cssi/SciSciGPT-SciSciNet huggingface dataset:
Validation of the IMPEDE VTE score for prediction of venous thromboembolism in multiple myeloma: a retrospective cohort study.

The IMPEDE VTE score has recently emerged as a novel risk prediction tool for venous thromboembolism (VTE) in multiple myeloma (MM). We retrospectively reviewed 839 patients with newly diagnosed MM between 2010 and 2015 at Cleveland Clinic and included 575 patients in final analysis to validate this score. The c-statistic of the IMPEDE VTE score to predict VTE within 6 months of treatment start was 0·68 (95% CI: 0·61-0·75). The 6-month cumulative incidence of VTE was 5·0% (95% CI: 2·1-7·9) in the low risk group, compared to 12·6% (95% CI: 8·9-16·4%) and 24·1% (95% CI: 12·2-36·1) in the intermediate and high risk groups (P < 0·001 for both). In addition, a higher proportion of patients in the VTE cohort had ECOG performance status of ≥2 as compared to the no VTE cohort (33% vs. 16%, P = 0·001). Other MM characteristics such as stage, immunoglobulin subtype, and cytogenetics were not predictors of VTE. In summary, we have validated the IMPEDE VTE score in our patient cohort and our findings suggest that it can be utilized as a VTE risk stratification tool in prospective studies looking into investigating VTE prophylaxis strategies in MM patients.
Polygenic risk scores for alcohol involvement relate to brain structure in substance-naïve children: Results from the ABCD study

Brain imaging-derived structural correlates of alcohol involvement have largely been speculated to arise as a consequence of alcohol exposure. However, they may also reflect predispositional risk. In substance naive children of European ancestry who completed the baseline session of the Adolescent Brain Cognitive Development (ABCD) Study (n = 3013), mixed-effects models estimated whether polygenic risk scores (PRS) for problematic alcohol use (PAU-PRS) and drinks per week (DPW-PRS) are associated with magnetic resonance imaging-derived brain structure phenotypes (i.e., total and regional: cortical thickness, surface area and volume; subcortical volume; white matter volume, fractional anisotropy, mean diffusivity). Follow-up analyses evaluated whether any identified regions were also associated with polygenic risk among substance naive children of African ancestry (n = 898). After adjustment for multiple testing correction, polygenic risk for PAU was associated with lower volume of the left frontal pole and greater cortical thickness of the right supramarginal gyrus (|βs| > 0.009; ps < 0.001; psfdr < 0.046; r2 s < 0.004). PAU PRS and DPW PRS showed nominally significant associations with a host of other regional brain structure phenotypes (e.g., insula surface area and volume). None of these regions showed any, even nominal association among children of African ancestry. Genomic liability to alcohol involvement may manifest as variability in brain structure during middle childhood prior to alcohol use initiation. Broadly, alcohol-related variability in brain morphometry may partially reflect predisposing genomic influence. Larger discovery genome-wide association studies and target samples of diverse ancestries are needed to determine whether observed associations may generalize across ancestral origins.
Perma.cc and Web Archival Dissonance with Copyright Law

Harvard’s Perma.cc offers the solution to link rot—the phenomenon that citations in academic journals to Web materials disappear with the passage of time, resulting in “broken links” and disappeara...
Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta-Transfer Learning

Most environmental data come from a minority of well-observed sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unobserved sites. Here, we demonstrate a novel transfer learning framework that accurately predicts temperature in unobserved lakes (targets) by borrowing models from highly observed lakes (sources). This method, Meta Transfer Learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-observed lakes using calibrated process-based modeling (PB) and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unobserved in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated process-based General Lake Model, where the median RMSE for the target lakes is $2.52^{\circ}C$. PB-MTL yielded a median RMSE of $2.43^{\circ}C$; PGDL-MTL yielded $2.16^{\circ}C$; and a PGDL-MTL ensemble of nine sources per target yielded $1.88^{\circ}C$. For sparsely observed target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.
Self-Supervised Test-Time Learning for Reading Comprehension.

Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods. In this work, we consider the task of unsupervised reading comprehension and present a method that performs “test-time learning” (TTL) on a given context (text passage), without requiring training on large-scale human-authored datasets containing context-question-answer triplets. This method operates directly on a single test context, uses self-supervision to train models on synthetically generated question-answer pairs, and then infers answers to unseen human-authored questions for this context. Our method achieves accuracies competitive with fully supervised methods and significantly outperforms current unsupervised methods. TTL methods with a smaller model are also competitive with the current state-of-the-art in unsupervised reading comprehension.
Explainable AI for Retinal Prostheses: Predicting Electrode Deactivation from Routine Clinical Measures

To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual's perceptual thresholds (‘system fitting’). Nonfunctional electrodes may then be deactivated to reduce power consumption and improve visual outcomes. However, thresholds vary drastically not just across electrodes but also over time, thus calling for a more flexible electrode deactivation strategy. Here we present an explainable artificial intelligence (XAI) model fit on a large longitudinal dataset that can 1) predict at which point in time the manufacturer chose to deactivate an electrode as a function of routine clinical measures (‘predictors') and 2) reveal which of these predictors were most important. The model predicted electrode deactivation from clinical data with 60.8% accuracy. Performance increased to 75.3% with system fitting data, and to 84% when thresholds from follow-up examinations were available. The model further identified subject age and time since blindness onset as important predictors of electrode deactivation. An accurate XAI model of electrode deactivation that relies on routine clinical measures may benefit both the retinal implant and wider neuroprosthetics communities.
Remarks on Joachimsthal Integral and Poritsky Property

The billiard in an ellipse has a conserved quantity, the Joachimsthal integral. We show that the existence of such an integral characterizes conics. We extend this result to the spherical and hyperbolic geometries and to higher dimensions. We connect the existence of Joachimsthal integral with the Poritsky property, a property of billiard curves, called so after H. Poritsky whose important paper Poritsky (Ann Math 51:446–470, 1950) was one of the early studies of the billiard problem.
A Statistical Framework for Model-Based Inverse Problems in Ultrasound Elastography

Model-based computational elasticity imaging of tissues can be posed as solving an inverse problem over finite elements spanning the displacement image. As most existing quasi-static elastography methods count on deterministic formulations of the forward model resulting in a constrained optimization problem, the impact of displacement observation errors has not been well addressed. To this end, we propose a new statistical technique that leads to a unified optimization problem for elasticity imaging. Our statistical model takes the imperfect nature of the displacement measurements into account, and leads to an observation model for the Young’s modulus that involves signal dependent colored noise. To solve the resulting regularized optimization problem, we propose a fixed-point algorithm that leverages proximal splitting methods. Preliminary qualitative and quantitative results demonstrate the effectiveness and robustness of the proposed methodology.
Robust Policy Iteration for Continuous-Time Linear Quadratic Regulation

This paper studies the robustness of policy iteration in the context of continuous-time infinite-horizon linear quadratic regulator (LQR) problem. It is shown that Kleinman's policy iteration algorithm is small-disturbance input-to-state stable, a property that is stronger than Sontag's local input-to-state stability but weaker than global input-to-state stability. More precisely, whenever the error in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded and enter a small neighborhood of the optimal solution of the LQR problem. Based on this result, an off-policy data-driven policy iteration algorithm for the LQR problem is shown to be robust when the system dynamics are subjected to small additive unknown bounded disturbances. The theoretical results are validated by a numerical example.
Game Theory without Partitions, and Applications to Speculation and Consensus

Decision theory and game theory are extended to allow for information processing errors. This extended theory is then used to reexamine market speculation and consensus, both when all actions (opinions) are common knowledge and when they may not be. Five axioms of information processing are shown to be especially important to speculation and consensus. They are called nondelusion, knowing that you know (KTYK), nested, balanced, and positively balanced. We show that it is necessary and sufficient that each agent's information processing errors be (1) nondeluded and balanced so that the agents cannot agree to disagree, (2) nondeluded and positively balanced so that it cannot be common knowledge that they are speculating, and (3) nondeluded and KTYK and nested so that agents cannot speculate in equilibrium. Each condition is strictly weaker than the next one, and the last is strictly weaker than partition information.
CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers—both of which are superior to previous approaches in precision and recall in three different test scenarios—and the first signedness classification algorithm, which exhibits a $>$ 97% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average $\ell ^1$ error is five times better (81% less) than all previous methods and exhibits lower average error, even when considering only signals that meet prior methods’ assumptions. Finally, CAN-D is implemented in lightweight hardware, allowing for an on-board diagnostic (OBD-II) plugin for real-time in-vehicle CAN decoding.
Entropy-Guided Control Improvisation

