cssi/SciSciGPT-SciSciNet huggingface dataset:
Fiber Deficiency and Colonic Disorders

It was agreed that better analytic techniques and bioassays of fiber and fiber composition are needed. Synthetics duplicating activities attributable to natural fiber represent an intriguing alternative for relating the range of fiber effects to the types of fiber in the diet of man. Although there is certainly variance in the literature to date, reports at this conference seemed to indicate that bile salt excretion was not increased by increased amounts of dietary fiber; rather, that bile salt metabolism seems to be altered, resulting in decreased deoxycholate and increased chenodeoxycholate. Electrical activity, manometric data, and motor transit studies must be correlated to give optimal information on bowel functions in relation to the intake of food. Further studies along these lines should be done in the normal “dirty” colon. More attention might be paid to the activity of the longitudinal muscle of the colon in the early pathogenesis of the diverticular disease. In general, there appears to be a correlation between low fiber intake and “diseases of civilization”, but nothing definitive can be assumed as yet as to cause and effect. Increased fat, animal protein, sugar, and other factors also deserve consideration. There are inconsistencies in the geographic data relating low intake of dietary fiber to disease; perhaps we should look for clues in these very inconsistencies. Certainly, regardless of the limitations of present data and the many gaps in our understanding of the possible relationship between the intake of food and diseases in man, this subject constitutes an important area for future research. The detailed proceedings of this conference are now in preparation for publication.
Recommendations for applying a multi-dimensional model of impulsive personality to diagnosis and treatment

The UPPS-P Model of Impulsive Personality, a prominent model of impulsive personality derived from the Five Factor Model of Personality, is a multi-dimensional model of impulsive personality that consists of negative urgency, lack of premeditation, lack of perseveration, sensation seeking, and positive urgency. The UPPS-P model has highlighted the importance of separating multidimensional traits due to the specificity of these traits corresponding to different risk behaviors. The goal of the current review paper is to make recommendations on how to apply the UPPS-P Model of Impulsive Personality, to diagnosis of and treatment for psychopathology. However, despite impulsivity being one of the most frequently used criteria for a number of clinical disorders, our review of the Diagnostic and Statistical Manual for Mental Disorders-5 found that the UPPS-P traits are not well represented in the diagnostic criteria, which we propose limits inferences about etiology and treatment targets. Additionally, research has largely focused on the importance of these traits for risk models; our review of the literature applying the UPPS-P traits to treatment processes and outcomes concluded that this area is not yet well studied. Here, we propose the specific application of the UPPS-P model to improve diagnosis and increase treatment effectiveness.
Breastfeeding as a Predictor of Serum Concentrations of Per- and Polyfluorinated Alkyl Substances in Reproductive-Aged Women and Young Children: A Rapid Systematic Review

Per- and polyfluorinated alkyl substances (PFASs) are synthetic chemicals widely detected in human serum, and at low levels in breast milk. We conducted a rapid systematic review on breastfeeding practices and serum concentrations of PFASs—specifically PFOS and PFOA—among reproductive-aged women and young children using the Navigation Guide systematic review methodology. We included 14 studies examining associations between breastfeeding and PFASs in infants/toddlers or pregnant/postnatal women. Breastfeeding was significantly associated with lower PFASs exposure among women and higher PFASs exposure among children. We concluded there was “sufficient” evidence supporting an association between breastfeeding and serum PFASs concentrations among women, and “limited” evidence of an association among children due to issues with sample size, confounding, and exposure assessment. These findings reinforce that lactation is an important excretion route of PFASs for women, and that breast milk may be an important exposure pathway for young children.
An Effective AODV-based Flooding Detection and Prevention for Smart Meter Network

