cssi/SciSciGPT-SciSciNet huggingface dataset:
Liquid–Liquid Phase Separation As the Second Step of Complex Coacervation

Liquid-liquid phase separation (LLPS) between tyrosine- and arginine-rich peptides are of biological importance. To understand the interactions between proteins in the condensed phase in close analogy to complex coacervation, we run multiple umbrella calculations between oligomers containing tyrosine (pY) and arginine (pR). We find pR-pY complexation to be energetically driven. Metadynamics simulations on monomers suggest that this energy of complexation is correlated with the number of π-cation bonds. Free energy calculations for the binding between pairs of poly glutamate-pR dimers show striking similarities between this process and LLPS. These calculations suggest that proteins containing arginine and tyrosine residues do not undergo complexation followed by coacervation. The mechanism, rather, is akin to phase separation of neutral polyion pairs.
Spectra of Regular Quantum Trees: Rogue Eigenvalues and Dependence on Vertex Condition

We investigate the spectrum of Schrodinger operators on finite regular metric trees through a relation to orthogonal polynomials that provides a graphical perspective. As the Robin vertex parameter tends to $$-\,\infty $$ , a narrow cluster of finitely many eigenvalues tends to $$-\,\infty $$ , while the eigenvalues above this cluster remain bounded from below. Certain “rogue” eigenvalues break away from this cluster and tend even faster toward $$-\,\infty $$ . The spectrum can be visualized as the intersection points of two objects in the plane—a spiral curve depending on the Schrodinger potential, and a set of curves depending on the branching factor, the diameter of the tree, and the Robin parameter.
Regular Polygonal Partitions of a Tverberg Type

A seminal theorem of Tverberg states that any set of $$T(r,d)=(r-1)(d+1)+1$$ points in $${\mathbb {R}}^{d}$$ can be partitioned into r subsets whose convex hulls have non-empty r-fold intersection. Almost any collection of fewer points in $${\mathbb {R}}^{d}$$ cannot be so divided, and in these cases we ask if the set can nonetheless be P(r, d)-partitioned, i.e., split into r subsets so that there exist r points, one from each resulting convex hull, which form the vertex set of a prescribed convex d-polytope P(r, d). Our main theorem shows that this is the case for any generic $$T(r,2)-2$$ points in the plane and any $$r\ge 3$$ when $$P(r,2)=P_{r}$$ is a regular r-gon, and moreover that $$T(r,2)-2$$ is tight. For higher dimensional polytopes and $$r=r_{1}\cdots r_{k}$$ , $$r_{i} \ge 3$$ , this generalizes to $$T(r,2k)-2k$$ generic points in $${\mathbb {R}}^{2k}$$ and orthogonal products $$P(r,2k)=P_{r_{1}}\times \cdots \times P_{r_{k}}$$ of regular polygons, and likewise to $$T(2r,2k+1)-(2k+1)$$ points in $${\mathbb {R}}^{2k+1}$$ and the product polytopes $$P(2r,2k+1)=P_{r_{1}}\times \cdots \times P_{r_{k}}\times P_{2}$$ . As with Tverberg’s original theorem, our results admit topological generalizations when r is a prime power, and, using the “constraint method” of Blagojevic, Frick, and Ziegler, allow for dimensionally restricted versions of a van Kampen–Flores type and colored analogues in the fashion of Soberon.
Towards Algorithmic Synthesis of Synchronization for Shared-Memory Concurrent Programs

We present a framework that takes a concurrent program composed of unsynchronized processes, along with a temporal specification of their global concurrent behaviour, and automatically generates a concurrent program with synchronization ensuring correct global behaviour. Our methodology supports finite-state concurrent programs composed of processes that may have local and shared variables, may be straight-line or branching programs, may be ongoing or terminating, and may have program-initialized or user-initialized variables. The specification language is an extension of propositional Computation Tree Logic (CTL) that enables easy specification of safety and liveness properties over control and data variables. The framework also supports synthesis of synchronization at different levels of abstraction and granularity.
Powering population health research: Considerations for plausible and actionable effect sizes

Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform calculations of power, sample size, and minimum detectable effect (MDE). E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to calculations of power, sample size and MDE for research proposals to study population health interventions. We show that plausible effects sizes for population health interventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied. Population health impact depends on the proportion of the population receiving the intervention. When adequately powered, even studies of interventions with small effect sizes can offer valuable evidence to inform population health if such interventions can be implemented broadly. Demonstrating the effectiveness of such interventions, however, requires large sample sizes.
Reversible Communicating Processes