High level declarative constraints provide a powerful (and popular) way to define and construct control policies; however, most synthesis algorithms do not support specifying the degree of randomness (unpredictability) of the resulting controller. In many contexts, e.g., patrolling, testing, behavior prediction, and planning on idealized models, predictable or biased controllers are undesirable. To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm that supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy. This framework, which extends the state-of-the-art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment, enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.
Sequential selection for accelerated life testing via approximate Bayesian inference

Accelerated life testing (ALT) is typically used to assess the reliability of material's lifetime under desired stress levels. Recent advances in material engineering have made a variety of material alternatives readily available. To identify the most reliable material setting with efficient experimental design, a sequential test planning strategy is preferred. To guarantee a tractable statistical mechanism for information collection and update, we develop explicit model parameter update formulas via approximate Bayesian inference. Theories show that our explicit update formulas give consistent parameter estimates. Simulation study and a case study show that the proposed sequential selection approach can significantly improve the probability of identifying the material alternative with best reliability performance over other design approaches.
On the Adversarial Robustness of Quantized Neural Networks

Reducing the size of neural network models is a critical step in moving AI from a cloud-centric to an edge-centric (i.e. on-device) compute paradigm. This shift from cloud to edge is motivated by a number of factors including reduced latency, improved security, and higher flexibility of AI algorithms across several application domains (e.g. transportation, healthcare, defense, etc.). However, it is currently unclear how model compression techniques may affect the robustness of AI algorithms against adversarial attacks. This paper explores the effect of quantization, one of the most common compression techniques, on the adversarial robustness of neural networks. Specifically, we investigate and model the accuracy of quantized neural networks on adversarially-perturbed images. Results indicate that for simple gradient-based attacks, quantization can either improve or degrade adversarial robustness depending on the attack strength.
Postbuckling of orthotropic composite plates loaded in compression

The nonlinear large deflection equations of von Karman are written for 'specially' orthotropic plates. The equations are then manipulated to determine the parameters required to establish postbuckling behavior. It is found that only two new parameters are needed beyond those required for buckling. By assuming trigonometric functions in one direction, the plate equations are converted into ordinary nonlinear differential equations which are solved numerically using a two point boundary problem solver that makes use of Newton's method. The postbuckling behavior is obtained for simply supported and clamped, long, rectangular, orthotropic plates covering the complete range of dimensions and material properties.
Improving Factual Consistency of Abstractive Summarization via Question Answering

A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. Through extensive experiments, we confirm that our method is effective in improving factual consistency and even overall quality of the summaries, as judged by both automatic metrics and human evaluation.
Natural Disaster Insurance for Sovereigns: Issues, Challenges and Optimality

Natural disasters are a source of economic risks in many countries, especially in smaller and lower-income states, and ex-ante preparedness is needed to manage the risks. The paper discusses sovereign experience with disaster insurance as a key instrument to mitigate the risks; proposes ways to judge the adequacy of insurance; and considers ways to enhance its use by vulnerable countries. The paper especially aims to inform policy decisions on disaster insurance. Through simulations of natural disasters and various insurance options, we find that sovereign decisions on optimal risk transfer involve balancing trade-offs between growth and debt, based on government risk preferences and country risk exposure. The choice of optimal insurance for smaller countries turns out to be more constrained by cost considerations due to their higher exposure, likely resulting in underinsurance; donor grants could help them achieve a more optimal protection. We also find that optimal insurance packages are those that are least costly relative to expected payouts (i.e. have the lowest insurance multiple), which are also the packages that insure less severe (more frequent) disasters.
A Strain-Specific Inhibitor of Receptor-Bound HIV-1 Targets a Pocket Near the Fusion Peptide and Offers a Template for Drug Design

Disruption of viral fusion represents a viable, albeit under-explored, target for HIV therapeutics. While studying the receptor-bound envelope glycoprotein conformation by cryo-EM, we identified a pocket near the base of the trimer containing a bound detergent molecule and performed in silico drug screening using a library of drug-like and commercially available molecules. After down-selection, we solved cryo-EM structures that validated binding of two small molecule hits in very similar manners to the predicted binding poses, including interactions with aromatic residues within the fusion peptide. One of the molecules demonstrated low micromolar inhibition of the autologous virus by utilizing a very rare phenylalanine in the fusion peptide and stabilizing the surrounding region. This work demonstrates that small molecules can target the fusion process, providing a new target for anti-HIV therapeutics, and highlights the need to explore how fusion peptide sequence variations affect receptor-mediated conformational states across diverse HIV strains.
Very high thermoelectric power factor near magic angle in twisted bilayer graphene

