cssi/SciSciGPT-SciSciNet huggingface dataset:
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

One of the most important characteristics of Quantitative Structure Activity Relashionships (QSAR) models is their predictive power. The latter can be defined as the ability of a model to predict accurately the target property (e.g., biological activity) of compounds that were not used for model development. We suggest that this goal can be achieved by rational division of an experimental SAR dataset into the training and test set, which are used for model development and validation, respectively. Given that all compounds are represented by points in multidimensional descriptor space, we argue that training and test sets must satisfy the following criteria: (i) Representative points of the test set must be close to those of the training set; (ii) Representative points of the training set must be close to representative points of the test set; (iii) Training set must be diverse. For quantitative description of these criteria, we use molecular dataset diversity indices introduced recently (Golbraikh, A., J. Chem. Inf. Comput. Sci., 40 (2000) 414-425). For rational division of a dataset into the training and test sets, we use three closely related sphere-exclusion algorithms. Using several experimental datasets, we demonstrate that QSAR models built and validated with our approach have statistically better predictive power than models generated with either random or activity ranking based selection of the training and test sets. We suggest that rational approaches to the selection of training and test sets based on diversity principles should be used routinely in all QSAR modeling research.
Machine-learned patterns suggest that diversification drives economic development

We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modelled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries' exports, PriSDA finds that what most distinguishes countries' export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of per capita income. The inferred model predicts that diversification drives growth in income, that diversified middle-income countries will grow the fastest, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.
A combinatorial model for the Menger curve

We represent the universal Menger curve as the topological realization |&#x1d544;| of the projective Fraisse limit &#x1d544; of the class of all finite connected graphs. We show that &#x1d544; satisfies combinatorial anal...
Entropic CLT for Smoothed Convolutions and Associated Entropy Bounds

We explore an asymptotic behavior of entropies for sums of independent random variables that are convolved with a small continuous noise.
Towards Interpretable Graph Modeling with Vertex Replacement Grammars

An enormous amount of real-world data exists in the form of graphs. Oftentimes, interesting patterns that describe the complex dynamics of these graphs are captured in the form of frequently reoccurring substructures. Recent work at the intersection of formal language theory and graph theory has explored the use of graph grammars for graph modeling and pattern mining. However, existing formulations do not extract meaningful and easily interpretable patterns from the data. The present work addresses this limitation by extracting a special type of vertex replacement grammar, which we call a KT grammar, according to the Minimum Description Length (MDL) heuristic. In experiments on synthetic and real-world datasets, we show that KT-grammars can be efficiently extracted from a graph and that these grammars encode meaningful patterns that represent the dynamics of the real-world system.
Transcriptome analysis reveals the difference between “healthy” and “common” aging and their connection with age‐related diseases

A key goal of aging research was to understand mechanisms underlying healthy aging and develop methods to promote the human healthspan. One approach is to identify gene regulations unique to healthy aging compared with aging in the general population (i.e., "common" aging). Here, we leveraged Genotype-Tissue Expression (GTEx) project data to investigate "healthy" and "common" aging gene expression regulations at a tissue level in humans and their interconnection with diseases. Using GTEx donors' disease annotations, we defined a "healthy" aging cohort for each tissue. We then compared the age-associated genes derived from this cohort with age-associated genes from the "common" aging cohort which included all GTEx donors; we also compared the "healthy" and "common" aging gene expressions with various disease-associated gene expressions to elucidate the relationships among "healthy," "common" aging and disease. Our analyses showed that 1. GTEx "healthy" and "common" aging shared a large number of gene regulations; 2. Despite the substantial commonality, "healthy" and "common" aging genes also showed distinct function enrichment, and "common" aging genes had a higher enrichment for disease genes; 3. Disease-associated gene regulations were overall different from aging gene regulations. However, for genes regulated by both, their regulation directions were largely consistent, implying some aging processes could increase the susceptibility to disease development; and 4. Possible protective mechanisms were associated with some "healthy" aging gene regulations. In summary, our work highlights several unique features of GTEx "healthy" aging program. This new knowledge could potentially be used to develop interventions to promote the human healthspan.
Transient invaders can induce shifts between alternative stable states of microbial communities