Abstract In this paper, we investigate flooding attack detection and prevention for smart meter networks. The default Ad hoc On-Demand Distance Vector (AODV) protocol is vulnerable to flooding attacks because intermediate meters forward packets blindly. To detect and prevent flooding attacks, we propose a new AODV-based routing protocol called FLOoding aWareness AODV (FLOW-AODV) considering both with and without Internet Protocol (IP) spoofing. If no IP spoofing exists, we modify the route request forwarding procedure by adding new features. The main idea is to enable one-hop neighboring meters to detect attackers by counting the received route requests from the same source. Specifically, when a meter receives a request, it first determines whether it is a one-hop neighbor to the source or not. If yes, the one-hop neighboring meter updates its request counter for every received request from that source. When the received requests exceed a predefined number (e.g., 2), it perceives the source as an attacker and just discards the following requests, so the flooding is avoided. In this way, the receiver can prevent flooding attacks when there is no IP spoofing from flooding attacker. If IP spoofing exists, the flooding attacker will hide its real IP address. In this case, we have extended our algorithm and included trustworthiness of each neighbor in the neighbor routing table. Extensive simulations are conducted in NS3 to evaluate the performance of FLOW-AODV. Results show that FLOW-AODV obtains considerably higher performance in terms of packet delivery ratio, and average delay compared with the default AODV routing protocol. In addition, the FLOW-AODV significantly improves network performance and eliminates congestion when multiple attackers are involved.
Why Atlas Hasn't Shrugged: Review Essay

Taxing the Rich: A History of Fiscal Fairness in the United State and Europe . By Kenneth Scheve & David Stasavage. Princeton, N.J.: Princeton University Press. 2016. Pp. xv, 266. $29.95.
Study on coalescence dynamics of unequal-sized microbubbles captive on solid substrate

Abstract The dynamics of bubble coalescence are of importance for a number of industrial processes, in which the size inequality of the parent bubbles plays a significant role in mass transport, topological change and overall motion. In this study, coalescence of unequal-sized microbubbles captive on a solid substrate was observed from cross-section view using synchrotron high-speed imaging technique and a microfluidic gas generation device. The bridging neck growth and surface wave propagation at the early stage of coalescence were investigated by experimental and numerical methods. The results show that theoretical half-power-law of neck growth rate is still valid when viscous effect is neglected. However, the inertial-capillary time scale is associated with the initial radius of the smaller parent microbubble. The surface wave propagation rate on the larger parent microbubble is proportional to the inertial-capillary time scale.
A Learn-to-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a ight control sys- tem to achieve desired closed-loop performance. The method combines efficient parameter estimation for identifying closed-loop dynamics models, with online nonlinear optimization for sequentially perturbing and updating control gains to improve performance. Prior in- formation on stability and control derivatives is not needed, nor is any knowledge about the control system architecture. Following convergence, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear ight dynamics simulation of the NASA FASER airplane. Convergence was attained using five piloted maneuvers that spanned approximately one minute of ight test time. Although demonstrated for a relatively simple case, the method is general and can be applied to other aircraft, axes, performance metrics, and control systems.
High-precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high-frequency (milliseconds) updates. A real-time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in-field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time-varying parameters. The capabilities of the real-time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.
Advance Selling to Strategic Consumers: Preorder Contingent Production Strategy with Advance Selling Target

Motivated by emerging industry practices, this paper studies the effectiveness of a new advance selling strategy in counteracting strategic consumer behavior: the preorder contingent production (PCP) strategy, where the seller's production decision is contingent on an advance selling target. We find that compared to other advance selling strategies, such as the traditional advance selling strategy and the capacity rationing strategy, the PCP strategy is effective in mitigating strategic waiting behavior and thus can significantly improve the seller's profit performance, especially when consumers’ discount factor is at a medium or high level, the production cost is not too high, or the market size has an unbalanced probability distribution. Moreover, when the market size is deterministic, we show that the PCP strategy can completely eliminate strategic waiting behavior and attain the seller's profit performance under myopic consumer behavior. Finally, we demonstrate that the benefits of the PCP strategy are robust under other model considerations.
NHA2 promotes cyst development in an in vitro model of polycystic kidney disease