Reversible distributed programs have the ability to abort unproductive computation paths and backtrack, while unwinding communication that occurred in the aborted paths. While it is natural to assume that reversibility implies full state recovery (as with traditional roll-back recovery protocols), an interesting alternative is to separate backtracking from local state recovery. For example, such a model could be used to create complex transactions out of nested compensable transactions where a programmer supplied compensation defines the work required to “unwind” a transaction. Reversible distributed computing has received considerable theoretical attention, but little reduction to practice; the few published implementations of languages supporting reversibility depend upon a high degree of central control. The objective of this paper is to demonstrate that a practical reversible distributed programming language can be efficiently implemented in a fully distributed manner. We discuss such a language, supporting CSP-style synchronous communication, embedded in Scala. While this language provided the motivation for the work described in this paper, our focus is upon the distributed implementation. In particular, we demonstrate that a “high-level” semantic model can be implemented using a simple point-to-point protocol.
Complementarity between Audited Financial Reporting and Voluntary Disclosure: The Case of Former Andersen Clients

Prior literature presents various perspectives on the role of financial reporting. One view is that mandatory periodic reporting disciplines managers and encourages timely voluntary disclosure. We examine this “confirmation hypothesis” using the shock to financial-reporting quality experienced by Arthur Andersen clients forced to switch auditors. Consistent with the confirmation hypothesis, we find that former Andersen clients increase disclosure after they change auditors. They increase forecasting frequency and enhance forecasting precision and specificity. We present additional cross-sectional evidence that shows Arthur Andersen clients with larger increases in financial-reporting quality increased their disclosure by relatively more, even within the sample of Arthur Andersen clients. We supplement our main findings with a battery of tests to reduce the possibility that alternative shocks and uncertainty drive our results. Our findings support complementarity between financial-reporting quality and voluntary disclosures.
Holistic assessment of competitiveness of Bulgarian farming enterprises

The problem of determining the level of competitiveness of various economic organizations is among the most topical academic and practical issues. However, there is no widely accepted and comprehensive framework for understanding and assessing the competitiveness of farming enterprises in different market, economic, institutional and natural environments. This presentation suggests and applies a holistic approach for assessing the competitiveness of Bulgarian farms as a whole and different specializations. This novel framework includes appropriate criteria, indicators and reference values for the four pillars of farm competitiveness – Economics efficiency, Financial endowment, Adaptability and Sustainability. The multi-criteria assessment of farm competitiveness in Bulgaria has found that the level of competitiveness of farms is at a good level, with low adaptive potential and economic efficiency, to the greatest extent contributing to lower competitiveness. More than a third of all farms in the country has a low level of competitiveness. The most competitive are the farms in the beekeeping, followed by field crops, mixed livestock and crop productions, and the lowest in grazing livestock. The proposed approach should be improved and applied more widely and periodically, increasing accuracy and representativeness.
Local Clustering in Contextual Multi-Armed Bandits

We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement. In practice, user dependency plays an essential role in the user’s actions, and thus the rewards. Clustering similar users can improve the quality of reward estimation, which in turn leads to more effective content recommendation and targeted advertising. Different from traditional clustering settings, we cluster users based on the unknown bandit parameters, which will be estimated incrementally. In particular, we define the problem of cluster detection in contextual MAB, and propose a bandit algorithm, LOCB, embedded with local clustering procedure. And, we provide theoretical analysis about LOCB in terms of the correctness and efficiency of clustering and its regret bound. Finally, we evaluate the proposed algorithm from various aspects, which outperforms state-of-the-art baselines.
Interpoint distance based two sample tests in high dimension

In this paper, we study a class of two sample test statistics based on inter-point distances in the high dimensional and low/medium sample size setting. Our test statistics include the well-known energy distance and maximum mean discrepancy with Gaussian and Laplacian kernels, and the critical values are obtained via permutations. We show that all these tests are inconsistent when the two high dimensional distributions correspond to the same marginal distributions but differ in other aspects of the distributions. The tests based on energy distance and maximum mean discrepancy mainly target the differences between marginal means and variances, whereas the test based on L1-distance can capture the difference in marginal distributions. Our theory sheds new light on the limitation of inter-point distance based tests, the impact of different distance metrics, and the behavior of permutation tests in high dimension. Some simulation results and a real data illustration are also presented to corroborate our theoretical findings.
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization.

