cssi/SciSciGPT-SciSciNet huggingface dataset:
Contrasting sensitivity of lake sediment n-alkanoic acids and n-alkanes to basin-scale vegetation and regional-scale precipitation δ2H in the Adirondack Mountains, NY (USA)

Abstract The hydrogen isotope values of plant waxes (δ2Hwax) primarily reflect plant source water. δ2Hwax preserved in lake sediments has therefore been widely used to investigate past hydroclimate. The processes by which plant waxes are integrated at regional and catchment scales are poorly understood and may affect the δ2Hwax values recorded in sediments. Here, we assess the variability of sedimentary δ2Hwax for two plant wax compound classes (n-alkanes and n-alkanoic acids) across 12 lakes in the Adirondack Mountains that receive similar regional precipitation δ2H but vary at the catchment-scale in terms of vegetation structure and basin morphology. Total long-chain (n-C27 to n-C35) alkane concentrations were similar across all sites (191 ± 53 µg/g TOC) while total long-chain (n-C28 and n-C30) alkanoic acid concentrations were more variable (117 ± 116 µg/g TOC) and may reflect shoreline vegetation composition. Lakes with shorelines dominated by evergreen gymnosperm plants had significantly higher concentrations of long-chain n-alkanoic acids relative to n-alkanes, consistent with our observations that deciduous angiosperms produced more long-chain n-alkanes than evergreen gymnosperms (471 and 33 µg/g TOC, respectively). In sediments, the most abundant chain lengths in each compound class were n-C29 alkane and n-C28 alkanoic acid, which had mean δ2H values of −188 ± 6‰ and −164 ± 9‰, respectively. Across sites, the range in sedimentary n-C29 alkane (22‰) and n-C28 alkanoic acid δ2H (35‰) was larger than expected based on the total range in modeled mean annual precipitation δ2H (4‰). We observed larger mean eapp (based on absolute values) for n-alkanes (−123‰) than for n-alkanoic acids (−97‰). Across sites, the δ2H offset between n-C29 alkane and the biosynthetic precursor n-C30 alkanoic acid (eC29-C30) ranged from −8 to −58‰, which was more variable than expected based on observations in temperate trees (−20 to −30‰). Sediments with greater aquatic organic matter contributions (lower C/N ratios) had significantly larger (absolute) eC29-C30 values, which may reflect long-chain n-alkanoic acids from aquatic sources. Concentration and δ2Hwax data in Adirondack lakes suggest that long-chain n-alkanes are more sensitive to regional-scale precipitation signals, while n-alkanoic acids are more sensitive to basin-scale differences in catchment vegetation and wax sourcing.
The Role of Interactivity in Local Differential Privacy

We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of existing lower bounds for local differential privacy apply only to sequentially interactive protocols, and before this paper it was not known whether fully interactive protocols were more powerful. We resolve this question. First, we classify locally private protocols by their compositionality, the multiplicative factor by which the sum of a protocol's single-round privacy parameters exceeds its overall privacy guarantee. We then show how to efficiently transform any fully interactive compositional protocol into an equivalent sequentially interactive protocol with a blowup in sample complexity linear in this compositionality. Next, we show that our reduction is tight by exhibiting a family of problems such that any sequentially interactive protocol requires this blowup in sample complexity over a fully interactive compositional protocol. We then turn our attention to hypothesis testing problems. We show that for a large class of compound hypothesis testing problems --- which include all simple hypothesis testing problems as a special case --- a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.
Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training

Training neural network (NN) often uses a machine learning framework such as TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes. Operations in NN training are typically implemented by the frameworks as primitives and represented as nodes in the dataflow graph. Training NN models in a dataflow-based machine learning framework involves a large number of fine-grained operations whcih present diverse memory access patterns and computation intensity. Managing and scheduling those operations is challenging, because we have to decide the number of threads to run each operation (concurrency control) and schedule those operations for good hardware utilization and system throughput. In this paper, we extend an existing runtime system (the TensorFlow runtime) to enable automatic concurrency control and scheduling of operations. We explore performance modeling to predict the performance of operations with various thread-level parallelism. Our performance model is highly accurate and lightweight. Leveraging the performance model, our runtime system employs a set of scheduling strategies that co-run operations to improve hardware utilization and system throughput. Our runtime system demonstrates a significant performance benefit. Comparing with using the recommended configurations for concurrency control and operation scheduling in TensorFlow, our approach achieves 36% performance (execution time) improvement on average (up to 49%) for four neural network models, and achieves high performance close to the optimal one manually obtained by the user.
Molten ionic oxides for CO2 capture at medium to high temperatures