Recent research on twisted bilayer graphene (TBG) uncovered that its twist-angle-dependent electronic structure leads to a host of unique properties, such as superconductivity, correlated insulating states, and magnetism. The flat bands that emerge at low twist angles in TBG result in sharp features in the electronic density of states (DOS), affecting transport. Here we show that they lead to superior and tuneable thermoelectric (TE) performance. Combining an iterative Boltzmann transport equation solver and electronic structure from an exact continuum model, we calculate thermoelectric transport properties of TBG at different twist angles, carrier densities, and temperatures. Our simulations show the room-temperature TE power factor (PF) in TBG reaches 40 mWm-1K-2, significantly higher than single-layer graphene (SLG) and among the highest reported to date. The peak PF is observed near the magic angle, at a twist angle of 1.3, and near complete band filling. We elucidate that its dependence is driven by two opposing forces: the band gap between the flat and remote bands suppresses bipolar transport and improves the Seebeck coefficient but the gap decreases with twist angle; on the other hand, the Fermi velocity, which impacts conductivity, is smallest at the magic angle and rises with twist angle. We observed a further increase in the PF of TBG with decreasing temperature. The strong TE performance, along with the ability to fine-tune transport using twist angle, makes TBG an interesting candidate for future research and applications in energy conversion and thermal sensing and management.
The design and implementation of a parallel unstructured Euler solver using software primitives

This paper is concerned with the implementation of a three-dimensional unstructured grid Euler-solver on massively parallel distributed-memory computer architectures. The goal is to minimize solution time by achieving high computational rates with a numerically efficient algorithm. An unstructured multigrid algorithm with an edge-based data structure has been adopted, and a number of optimizations have been devised and implemented in order to accelerate the parallel communication rates. The implementation is carried out by creating a set of software tools, which provide an interface between the parallelization issues and the sequential code, while providing a basis for future automatic run-time compilation support. Large practical unstructured grid problems are solved on the Intel iPSC/860 hypercube and Intel Touchstone Delta machine. The quantitative effect of the various optimizations are demonstrated, and we show that the combined effect of these optimizations leads to roughly a factor of three performance improvement. The overall solution efficiency is compared with that obtained on the CRAY-YMP vector supercomputer.
Ordered intricacy of Shilnikov saddle-focus homoclinics in symmetric systems

Using the technique of Poincare return maps, we disclose an intricate order of subsequent homoclinic bifurcations near the primary figure-8 connection of the Shilnikov saddle-focus in systems with reflection symmetry. We also reveal admissible shapes of the corresponding bifurcation curves in a parameter space. Their scalability ratio and organization are proven to be universal for such homoclinic bifurcations of higher orders. Two applications with similar dynamics due to the Shilnikov saddle-foci are used to illustrate the theory: a smooth adaptation of the Chua circuit and a 3D normal form.
Adaptive Active-Passive Networked Multiagent Systems

Active-passive multiagent systems consist of agents subject to inputs (active agents) and agents with no inputs (passive agents), where active and passive agent roles are considered to be interchangeable in order to capture a wide array of applications. A challenge in the control of active-passive multiagent systems is the presence of information exchange uncertainties that can yield to undesirable closed-loop system performance. Motivated by this standpoint, this paper proposes an adaptive control algorithm for this class of multiagent systems to suppress the negative effects of information exchange uncertainties. Specifically, by estimating these uncertainties, the proposed adaptive control architecture has the ability to recover the active-passive multiagent system performance in a distributed manner. As a result, the agents converge to a user-adjustable neighborhood of the average of the applied inputs to the active agents. The efficacy of the proposed control architecture is also validated from a human-robot collaboration perspective, where a human is visiting several task locations, and the multiagent system identifies these locations and move toward them as a coverage control problem.
Coordinated Scheduling of Electric Vehicles Within Zero Carbon Emission Hybrid AC/DC Microgrids