Microbial dispersal often leads to the arrival of outsider organisms into ecosystems. When their arrival gives rise to successful invasions, outsider species establish within the resident community, which can markedly alter the ecosystem. Seemingly less influential, the potential impact of unsuccessful invaders that interact only transiently with the community has remained largely ignored. Here, we experimentally demonstrate that these transient invasions can induce a lasting transition to an alternative stable state, even when the invader species itself does not survive the transition. First, we develop a mechanistic understanding of how environmental changes caused by these transient invaders can drive a community shift in a simple, bistable model system. Beyond this, we show that transient invaders can also induce switches between stable states in more complex communities isolated from natural soil samples. Our results demonstrate that short-term interactions with an invader species can induce lasting shifts in community composition and function.
Histologic safety of transcranial focused ultrasound neuromodulation and magnetic resonance acoustic radiation force imaging in rhesus macaques and sheep

Abstract Background Neuromodulation by transcranial focused ultrasound (FUS) offers the potential to non-invasively treat specific brain regions, with treatment location verified by magnetic resonance acoustic radiation force imaging (MR-ARFI). Objective To investigate the safety of these methods prior to widespread clinical use, we report histologic findings in two large animal models following FUS neuromodulation and MR-ARFI. Methods Two rhesus macaques and thirteen Dorset sheep were studied. FUS neuromodulation was targeted to the primary visual cortex in rhesus macaques and to subcortical locations, verified by MR-ARFI, in eleven sheep. Both rhesus macaques and five sheep received a single FUS session, whereas six sheep received repeated sessions three to six days apart. The remaining two control sheep did not receive ultrasound but otherwise underwent the same anesthetic and MRI procedures as the eleven experimental sheep. Hematoxylin and eosin-stained sections of brain tissue (harvested zero to eleven days following FUS) were evaluated for tissue damage at FUS and control locations as well as tissue within the path of the FUS beam. TUNEL staining was used to evaluate for the presence of apoptosis in sheep receiving high dose FUS. Results No FUS-related pre-mortem histologic findings were observed in the rhesus macaques or in any of the examined sheep. Extravascular red blood cells (RBCs) were present within the meninges of all sheep, regardless of treatment group. Similarly, small aggregates of perivascular RBCs were rarely noted in non-target regions of neural parenchyma of FUS-treated (8/11) and untreated (2/2) sheep. However, no concurrent histologic abnormalities were observed, consistent with RBC extravasation occurring as post-mortem artifact following brain extraction. Sheep within the high dose FUS group were TUNEL-negative at the targeted site of FUS. Conclusions The absence of FUS-related histologic findings suggests that the neuromodulation and MR-ARFI protocols evaluated do not cause tissue damage.
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks.

It has been demonstrated that very simple attacks can fool highly-sophisticated neural network architectures. In particular, so-called adversarial examples, constructed from perturbations of input data that are small or imperceptible to humans but lead to different predictions, may lead to an enormous risk in certain critical applications. In light of this, there has been a great deal of work on developing adversarial training strategies to improve model robustness. These training strategies are very expensive, in both human and computational time. To complement these approaches, we propose a very simple and inexpensive strategy which can be used to ``retrofit'' a previously-trained network to improve its resilience to adversarial attacks. More concretely, we propose a new activation function---the JumpReLU---which, when used in place of a ReLU in an already-trained model, leads to a trade-off between predictive accuracy and robustness. This trade-off is controlled by the jump size, a hyper-parameter which can be tuned during the validation stage. Our empirical results demonstrate that this increases model robustness, protecting against adversarial attacks with substantially increased levels of perturbations. This is accomplished simply by retrofitting existing networks with our JumpReLU activation function, without the need for retraining the model. Additionally, we demonstrate that adversarially trained (robust) models can greatly benefit from retrofitting.
Quantitative $$l^p$$-Improving for Discrete Spherical Averages Along the Primes

We show quantitative (in terms of the radius) $$l^p$$-improving estimates for the discrete spherical averages along the primes. These averaging operators were defined in [1] and are discrete, prime variants of Stein’s spherical averages. The proof uses a precise decomposition of the Fourier multiplier.
Downlink Analysis of NOMA-Enabled Cellular Networks With 3GPP-Inspired User Ranking