The development of efficient low cost CO2 capture systems is a critical challenge for mitigating climate change while meeting global energy demand. Herein, we demonstrate the first liquid absorbents for CO2 capture at medium to high temperatures (500 to 700 °C). Molten ionic oxides based on sodium borate and the mixed alkali-metal borates show remarkably fast kinetics and intrinsic regenerability, with no observable deterioration in performance over multiple absorption–desorption cycles under both temperature- and pressure-swing operations. The behavior of the molten ionic oxides is ascribed to the instantaneous formation of carbonate ions in the molten oxides without the diffusional transport restrictions imposed by solid product layers characteristic of solid adsorbents. The new liquid absorbents will enable continuous processing and thermal integration via a simple absorber–desorber arrangement, thereby overcoming the challenges previously restraining high temperature CO2 capture and opening up new opportunities in clean energy production.
Molecular function limits divergent protein evolution on planetary timescales.

Functional conservation is known to constrain protein evolution. Nevertheless, the long-term divergence patterns of proteins maintaining the same molecular function and the possible limits of this divergence have not been explored in detail. We investigate these fundamental questions by characterizing the divergence between ancient protein orthologs with conserved molecular function. Our results demonstrate that the decline of sequence and structural similarities between such orthologs significantly slows down after ~1-2 billion years of independent evolution. As a result, the sequence and structural similarities between ancient orthologs have not substantially decreased for the past billion years. The effective divergence limit (>25% sequence identity) is not primarily due to protein sites universally conserved in all linages. Instead, less than four amino acid types are accepted, on average, per site across orthologous protein sequences. Our analysis also reveals different divergence patterns for protein sites with experimentally determined small and large fitness effects of mutations. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Ultra-high capacity optical transmission technologies for 100 Tb/s and beyond

This presentation reviews recent advances in 100-Tb/s-class ultra-high capacity transmission systems with 400-Gb/s channel rates focusing on spectrally-efficient higher-order multi-level modulation and single-carrier frequency-division multiplexing techniques.
A Staggered Semi-analytic Method for Simulating Dust Grains Subject to Gas Drag

Numerical simulations of dust-gas dynamics are one of the fundamental tools in astrophysical research, such as the study of star and planet formation. It is common to find tightly coupled dust and gas in astrophysical systems, which demands that any practical integration method be able to take time steps $\Delta t$ much longer than the stopping time $t_{\rm s}$ due to drag. A number of methods have been developed to ensure stability in this stiff ($\Delta t\gg t_{\rm s}$) regime, but there remains large room for improvement in terms of accuracy. In this paper, we describe an easy-to-implement method, the "staggered semi-analytic method" (SSA), and conduct numerical tests to compare it to other implicit and semi-analytic methods, including the $2^{\rm nd}$ order implicit method and the Verlet method. SSA makes use of a staggered step to better approximate the terminal velocity in the stiff regime. In applications to protoplanetary disks, this not only leads to orders-of-magnitude higher accuracy than the other methods, but also provides greater stability, making it possible to take time steps 100 times larger in some situations. SSA is also $2^{\rm nd}$ order accurate and symplectic when $\Delta t \ll t_{\rm s}$. More generally, the robustness of SSA makes it applicable to linear dust-gas drag in virtually any context.
Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores.

Motivation Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein-protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein-protein inter-residue contacts remains relatively limited. Results In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein-protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN. Availability and implementation https://github.com/BioMLBoston/PatchDCA. Supplementary information Supplementary data are available at Bioinformatics online.
Scenario Generalization of Data-driven Imitation Models in Crowd Simulation

Crowd simulation, the study of the movement of multiple agents in complex environments, presents a unique application domain for machine learning. One challenge in crowd simulation is to imitate the movement of expert agents in highly dense crowds. An imitation model could substitute an expert agent if the model behaves as good as the expert. This will bring many exciting applications. However, we believe no prior studies have considered the critical question of how training data and training methods affect imitators when these models are applied to novel scenarios. In this work, a general imitation model is represented by applying either the Behavior Cloning (BC) training method or a more sophisticated Generative Adversarial Imitation Learning (GAIL) method, on three typical types of data domains: standard benchmarks for evaluating crowd models, random sampling of state-action pairs, and egocentric scenarios that capture local interactions. Simulated results suggest that (i) simpler training methods are overall better than more complex training methods, (ii) training samples with diverse agent-agent and agent-obstacle interactions are beneficial for reducing collisions when the trained models are applied to new scenarios. We additionally evaluated our models in their ability to imitate real world crowd trajectories observed from surveillance videos. Our findings indicate that models trained on representative scenarios generalize to new, unseen situations observed in real human crowds.
Output coupling from x-ray free-electron laser cavities with intracavity beam splitters