The use of min-max optimization in adversarial training of deep neural network classifiers and training of generative adversarial networks has motivated the study of nonconvex-nonconcave optimization objectives, which frequently arise in these applications. Unfortunately, recent results have established that even approximate first-order stationary points of such objectives are intractable, even under smoothness conditions, motivating the study of min-max objectives with additional structure. We introduce a new class of structured nonconvex-nonconcave min-max optimization problems, proposing a generalization of the extragradient algorithm which provably converges to a stationary point. The algorithm applies not only to Euclidean spaces, but also to general $\ell_p$-normed finite-dimensional real vector spaces. We also discuss its stability under stochastic oracles and provide bounds on its sample complexity. Our iteration complexity and sample complexity bounds either match or improve the best known bounds for the same or less general nonconvex-nonconcave settings, such as those that satisfy variational coherence or in which a weak solution to the associated variational inequality problem is assumed to exist.
Fluorescence microscopy datasets for training deep neural networks.

Background Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal-to-noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample. Findings To use deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high-quality datasets that can be used to train and evaluate deep learning methods under development. Conclusion The availability of high-quality data is vital for training convolutional neural networks that are used in current machine learning approaches.
Identification of cell-type-specific marker genes from co-expression patterns in tissue samples.

Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.
Economic Downturns and the Informativeness of Management Earnings Forecasts

Economic downturns create uncertainty about a firm’s operations and make it disproportionately harder for outside market participants to assess the firm’s prospects. We posit that in this environment, management earnings forecasts will be more informative to investors and analysts. Consistent with this prediction, we find larger stock price reactions and analyst forecast revisions to news in management forecasts during downturns. Holding the amount of news in forecasts constant, stock price reactions to management forecasts are also greater than those to analyst forecasts. We also find that relative to analyst forecasts, management forecast accuracy increases during downturns, suggesting that investors justifiably assess management forecasts as more informative. Overall, we document that macroeconomic conditions create time-series variation in the informativeness of different sources of information to outside market participants.
Transcranial Direct Current Stimulation (tDCS) Augments the Effects of Gamified, Mobile Attention Bias Modification

Anxiety-related attention bias (AB) is the preferential processing of threat observed in clinical and sub-clinical anxiety. Attention bias modification training (ABMT) is a computerized cognitive training technique designed to systematically direct attention away from threat and ameliorate AB, but mixed and null findings have highlighted gaps in our understanding of mechanisms underlying ABMT and how to design the most effective delivery systems. One neuromodulation technique, transcranial direct current stimulation (tDCS) across the prefrontal cortex (PFC) may augment the effects of ABMT by strengthening top-down cognitive control processes, but the evidence base is limited and has not been generalized to current approaches in digital therapeutics, such as mobile applications. The present study was a single-blind randomized sham-controlled design. We tested whether tDCS across the PFC, versus sham stimulation, effectively augments the beneficial effects of a gamified ABMT mobile app. Thirty-eight adults (Mage = 23.92, SD = 4.75; 18 females) evidencing low-to-moderate anxiety symptoms were randomly assigned to active or sham tDCS for 30-minutes while receiving ABMT via a mobile app. Participants reported on potential moderators of ABMT, including life stress and trait anxiety. ECG was recorded during a subsequent stressor to generate respiratory sinus arrhythmia (RSA) suppression as a metric of stress resilience. ABMT delivered via the app combined with tDCS (compared to sham) reduced AB and boosted stress resilience measured via RSA suppression, particularly for those reporting low life stress. Our results integrating tDCS with ABMT provide insight into the mechanisms of AB modulation and support ongoing evaluations of enhanced ABMT reliability and effectiveness via tDCS.
Mutant SETBP1 enhances NRAS-driven MAPK pathway activation to promote aggressive leukemia

Mutations in SET-binding protein 1 (SETBP1) are associated with poor outcomes in myeloid leukemias. In the Ras-driven leukemia, juvenile myelomonocytic leukemia, SETBP1 mutations are enriched in relapsed disease. While some mechanisms for SETBP1-driven oncogenesis have been established, it remains unclear how SETBP1 specifically modulates the biology of Ras-driven leukemias. In this study, we found that when co-expressed with Ras pathway mutations, SETBP1 promoted oncogenic transformation of murine bone marrow in vitro and aggressive myeloid leukemia in vivo. We demonstrate that SETBP1 enhances the NRAS gene expression signature, driving upregulation of mitogen-activated protein kinase (MAPK) signaling and downregulation of differentiation pathways. SETBP1 also enhances NRAS-driven phosphorylation of MAPK proteins. Cells expressing NRAS and SETBP1 are sensitive to inhibitors of the MAPK pathway, and treatment with the MEK inhibitor trametinib conferred a survival benefit in a mouse model of NRAS/SETBP1-mutant disease. Our data demonstrate that despite driving enhanced MAPK signaling, SETBP1-mutant cells remain susceptible to trametinib in vitro and in vivo, providing encouraging preclinical data for the use of trametinib in SETBP1-mutant disease.
Trump’s (mis)management of the COVID-19 pandemic in the US