This paper provides a comprehensive downlink analysis of non-orthogonal multiple access (NOMA) enabled cellular networks using tools from stochastic geometry. As a part of this analysis, we develop a novel 3GPP-inspired user ranking technique to construct a user cluster for the non-orthogonal transmission by grouping users from the cell center (CC) and cell edge (CE) regions. This technique allows the pairing of users with distinct link qualities, which is imperative for harnessing NOMA performance gains. The analysis is performed from the perspective of the typical cell , which is significantly different from the standard stochastic geometry-based approach of analyzing the performance of the typical user . For this setting, we first derive the moments of the meta distributions for the CC and CE users under NOMA and orthogonal multiple access (OMA). Using this, we then derive the distributions of the transmission rates and mean packet delays under non-real time (NRT) and real-time (RT) service models, respectively, for both CC and CE users. Finally, we study two resource allocation (RA) techniques to maximize the cell sum-rate ( $\mathtt {CSR}$ ) under NRT service, and the sum effective capacity ( $\mathtt {SEC}$ ) under RT service. In addition to providing several useful design insights, our results demonstrate that NOMA provides improved rate region and higher $\mathtt {CSR}$ as compared to OMA. In addition, we also show that NOMA provides better $\mathtt {SEC}$ as compared to OMA for the higher user density.
Sum-of-Squares Optimization and the Sparsity Structure of Equiangular Tight Frames

Equiangular tight frames (ETFs) may be used to construct examples of feasible points for semidefinite programs arising in sum-of-squares (SOS) optimization. We show how generalizing the calculations in a recent work of the authors’ that explored this connection also yields new bounds on the sparsity of (both real and complex) ETFs. One corollary shows that Steiner ETFs corresponding to finite projective planes are optimally sparse in the sense of achieving tightness in a matrix inequality controlling overlaps between sparsity patterns of distinct rows of the synthesis matrix. We also formulate several natural open problems concerning further generalizations of our technique.
Utilizing Information Optimally to Influence Distributed Network Routing

How can a system designer exploit system-level knowledge to derive incentives to optimally influence social behaviorƒ The literature on network routing contains many results studying the application of monetary tolls to influence behavior and improve the efficiency of self-interested network traffic routing. These results typically fall into two categories: (1) optimal tolls which incentivize socially-optimal behavior for a known realization of the network and population, or (2) robust tolls which provably reduce congestion given uncertainty regarding networks and user types, but may fail to optimize routing in general. This paper advances the study of robust influencing, mechanisms asking how a system designer can optimally exploit additional information regarding the network structure and user price sensitivities to design pricing mechanisms which influence behavior. We design optimal scaled marginal-cost pricing mechanisms for a class of parallel-network routing games and derive the tight performance guarantees when the network structure and/or the average user price-sensitivity is known. Our results demonstrate that from the standpoint of the system operator, in general it is more important to know the structure of the network than it is to know distributional information regarding the user population.
Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we focus on the LTE-Unlicensed (LTE-U) specification developed by the LTE-U Forum, which uses the duty-cycle approach for fair coexistence. The specification suggests reducing the duty cycle at the LTE-U base-station (BS) when the number of co-channel Wi-Fi basic service sets (BSSs) increases from one to two or more. However, without decoding the Wi-Fi packets, detecting the number of Wi-Fi BSSs operating on the channel in real-time is a challenging problem. In this work, we demonstrate a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration. It is relatively straightforward to observe only the energy values during the LTE-U BS OFF time compared to decoding the entire Wi-Fi packet, which would require a full Wi-Fi receiver at the LTE-U base-station. We implement and validate the proposed ML-based approach by real-time experiments and demonstrate that there exist distinct patterns between the energy distributions between one and many Wi-Fi AP transmissions. The proposed ML-based approach results in a higher accuracy (close to 99% in all cases) as compared to the existing auto-correlation (AC) and energy detection (ED) approaches.
Stack words and a bound for 3-stack sortable permutations

Abstract We use stack words to find a new, simple proof for the best known upper bound for the number of 3-stack sortable permutations of a given length. This is the first time that stack words are used to obtain such a result.
Response of SDO/HMI Observables to Heating of the Solar Atmosphere by Precipitating High-energy Electrons

We perform an analysis of the line-of-sight (LOS) observables of the SDO/HMI for models of the solar atmosphere heated by precipitating high-energy electrons during solar flares. The radiative hydrodynamic (RADYN) flare models are obtained from the F-CHROMA database. The Stokes profiles for the Fe 6173 A line observed by SDO/HMI are calculated using the radiative transfer code RH1.5D assuming statistical equilibrium for atomic level populations and imposing uniform background vertical magnetic field of various strength. The SDO/HMI observing sequence and LOS data processing pipeline algorithm are applied to derive the observables (continuum intensity, line depth, Doppler velocity, LOS magnetic field). Our results reveal that the strongest deviations of the observables from the actual spectroscopic line parameters are found for the model with a total energy deposited of $E_{total}=1.0\times{}10^{12}$ erg cm$^{-2}$, injected with power-law spectral index of $\delta=3$ above a low-energy cutoff of $E_{c}=25$ keV. The magnitudes of the velocity and magnetic field deviations depend on the imposed magnetic field, and can reach 0.35 km/s for LOS velocities, 90 G for LOS magnetic field, and 3\% for continuum enhancement for the 1000 G imposed LOS magnetic field setup. For $E_{total}\geq{}3.0\times{}10^{11}$ erg cm$^{-2}$ models, the velocity and magnetic field deviations are most strongly correlated with the energy flux carried by $\sim$50 keV electrons, and the continuum enhancement is correlated with the synthesized $\sim$55-60 keV hard X-ray photon flux. The relatively low magnitudes of perturbations of the observables and absence of magnetic field sign reversals suggest that the considered radiative hydrodynamic beam heating models augmented with the uniform vertical magnetic field setups cannot explain strong transient changes found in the SDO/HMI observations.
The impact of the third sector of R&D on the innovative performance of entrepreneurial firms