Permeable mirrors are typically used for coupling photons out of laser cavities. A similar approach was proposed for output coupling photons from the cavities of x-ray free-electron laser (XFEL) oscillators. One of the Bragg-reflecting crystal mirrors is thin, just a few extinction length, and is used as a permeable mirror with reduced reflectivity. However, this method is very often limited to extractions of only a few tenths of the intracavity power. Other cavity-based XFELs, such as the high-gain regenerative amplifier XFEL, require much higher outcoupling efficiency. Here, alternative schemes are proposed and analyzed for coupling x-ray photons out of XFEL cavities using intracavity Bragg-reflecting x-ray-transparent diamond crystal beam splitters, with all cavity crystal mirrors being thick and featuring high reflectivity. The intracavity beam splitters are efficient and flexible in terms of the amount of the power they are capable of coupling out of the cavity, an amount that can be varied promptly from zero to close to 100%. The stability and performance of the cavity are now separated from the outcoupling and can be developed and optimized individually. The schemes can be readily extended to multibeam outcoupling. Other types of beam splitters are discussed as well.
An Optimal Vector Clock Algorithm for Multithreaded Systems

Tracking causality (or happened-before relation) between events is useful for many applications such as debugging and recovery from failures. Consider a concurrent system with n threads and m objects. For such systems, either a vector clock of size n is used with one component per thread or a vector clock of size m is used with one component per object. A natural question is whether one can use a vector clock of size strictly less than the minimum of m and n to timestamp events. We give an algorithm in this paper that uses a hybrid of thread and object components. Our algorithm is guaranteed to return the minimum number of components necessary for vector clocks. We first consider the case when the interaction between objects and threads is statically known. This interaction is modeled by a thread-object bipartite graph. Our algorithm is based on finding the maximum bipartite matching of such a graph and then applying Konig-Egervary Theorem to compute the minimum vertex cover to determine the optimal number of components necessary for the vector clock. We also propose two mechanisms to compute such an vector clock when computation is revealed in an online fashion. Evaluation on different types of graphs indicates that our offline algorithm generates a size vector clock which is significantly less than the minimum of m and n. These mechanisms are more effective when the underlying bipartite graph is not dense.
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing

It is our great pleasure to welcome you to the 2010 ACM Multimedia Workshop on Mobile Cloud Media Computing -- MCMC'10. The mission of this workshop is to timely address the challenges and opportunities in applying advanced multimedia technology, cloud computing, pattern recognition, signal processing, user interface, and graphics techniques to mobile systems, given the proliferating market of mobile and portable devices that have been widely spreading in both consumer (e.g. smart phones, music/video player, mobile TV, and digital cameras) and enterprise markets (e.g. medical, control, surveillance, etc.). MCMC brings together relevant researchers to explore intelligent multimedia technologies and applications for mobile devices, especially to leverage the emerging media cloud computing platform. The call for papers attracted 20 submissions from Asia, Europe, and the United States. Among them there are 4 invited papers. The program committee accepted 8 papers from general submission. Together with the 4 invited papers, this workshop covers a variety of topics, including mobile experience computing, mobile networking and content delivery, multimedia computing on mobile devices, and some other interesting mobile multimedia applications. The highlight of the technical program is the keynote speech by Prof. Ramesh Jain on Crawling Is Difficult in Clouds. We hope that the proceedings based on the technical program will serve as a valuable reference for mobile media and cloud media researchers and developers.
On the number of variables to use in principal component regression

We study least squares linear regression over $N$ uncorrelated Gaussian features that are selected in order of decreasing variance. When the number of selected features $p$ is at most the sample size $n$, the estimator under consideration coincides with the principal component regression estimator; when $p>n$, the estimator is the least $\ell_2$ norm solution over the selected features. We give an average-case analysis of the out-of-sample prediction error as $p,n,N \to \infty$ with $p/N \to \alpha$ and $n/N \to \beta$, for some constants $\alpha \in [0,1]$ and $\beta \in (0,1)$. In this average-case setting, the prediction error exhibits a ``double descent'' shape as a function of $p$. We also establish conditions under which the minimum risk is achieved in the interpolating ($p>n$) regime.
The Impact of the General Data Protection Regulation on Internet Interconnection