Microgrids with AC/DC architecture benefit from advantages of both AC and DC power. In this paper, daily operation problem for a zero-carbon AC/DC microgrid in presence of electric vehicles (EVs) is considered. In this framework, EVs' batteries are mobile energy storage systems, which allow desirable operation of the microgrid during peak demand hours. This study shows in absence of storage system, EVs' batteries can be properly managed to satisfy the system requirements. In the case studies, several sensitivity analyses based on variations in battery degradation costs, solar irradiance, and inverter capacity are investigated.
Unveiling the Effects of Foreign Exchange Interventions: Evidence from the Kyrgyz Republic

This paper analyzes determinants and consequences of FX interventions in the Kyrgyz Republic. Most of the literature on the topic focuses on advanced and emerging economies and this paper provides new evidence from a low-income country. We find that FX interventions take place in response to movements in the exchange rate and its volatility. There is also evidence of 'leaning against the wind', which is more pronounced for relatively larger FX sales and purchases. The 'leaning against the wind' is asymmetric toward FX sales and largely reflects leaning against depreciation of domestic currency. We document a varying degree of de-facto exchange rate stability despite the de-jure floating exchange rate regime. During most of the sample, the exchange rate management index was relatively low in line with the floating exchange rate regime, with the exception of the period from 2018 Q4 until the COVID-19 shock, during which the exchange rate management index was relatively high.
Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping

Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used In neuromorphic systems to Implement high-density and low-power analog synaptic weights. Unfortunately, an RRAM cell can switch its state after reading its content a certain number of times. Such behavior challenges the integrity and program-onee-read-many-times philosophy of implementing machine learning inference on neuromorphic systems, impacting the Quality-of-Serviee (QoS). Elevated temperatures and frequent usage can significantly shorten the number of times an RRAM cell can be reliably read before it becomes absolutely necessary to reprogram. We propose an architectural solution to extend the read endurance of RRAM-based neuromorphic systems. We make two key contributions. First, we formulate the read endurance of an RRAM cell as a function of the programmed synaptic weight and its activation within a machine learning workload. Second, we propose an intelligent workload mapping strategy incorporating the endurance formulation to place the synapses of a machine learning model onto the RRAM cells of the hardware. The objective is to extend the inference lifetime, defined as the number of times the model can be used to generate output (inference) before the trained weights need to be reprogrammed on the RRAM cells of the system. We evaluate our architectural solution with machine learning workloads on a eyele-aeeurate simulator of an RRAM-based neuromorphic system. Our results demonstrate a significant increase in inference lifetime with only a minimal performance impact.
Qubit-efficient entanglement spectroscopy using qubit resets

One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop qubit-efficient quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the n-th power of the density operator of a quantum system, $Tr(\rho^n)$, (related to the Renyi entropy of order n) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of n, are variants of previous algorithms with width proportional to n, an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of effective circuit depth as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating $Tr(\rho^n)$ for larger n than possible with previous algorithms.
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised

Question Answering (QA) is key for making possible a robust communication between human and machine. Modern language models used for QA have surpassed the human-performance; however, these models require large amounts of human-generated training data which are costly and time-consuming to create. This paper studies augmenting human-made datasets with synthetic data as a way of surmounting this problem. A state-of-the-art model based on deep transformers is used to inspect the impact of using synthetic answerable and unanswerable questions to complement a well-known human-made dataset. The results indicate a tangible improvement in the performance of the language model (measured in terms of F1 and EM scores) trained on the mixed dataset. Specifically, unanswerable question-answers prove more effective in boosting the model: the F1 score gain from adding to the original dataset the answerable, unanswerable, and combined question-answers were 1.3%, 5.0%, and 6.7%, respectively.
Handling Qualities of a Capsule Spacecraft During Atmospheric Entry