Entrepreneurial firms that rely on public research institutes, the third sector of R&D, are also firms that are more innovative in terms of introducing new or significantly improved goods or services to the market. This finding is based on an analysis of 4004 knowledge-intensive entrepreneurial (KIE) firms located in ten European Union countries. We interpret our findings as suggestive evidence of the importance of policymakers continuing to support financially public research institutions.
Open source software tool for the automated detection and characterization of epithelial cells from trace biological samples.

Abstract This paper presents a strategy for an unsupervised workflow for identifying epithelial cells in microscopic images and characterizing their morphological and/or optical properties. The proposed method can be used on cells that have been stained with fluorescent dyes and imaged using conventional optical microscopes. The workflow was tested on cell populations that were imaged directly on touch/contact surfaces and stained with nucleic acid dyes to visualize genetic content. Our results show that this approach could be a useful strategy for characterizing differences in staining efficiency and/or morphological properties of individual cells or aggregate populations within a biological sample. Further, they can potentially reduce the laborious nature of microscopic analysis and increase throughput and reproducibility of similar studies.
Pair-production supernovae: Theory and observation

I review the physical properties of pair-production supernovae (PPSNe) as well as the prospects for them to be constrained observationally. In very massive (140-260 solar mass) stars, much of the pressure support comes from the radiation field, meaning that they are loosely bound, with an adiabatic coefficient that is close to the minimum stable value. Near the end of C/O burning, the central temperature increases to the point that photons begin to be converted into electron-positron pairs, softening gamma below this critical value. The result is a runaway collapse, followed by explosive burning that completely obliterates the star. While these explosions can be up to 100 times more energetic that core collapse and Type Ia supernovae, their peak luminosities are only slightly greater. However, due both to copious Ni-56 production and hydrogen recombination, they are brighter much longer, and remain observable for ~ 1 year. Since metal enrichment is a local process, PPSNe should occur in pockets of metal-free gas over a broad range of redshifts, greatly enhancing their detectability, and distributing their nucleosyntehtic products about the Milky Way. This means that measurements of the abundances of metal-free stars should be thought of as directly constraining these objects. It also means that ongoing supernova searches, already provide weak constraints for PPSN models. A survey with the NIRCam instrument on JWST, on the other hand, would be able to extend these limits to z ~ 10. Observing a 0.3 deg^2 patch of sky for one week per year for three consecutive years, such a program would either detect or rule out the existence of these remarkable objects.
Dirac’s Legacy: Light-Cone Quantization

In recent years light-cone quantization of quantum field theory has emerged as a promising method for solving problems in the strong coupling regime. This approach has a number of unique features that make it particularly appealing, most notably, the ground state of the free theory is also a ground state of the full theory.
Progressive multifocal leukoencephalopathy.

Treatment of progressive multifocal leukoencephalopathy (PML) in a patient with exogenous immunosuppression starts with discontinuation of immunosuppressive medication. The restored host immunity will clear JC virus, the cause of PML, from the brain via cell-mediated immune mechanisms. Patients with solid-organ transplants will lose the transplanted organ, however, and patients who have auto-immune disorders may experience exacerbation of their underlying disease. These factors need to be weighed against the potentially fatal nature of PML. If the patient’s immunosupression is AIDS-related, highly active antiretroviral therapy (HAART) should be initiated if it has not previously been used. If the patient is already receiving HAART, the therapy should be changed to optimize treatment, with the goals of a nondetectable HIV viral load and normalization or near normalization of the CD4 count. For non-AIDS PML patients, daily intravenous cytosine arabinoside for 5 days can be offered if the patient is not pancytopenic and can tolerate a chemotherapeutic agent. For AIDS patients with PML or failing non-AIDS patients with neurologic deterioration, cidofovir can be considered. These therapies can be offered if neurologic stabilization satisfies the quality-of-life goals for the patient. For patients intolerant of other therapies or unsuited to them, oral mirtazapine or risperidone can be considered. The safety of these agents has been established in the treatment of psychiatric disease, but their efficacy has not yet been proven. Small interfering RNA (siRNA) therapy holds the promise of specific antiviral therapy, but delivery methods, safety, and efficacy are yet to be established.
Open microfluidic coculture reveals paracrine signaling from human kidney epithelial cells promotes kidney specificity of endothelial cells.