Abstract The Internet comprises thousands of independently operated networks, interconnected using bi-laterally negotiated data exchange agreements. The European Union (EU)'s General Data Pro-tection Regulation (GDPR) imposes strict restrictions on handling of personal data of European Economic Area (EEA) residents. A close examination of the text of the law suggests signifi-cant cost to application firms. Available empirical evidence confirms reduction in data usage in the EEA relative to other markets. We investigate whether this decline in derived demand for data exchange impacts EEA networks' decisions to interconnect relative to those of non-EEA OECD networks. Our data consists of a large sample of interconnection agreements between networks globally in 2015–2019. All evidence estimates zero effects: the number of observed agreements, the inferred agreement types, and the number of observed IP-address-level inter-connection points per agreement. We also find economically small effects of the GDPR on the entry and the observed number of customers of networks. We conclude there is no visible short run effects of the GDPR on these measures at the internet layer.
Rigid Graph Alignment

An increasingly important class of networks is derived from physical systems that have a spatial basis. Specifically, nodes in the network have spatial coordinates associated with them, and conserved edges in two networks being aligned have correlated distance measures. An example of such a network is the human brain connectome – a network of co-activity of different regions of the brain, as observed in a functional MRI (fMRI). Here, the problem of identifying conserved patterns corresponds to the alignment of connectomes. In this context, one may structurally align the brains through co-registration to a common coordinate system. Alternately, one may align the networks, ignoring the structural basis of co-activity. In this paper, we formulate a novel problem – rigid graph alignment, which simultaneously aligns the network, as well as the underlying structure. We formally specify the problem and present a method based on expectation maximization, which alternately aligns the network and the structure via rigid body transformations. We demonstrate that our method significantly improves the quality of network alignment in synthetic graphs. We also apply rigid graph alignment to functional brain networks derived from 20 subjects drawn from the Human Connectome Project (HCP), and show over a two-fold increase in quality of alignment. Our results are broadly applicable to other applications and abstracted networks that can be embedded in metric spaces – e.g., through spectral embeddings.
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling

Motivated by the many real-world applications of reinforcement learning (RL) that require safe-policy iterations, we consider the problem of off-policy evaluation (OPE) --- the problem of evaluating a new policy using the historical data obtained by different behavior policies --- under the model of nonstationary episodic Markov Decision Processes (MDP) with a long horizon and a large action space. Existing importance sampling (IS) methods often suffer from large variance that depends exponentially on the RL horizon $H$. To solve this problem, we consider a marginalized importance sampling (MIS) estimator that recursively estimates the state marginal distribution for the target policy at every step. MIS achieves a mean-squared error of $$ \frac{1}{n} \sum_{t=1}^H\mathbb{E}_{\mu}\left[\frac{d_t^\pi(s_t)^2}{d_t^\mu(s_t)^2} \Var_{\mu}\left[\frac{\pi_t(a_t|s_t)}{\mu_t(a_t|s_t)}\big( V_{t+1}^\pi(s_{t+1}) + r_t\big) \middle| s_t\right]\right] + \tilde{O}(n^{-1.5}) $$ where $\mu$ and $\pi$ are the logging and target policies, $d_t^{\mu}(s_t)$ and $d_t^{\pi}(s_t)$ are the marginal distribution of the state at $t$th step, $H$ is the horizon, $n$ is the sample size and $V_{t+1}^\pi$ is the value function of the MDP under $\pi$. The result matches the Cramer-Rao lower bound in [Jiang and Li, 2016] up to a multiplicative factor of $H$. To the best of our knowledge, this is the first OPE estimation error bound with a polynomial dependence on $H$. Besides theory, we show empirical superiority of our method in time-varying, partially observable, and long-horizon RL environments.
Autonomous navigation of shape-shifting microswimmers

Previous demonstrations of chemotaxic behavior by self-propelled particles have relied on external gradients to orient particles in a preferred direction. In this work, the authors propose a different strategy based on stimuli-responsive, shape-shifting particles that use engineered feedback between local sensing and particle motion to navigate heterogeneous environments. By controlling the particle shape and its stimulus response, particles can be rationally designed to swim up (or down) stimulus gradients---even those too weak to be felt directly by the particle.
Generalized bipyramids and hyperbolic volumes of alternating k-uniform tiling links

