit of a dietary source to increase urine pH and citrate in stone formers has been ongoing for >30 years. Early evidence showed that orange juice (OJ) contains alkali and citrate, ...
Caffeine Use and Associations With Sleep in Adolescents With and Without ADHD.

Objective The objective of this study was to compare caffeine consumption in the morning, afternoon, and evening in adolescents with and without attention-deficit/hyperactivity disorder (ADHD) and examine associations with sleep functioning. Methods Participants were 302 adolescents (ages 12-14) with (N = 140) and without (N = 162) ADHD. Adolescents wore actigraph watches to assess total sleep time and wake after sleep onset and reported on sleep-wake problems and the number of caffeinated beverages consumed per day in the morning, afternoon, and evening. Parents reported on adolescents' difficulties initiating and maintaining sleep. Chi-square tests, odds ratios, and path analyses were conducted. Results Analyses controlled for sex, medication status, and pubertal development. Adolescents with ADHD were 2.47 times more likely to consume caffeine in the afternoon and evening than adolescents without ADHD. Path analyses indicated significant associations between afternoon caffeine use and more self-reported sleep problems for adolescents with and without ADHD, and an association between evening caffeine use and self-reported sleep problems only in adolescents with ADHD. Afternoon caffeine use was associated with parent-reported sleep problems in adolescents with ADHD only. Caffeine use was not associated with actigraphy-assessed sleep. Conclusion This is the first study to show that adolescents with ADHD consume more caffeine than peers during later times of the day. Additionally, caffeine use is more consistently associated with poorer subjective sleep functioning in adolescents with ADHD. Pediatricians and mental health professionals should assess for caffeine use in adolescents with ADHD and co-occurring sleep problems.
Financial Intermediary Capital

We propose a dynamic theory of financial intermediaries that are better able to collateralize claims than households, that is, have a collateralization advantage. Intermediaries require capital as they have to finance the additional amount that they can lend out of their own net worth. The net worth of financial intermediaries and the corporate sector are both state variables affecting the spread between intermediated and direct finance and the dynamics of real economic activity, such as investment, and financing. The accumulation of net worth of intermediaries is slow relative to that of the corporate sector. The model is consistent with key stylized facts about macroeconomic downturns associated with a credit crunch, namely, their severity, their protractedness, and the fact that the severity of the credit crunch itself affects the severity and persistence of downturns. The model captures the tentative and halting nature of recoveries from crises.
Nonlinear Mixed Effects Models for Longitudinal Data

The analysis of longitudinal data may require a mixed-effects model, incorporating parameters for fixed effects associated with the whole population and also parameters describing distributions of random effects associated with individual subjects. These may enter the model nonlinearly, as in compartment models used in pharmacokinetics. Maximum likelihood estimation is carried out by numerical optimization. Keywords: repeated measures; growth curves; pharmacokinetics; compartment models; random effects
Disruption of cross-feeding interactions by invading taxa can cause invasional meltdown in microbial communities

The strength of biotic interactions within an ecological community affects the susceptibility of the community to invasion by introduced taxa. In microbial communities, cross-feeding is a widespread type of biotic interaction that has the potential to affect community assembly and stability. Yet, there is little understanding of how the presence of cross-feeding within a community affects invasion risk. Here, I develop a metabolite-explicit model where native microbial taxa interact through both cross-feeding and competition for metabolites. I use this model to study how the strength of biotic interactions, especially cross-feeding, influence whether an introduced taxon can join the community. I found that stronger cross-feeding and competition led to much lower invasion risk, as both types of biotic interactions lead to greater metabolite scarcity for the invader. I also evaluated the impact of a successful invader on community composition and structure. The effect of invaders on the native community was greatest at intermediate levels of cross-feeding; at this 'critical' level of cross-feeding, successful invaders generally cause decreased diversity, decreased productivity, greater metabolite availability, and decreased quantities of metabolites exchanged among taxa. Furthermore, these changes resulting from a successful primary invader made communities further susceptible to future invaders. The increase in invasion risk was greatest when the network of metabolite exchange between taxa was minimally redundant. Thus, this model demonstrates a case of invasional meltdown that is mediated by initial invaders disrupting the metabolite exchange networks of the native community.
Thermionic Emission from a Planar Tantalum Crystal