A piloted simulation was conducted to study handling qualities for capsule spacecraft entering the Earth s atmosphere. Eight evaluation pilots, including six pilot astronauts, provided Cooper-Harper ratings, workload ratings, and qualitative comments. The simulation began after descending through the atmospheric entry interface point and continued until the drogue parachutes deployed. There were two categories of piloting tasks, both of which required bank angle control. In one task category, the pilot followed a closed-loop bank angle command computed by the backup guidance system to manage g-loads during entry. In the other task category, the pilot used intuitive rules to determine the desired bank angle independently, based on an open-loop schedule of vertical speed, Mach, and total energy specified at several range-to-target gates along the entry trajectory. Pilots were able to accurately track the bank angle guidance commands and steered the capsule toward the recovery site with essentially the same range error as the benchmark autopilot trajectory albeit with substantially higher propellant usage, and the handling qualities for this task were satisfactory. Another key result was that the complex piloting task of atmospheric entry could be performed satisfactorily, even in the presence of large dispersions, by controlling bank angle to follow a simple open-loop schedule.
Lebesgue Space Estimates for Spherical Maximal Functions on Heisenberg Groups

We prove $L^p\to L^q$ estimates for local maximal operators associated with dilates of codimension two spheres in Heisenberg groups; these are sharp up to two endpoints. The results can be applied to improve currently known bounds on sparse domination for global maximal operators. We also consider lacunary variants, and extensions to Metivier groups.
A Class of Recursive Sets

In this article one builds a class of recursive sets, one establishes properties of these sets and one proposes applications. This article widens some results of C. P. Popovici, L. Livovschi, H. Georgescu. N. Ţandareanu, ”Curs de bazele informaticii (funcţii booleene si circuite combinaţionale)”, Tipografia Universitaţii din Bucuresti, 1976.
Protein-coding repeat polymorphisms strongly shape diverse human phenotypes.

Many human proteins contain domains that vary in size or copy number because of variable numbers of tandem repeats (VNTRs) in protein-coding exons. However, the relationships of VNTRs to most pheno...
Fabrication and characterization methods for investigating cell-matrix interactions in environments possessing spatial orientation heterogeneity.

Abstract Fibrillar collagen is a ubiquitous structural protein that plays a significant role in determining the mechanical properties of various tissues. The constituent collagen architecture can give direct insight into the respective functional role of the tissue due to the strong structure-function relationship that is exhibited. In such tissues, matrix structure can vary across local subregions contributing to mechanical heterogeneity which can be implicated in tissue function or failure. The post-myocardial infarction scar environment is an example of note where mechanically insufficient collagen can result in impaired cardiac function and possibly tissue rupture due to post-MI cellular response and matrix interactions. In order to further develop the understanding of cell-matrix interactions within heterogeneous environments, we developed a method of heterogeneous collagen gel fabrication which produces a region of randomly oriented fibers directly adjacent to an interconnected region of anisotropic alignment. To fully capture and evaluate the degree of alignment and spatial orientation heterogeneity, several image processing and automated analysis methods were employed. Our analysis revealed the successful fabrication of an interconnected spatially heterogeneous collagen gel possessing distinct regions of random or preferential alignment. Additionally, embedded cell populations were observed to recognize and reorient with their underlying and surrounding architectures through our cell-centric analysis techniques. Statement of significance Fibrillar collagen is a structural protein that contributes to the architecture-function relationship exhibited by various tissues where mechanically insufficient collagen architecture can lead to tissue failure. One environment where this can occur is the post-myocardial infarction scar environment where too much or too little collagen accumulation coupled with spatial fiber orientation heterogeneity can lead to environments incapable of normal mechanical functionality. While there are methodologies capable of generating aligned constructs, they do so with varying degrees of control and complexity with many producing uniform construct alignment. The presented platform is simple and produces continuous constructs possessing inherent spatial orientation heterogeneity. Coupling this with image processing and automated analysis methods enables the probing of fundamental cell-matrix interactions within heterogeneous environments.
The Daphnia carapace and the origin of novel structures

Understanding how novel structures arise is a central question in evolution. The carapace of the waterflea Daphnia is a bivalved cape of exoskeleton that has been proposed to be one of many novel arthropod structures that arose through repeated co-option of genes that also pattern insect wings. To determine whether the Daphnia carapace is a novel structure, we compare the expression of pannier, araucan, and vestigial between Daphnia, Parhyale, and Tribolium. Our results suggest that the Daphnia carapace did not arise by co-option, but instead derives from an ancestral exite (lateral lobe) that emerges from a proximal leg segment that was incorporated into the Daphnia body wall. The Daphnia carapace therefore appears to be homologous to the Parhyale tergal plate and the insect wing. Remarkably, the vestigial-positive region that gives rise to the Daphnia carapace appears to be present in Parhyale and Tribolium as a small, inconspicuous protrusion. Similarly, the vestigial-positive developmental fields that form tergal plates in Parhyale appear to be present in Daphnia, even though Daphnia does not form tergal plates. Thus, rather than a novel structure resulting from gene co-option, the Daphnia carapace appears to have arisen from a shared, ancestral developmental field that persists in a cryptic state in other arthropod lineages. Cryptic persistence of unrecognized serially homologous developmental fields may thus be a general solution for the origin of novel structures. Our simple molecular triangulation strategy, which does not require functional studies, can illuminate the homologies of long-debated structures on the legs and body wall of arthropods.
RHAMMB-mediated bifunctional nanotherapy targeting Bcl-xL and mitochondria for pancreatic neuroendocrine tumor treatment.