Abstract We present explicit geometric decompositions of the hyperbolic complements of alternating k-uniform tiling links, which are alternating links whose projection graphs are k-uniform tilings of S 2 , E 2 , or H 2 . A consequence of this decomposition is that the volumes of spherical alternating k-uniform tiling links are precisely twice the maximal volumes of the ideal Archimedean solids of the same combinatorial description, and the hyperbolic structures for the hyperbolic alternating tiling links come from the equilateral realization of the k-uniform tiling on H 2 . In the case of hyperbolic tiling links, we are led to consider links embedded in thickened surfaces S g × I with genus g ≥ 2 and totally geodesic boundaries. We generalize the bipyramid construction of Adams to truncated bipyramids and use them to prove that the set of possible volume densities for all hyperbolic links in S g × I , ranging over all g ≥ 2 , is a dense subset of the interval [ 0 , 2 v oct ] , where v oct ≈ 3.66386 is the volume of the ideal regular octahedron.
A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

Abstract Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by eliminating the laborious step of having a human expert manually segment the lesion on each brain scan. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We trained and tested our method on datasets from three sources: Medical College of Wisconsin (MCW), Kessler Foundation (KF), and the publicly available Anatomical Tracings of Lesions After Stroke (ATLAS) dataset. To promote wide applicability, lesions were included from both subacute (1 to 5 weeks) and chronic ( > 3 months) phases post stroke, and were of both hemorrhagic and ischemic etiology. Cross-study validation results (with independent training and validation datasets) were obtained to compare with previous methods based on naive Bayes, random forests, and three recently published convolutional neural networks. Model performance was quantified in terms of the Dice coefficient, a measure of spatial overlap between the model-identified lesion and the human expert-identified lesion, where 0 is no overlap and 1 is complete overlap. Training on the KF and MCW images and testing on the ATLAS images yielded a mean Dice coefficient of 0.54. This was reliably better than the next best previous model, UNet, at 0.47. Reversing the train and test datasets yields a mean Dice of 0.47 on KF and MCW images, whereas the next best UNet reaches 0.45. With all three datasets combined, the current system compared to previous methods also attained a reliably higher cross-validation accuracy. It also achieved high Dice values for many smaller lesions that existing methods have difficulty identifying. Overall, our system is a clear improvement over previous methods for automating stroke lesion segmentation, bringing us an important step closer to the inter-rater accuracy level of human experts.
High electrical conductivity in the epitaxial polar metals LaAuGe and LaPtSb

Polar metals are an intriguing class of materials that simultaneously host free carriers and polar structural distortions. Despite the name “polar metal,” however, most well-studied polar metals are poor electrical conductors. Here, we demonstrate the molecular beam epitaxial growth of LaPtSb and LaAuGe, two polar metal compounds whose electrical resistivity is an order of magnitude lower than the well studied oxide polar metals. These materials belong to a broad family of ABC intermetallics adopting the stuffed wurtzite structure, also known as hexagonal Heusler compounds. Scanning transmission electron microscopy reveals a polar structure with unidirectionally buckled BC (PtSb and AuGe) planes. Magnetotransport measurements demonstrate good metallic behavior with low residual resistivity (ρLaAuGe = 59.05 μΩ cm and ρLaAPtSb = 27.81 μΩ cm at 2 K) and high carrier density (nh ∼ 1021 cm−3). Photoemission spectroscopy measurements confirm the band metallicity and are in quantitative agreement with density functional theory (DFT) calculations. Through DFT-chemical pressure and crystal orbital Hamilton population analyses, the atomic packing factor is found to support the polar buckling of the structure although the degree of direct interlayer B–C bonding is limited by repulsion at the A–C contacts. When combined with predicted ferroelectric hexagonal Heuslers, these materials provide a new platform for fully epitaxial, multiferroic heterostructures.
Lockless Transaction Isolation in Hyperledger Fabric

Hyperledger Fabric is a distributed operating system for permissioned blockchains hosted by the Linux Foundation. It is the first truly extensible blockchain system for running distributed applications at enterprise grade scale. To achieve this, Hyperledger Fabric introduces a novel execute-order-validate blockchain architecture, allowing parallelization of transaction execution and validation. However, this raises the need for transaction isolation. Today transaction isolation is attained by locking the entire state database during simulation of transactions and database updates. This lock is one of the major performance bottlenecks as observed by previous work. This work presents a new lock-free approach for providing transaction isolation. It harnesses the already existing versioning of key-value pairs in the database, used primarily for a read-write conflict detection during the validation phase, to create a version-based snapshot isolation. We further implement and evaluate our new approach. We show that our solution outperforms the current implementation by 8.1x and that it is comparable to the optimal solution where no isolation mechanism is applied.
The frequency of cortical microstimulation shapes artificial touch