Crises are a time when people look to their political leaders for action. For a President who was a master of shaping political narratives, COVID-19 offered an opportunity to use his messaging skil...
The Case of Undetected Fraud: Can Audit Firm Policies or Highlighting the Fraud Examiner’s Role Reduce Juror Assessments of Auditor Negligence?

This study explores two potential safeguards against liability when auditors exercise professional skepticism, but do not detect a fraud: (1) a firm policy requiring a specific level of professional skepticism in high-risk audit areas and (2) providing jurors with key differences between the role of a fraud examiner and the role of an auditor as a reference point for judging the auditor’s performance. We find that describing a specific firm policy to jurors does not significantly decrease negligence findings for auditors who do not detect a fraud. However, providing the role of a fraud examiner as a reference point (e.g., scope of work, testing approaches) does significantly decrease negligence findings. Encouragingly, we also find some evidence that jurors are apt to decrease negligence findings when auditors exercise higher skepticism, but ultimately do not detect a fraud.
Racial and ethnic disparities among participants in precision oncology clinical studies.

3014Background: Precision medicine has revolutionized oncologic care in the United States (US) in the past two decades. While the US cancer population is rapidly diversifying, enrollment of a diver...
How Do Peer Awards Motivate Creative Content? Experimental Evidence from Reddit

We theorize peer awards’ effects on the volume and novelty of creative user-generated content (UGC) produced at online platform communities. We then test our hypotheses via a randomized field exper...
The Relationship Dilemma: Why Do Banks Differ in the Pace at Which They Adopt New Technology?

India introduced credit scoring technology in 2007. We study its adoption by the two main types of banks operating there, new private banks (NPBs) and state-owned public sector banks (PSBs). NPBs start checking the credit scores of most borrowers before lending soon after the technology is introduced. PSBs do so equally quickly for new borrowers but very slowly for prior clients, although lending without checking scores is reliably associated with more delinquencies. We show that an important factor explaining the difference in adoption is the stickiness of past bank structures and associated managerial practices. Past practices hold back better practices today.
Gradient ambient obstruction solitons on homogeneous manifolds

We examine homogeneous solitons of the ambient obstruction flow and, in particular, prove that any compact ambient obstruction soliton with constant scalar curvature is trivial. Focusing on dimension 4, we show that any homogeneous gradient Bach soliton that is steady must be Bach flat, and that the only non-Bach-flat shrinking gradient solitons are product metrics on $$\mathbb {R}^2\times S^2$$ and $$\mathbb {R}^2 \times H^2$$ . We also construct a non-Bach-flat expanding homogeneous gradient Bach soliton. We also establish a number of results for solitons to the geometric flow by a general tensor q.
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from. We propose a two-step method to interpret summarization model decisions. We first analyze the model’s behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between? After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods. We compare these techniques based on their ability to select content and reconstruct the model’s predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token. While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.
A triple threat: the Parastagonospora nodorum SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat

Parastagonospora nodorum is a fungal pathogen of wheat. As a necrotrophic specialist, it deploys effector proteins that target dominant host susceptibility genes to elicit programmed cell death (PCD). Here we identify and functionally validate the effector targeting the host susceptibility genes Snn2, Snn6 and Snn7. We utilized whole-genome sequencing, association mapping, gene-disrupted mutants, gain-of-function transformants, virulence assays, bioinformatics and quantitative PCR to characterize these interactions. A single proteinaceous effector, SnTox267, targeted Snn2, Snn6 and Snn7 to trigger PCD. Snn2 and Snn6 functioned cooperatively to trigger PCD in a light-dependent pathway, whereas Snn7-mediated PCD functioned in a light-independent pathway. Isolates harboring 20 SnTox267 protein isoforms quantitatively varied in virulence. The diversity and distribution of isoforms varied between populations, indicating adaptation to local selection pressures. SnTox267 deletion resulted in the upregulation of effector genes SnToxA, SnTox1 and SnTox3. We validated a novel effector operating in an inverse-gene-for-gene manner to target three genetically distinct host susceptibility genes and elicit PCD. The discovery of the complementary gene action of Snn2 and Snn6 indicates their potential function in a guard or decoy model. Additionally, differences in light dependency in the elicited pathways and upregulation of unlinked effectors sheds new light onto a complex fungal necrotroph-host interaction.
VARIATION IN TREATMENT APPROACHES TO ADOLESCENT MIDSHAFT CLAVICLE FRACTURES IN PEDIATRIC VERSUS ADULT HOSPITALS