In this investigation two (211) crystals in tantalum ribbon are used as emitter and collector in a three-element retarding-potential experiment. The electrons from one crystal are accelerated through a small aperture which, in conjunction with a strong axial magnetic field, collimates a beam and directs it to the collector crystal. The uniform work-function of single crystals, plane geometry, and the collimating magnetic field completely remove the deleterious effects of conventional retarding-potential experiments that prevent the exact determination of the energy distribution. The resulting retarding-potential plot indicates close agreement with the theoretically predicted two intersecting straight lines on semilogarithmic paper, with the transition region extending less than 20 mv. This indicates a Maxwellian distribution with no large energy-dependent reflection. A slowly varying 6% reflection, as predicted by quantum mechanics, is observed. The temperature, the saturated current density, and the temperature derivative of the work-function, as found from different retarding-potential plots, are used in a Richardson analysis to derive the thermionic constants. For the clean (211) tantalum surface that was used, the work-function, which is practically temperature-independent, is 4.352\ifmmode\pm\else\textpm\fi{}0.01 volts, and the emission constant $A$ is 120 amp/${\mathrm{cm}}^{2}$ \ifmmode^\circ\else\textdegree\fi{}${\mathrm{K}}^{2}$. For differing surfaces resulting from stable layers of foreign adsorbed atoms, the $A$, as calculated from a Richardson plot, was demonstrated to differ from 120 solely because of the temperature variation of the work-function.
Lorentz invariance relations for twist-3 quark distributions

We calculate twist-3 parton ditribution functions (PDFs) using cut and uncut diagrams. Uncut diagrams lead to a Dirac delta function term. No such term appears when cut diagrams are used. We show that a δ(x) is necessary to satisfy the Lorentz invariance relations of twist-3 PDFs, except for the Burkhardt-Cottingham sum rule in QCD.
Getting ahead in the race for a cure: How nonprofits are financing biomedical R&D

Abstract In recent years, nonprofit firms focused on specific diseases have increased their grantmaking efforts in the search of a cure. They have become more aggressive in directly funding research and lobbying for public support, even if their cause affects a small number of people. This paper contributes to the literatures on R&D financing by developing the first production function for disease and medical research nonprofits, a growing funder of biomedical R&D. Using IRS data, the model estimates the role of market competition and firm demographics on the adoption of grantmaking and lobbying strategies. Most notably, results provide evidence that firms in more geographically concentrated (less competitive markets) are more likely to adopt a lobbying strategy and less likely to be grantmaking on the extensive margin. Descriptive cases also illustrate funding discrepancies between charitable and government support across disease prevalence.
Linear Progress with Exponential Decay in Weakly Hyperbolic Groups

A random walk $w_n$ on a separable, geodesic hyperbolic metric space $X$ converges to the boundary $\partial X$ with probability one when the step distribution supports two independent loxodromics. In particular, the random walk makes positive linear progress. Progress is known to be linear with exponential decay when (1) the step distribution has exponential tail and (2) the action on $X$ is acylindrical. We extend exponential decay to the non-acylindrical case.
Promoting Latino Self-Management Through Use of Information Visualizations: A Case Study in New York City

Social determinants of health, including low health literacy and limited English proficiency, and other factors place Latinos at high risk for health disparities related to chronic diseases. Such diseases require self-management skills as well as medical management. Well-designed visualizations are suitable for visualizing data related to self-management because they can help narrow the comprehension gap between individuals with low and high levels of health literacy by leveraging existing visual analysis skills while reducing the demand on literacy and numeracy competencies. Use of information visualizations also supports a common visual representation across languages to address limited English proficiency. This report illustrates the use of information visualizations for communication related to self-management through research-based case examples and summarizes key lessons from studies with Latinos in New York City.
Copper diffusion characteristics in single crystal and polycrystalline TaN

TaN has become a very promising diffusion barrier material for Cu interconnections, due to the high thermal stability requirement and thickness limitation for next generation ULSI devices. TaN has a variety of phases and Cu diffusion characteristics vary with different phases and microstructures. We have investigated the diffusivity of copper in single-crystal (NaCl-structured) and polycrystalline TaN thin films grown by pulsed laser deposition. The polycrystalline TaN films were grown directly on Si(100), while the single crystal films were grown with TiN buffer layers. Both of poly and single-crystal films with Cu overlayers were annealed at 500 °C, 600 °C, 650 °C, and 700 °C in vacuum to study the copper diffusion characteristics. The diffusion of copper into TaN was studied using STEM-Z contrast, where the contrast is proportional to Z (atomic number), and TEM. The diffusion distances are found to be about 5nm at 650°C for 30 min annealing. The diffusivity of Cu into single crystal TaN follows the relation D = (160±9.5)exp[-(3.27 ±0.1) eV / k B T ] cm 2 s -1 in the temperature range of 600°C to 700°C. We observe that Cu diffusion in polycrystalline TaN thin films is nonuniform with enhanced diffusivities along the grain boundary.
Genome-wide studies reveal the essential and opposite roles of ARID1A in controlling human cardiogenesis and neurogenesis from pluripotent stem cells.