Intracortical microstimulation (ICMS) of the somatosensory cortex evokes vivid tactile sensations and can be used to convey sensory feedback from brain-controlled bionic hands. Changes in ICMS frequency lead to changes in the resulting sensation, but the discriminability of frequency has only been investigated over a narrow range of low frequencies. Furthermore, the sensory correlates of changes in ICMS frequency remain poorly understood. Specifically, it remains to be elucidated whether changes in frequency only modulate sensation magnitude—as do changes in amplitude—or whether they also modulate the quality of the sensation. To fill these gaps, we trained monkeys to discriminate the frequency of ICMS pulse trains over a wide range of frequencies (from 10 to 400 Hz). ICMS amplitude also varied across stimuli to dissociate sensation magnitude from ICMS frequency and ensure that animals could not make frequency judgments based on magnitude. We found that animals could consistently discriminate ICMS frequency up to ∼200 Hz but that the sensory correlates of frequency were highly electrode dependent: On some electrodes, changes in frequency were perceptually distinguishable from changes in amplitude—seemingly giving rise to a change in sensory quality; on others, they were not. We discuss the implications of our findings for neural coding and for brain-controlled bionic hands.
Ampakine pretreatment enables a single brief hypoxic episode to evoke phrenic motor facilitation.

A single brief episode of hypoxia (e.g., 3–5 min) does not evoke long-lasting increases in respiratory motor output after the hypoxia is concluded. Ampakines are a class of drugs that enhance AMPA ...
Linear size sparsifier and the geometry of the operator norm ball

The Matrix Spencer Conjecture asks whether given n symmetric matrices in Rn×n with eigenvalues in [−1, 1] one can always find signs so that their signed sum has singular values bounded by [MATH HERE]. The standard approach in discrepancy requires proving that the convex body of all good fractional signings is large enough. However, this question has remained wide open due to the lack of tools to certify measure lower bounds for rather small non-polyhedral convex sets. A seminal result by Batson, Spielman and Srivastava from 2008 shows that any undirected graph admits a linear size spectral sparsifier. Again, one can define a convex body of all good fractional signings. We can indeed prove that this body is close to most of the Gaussian measure. This implies that a discrepancy algorithm by the second author can be used to sample a linear size sparsifer. In contrast to previous methods, we require only a logarithmic number of sampling phases.
Assessing a rural academic-community partnership using ripple effect mapping

As Area Health Education Centers (AHECs) expand their efforts to improve the distribution and diversity of the healthcare workforce, one common question is how these activities impact their local c...
Template plasmid integration in germline genome-edited cattle.

Abstract We analyzed publicly available whole genome sequencing data from cattle which were germline genome-edited to introduce polledness. Our analysis discovered the unintended heterozygous integration of the plasmid and a second copy of the repair template sequence, at the target site. Our finding underscores the importance of employing screening methods suited to reliably detect the unintended integration of plasmids and multiple template copies.
Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks

The high energy cost of processing deep convolutional neural networks impedes their ubiquitous deployment in energy-constrained platforms such as embedded systems and IoT devices. This article introduces convolutional layers with pre-defined sparse 2D kernels that have support sets that repeat periodically within and across filters. Due to the efficient storage of our periodic sparse kernels, the parameter savings can translate into considerable improvements in energy efficiency due to reduced DRAM accesses, thus promising significant improvements in the trade-off between energy consumption and accuracy for both training and inference. To evaluate this approach, we performed experiments with two widely accepted datasets, CIFAR-10 and Tiny ImageNet in sparse variants of the ResNet18 and VGG16 architectures. Compared to baseline models, our proposed sparse variants require up to $\mathord {\sim }82\%$ ∼ 82 % fewer model parameters with $5.6\times$ 5 . 6 × fewer FLOPs with negligible loss in accuracy for ResNet18 on CIFAR-10. For VGG16 trained on Tiny ImageNet, our approach requires $5.8 \times$ 5 . 8 × fewer FLOPs and up to $\mathord {\sim }83.3\%$ ∼ 83 . 3 % fewer model parameters with a drop in top-5 (top-1) accuracy of only 1.2% ( $\mathord {\sim }2.1\%$ ∼ 2 . 1 % ). We also compared the performance of our proposed architectures with that of ShuffleNet and MobileNetV2. Using similar hyperparameters and FLOPs, our ResNet18 variants yield an average accuracy improvement of $\mathord {\sim }2.8\%$ ∼ 2 . 8 % .
Microglia in the developing retina couple phagocytosis with the progression of apoptosis via P2RY12 signaling