Endothelial cells (ECs) from different human organs possess organ-specific characteristics that support specific tissue regeneration and organ development. EC specificity is identified by both intr...
Performance Analysis of Software Defined Network Concepts in Networked Embedded Systems

Software-Defined Networking (SDN) has been largely used in wired networks to separate the control plane and the data plane and enable users to programmatically change the networking capabilities. This investigation focused on understanding how SDN concepts could be applied in a networked embedded systems environment where the nodes have limited capabilities and the wireless links have limited bandwidth. The goals were to realize the benefits of SDN while maintaining the network’s topology and connectivity. A SDN controller was implemented on BeagleBone Black Board and interfaced with a networked embedded system via a SINK node. The approach was validated through simulations and experiments based on a physical testbed in multiple scenarios. In the future, the design can be extended to a fully capable Wireless-SDN system for use in a variety of applications such as Healthcare Systems, Internet of Things and Advanced Manufacturing Systems.
Proof of a conjecture of Chan, Robbins, and Yuen

Using the celebrated Morris Constant Term Identity, we deduce a recent conjecture of Chan, Robbins, and Yuen (math.CO/9810154), that asserts that the volume of a certain $n(n-1)/2$-dimensional polytope is given by the product of the first n-1 Catalan numbers.
Herbig-Haro Objects

The available observational information on the geometrical structure, emission line and continuous spectra, line profiles, radial velocities and linear polarization of Herbig-Haro objects is briefly reviewed. We emphasize the inhomogeneous structure of the “classical” Herbig-Haro objects and the appearance of small “condensations” with radii of ~300-900 a.u. The apparent paradox of the presence of “gaseous nebula type” as well as “reflection type” Herbig-Haro objects is discussed. A purely empirical model of the regions of line formation is discussed. It shows that regions of low density (N e ~10 3 cm -3 ) cover the space between the condensations and most of the volume of the condensations themselves. Only between 0.1 and 1% of the volume of the condensations is covered by a high density medium (N e ~ 4 x 10 4 cm -3 , N ~10 5 cm -3 ) which, however, contributes very strongly to the formation of the spectrum. Different theoretical models for the line forming regions are discussed. We strongly favor the shock wave theory in which the emission lines are formed in the cooling regions of (running) shock waves. The general agreement between observations and the new calculations by Raymond is emphasized, and the few remaining discrepancies are discussed. The possibilities of explaining other properties of Herbig-Haro objects (including time scales, sizes and filling factors of condensations) are described.
Deep Learning Based Detection of Acute Aortic Syndrome in Contrast CT Images

Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta. We have developed an end-to-end automatic approach to detect AAS in computed tomography (CT) images. Our approach consists of two steps. At first, we extract N cross sections along the segmented aorta centerline for each CT scan. These cross sections are stacked together to form a new volume which is then classified using two different classifiers, a 3D convolutional neural network (3D CNN) and a multiple instance learning (MIL). We trained, validated, and compared two models on 2291 contrast CT volumes. We tested on a set aside cohort of 230 normal and 50 positive CT volumes. Our models detected AAS with an Area under Receiver Operating Characteristic curve (AUC) of 0.965 and 0.985 using 3DCNN and MIL, respectively.
Transfer Reinforcement Learning under Unobserved Contextual Information

In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data constitute the experience of the demonstrator. Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator’s data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner.
Heterogeneous T cell motility behaviors emerge from a coupling between speed and turning in vivo

T cells in vivo migrate primarily via undirected random walks, but it remains unresolved how these random walks generate an efficient search. Here, we use light sheet microscopy of T cells in the larval zebrafish as a model system to study motility across large populations of cells over hours in their native context. We show that cells do not perform Levy flight; rather, there is substantial cell-to-cell variability in speed, which persists over timespans of a few hours. This variability is amplified by a correlation between speed and directional persistence, generating a characteristic cell behavioral manifold that is preserved under a perturbation to cell speeds, and seen in Mouse T cells and Dictyostelium. Together, these effects generate a broad range of length scales over which cells explore in vivo.
Animal biosynthesis of complex polyketides in a photosynthetic partnership