OBJECTIVE The primary objective of this study was to identify differences in the treatment approach for isolated, displaced midshaft clavicle fractures in adolescent patients 15-18 years of age at adult versus pediatric hospitals. The secondary objective of this study was to identify factors associated with the surgical treatment of these fractures in this age group. DESIGN Retrospective cohort study. SETTING Two adult and one pediatric tertiary care referral hospitals. PATIENTS/PARTICIPANTS Adolescent patients 15-18 years of age with isolated, displaced midshaft clavicle fractures treated at 2 adult tertiary care referral hospitals and one pediatric tertiary care referral hospital were identified. A total of 214 patients, 105 from the adult hospitals and 109 from the pediatric hospital, were included. INTERVENTION Nonoperative versus surgical treatment of clavicle fractures. MAIN OUTCOME MEASUREMENT Surgical treatment. RESULTS A multivariable logistic regression analysis showed that superior-inferior fracture displacement [odds ratio (OR) 1.13, 95% confidence interval (CI) 1.06-1.20], dominant upper extremity injury (OR 2.60, 95% CI 1.19-5.67), and treatment at an adult hospital (OR 5.28, 95% CI 2.28-12.2) were independently associated with the surgical treatment of adolescent displaced midshaft clavicle fractures. CONCLUSIONS After controlling for relevant demographic and fracture characteristics, adolescent patients treated at adult hospitals for displaced midshaft clavicle fractures have more than 5 times the odds of surgical treatment than those treated at a pediatric hospital. Significant practice variation across institutions reflects ongoing controversy in surgical indications and underscores the need for high-quality prospective outcome studies. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Augmenting high-dimensional nonlinear optimization with conditional GANs

Many mathematical optimization algorithms fail to sufficiently explore the solution space of high-dimensional nonlinear optimization problems due to the curse of dimensionality. This paper proposes generative models as a complement to optimization algorithms to improve performance in problems with high dimensionality. To demonstrate this method, a conditional generative adversarial network (C-GAN) is used to augment the solutions produced by a genetic algorithm (GA) for a 311-dimensional nonconvex multi-objective mixed-integer nonlinear optimization. The C-GAN, composed of two networks with three fully connected hidden layers, is trained on solutions generated by GA, and then given sets of desired labels (i.e., objective function values), generates complementary solutions corresponding to those labels. Six experiments are conducted to evaluate the capabilities of the proposed method. The generated complementary solutions are compared to the original solutions in terms of optimality and diversity. The generative model generates solutions with objective functions up to 79% better, and with hypervolumes up to 58% higher, than the original solutions. These findings show that a C-GAN with even a simple training approach and architecture can, with a much shorter runtime, highly improve the diversity and optimality of solutions found by an optimization algorithm for a high-dimensional nonlinear optimization problem. [GitHub repository: https://github.com/PouyaREZ/GAN_GA]
Bortezomib Inhibits Lung Fibrosis and Fibroblast Activation Without Proteasome Inhibition.

The FDA-approved proteasomal inhibitor bortezomib (BTZ) has attracted interest for its potential anti-fibrotic actions. However, neither its in vivo efficacy in lung fibrosis nor its dependence on proteasome inhibition has been conclusively defined. In this study, we assessed the therapeutic efficacy of BTZ in a mouse model of pulmonary fibrosis, developed an in vitro protocol to define its actions on diverse fibroblast activation parameters, determined its reliance on proteasome inhibition for these actions in vivo and in vitro and explored alternative mechanisms of action. The therapeutic administration of BTZ diminished the severity of pulmonary fibrosis without reducing proteasome activity in the lung. In experiments designed to mimic this lack of proteasome inhibition in vitro, BTZ reduced fibroblast proliferation, differentiation into myofibroblasts, and collagen synthesis. It promoted de-differentiation of myofibroblasts and overcame their characteristic resistance to apoptosis. Mechanistically, BTZ inhibited kinases important for fibroblast activation while inducing expression of dual-specificity phosphatase 1 or DUSP1, and knockdown of DUSP1 abolished its anti-fibrotic actions in fibroblasts. Collectively, these findings suggest that BTZ exhibits a multidimensional profile of robust inhibitory actions on lung fibroblasts as well as anti-fibrotic actions in vivo. Unexpectedly, these actions appear to be independent of proteasome inhibition, and instead attributable to induction of DUSP1.
Image-based model predictive control via dynamic mode decomposition