Background Microglia colonize the developing vertebrate central nervous system coincident with the detection of developmental apoptosis. Our understanding of apoptosis in intact tissue in relation to microglial clearance of dying cells is largely based on fixed samples, which is limiting given that microglia are highly motile and mobile phagocytes. Here, we used a system of microglial depletion and in vivo real-time imaging in zebrafish to directly address microglial phagocytosis of apoptotic cells during normal retinal development, the relative timing of phagocytosis in relation to apoptotic progression, and the contribution of P2RY12 signaling to this process. Results The depletion of microglia resulted in accumulation of numerous apoptotic cells in the retina. Real-time imaging revealed precise timing of microglial engulfment with the progression of apoptosis, and dynamic movement and displacement of engulfed apoptotic cells. Inhibition of P2RY12 signaling delayed microglial clearance of apoptotic cells. Conclusions Microglial engulfment of dying cells is coincident with apoptotic progression and requires P2RY12 signaling, indicating that microglial P2RY12 signaling is shared between development and injury response. Our work provides important in vivo insight into the dynamics of apoptotic cell clearance in the developing vertebrate retina and provides a basis to understand microglial phagocytic behavior in health and disease.
The Secreted Acid Phosphatase Domain-Containing GRA44 from Toxoplasma gondii Is Required for c-Myc Induction in Infected Cells.

ABSTRACT During host cell invasion, the eukaryotic pathogen Toxoplasma gondii forms a parasitophorous vacuole to safely reside within the cell, while it is partitioned from host cell defense mechanisms. From within this safe niche, parasites sabotage multiple host cell systems, including gene expression, apoptosis, and intracellular immune recognition, by secreting a large arsenal of effector proteins. Many parasite proteins studied for active host cell manipulative interactions have been kinases. The translocation of effectors from the parasitophorous vacuole into the host cell is mediated by a putative translocon complex, which includes the proteins MYR1, MYR2, and MYR3. Whether other proteins are involved in the structure or regulation of this putative translocon is not known. We have discovered that the secreted protein GRA44, which contains a putative acid phosphatase domain, interacts with members of this complex and is required for host cell effects downstream of effector secretion. We have determined that GRA44 is processed in a region with homology to sequences targeted by protozoan proteases of the secretory pathway and that both major cleavage fragments are secreted into the parasitophorous vacuole. Immunoprecipitation experiments showed that GRA44 interacts with a large number of secreted proteins, including MYR1. Importantly, conditional knockdown of GRA44 resulted in a lack of host cell c-Myc upregulation, which mimics the phenotype seen when members of the translocon complex are genetically disrupted. Thus, the putative acid phosphatase GRA44 is crucial for host cell alterations during Toxoplasma infection and is associated with the translocon complex which Toxoplasma relies upon for success as an intracellular pathogen. IMPORTANCE Approximately one-third of humans are infected with the parasite Toxoplasma gondii. Toxoplasma infections can lead to severe disease in those with a compromised or suppressed immune system. Additionally, infections during pregnancy present a significant health risk to the developing fetus. Drugs that target this parasite are limited, have significant side effects, and do not target all disease stages. Thus, a thorough understanding of how the parasite propagates within a host is critical in the discovery of novel therapeutic targets. Toxoplasma replication requires that it enter the cells of the infected organism. In order to survive the environment inside a cell, Toxoplasma secretes a large repertoire of proteins, which hijack a number of important cellular functions. How these Toxoplasma proteins move from the parasite into the host cell is not well understood. Our work shows that the putative phosphatase GRA44 is part of a protein complex responsible for this process.
Hybrid Scheduling in Heterogeneous Half- and Full-Duplex Wireless Networks

Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized Greedy Maximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid -GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughput-optimal . Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of H-GMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16– $30\times $ better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm.
Diffuse X-ray scattering from correlated motions in a protein crystal

Protein dynamics are integral to biological function, yet few techniques are sensitive to collective atomic motions. A long-standing goal of X-ray crystallography has been to combine structural information from Bragg diffraction with dynamic information contained in the diffuse scattering background. However, the origin of macromolecular diffuse scattering has been poorly understood, limiting its applicability. We present a finely sampled diffuse scattering map from triclinic lysozyme with unprecedented accuracy and detail, clearly resolving both the inter- and intramolecular correlations. These correlations are studied theoretically using both all-atom molecular dynamics and simple vibrational models. Although lattice dynamics reproduce most of the diffuse pattern, protein internal dynamics, which include hinge-bending motions, are needed to explain the short-ranged correlations revealed by Patterson analysis. These insights lay the groundwork for animating crystal structures with biochemically relevant motions.
The Americans with disabilities act and healthcare employer-mandated vaccinations.