Complex polyketides are typically associated with microbial metabolism. Here, we report that animals also make complex, microbe-like polyketides. We show there is a widespread branch of fatty acid synthase- (FAS)-like polyketide synthase (PKS) proteins, which sacoglossan animals use to synthesize complex products. The purified sacogolassan protein EcPKS1 uses only methylmalonyl-CoA as a substrate, otherwise unknown in animal lipid metabolism. Sacoglossans are sea slugs, some of which eat algae, digesting the cells but maintaining functional chloroplasts. Here, we provide evidence that polyketides support this unusual photosynthetic partnership. The FAS-like PKS family represents an uncharacterized branch of polyketide and fatty acid metabolism, encoding a large diversity of biomedically relevant animal enzymes and chemicals awaiting discovery. The biochemical characterization of an intact animal polyketide biosynthetic enzyme opens the door to understanding the immense untapped metabolic potential of metazoans.
Links in surfaces and Laplacian modules

Laplacian matrices of signed graphs in surfaces S are used to define module and polynomial invariants of ℤ/2-homologically trivial links in S × [0, 1]. Information about virtual genus is obtained.
Asymmetric information and personal affiliations in brokered housing transactions

This research provides evidence on the role of information asymmetry and personal affiliations in housing transactions using a novel dataset that allows observation of detailed information about the property, broker, and seller. Sellers holding a real estate license are found to obtain about 1.6% (or $3,200) more for their properties than unlicensed sellers, and sellers personally affiliated to a real estate licensee (e.g., spouses, siblings, parents) also obtain a premium of about the same size, all else equal. There is evidence from appraisal training that knowledge of property valuation further increases the price disparities, while informative public property records reduce price disparities. Agents and personal affiliates of agents appear to use information advantages to time the market and capture capital gains. These new findings provide insights into the behaviors of market participants in asymmetric information environments.
Reducing shock imminence eliminates poor avoidance in rats

In signaled active avoidance (SigAA), rats learn to suppress Pavlovian freezing and emit actions to remove threats and prevent footshocks. SigAA is critical for understanding aversively motivated instrumental behavior and anxiety-related active coping. However, with standard protocols ∼25% of rats exhibit high freezing and poor avoidance. This has dampened enthusiasm for the paradigm and stalled progress. We demonstrate that reducing shock imminence with long-duration warning signals leads to greater freezing suppression and perfect avoidance in all subjects. This suggests that instrumental SigAA mechanisms evolved to cope with distant harm and protocols that promote inflexible Pavlovian reactions are poorly designed to study avoidance.
Syntactic Data Augmentation Increases Robustness to Inference Heuristics

Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets. We hypothesize that this issue is not primarily caused by the pretrained model’s limitations, but rather by the paucity of crowdsourced NLI examples that might convey the importance of syntactic structure at the fine-tuning stage. We explore several methods to augment standard training sets with syntactically informative examples, generated by applying syntactic transformations to sentences from the MNLI corpus. The best-performing augmentation method, subject/object inversion, improved BERT’s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73, without affecting performance on the MNLI test set. This improvement generalized beyond the particular construction used for data augmentation, suggesting that augmentation causes BERT to recruit abstract syntactic representations.
Performance-based post-earthquake decision making for instrumented buildings

This paper develops a decision making framework for post-earthquake assessment of instrumented buildings in a manner consistent with performance-based design criteria. This framework is achieved by simultaneously combining and advancing existing knowledge from seismic structural health monitoring and performance-based earthquake engineering paradigms. The framework consists of (1) measurement, (2) uncertainty modeling, (3) dynamic response reconstruction, (4) damage estimation, and (5) performance-based assessment and decision making. In particular, the main objective is to reconstruct inter-story drifts with a probabilistic measure of exceeding performance-based acceptance limits and determine the post-earthquake re-occupancy classification of the instrumented building of interest. Since the proposed framework is probabilistic, the outcome can be used to obtain the probability of losses based on the defined decision variables and be integrated into a risk-based decision making process by city officials, building owners, and emergency managers. The framework is illustrated using data from the Van Nuys hotel testbed, a seven-story reinforced concrete building instrumented by the California Strong Motion Instrumentation Program (CSMIP Station 24386).
Using Waste Carbon Feedstocks to Produce Chemicals