Key points Significant and selective up-regulation of the Na+ /H+ exchanger NHA2 (SLC9B2) was observed in cysts of patients with autosomal dominant polycystic kidney disease. Using the MDCK cell model of cystogenesis, it was found that NHA2 increases cyst size. Silencing or pharmacological inhibition of NHA2 inhibits cyst formation in vitro. Polycystin-1 represses NHA2 expression via Ca2+ /NFAT signalling whereas the dominant negative membrane-anchored C-terminal fragment (PC1-MAT) increased NHA2 levels. Drugs (caffeine, theophylline) and hormones (vasopressin, aldosterone) known to exacerbate cysts elicit NHA2 expression. Taken together, the findings reveal NHA2 as a potential new player in salt and water homeostasis in the kidney and in the pathogenesis of polycystic kidney disease. Abstract Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in PKD1 and PKD2 encoding polycystin-1 (PC1) and polycystin-2 (PC2), respectively. The molecular pathways linking polycystins to cyst development in ADPKD are still unclear. Intracystic fluid secretion via ion transporters and channels plays a crucial role in cyst expansion in ADPKD. Unexpectedly, we observed significant and selective up-regulation of NHA2, a member of the SLC9B family of Na+ /H+ exchangers, that correlated with cyst size and disease severity in ADPKD patients. Using three-dimensional cultures of MDCK cells to model cystogenesis in vitro, we showed that ectopic expression of NHA2 is causal to increased cyst size. Induction of PC1 in MDCK cells inhibited NHA2 expression with concordant inhibition of Ca2+ influx through store-dependent and -independent pathways, whereas reciprocal activation of Ca2+ influx by the dominant negative membrane-anchored C-terminal tail fragment of PC1 elevated NHA2. We showed that NHA2 is a target of Ca2+ /NFAT signalling and is transcriptionally induced by methylxanthine drugs such as caffeine and theophylline, which are contraindicated in ADPKD patients. Finally, we observed robust induction of NHA2 by vasopressin, which is physiologically consistent with increased levels of circulating vasopressin and up-regulation of vasopressin V2 receptors in ADPKD. Our findings have mechanistic implications on the emerging use of vasopressin V2 receptor antagonists such as tolvaptan as safe and effective therapy for polycystic kidney disease and reveal a potential new regulator of transepithelial salt and water transport in the kidney.
Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimization

This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies. The planner optimizes both the contact sequence and the robot state trajectories. A high-level tree search is conducted to iteratively grow a contact transition tree. At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy. Also, at each node of the tree, the optimizer attempts to find a self-motion (inertial shaping movement)to eliminate kinetic energy. This paper also presents an efficient and effective method to generate initial seeds to facilitate trajectory optimization. Experiments demonstrate show that our proposed algorithm can generate complex stabilization strategies for a simulated planar robot under varying initial pushes and environment shapes.
Model-based feedback control of oil production in oil-rim reservoirs under gas coning conditions

In oil-rim reservoirs, oil is typically extracted at constant rate. However, the constant-rate oil production strategy may lead to suboptimal solutions as it does not take into account the process dynamics such as how the gas/oil contact (GOC) near the wellbore dynamically changes with time during oil production. Motivated by this, initially, we present a gas-rate dependent gas/oil ratio model to describe gas coning phenomena. Second, a numerical scheme is developed to describe the spatiotemporal evolution of the GOC height during oil production. Third, a reduced-order model is constructed, which is used to design a Luenberger observer for state estimation. Lastly, the model predictive control theory is applied for the design of a feedback control system to compute the optimal oil production profile that maximizes the Net Present Value (NPV) of oil produced from a well before gas breakthrough. We demonstrate that the proposed control scheme is able to compute the oil production profile before gas breakthrough which has a higher NPV compared to other state-of-the-art oil production strategies including Hasan's control law and the conventional constant-rate oil production strategies.
VizML: A Machine Learning Approach to Visualization Recommendation

Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify five key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X- or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models. We report and interpret feature importances from one of these baseline models. To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting consensus visualization type, and exceeds that of other visualization recommender systems.
Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval

Cross-modal retrieval between visual data and natural language description remains a long-standing challenge in multimedia. While recent image-text retrieval methods offer great promise by learning deep representations aligned across modalities, most of these methods are plagued by the issue of training with small-scale datasets covering a limited number of images with ground-truth sentences. Moreover, it is extremely expensive to create a larger dataset by annotating millions of images with sentences and may lead to a biased model. Inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation. Specifically, our main idea is to leverage web images and corresponding tags, along with fully annotated datasets, in training for learning the visual-semantic joint embedding. We propose a two-stage approach for the task that can augment a typical supervised pair-wise ranking loss based formulation with weakly-annotated web images to learn a more robust visual-semantic embedding. Experiments on two standard benchmark datasets demonstrate that our method achieves a significant performance gain in image-text retrieval compared to state-of-the-art approaches.
A user-driven free speech application for anonymous and verified online, public group discourse