Early human heart and brain development simultaneously occur during embryogenesis. Notably, in human newborns, congenital heart defects strongly associate with neurodevelopmental abnormalities, suggesting a common gene or complex underlying both cardiogenesis and neurogenesis. However, due to lack of in vivo studies, the molecular mechanisms that govern both early human heart and brain development remain elusive. Here, we report ARID1A, a DNA-binding subunit of the SWI/SNF epigenetic complex, controls both neurogenesis and cardiogenesis from human embryonic stem cells (hESCs) through distinct mechanisms. Knockout-of-ARID1A (ARID1A−/−) leads to spontaneous differentiation of neural cells together with globally enhanced expression of neurogenic genes in undifferentiated hESCs. Additionally, when compared with WT hESCs, cardiac differentiation from ARID1A −/− hESCs is prominently suppressed, whereas neural differentiation is significantly promoted. Whole genome-wide scRNA-seq, ATAC-seq, and ChIP-seq analyses reveal that ARID1A is required to open chromatin accessibility on promoters of essential cardiogenic genes, and temporally associated with key cardiogenic transcriptional factors T and MEF2C during early cardiac development. However, during early neural development, transcription of most essential neurogenic genes is dependent on ARID1A, which can interact with a known neural restrictive silencer factor REST/NRSF. We uncover the opposite roles by ARID1A to govern both early cardiac and neural development from pluripotent stem cells. Global chromatin accessibility on cardiogenic genes is dependent on ARID1A, whereas transcriptional activity of neurogenic genes is under control by ARID1A, possibly through ARID1A-REST/NRSF interaction.
Geometric potentials from deep learning improve prediction of CDR H3 loop structures

Motivation Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. Results When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 A). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 A on the Rosetta antibody benchmark. Availability and implementation DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. Supplementary information Supplementary data are available at Bioinformatics online.
Leukoencephalopathy and cerebral edema as the presenting manifestations of SLE in an ANA-negative adolescent female: a case report and review of literature.

Systemic lupus erythematosus (SLE) is an autoimmune disease with various clinical manifestations involving multiple organ systems. Neuropsychiatric manifestations of SLE have been associated with increased morbidity and mortality, thus it is important to recognize and diagnose the disease entity and treat early. When neuropsychiatric symptoms are involved, typically there are many other systemic features to aid in the diagnosis of SLE. Many autoantibodies have been discovered and are used to help diagnose SLE. The antibody present in most cases of pediatric SLE, as well as in many other rheumatic diseases, is the nonspecific antinuclear antibody (ANA). The ANA is a commonly used screening tool by primary care physicians when evaluating a patient with a possible rheumatic disorder. However, a small subset of SLE patients, 1–5%, present with a negative ANA, and it is important to keep SLE on the differential diagnosis in specific instances when a thorough infectious, metabolic and neurological workup has been completed and proven to be inconclusive. This case involves a Hispanic adolescent female with a negative ANA who presented with diffuse cerebral edema secondary to leukoencephalopathy due to SLE with central nervous system involvement. She was normotensive on presentation and relatively symptom free aside from headache. She had an extensive workup while inpatient involving metabolic, infectious disease, rheumatology, and neurology prior to obtaining the diagnosis of SLE. She was treated with cyclophosphamide and rituximab with appropriate disease response. A review of the literature revealed 12 cases with SLE presenting with or developing diffuse cerebral edema and/or leukoencephalopathy. Our patient’s case differs in that she was also ANA negative despite other autoantibody positivity. While she did have low complements and transient leukopenia, she did not present with other signs of organ involvement, which made the diagnosis of SLE with neuropsychiatric involvement quite challenging. We discuss the importance of keeping SLE on the differential diagnosis despite a negative ANA in complex cases after thorough workup has been unrevealing, and to consider initial screening with not only the ANA but also dsDNA and complements to avoid missed diagnoses.
Application of the hierarchical bootstrap to multi-level data in neuroscience.

A common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g., Student’s t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data, and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in Bengalese finches (Lonchura striata var. domestica) and second quantifying changes in behavior under optogenetic control in flies (Drosophila melanogaster).