Emerging carbon capture utilization (CCU) technologies potentially allow chemical companies and other manufacturers to capture waste carbon—in the form of carbon monoxide (CO) and/or carbon dioxide...
Seeded graph matching via large neighborhood statistics

We study a well known noisy model of the graph isomorphism problem. In this model, the goal is to perfectly recover the vertex correspondence between two edge-correlated Erdős-Renyi random graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. For seeded problems, our result provides a significant improvement over previously known results. We show that it is possible to achieve the information-theoretic limit of graph sparsity in time polynomial in the number of vertices $n$. Moreover, we show the number of seeds needed for exact recovery in polynomial-time can be as low as $n^{3\epsilon}$ in the sparse graph regime (with the average degree smaller than $n^{\epsilon}$) and $\Omega(\log n)$ in the dense graph regime. Our results also shed light on the unseeded problem. In particular, we give sub-exponential time algorithms for sparse models and an $n^{O(\log n)}$ algorithm for dense models for some parameters, including some that are not covered by recent results of Barak et al.
The regulatory landscape of early maize inflorescence development

The functional genome of agronomically important plant species remains largely unexplored, yet presents a virtually untapped resource for targeted crop improvement. Functional elements of regulatory DNA revealed through profiles of chromatin accessibility can be harnessed for fine-tuning gene expression to optimal phenotypes in specific environments. Here, we investigate the non-coding regulatory space in the maize (Zea mays) genome during early reproductive development of pollen- and grain-bearing inflorescences. Using an assay for differential sensitivity of chromatin to micrococcal nuclease (MNase) digestion, we profile accessible chromatin and nucleosome occupancy in these largely undifferentiated tissues and classify at least 1.6% of the genome as accessible, with the majority of MNase hypersensitive sites marking proximal promoters, but also 3′ ends of maize genes. This approach maps regulatory elements to footprint-level resolution. Integration of complementary transcriptome profiles and transcription factor occupancy data are used to annotate regulatory factors, such as combinatorial transcription factor binding motifs and long non-coding RNAs, that potentially contribute to organogenesis, including tissue-specific regulation between male and female inflorescence structures. Finally, genome-wide association studies for inflorescence architecture traits based solely on functional regions delineated by MNase hypersensitivity reveals new SNP-trait associations in known regulators of inflorescence development as well as new candidates. These analyses provide a comprehensive look into the cis-regulatory landscape during inflorescence differentiation in a major cereal crop, which ultimately shapes architecture and influences yield potential.
Blind Dynamic Resource Allocation in Closed Networks via Mirror Backpressure

We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with finite, fixed number of supply units which circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the "origin" to the "destination" of the customer. We consider general controls including entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a novel family of Mirror Backpressure (MBP) control policies. The MBP policies are simple and practical, and crucially do not need any statistical knowledge of the demand (customer) arrival rates. Under mild conditions, we show that MBP policies lose at most O(K/T +1/K) payoff per customer relative to the optimal policy that knows the demand arrival rates, where K is the number of supply units and T is the total number of customers over the time horizon. The key technical challenge we overcome is that the number of supply units at any node can never be negative. Simulation results in a realistic ridehailing environment support our theoretical findings.
Dryland Cropping Systems, Weed Communities, and Disease Status Modulate the Effect of Climate Conditions on Wheat Soil Bacterial Communities

ABSTRACT Little knowledge exists on how soil bacteria in agricultural settings are impacted by management practices and environmental conditions in current and predicted climate scenarios. We assessed the impact of soil moisture, soil temperature, weed communities, and disease status on soil bacterial communities in three cropping systems: (i) conventional no-till (CNT) systems utilizing synthetic pesticides and herbicides, (ii) USDA-certified tilled organic (OT) systems, and (iii) USDA-certified organic systems with sheep grazing (OG). Sampling date within the growing season and associated soil temperature and moisture exerted the greatest effect on bacterial communities, followed by cropping system, Wheat streak mosaic virus (WSMV) infection status, and weed community. Soil temperature was negatively correlated with bacterial richness and evenness, while soil moisture was positively correlated with bacterial richness and evenness. Soil temperature and soil moisture independently altered soil bacterial community similarity between treatments. Inoculation of wheat with WSMV altered the associated soil bacteria, and there were interactions between disease status and cropping system, sampling date, and climate conditions, indicating the effect of multiple stressors on bacterial communities in soil. In May and July, cropping system altered the effect of climate change on the bacterial community composition in hotter conditions and in hotter and drier conditions compared to ambient conditions, in samples not treated with WSMV. Overall, this study indicates that predicted climate modifications as well as biological stressors play a fundamental role in the impact of cropping systems on soil bacterial communities. IMPORTANCE Climate change is affecting global moisture and temperature patterns, and its impacts are predicted to worsen over time, posing progressively larger threats to food production. In the Northern Great Plains of the United States, climate change is forecast to increase temperature and decrease precipitation during the summer, and it is expected to negatively affect cereal crop production and pest management. In this study, temperature, soil moisture, weed communities, and disease status had interactive effects with cropping system on bacterial communities. As local climates continue to shift, the dynamics of above- and belowground associated biodiversity will also shift, which will impact food production and increase the need for more sustainable practices.
Robust LQR for Uncertain Discrete-Time Systems using Polynomial Chaos.