Online social networks are major hubs of communications that are used to share information by societies worldwide. However, the ability to freely communicate on these platforms is increasingly restricted in countries across the globe and existing technological solutions do not fully address the needs of affected communities. In this paper we explore the design process of SecurePost, a novel tool that allows verified group anonymity to those communicating publicly on online social networks. We present survey-based research and ethnographic interviews of communities vulnerable to censorship conducted in Zambia, Turkey, and Mongolia between 2013 to 2016. We use analysis of this data to ground our work. We explore needs and requirements of users such as modes of censorship, resistance to network disruption, and appropriate platform consideration. We outline our technological solution and expand on how on-the-ground research of user communities guides technological requirements.
First genome-wide analysis of the endangered, endemic lichen Cetradonia linearis reveals isolation by distance and strong population structure.

PREMISE OF THE STUDY Lichenized fungi are evolutionarily diverse and ecologically important, but little is known about the processes that drive their diversification and genetic differentiation. Distributions are often assumed to be wholly shaped by ecological requirements rather than dispersal limitations. Furthermore, although asexual and sexual reproductive structures are observable, the lack of information about recombination rates makes inferences about reproductive strategies difficult. We investigated the population genomics of Cetradonia linearis, a federally endangered lichen in the southern Appalachians of eastern North America, to test the relative contributions of environmental and geographic distance in shaping genetic structure, and to characterize the mating system and genome-wide recombination. METHODS Whole-genome shotgun sequencing was conducted to generate data for 32 individuals of C. linearis. A reference genome was assembled, and reads from all samples were aligned to generate a set of single-nucleotide polymorphisms for further analyses. KEY RESULTS We found evidence for low rates of recombination and for isolation by distance, but not for isolation by environment. The species is putatively unisexual, given that only one mating-type locus was found. Hindcast species distribution models and the distribution of genetic diversity support C. linearis having a larger range during the Last Glacial Maximum in the southern portion of its current extent. CONCLUSIONS Our findings contribute to the understanding of factors that shape genetic diversity in C. linearis and in fungi more broadly. Because all populations are highly genetically differentiated, the extirpation of any population would mean the loss of unique genetic diversity; therefore, our results support the continued conservation of this species.
How do Foreign Firms Manage Their Exposure to United States Portfolio Flows

From 1990 to 2013, US portfolio flows to foreign stocks and bonds nearly quadrupled. Across 44 countries, I show that non-US firms with stock returns that are more sensitive to these cross-border flows are more likely to sell stocks and bonds, but display lower investment and employment growth rates. Using large natural and industrial disasters, I document that after deadly disasters occur, firms that are more sensitive to US flows are less likely to tap capital markets and tend to cut their real behaviors at higher rates, as would be expected if capital flight could potentially spill over to the real economy.
Integral points close to a space curve

We establish sharp lower and upper bounds for the number of integral points near dilations of a space curve with nowhere vanishing torsion.
Decoding and reading comprehension: A test of the decoding threshold hypothesis

We report results of 2 studies examining the relation between decoding and reading comprehension. Based on our analysis of prominent reading theories such as the Simple View of Reading (Gough & Tunmer, 1986), the Lexical Quality Hypothesis (Perfetti & Hart, 2002) and the Self-Teaching Hypothesis (Share, 1995), we propose the Decoding Threshold Hypothesis, which posits that the relation between decoding and reading comprehension can only be reliably observed above a certain decoding threshold. In Study 1, the Decoding Threshold Hypothesis was tested in a sample of over 10,000 Grade 5–10 students. Using quantile regression, classification analysis (Receiver Operating Characteristics) and broken-line regression, we found a reliable decoding threshold value below that there was no relation between decoding and reading comprehension, and above which the two measures showed a positive linear relation. Study 2 is a longitudinal analysis of over 30,000 students’ reading comprehension growth as a function of their initial decoding status. Results showed that scoring below the decoding threshold was associated with stagnant growth in reading comprehension. We argue that the Decoding Threshold Hypothesis has the potential to explain differences in the prominent reading theories in terms of the role of decoding in reading comprehension in students at Grade 5 and above. Furthermore, the identification of decoding threshold also has implications for reading practice. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Structural Subnetwork Evolution Across the Life-Span: Rich-Club, Feeder, Seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available life-span study. We demonstrate changes in rich-club membership with age alongside a shift in importance from ’peripheral’ seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span.
ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM

CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). In this paper, we propose two extensions on this robustness score. First, we provide a new formal robustness guarantee for classifier functions that are twice differentiable. We apply extreme value theory on the new formal robustness guarantee and the estimated robustness is called second-order CLEVER score. Second, we discuss how to handle gradient masking, a common defensive technique, using CLEVER with Backward Pass Differentiable Approximation (BPDA). With BPDA applied, CLEVER can evaluate the intrinsic robustness of neural networks of a broader class – networks with non-differentiable input transformations. We demonstrate the effectiveness of CLEVER with BPDA in experiments on a 121-layer Densenet model trained on the ImageNet dataset.
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies

We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies. Unlike existing hierarchical multitask RL approaches that explicitly describe what the agent should do at a high level, our problem only describes properties of subtasks and relationships among them, which requires the agent to perform complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural subtask graph solver (NSGS) which encodes the subtask graph using a recursive neural network embedding. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy, graph reward propagation, to pre-train our NSGS agent and further finetune it through actor-critic method. The experimental results on two 2D visual domains show that our agent can perform complex reasoning to find a near-optimal way of executing the subtask graph and generalize well to the unseen subtask graphs. In addition, we compare our agent with a Monte-Carlo tree search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of NSGS can be further improved by combining it with MCTS.
Spectral approximation of a variable coefficient fractional diffusion equation in one space dimension

Abstract In this article we consider the approximation of a variable coefficient (two-sided) fractional diffusion equation (FDE), having unknown u . By introducing an intermediate unknown, q , the variable coefficient FDE is rewritten as a lower order, constant coefficient FDE. A spectral approximation scheme, using Jacobi polynomials, is presented for the approximation of q, q N . The approximate solution to u, u N , is obtained by post processing q N . An a priori error analysis is given for ( q − q N ) and ( u − u N ) . Two numerical experiments are presented whose results demonstrate the sharpness of the derived error estimates.
On the Uncertainty of Information Retrieval in Associative Memories

We (people) are memory machines. Our decision processes, emotions, and interactions with the world around us are based on and driven by associations to our memories. This natural association paradigm will become critical in future memory systems, namely, the key question will not be “How do I store more information?” but rather “Do I have the relevant information? How do I retrieve it?” The focus of this paper is to make a first step in this direction. We define and solve a very basic problem in associative retrieval. Given a word $W$ , the words in the memory, which are $t$ -associated with $W$ , are the words in the ball of radius $t$ around $W$ . In general, given a set of words, say $W$ , $X$ , and $Y$ , the words that are $t$ -associated with $\{W,X,Y\}$ are those in the memory that are within distance $t$ from all the three words. Our main goal is to study the maximum size of the $t$ -associated set as a function of the number of input words and the minimum distance of the words in memory—we call this value the uncertainty of an associative memory . In this paper, we consider the Hamming distance and derive the uncertainty of the associative memory that consists of all the binary vectors with an arbitrary number of input words. In addition, we study the retrieval problem, namely, how do we get the $t$ -associated set given the inputs? We note that this paradigm is a generalization of the sequences reconstruction problem that was proposed by Levenshtein (2001). In this model, a word is transmitted over multiple channels. A decoder receives all the channel outputs and decodes the transmitted word. Levenshtein computed the minimum number of channels that guarantee a successful decoder—this value happens to be the uncertainty of an associative memory with two input words.
Scleraxis lineage cells contribute to organized bridging tissue during tendon healing and identify a subpopulation of resident tendon cells