Abstract We present a data-driven model predictive control (MPC) framework for systems with high state–space dimensionality. This work is motivated by the need to exploit sensor data that appears in the form of images (e.g., 2D or 3D spatial fields captured by thermal cameras). We propose to use dynamic mode decomposition (DMD) to directly build a low-dimensional model from image data and we use such model to obtain a tractable MPC controller. We demonstrate the scalability of this approach (which we call DMD-MPC) by using a 2D thermal diffusion system. Here, we assume that the evolution of the thermal field is captured by 50 x 50 pixel images, which results in a 2500-dimensional state–space. We show that the dynamics of this high-dimensional space can be accurately captured by using a 40-dimensional DMD model and we show that the field can be manipulated satisfactorily by using an MPC controller that embeds the low-dimensional DMD model. We also show that the DMD-MPC controller outperforms a standard MPC controller that uses data collected from a finite set of spatial locations (proxy locations) to manipulate the high-dimensional thermal field. This illustrates the value of information embedded in image data.
Shining a light on Laurentian Great Lakes cisco (Coregonus artedi): How ice coverage may impact embryonic development☆

Abstract Changes in winter conditions, such as decreased ice coverage and duration, have been observed in the Laurentian Great Lakes for more than 20 years. Such changes have been hypothesized to be linked to low Coregonus spp. survival to age-1 as most cisco (Coregonus artedi) populations are autumn spawners whose embryos incubate under ice throughout the winter. The quantity of light during winter is regulated by ice coverage, and light affects embryo survival and development in some teleosts. We experimentally evaluated how cisco embryos from lakes Superior and Ontario respond to three light treatments that represented day-light intensity under 0–10, 40–60, and 90–100% ice coverage. Embryonic response measures included two developmental factors (embryo survival and incubation period) and two morphological traits (length-at-hatch and yolk-sac volume). Embryo survival was highest at the medium light treatment and decreased at high and low treatments for both populations, suggesting cisco may be adapted to withstand some light exposure from inter-annual variability in ice coverage. Light intensity had no overall effect on length of incubation. Increasing light intensity decreased length-at-hatch in Lake Superior but had no effect in Lake Ontario. Yolk-sac volume was positively correlated with increasing light in Lake Superior and negatively correlated in Lake Ontario. Contrasting responses in embryo development between lakes suggests differences in populations’ response to light is flexible. Our results provide a step towards better understanding the high variability observed in coregonine recruitment and may help predict what the future of this species may look like under current climate trends.
An Alternate Method for Estimating Dynamic Height from XBT Profiles Using Empirical Vertical Modes

A technique is presented that applies modal decomposition to estimate dynamic height (0-450 db) from Expendable BathyThermograph (XBT) temperature profiles. Salinity-Temperature-Depth (STD) data are used to establish empirical relationships between vertically integrated temperature profiles and empirical dynamic height modes. These are then applied to XBT data to estimate dynamic height. A standard error of 0.028 dynamic meters is obtained for the waters of the Gulf of Alaska- an ocean region subject to substantial freshwater buoyancy forcing and with a T-S relationship that has considerable scatter. The residual error is a substantial improvement relative to the conventional T-S correlation technique when applied to this region. Systematic errors between estimated and true dynamic height were evaluated. The 20-year-long time series at Ocean Station P (50 deg N, 145 deg W) indicated weak variations in the error interannually, but not seasonally. There were no evident systematic alongshore variations in the error in the ocean boundary current regime near the perimeter of the Alaska gyre. The results prove satisfactory for the purpose of this work, which is to generate dynamic height from XBT data for coanalysis with satellite altimeter data, given that the altimeter height precision is likewise on the order of 2-3 cm. While the technique has not been applied to other ocean regions where the T-S relation has less scatter, it is suggested that it could provide some improvement over previously applied methods, as well.
Robust estimation via generalized quasi-gradients