Battles around workplace vaccination policies often focus on the annual influenza vaccine, but many healthcare employers impose requirements for additional vaccines because of the increased likelihood that employees in this sector will interact with populations at increased risk of acquiring or experiencing harmful sequelae of vaccine-preventable diseases. The federal Centers for Disease Control and Prevention and many states recommend healthcare employees receive numerous vaccines, including measles, mumps, and rubella (“MMR”); tetanus, diphtheria, and pertussis (“Tdap”). However, recent outbreaks of once-eliminated diseases that are now resurgent and the rising antivaccination movement raise questions about how far employers can go to mandate vaccinations. While healthcare institutions are increasingly mandating that employees receive vaccinations, employee objections to vaccines, including litigation, have increased in recent years. Employer policies must comply with the Americans with Disabilities Act (“ADA”), which prohibits discrimination on the basis of disability. Although the ADA permits mandatory vaccine policies under certain circumstances, employers must consider reasonable accommodations, which are changes to the job or work environment that permit the employee with a disability to perform the essential functions of the job, within certain limits. This article analyzes two recent cases which suggest how employers seeking to protect their workforce and the patients they serve by requiring vaccines can work within the framework of the ADA to implement these policies.
Are 2D fingerprints still valuable for drug discovery

Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein-ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprint-based methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein-ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein-ligand binding affinity predictions.
A Combinatorial Proof on Partition Function Parity.

One of the most basic results on the number-theoretic properties of the partition function p(n) is that p(n) takes each value of parity infinitely often. First proved by Kolberg in 1959, this statement was strengthened by Kolberg and Subbarao in 1966 to say that both p(2n) and p(2n + 1) take each value of parity infinitely often. These results have received several other proofs, each relying to a certain extent on manipulating generating functions. We give a new, self-contained proof of Subbarao’s result by constructing a series of bijections and involutions, along the way getting a more general theorem concerning the enumeration of a special subset of integer partitions.
The SME with Gravity and Explicit Diffeomorphism Breaking

This overview looks at what happens when the Standard-Model Extension (SME) is used to investigate gravity theories with explicit diffeomorphism breaking. It is shown that when matter-gravity couplings are included, the SME generally maintains consistency with the Bianchi identities, and it therefore provides a useful phenomenological framework for investigating the effects of explicit diffeomorphism breaking in gravity theories.
Dugesia japonica is the best suited of three planarian species for high-throughput toxicology screening

High-throughput screening (HTS) using new approach methods is revolutionizing toxicology. Asexual freshwater planarians are a promising invertebrate model for neurotoxicity HTS because their diverse behaviors can be used as quantitative readouts of neuronal function. Currently, three planarian species are commonly used in toxicology research: Dugesia japonica, Schmidtea mediterranea, and Girardia tigrina. However, only D. japonica has been demonstrated to be suitable for HTS. Here, we assess the two other species for HTS suitability by direct comparison with D. japonica. Through quantitative assessments of morphology and multiple behaviors, we assayed the effects of 4 common solvents (DMSO, ethanol, methanol, ethyl acetate) and a negative control (sorbitol) on neurodevelopment. Each chemical was screened blind at 5 concentrations at two time points over a twelve-day period. We obtained two main results: First, G. tigrina and S. mediterranea planarians showed significantly reduced movement compared to D. japonica under HTS conditions, due to decreased health over time and lack of movement under red lighting, respectively. This made it difficult to obtain meaningful readouts from these species. Second, we observed species differences in sensitivity to the solvents, suggesting that care must be taken when extrapolating chemical effects across planarian species. Overall, our data show that D. japonica is best suited for behavioral HTS given the limitations of the other species. Standardizing which planarian species is used in neurotoxicity screening will facilitate data comparisons across research groups and accelerate the application of this promising invertebrate system for first-tier chemical HTS, helping streamline toxicology testing.