During tendon healing, it is postulated that tendon cells drive tissue regeneration, whereas extrinsic cells drive pathologic scar formation. Tendon cells are frequently described as a homogenous, fibroblast population that is positive for the marker Scleraxis (Scx). It is controversial whether tendon cells localize within the forming scar tissue during adult tendon healing. We have previously demonstrated that S100 calcium-binding protein A4 (S100a4) is a driver of tendon scar formation and marks a subset of tendon cells. The relationship between Scx and S100a4 has not been explored. In this study, we assessed the localization of Scx lineage cells (ScxLin) following adult murine flexor tendon repair and established the relationship between Scx and S100a4 throughout both homeostasis and healing. We showed that adult ScxLin localize within the scar tissue and organize into a cellular bridge during tendon healing. Additionally, we demonstrate that markers Scx and S100a4 label distinct populations in tendon during homeostasis and healing, with Scx found in the organized bridging tissue and S100a4 localized throughout the entire scar region. These studies define a heterogeneous tendon cell environment and demonstrate discrete contributions of subpopulations during healing. These data enhance our understanding and ability to target the cellular environment of the tendon.-Best, K. T., Loiselle, A. E. Scleraxis lineage cells contribute to organized bridging tissue during tendon healing and identify a subpopulation of resident tendon cells.
International Risk Sharing in Overlapping Generations Models

Abstract We propose a way to address the Backus–Smith puzzle that, instead of relying on extreme parameter values or complex modeling assumptions, simply switches the framework from infinitely lived agents to overlapping generations. Young agents face nondiversifiable wage risk that leads to a low degree of risk sharing within each country. Subsequently, international price movements are not sufficient to achieve the high consumption-real exchange rate correlation produced in standard infinitely lived agent DSGE models.
Population reconstructions for humans and megafauna suggest mixed causes for North American Pleistocene extinctions

Dozens of large mammals such as mammoth and mastodon disappeared in North America at the end of the Pleistocene with climate change and "overkill" by human hunters the most widely-argued causes. However, the population dynamics of humans and megafauna preceding extinctions have received little attention even though such information may be telling as we expect increasing human populations to be correlated with megafaunal declines if hunting caused extinctions. No such trends are expected if climate change was the primary cause. We present tests of these hypotheses here by using summed calibrated radiocarbon date distributions to reconstruct population levels of megafauna and humans. The results suggest that the causes for extinctions varied across taxa and by region. In three cases, extinctions appear linked to hunting, while in five others they are consistent with the ecological effects of climate change and in a final case, both hunting and climate change appear responsible.
Counting standard contributions to measure the value of patent portfolios - A tale of apples and oranges

Abstract Measuring the value of portfolios of Standard-Essential Patents (SEP) is a difficult and controversial exercise. It has recently been suggested to use counts of technical contributions to Standard Development Organizations (SDO) as an indicator of the share of the value of a standard created by an SEP holder. Analyzing a comprehensive database of contributions to the Third Generation Partnership Project (3GPP), I find that contributions are highly heterogeneous in type, outcome, and impact. The standardization process is not intended to screen contributions for the significance of the contribution they make to the standard. Against this background, contribution counts are not a suitable basis for apportioning the value of a standard between different SEP holders. The measure is prone to be easily manipulated. Furthermore, apportioning royalty payments by contribution counts would exacerbate commercial considerations and opportunistic strategies, which could hamper the technical work of SDO working groups.
Reducing Tail Latencies while Improving Resiliency to Timing Errors for Stream Processing Workloads

Stream processing is an increasingly popular model for online data processing that can be partitioned into streams of elements. It is commonly used in real-time data analytics services, such as processing Twitter tweets and Internet of Things (IoT) device feeds. Current stream processing frameworks boast high throughput and low average latency. However, users of these frameworks may desire lower tail latencies and better real-time performance for their applications. In practice, there are a number of errors that can affect the performance of stream processing applications, such as garbage collection and resource contention. For some applications, these errors may cause unacceptable violations of real-time constraints. In this paper we propose applying redundancy in the data processing pipeline to increase the resiliency of stream processing applications to timing errors. This results in better real-time performance and a reduction in tail latency. We present a methodology and apply this redundancy in a framework based on Twitter's Heron. Finally, we evaluate the effectiveness of this technique against a range of injected timing errors using benchmarks from Intel's Storm Benchmark. Our results show that redundant tuple processing can effectively reduce the tail latency, and that the number of missed deadlines can also be reduced by up to 94% in the best case. We also study the potential effects of duplication when applied at different stages in the topology. For the topologies in this paper, we further observe that duplication is most effective when computation is redundant at the first bolt. Finally, we evaluate the additional overhead that duplicating tuples brings to a stream processing topology. Our results also show that computation overhead scales slower than communication, and that the real-time performance is improved in spite of the overheads. Overall we conclude that redundancy through duplicated tuples is indeed a powerful tool for increasing the resiliency to intermittent runtime timing errors.
Towards Self-Adaptive Metric Learning On the Fly