We explore why many recently proposed robust estimation problems are efficiently solvable, even though the underlying optimization problems are non-convex. We study the loss landscape of these robust estimation problems, and identify the existence of "generalized quasi-gradients". Whenever these quasi-gradients exist, a large family of low-regret algorithms are guaranteed to approximate the global minimum; this includes the commonly-used filtering algorithm. For robust mean estimation of distributions under bounded covariance, we show that any first-order stationary point of the associated optimization problem is an {approximate global minimum} if and only if the corruption level $\epsilon < 1/3$. Consequently, any optimization algorithm that aproaches a stationary point yields an efficient robust estimator with breakdown point $1/3$. With careful initialization and step size, we improve this to $1/2$, which is optimal. For other tasks, including linear regression and joint mean and covariance estimation, the loss landscape is more rugged: there are stationary points arbitrarily far from the global minimum. Nevertheless, we show that generalized quasi-gradients exist and construct efficient algorithms. These algorithms are simpler than previous ones in the literature, and for linear regression we improve the estimation error from $O(\sqrt{\epsilon})$ to the optimal rate of $O(\epsilon)$ for small $\epsilon$ assuming certified hypercontractivity. For mean estimation with near-identity covariance, we show that a simple gradient descent algorithm achieves breakdown point $1/3$ and iteration complexity $\tilde{O}(d/\epsilon^2)$.
Constraints on power law cosmology from cosmic chronometer, standard ruler, and standard candle data

In this paper I compare the quality of the fit of a simple power law model of cosmic expansion to the standard $\Lambda$CDM model. I analyze a data set consisting of cosmic chronometer, standard ruler, and standard candle measurements, finding that the $\Lambda$CDM model provides a better fit to most combinations of these data than does the power law model
Charting Our First Steps: An Update on the AGS Initiative to Address the Intersection of Structural Racism and Ageism.

With its long commitment to addressing ageism in healthcare, the American Geriatrics Society (AGS) began addressing the intersection of racism and ageism in 2020. An AGS writing group developed five priorities that tap into our shared values as individuals and as an organization, while remaining broad enough to allow for future flexibility, growth, and opportunities. AGS members then participated in focus groups to identify an initial set of strategies that individuals, our Society, and our partners can adopt together to achieve these priorities. The AGS has also taken some smaller steps as we work to infuse attention to equity and eliminating bias across our portfolio.
Chromatin-mediated alternative splicing regulates cocaine-reward behavior

Neuronal alternative splicing is a key gene regulatory mechanism in the brain. However, the spliceosome machinery is insufficient to fully specify splicing complexity. In considering the role of the epigenome in activity-dependent alternative splicing, we and others find the histone modification H3K36me3 to be a putative splicing regulator. In this study, we found that mouse cocaine self-administration caused widespread differential alternative splicing, concomitant with the enrichment of H3K36me3 at differentially spliced junctions. Importantly, only targeted epigenetic editing can distinguish between a direct role of H3K36me3 in splicing and an indirect role via regulation of splice factor expression elsewhere on the genome. We targeted Srsf11, which was both alternatively spliced and H3K36me3 enriched in the brain following cocaine self-administration. Epigenetic editing of H3K36me3 at Srsf11 was sufficient to drive its alternative splicing and enhanced cocaine self-administration, establishing the direct causal relevance of H3K36me3 to alternative splicing of Srsf11 and to reward behavior.
Two-Level Private Information Retrieval

In the conventional robust $T$ -colluding private information retrieval (PIR) system, the user needs to retrieve one of the possible messages while keeping the identity of the requested message private from any $T$ colluding servers. Motivated by the possible heterogeneous privacy requirements for different messages, we consider the ( $N, T_{1}: K_{1}, T_{2}: K_{2}$ ) two-level PIR system, where $K_{1}$ messages need to be retrieved privately against $T_{1}$ colluding servers, and all the messages need to be retrieved privately against $T_{2}$ colluding servers where $T_{2}\leq T_{1}$ . We obtain a lower bound to the capacity by proposing a novel coding scheme, namely the non-uniform successive cancellation scheme. A capacity upper bound is also derived. The gap between the upper bound and the lower bound is analyzed, and shown to vanish when $T_{1}=T_{2}$ .
State-operator correspondence in celestial conformal field theory

The bulk-to-boundary dictionary for 4D celestial holography is given a new entry defining 2D boundary states living on oriented circles on the celestial sphere. The states are constructed using the 2D CFT state-operator correspondence from operator insertions corresponding to either incoming or outgoing particles which cross the celestial sphere inside the circle. The BPZ construction is applied to give an inner product on such states whose associated bulk adjoints are shown to involve a shadow transform. Scattering amplitudes are then given by BPZ inner products between states living on the same circle but with opposite orientations. 2D boundary states are found to encode the same information as their 4D bulk counterparts, but organized in a radically different manner.
Effect of nozzle curvature on supersonic gas jets used in laser–plasma acceleration