In this paper, a polynomial chaos based framework for designing controllers for discrete time linear systems with probabilistic parameters is presented. Conditions for exponential-mean-square stability for such systems are derived and algorithms for synthesizing optimal quadratically stabilizing controllers are proposed in a convex optimization formulation. The solution presented is demonstrated on the derived discrete-time models of a nonlinear F-16 aircraft model trimmed at a set of chosen points.
On the Maximal Rate of Convergence Under the Ricci Flow

We estimate from above the rate at which a solution to the normalized Ricci flow on a closed manifold may converge to a limit soliton. Our main result implies that any solution which converges modulo diffeomorphisms to a soliton faster than any fixed exponential rate must itself be self-similar.
Optimization Based Planner-Tracker Design for Safety Guarantees.

We present a safe-by-design approach to path planning and control for nonlinear systems. The planner uses a low fidelity model of the plant to compute reference trajectories by solving an MPC problem, while the plant being controlled utilizes a feedback control law that tracks those trajectories with an upper-bound on the tracking error. Our main goal is to allow for maximum permissiveness (that is, room for constraint feasibility) of the planner, while maintaining safety after accounting for the tracking error bound. We achieve this by parametrizing the state and input constraints imposed on the planner and deriving corresponding parametrized tracking control laws and tracking error bounds, which are computed offline through Sum-of-Squares programming. The parameters are then optimally chosen to maximize planner permissiveness, while guaranteeing safety.
A Single Radioprotective Dose of Prostaglandin E2 Blocks Irradiation-Induced Apoptotic Signaling and Early Cycling of Hematopoietic Stem Cells

Summary Ionizing radiation exposure results in acute and delayed bone marrow suppression. Treatment of mice with 16,16-dimethyl prostaglandin E2 (dmPGE2) prior to lethal ionizing radiation (IR) facilitates survival, but the cellular and molecular mechanisms are unclear. In this study we show that dmPGE2 attenuates loss and enhances recovery of bone marrow cellularity, corresponding to a less severe hematopoietic stem cell nadir, and significantly preserves long-term repopulation capacity and progenitor cell function. Mechanistically, dmPGE2 suppressed hematopoietic stem cell (HSC) proliferation through 24 h post IR, which correlated with fewer DNA double-strand breaks and attenuation of apoptosis, mitochondrial compromise, oxidative stress, and senescence. RNA sequencing of HSCs at 1 h and 24 h post IR identified a predominant interference with IR-induced p53-downstream gene expression at 1 h, and confirmed the suppression of IR-induced cell-cycle genes at 24 h. These data identify mechanisms of dmPGE2 radioprotection and its potential role as a medical countermeasure against radiation exposure.
An Off-Chip Attack on Hardware Enclaves via the Memory Bus

This paper shows how an attacker can break the confidentiality of a hardware enclave with Membuster, an off-chip attack based on snooping the memory bus. An attacker with physical access can observe an unencrypted address bus and extract fine-grained memory access patterns of the victim. Membuster is qualitatively different from prior on-chip attacks to enclaves and is more difficult to thwart. We highlight several challenges for Membuster. First, DRAM requests are only visible on the memory bus at last-level cache misses. Second, the attack needs to incur minimal interference or overhead to the victim to prevent the detection of the attack. Lastly, the attacker needs to reverse-engineer the translation between virtual, physical, and DRAM addresses to perform a robust attack. We introduce three techniques, critical page whitelisting, cache squeezing, and oracle-based fuzzy matching algorithm to increase cache misses for memory accesses that are useful for the attack, with no detectable interference to the victim, and to convert memory accesses to sensitive data. We demonstrate Membuster on an Intel SGX CPU to leak confidential data from two applications: Hunspell and Memcached. We show that a single uninterrupted run of the victim can leak most of the sensitive data with high accuracy.