Good quality similarity metrics can significantly facilitate the performance of many large-scale, real-world applications. Existing studies have proposed various solutions to learn a Mahalanobis or bilinear metric in an online fashion by either restricting distances between similar (dissimilar) pairs to be smaller (larger) than a given lower (upper) bound or requiring similar instances to be separated from dissimilar instances with a given margin. However, these linear metrics learned by leveraging fixed bounds or margins may not perform well in real-world applications, especially when data distributions are complex. We aim to address the open challenge of “Online Adaptive Metric Learning” (OAML) for learning adaptive metric functions on-the-fly. Unlike traditional online metric learning methods, OAML is significantly more challenging since the learned metric could be non-linear and the model has to be self-adaptive as more instances are observed. In this paper, we present a new online metric learning framework that attempts to tackle the challenge by learning a ANN-based metric with adaptive model complexity from a stream of constraints. In particular, we propose a novel Adaptive-Bound Triplet Loss (ABTL) to effectively utilize the input constraints, and present a novel Adaptive Hedge Update (AHU) method for online updating the model parameters. We empirically validates the effectiveness and efficacy of our framework on various applications such as real-world image classification, facial verification, and image retrieval.
Management of carotid artery stenosis in patients with coexistent unruptured intracranial aneurysms.

Objective Concomitant unruptured intracranial aneurysms (UIAs) are present in patients with carotid artery stenosis not infrequently and result in unique management challenges. Thus, we investigated the risk of rupture of an aneurysm after revascularization of a carotid artery in a contemporary consecutive series of patients seen at our institution. Methods Data from patients who underwent a carotid revascularization in the presence of at least one concomitant UIA at our institution from 1991 to 2018 were retrospectively reviewed. Patients were evaluated for the incidence of aneurysm rupture within 30 days (early period) and after 30 days (late period) of carotid revascularization, as well as for the incidence of periprocedural complications from the treatment of carotid stenosis and/or UIA. Results Our study included 53 patients with 63 concomitant UIAs. There was no rupture within 30 days of carotid revascularization. The overall risk of rupture was 0.87% per patient-year. Treatment (coiling or clipping) of a concomitant UIA, if pursued, could be performed successfully after carotid revascularization. Conclusions Carotid artery revascularization in the setting of a concomitant UIA can be performed safely without an increased 30-day or late-term risk of rupture. If indicated, treatment of the UIA can take place after the patient recovers from the carotid procedure.
Demagoguery as Pathology: Interpreting European Politics Today

AbstractThe End of Europe by James Kirchick and The Strange Death of Europe by Douglas Murray evince many of the very same political and discursive pathologies which they successfully diagnose in E...
Ultra-performance liquid chromatography tandem mass spectrometry method for the determination of AZ66, a sigma receptor ligand, in rat plasma and its application to in vivo pharmacokinetics.

Methamphetamine abuse continues as a major problem in the USA owing to its powerful psychological addictive properties. AZ66, 3-(4-(4-cyclohexylpiperazine-1-yl)pentyl)-6-fluorobenzo(d)thiazole-2(3H)-one, an optimized sigma receptor ligand, is a promising therapeutic agent against methamphetamine. To study the in vivo pharmacokinetics of this novel sigma receptor ligand in rats, a sensitive ultra-performance liquid chromatography/tandem mass spectrometry (UPLC/MS/MS) method was developed in rat plasma and validated. The developed method requires a small volume of plasma (100 µL) and a simple liquid-liquid extraction. The chromatographic separations were achieved in 3.3 min using an Acquity UPLC BEH Shield RP18 column. The mass spectrophotometric detection was carried out using a Waters Micromass Quattro MicroTM triple-quadrupole system. Multiple reaction monitoring was used for the quantitation with transitions m/z 406 → m/z 181 for AZ66 and m/z 448 → m/z 285 for aripiprazole. The method was validated over a concentration range of 1-3500 ng/mL and the lower limit of quantitation was determined to be 1 ng/mL. Validation of the assay demonstrated that the developed UPLC/MS/MS method was sensitive, accurate and selective for the determination of AZ66 in rat plasma. The present method has been successfully applied to an i.v. pharmacokinetic study in Sprague-Dawley rats.
Picosecond Photoinduced Reflectivity Studies of GaN Prepared by Lateral Epitaxial Overgrowth