The incidence of pancreatic neuroendocrine tumor (PNET) has continued to rise. Due to their indolent feature, PNET patients often present with incurable, metastatic diseases. Novel therapies are urgently needed. We have previously shown that Receptor for Hyaluronic Acid-Mediated Motility isoform B (RHAMMB) and Bcl-xL are upregulated in PNETs and both of them promote PNET metastasis. Because RHAMM protein is undetectable in most adult tissues, we hypothesized that RHAMMB could be a gateway for nanomedicine delivery into PNETs. To test this, we developed a RHAMMB-targeting nanoparticle (NP). Inside this NP, we assembled small interfering RNA (siRNA) against Bcl-xL (siBcl-xL) and mitochondria-fusing peptide KLA. We demonstrated that RHAMMB-positive PNETs picked up the RHAMMB-targeting NPs. siBcl-xL or KLA alone killed only 30% of PNET cells. In contrast, a synergistic killing effect was achieved with the co-delivery of siBcl-xL and KLA peptide in vitro. Unexpectedly, siBcl-xL induced cell death before reducing Bcl-xL protein levels. The systemically injected RHAMMB-targeting NPs carrying siBcl-xL and KLA peptide significantly reduced tumor burden in mice bearing RHAMMB-positive PNETs. Together, these findings indicate that the RHAMMB-targeting nanotherapy serves as a promising drug delivery system for PNET and possibly other malignancies with upregulated RHAMMB. The combination of siBcl-xL and KLA peptide can be a therapy for PNET treatment.
Diverse arsenic-containing lipids in the surface ocean

Abstract Arsenic is present at nanomolar levels throughout the surface ocean, and microbes assimilate this toxic element due to its similarity to inorganic phosphorus. Although the concentration and characterization of dissolved arsenic has been a focus of ocean studies, the size of the particulate arsenic pool and its partitioning into organic molecules within the microbial community is not known. We measured the particulate pool of arsenic in five surface samples from the open ocean and determined the contribution of arsenic-containing lipids to this pool. Here we show that the accumulation of arsenic into lipids is a widespread phenomenon in the surface ocean. Particulate arsenic concentrations were 15 to 42 pmol L−1 with 7–20% of the particulate arsenic pool in the form of arsenolipids. We characterized these arsenolipids and found that arsenosugar phospholipids dominated the arsenolipid pools in our samples with a minor component of arsenohydrocarbons and other unidentified lipids. A significant portion of the arsenosugar phospholipids (up to 35%) were present as previously undescribed mixed acyl ether lipids, suggesting a bacterial source. Scientific significance statement Marine microbes experience constant exposure to the toxic element arsenic. However, there are no baseline measurements of how much arsenic accumulates in microbial communities nor do we know the full spectrum of arsenic containing biomolecules produced in marine systems. In culture-based studies there is strong evidence that phytoplankton synthesize arsenic-containing lipids, but these lipids have not been observed in natural communities of marine microbes. Here we make measurements of bulk particulate arsenic at five sites in the surface open ocean and show that a significant portion of this particulate arsenic is present as complex arsenic-containing lipids. We characterize this arsenolipid pool chemically and quantitatively to show a variety of chemically distinct and quantitatively significant lipids that expand our understanding of marine arsenic biogeochemistry. Data availability statement Mass spectrometry data and its associated metadata will be available on Dryad with publication. Scripts for data processing and figure generation found on github at https://github.com/kheal/particulate_As_data_analysis.
Generalized Boltzmann hierarchy for massive neutrinos in cosmology