Supersonic gas jets produced by converging–diverging nozzles are commonly used as targets for laser–plasma acceleration (LPA) experiments. A major point of interest for these targets is the gas density at the region of interaction where the laser ionizes the gas plume to create a plasma, providing the acceleration structure. Tuning the density profiles at this interaction region is crucial to LPA optimization. A “flat-top” density profile is desired at the line of interaction to control laser propagation and high-energy electron acceleration, while a short high-density profile is often preferred for acceleration of lower-energy tightly focused laser–plasma interactions. A particular design parameter of interest is the curvature of the nozzle's diverging section. We examine three nozzle designs with different curvatures: the concave “bell,” straight conical, and convex “trumpet” nozzles. We demonstrate that for mm-scale axisymmetric nozzles that, at mm-scale distances from the nozzle exit, curvature significantly impacts shock formation and the resulting gas jet density field and, therefore, is an essential parameter in LPA gas jet design. We show that bell nozzles are able to produce focused regions of gas with higher densities. We find that the trumpet nozzle, similar to straight and bell nozzles, can produce flat-top profiles if optimized correctly and can produce flatter profiles at the cost of slightly wider edges. An optimization procedure for the trumpet nozzle is derived and compared to the straight nozzle optimization process. We present results for different nozzle designs from computational fluid dynamics simulations performed with the program ANSYS Fluent and verify them experimentally using neutral density interferometry.
Roman Jackiw and Chern–Simons theories

I recount my personal experience interacting with Roman Jackiw in the 1980s, when we both worked on Chern-Simons theories in three dimensions.
Design Philanthropy: Challenges and Opportunities in the Evolution of Philanthropic Giving:

Over the past several decades large philanthropies have adopted aggressive approaches to education reform that scholars have labeled venture philanthropy. These efforts focused on broad changes to ...
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables

We explore how high-speed robot arm motions can dynamically manipulate ropes and cables to vault over obstacles, knock objects from pedestals, and weave between obstacles. In this paper, we propose a self-supervised learning framework that enables a UR5 robot to perform these three tasks. The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform a task with varying obstacle and target locations. The trajectory function computes minimum-jerk motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs to find the shortest and fastest feasible motion. We experiment with 5 physical cables with different thickness and mass and compare performance against two baselines in which a human chooses the apex point. Results suggest that a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7 %, 36.7 %, and 15.0 %, and a baseline with human-specified, task-specific apex points achieves 66.7 %, 56.7 %, and 15.0 % success rate respectively, while the robot using the learned apex point can achieve success rates of 81.7 % in vaulting, 65.0 % in knocking, and 60.0 % in weaving. Code, data, and supplementary materials are available at https://sites.google.com/berkeley.edu/dynrope/home.
Longest cycles in 3-connected hypergraphs and bipartite graphs

In the language of hypergraphs, our main result is a Dirac-type bound: we prove that every $3$-connected hypergraph $H$ with $ \delta(H)\geq \max\{|V(H)|, \frac{|E(H)|+10}{4}\}$ has a hamiltonian Berge cycle. This is sharp and refines a conjecture by Jackson from 1981 (in the language of bipartite graphs). Our proofs are in the language of bipartite graphs, since the incidence graph of each hypergraph is bipartite.
Geometric Solutions for General Actuator Routing on Inflated-Beam Soft Growing Robots

Continuum and soft robots can leverage complex actuator shapes to take on useful shapes while actuating only a few of their many degrees of freedom. Continuum robots that also grow increase the range of potential shapes that can be actuated and enable easier access to constrained environments. Existing models for describing the complex kinematics involved in general actuation of continuum robots rely on simulation or well-behaved stress–strain relationships, but the nonlinear behavior of the thin-walled inflated-beams used in growing robots makes these techniques difficult to apply. Here, we derive kinematic models of single, generally routed tendon paths on a soft pneumatic backbone of inextensible but flexible material from geometric relationships alone. This allows for forward modeling of the resulting shapes with only knowledge of the geometry of the system. We show that this model can accurately predict the shape of the whole robot body and how the model changes with actuation type. We also demonstrate the use of this kinematic model for inverse design, where actuator designs are found based on desired final robot shapes. We deploy these designed actuators on soft pneumatic growing robots to show the benefits of simultaneous growth and shape change.
Roth's Theorem over arithmetic function fields

Roth's theorem is extended to finitely generated field extensions of $\Bbb Q$, using Moriwaki's framework for heights.