The pump-probe technique has been used to perform room temperature studies of the photoinduced changes in the reflectivity ΔR associated with exciton and carrier dynamics in GaN prepared by lateral epitaxial overgrowth. For resonant excitation of cold excitons, the ΔR decay possesses a 720 ps component attributed to the free exciton lifetime in this high quality material. For electrons with small excess energy ( 100 meV) electron relaxation are attributed to electron-hole scattering in conjunction with the screened electron-LO phonon interaction.
Guanabenz Reverses a Key Behavioral Change Caused by Latent Toxoplasmosis in Mice by Reducing Neuroinflammation

ABSTRACT Toxoplasma gondii is an intracellular parasite that has infected one-third of humans. The infection is permanent because the replicative form (tachyzoite) converts into a latent tissue cyst form (bradyzoite) that evades host immunity and is impervious to current drugs. The continued presence of these parasitic cysts hinders treatment and leads to chronic infection that has been linked to behavioral changes in rodents and neurological disease in humans. How these behavioral changes occur, and whether they are due to parasite manipulation or the host response to infection, remains an outstanding question. We previously showed that guanabenz possesses antiparasitic activity; here, we show that guanabenz reproducibly lowers brain cyst burden up to 80% in chronically infected male and female BALB/cJ mice when given intraperitoneally but not when administered by gavage or in food. Regardless of the administration route, guanabenz reverses Toxoplasma-induced hyperactivity in latently infected mice. In contrast, guanabenz increases cyst burden when given to chronically infected C57BL/6J mice yet still reverses Toxoplasma-induced hyperactivity. Examination of the brains from chronically infected BALB/cJ and C57BL/6J mice shows that guanabenz decreases inflammation and perivascular cuffing in each strain. Our study establishes a robust model for cyst reduction in BALB/cJ mice and shows for the first time that it is possible to reverse a key behavioral change associated with latent toxoplasmosis. The rescue from parasite-induced hyperactivity correlates with a decrease in neuroinflammation rather than reduced cyst counts, suggesting that some behavioral changes arise from host responses to infection. IMPORTANCEToxoplasma gondii is a common parasite of animals, including up to one-third of humans. The single-celled parasite persists within hosts for the duration of their life as tissue cysts, giving rise to chronic infection. Latent toxoplasmosis is correlated with neurological dysfunction in humans and results in dramatic behavioral changes in rodents. When infected, mice and rats adapt behaviors that make them more likely to be devoured by cats, the only host that supports the sexual stage of the parasite. In this study, we establish a new mouse model of tissue cyst depletion using a drug called guanabenz and show that it is possible to reverse a key behavior change back to normal in infected animals. We also show that the mechanism appears to have nothing to do with parasite cyst burden but rather the degree of neuroinflammation produced by chronic infection.
Adversarially Robust Submodular Maximization under Knapsack Constraints

We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings. For a single knapsack constraint, our algorithm outputs a robust summary of almost optimal (up to polylogarithmic factors) size, from which a constant-factor approximation to the optimal solution can be constructed. For multiple knapsack constraints, our approximation is within a constant-factor of the best known non-robust solution. We evaluate the performance of our algorithms by comparison to natural robustifications of existing non-robust algorithms under two objectives: 1) dominating set for large social network graphs from Facebook and Twitter collected by the Stanford Network Analysis Project (SNAP), 2) movie recommendations on a dataset from MovieLens. Experimental results show that our algorithms give the best objective for a majority of the inputs and show strong performance even compared to offline algorithms that are given the set of removals in advance.