Boltzmann solvers are an important tool for the computation of cosmological observables in the linear regime. In the presence of massive neutrinos, they involve solving the Boltzmann equation followed by an integration in momentum space to arrive at the desired fluid properties, a procedure which is known to be computationally slow. In this work we introduce the so-called generalized Boltzmann hierarchy (GBH) for massive neutrinos in cosmology, an alternative to the usual Boltzmann hierarchy, where the momentum dependence is integrated out leaving us with a two-parameter infinite set of ordinary differential equations. Along with the usual expansion in multipoles, there is now also an expansion in higher velocity weight integrals of the distribution function. Using a toy code, we show that the GBH produces the density contrast neutrino transfer function to a $\ensuremath{\lesssim}0.5%$ accuracy at both large and intermediate scales compared to the neutrino free-streaming scale, thus providing a proof-of-principle for the GBH. We comment on the implementation of the GBH in a state of the art Boltzmann solver.
A spectral sequence for Dehn fillings

We study how the cohomology of a type $F_\infty$ relatively hyperbolic group pair $(G,\mathcal{P})$ changes under Dehn fillings (i.e. quotients of group pairs). For sufficiently long Dehn fillings where the quotient pair $(\bar{G},\bar{\mathcal{P}})$ is type $F_\infty$, we show that there is a spectral sequence relating the cohomology groups $H^i(G,\mathcal{P};\mathbb{Z}G)$ and $H^i\left(\bar{G},\bar{\mathcal{P}};\mathbb{Z}\bar{G}\right)$. As a consequence, we show that essential cohomological dimension is preserved under these Dehn fillings. This suggests that the topological dimension of the Bowditch boundary is preserved under sufficiently long Dehn fillings.
Microbiome First Approaches to Rescue Public Health and Reduce Human Suffering

The is a sequential article to an initial review suggesting that Microbiome First medical approaches to human health and wellness could both aid the fight against noncommunicable diseases and conditions (NCDs) and help to usher in sustainable healthcare. This current review article specifically focuses on public health programs and initiatives and what has been termed by medical journals as a catastrophic record of recent failures. Included in the review is a discussion of the four priority behavioral modifications (food choices, cessation of two drugs of abuse, and exercise) advocated by the World Health Organization as the way to stop the ongoing NCD epidemic. The lack of public health focus on the majority of cells and genes in the human superorganism, the microbiome, is highlighted as is the “regulatory gap” failure to protect humans, particularly the young, from a series of mass population toxic exposures (e.g., asbestos, trichloroethylene, dioxin, polychlorinated biphenyls, triclosan, bisphenol A and other plasticizers, polyfluorinated compounds, herbicides, food emulsifiers, high fructose corn syrup, certain nanoparticles, endocrine disruptors, and obesogens). The combination of early life toxicity for the microbiome and connected human physiological systems (e.g., immune, neurological), plus a lack of attention to the importance of microbial rebiosis has facilitated rather than suppressed, the NCD epidemic. This review article concludes with a call to place the microbiome first and foremost in public health initiatives as a way to both rescue public health effectiveness and reduce the human suffering connected to comorbid NCDs.
Fractons and Exotic Symmetries from Branes

The emerging study of fractons, a new type of quasi-particle with restricted mobility, has motivated the construction of several classes of interesting continuum quantum field theories with novel properties. One such class consists of foliated field theories which, roughly, are built by coupling together fields supported on the leaves of foliations of spacetime. Another approach, which we refer to as exotic field theory, focuses on constructing Lagrangians consistent with special symmetries (like subsystem symmetries) that are adjacent to fracton physics. A third framework is that of infinite-component Chern-Simons theories, which attempts to generalize the role of conventional Chern-Simons theory in describing (2+1)D Abelian topological order to fractonic order in (3+1)D. The study of these theories is ongoing, and many of their properties remain to be understood. Historically, it has been fruitful to study QFTs by embedding them into string theory. One way this can be done is via D-branes, extended objects whose dynamics can, at low energies, be described in terms of conventional quantum field theory. QFTs that can be realized in this way can then be analyzed using the rich mathematical and physical structure of string theory. In this paper, we show that foliated field theories, exotic field theories, and infinite-component Chern-Simons theories can all be realized on the world-volumes of branes. We hope that these constructions will ultimately yield valuable insights into the physics of these interesting field theories.