cssi/SciSciGPT-SciSciNet huggingface dataset:
PLANNING YOUR NEXT REVISION: STEP BY STEP

Understand the nature of the problem. Revision TKR is a major undertaking and should be focused on problem solving. Know the problem!- Pain is not a diagnosis. Review history of problem. Think of possibilities: Infection, loosening, instability, stiffness, malalignment, poor kinematics. Ensure an adequate workup - History, Exam; Imaging: Radiographs: consider long alignment films, MR for soft tissue issues: Clunk, recurrent hemarthroses; CT scan: Remains gold standard for rotational alignment. Labs: CBC, ESR, C-reactive protein. Aspiration: Cell count, Culture. Assessment of where the patient is currently! Synthesise a working diagnosis and formulate a provisional plan. Revise “part of knee”: you better know what9s in there! Revise “all of knee”. How will you get there? Think old incisions How will get the parts out? What tools do you need? High speed burrs / diamond tip wheel /long thin saw blades; Osteotomes; Implant specific tools. Once everything is out: What do you have left? Soft tissue defects, Bone defects. How to “rebuild”: Constraint for soft tissue defects, Stems for mechanical stability, Cones / augments / bone graft for osseous defects; Fixation: Cement, Cementless.
Geochemistry of Martian basalts with constraints on magma genesis

Abstract Over the past decade, our knowledge of the geochemical and mineralogical diversity of Martian rocks has greatly increased. Rocks on the surface of Mars are older than most Martian meteorites and have a wider range in bulk chemistry (higher SiO 2 , higher total alkalis, and lower MgO contents). Recent Martian meteorite discoveries (NWA 7034, NWA 7635, and NWA 8159) are also significantly older than the shergottites and NWA 7034 contains clasts which have a similar range in chemistry of the surface basalts. In this invited review, I summarize what is known about the bulk chemistry (major elements) of Martian igneous rocks and use the chemistry to constrain the formation conditions in the interior, and how these conditions have changed through time. The basalts at Gale Crater are consistent with forming from a metasomatized mantle source which affected not only the chemistry of the basalts, but also the formation conditions. Basalts at Gusev Crater, Bounce Rock at Meridiani Planum, and those basalts not affected by metasomatism like those at Gale Crater have a mantle potential temperature of ~ 1450 °C for the Noachian. Conversely, Martian shergottites have a much higher mantle potential temperature (~ 1745 °C), which has always been problematic to explain for such young rocks. New formation conditions calculations are consistent with previous models of formation by a hot mantle plume, but solve the pressure of formation paradox from the previous model. Combining the calculations for mantle potential temperature with previous estimates for GRS measurements of surface volcanics from the Hesperian and Amazonian, I calculate a baseline cooling curve and mantle melting conditions for the Martian interior through time.
Aircraft Anti-lock Brake System (ABS) with Fuzzy Logic Control

Abstract Fuzzy logic is used to deal with nonlinear ABS that contains incomplete or uncertain characteristics. A nonlinear interpolation design from given look-up tables with fuzzy logic is proposed. The resulting continuous interpolated nonlinear characteristics are used as identified parameters in the system design. The heuristic rule-based fuzzy logic controller is shown to achieve maximum dragging force in simulation.
Vesicoureteral Reflux in Asymptomatic Siblings of Patients with Known Reflux: Radionuclide Cystography

The familial nature of vesicoureteral reflux among siblings of patients with vesicoureteral reflux has been reported to be from 8% to 32%. These included both symptomatic and asymptomatic siblings. The incidence of vesicoureteral reflux in asymptomatic siblings, however, has not been studied extensively. Sixty asymptomatic siblings of patients known to have vesicoureteral reflux were studied with radionuclide voiding cystography. Their ages ranged from 2 months to 15 years (mean, 4.2 years). Vesicoureteral reflux was detected in 27 of 60 (45%) of the siblings. Vesicoureteral reflux was unilateral in 15 and bilateral in 12 of the siblings. Radionuclide cystography is more sensitive than radiographic cystography and results in a very low radiation dose to the patient. The gonadal dose with radionuclide cystography is only 1.0 to 2.0 mrads. Because of these features, radionuclide cystography is a nearly ideal technique for the diagnosis of vesicoureteral reflux in siblings of patients with known vesicoureteral reflux. All siblings (symptomatic or asymptomatic) of patients with known vesicoureteral reflux should have a screening radionuclide cystography.
Photon Entropy Control and Near-Field Radiative Coupling Improve Efficiency of Thermoradiative Cells

Efficiency of thermoradiative cells can be increased by spectrally selecting high-entropy long-wavelength infrared photons for radiative energy exchange. Furthermore, near-field coupling to phonon polaritons in the heat sink increases both conversion efficiency and power density.
Adenocarcinoma of the Canine Prostate: Immunohistochemical Examination for Secretory Antigens

Thirty-one canine adenocarcinomas of prostatic origin were examined immunohistochemically with antibodies generated against three prostate-specific antigens by using a peroxidase-antiperoxidase technique. Primary antibodies included two generated with human prostatic antigens (PSA, PSAP) and one generated with a canine prostatic antigen (CPSP). Eight of 31 adenocarcinomas were positive with CPSP, two were positive with PSA, and three were PSAP positive. Normal and hyperplastic canine prostatic epithelium was strongly positive with all three markers. These results contrast with those in human studies in which PSA and PSAP stain normal and neoplastic epithelium with comparable intensity in all but the most anaplastic tumors.
Untangling Planar Curves

Any generic closed curve in the plane can be transformed into a simple closed curve by a finite sequence of local transformations called homotopy moves. We prove that simplifying a planar closed curve with n self-crossings requires $$\Theta (n^{3/2})$$ homotopy moves in the worst case. Our algorithm improves the best previous upper bound $$O(n^2)$$ , which is already implicit in the classical work of Steinitz; the matching lower bound follows from the construction of closed curves with large defect, a topological invariant of generic closed curves introduced by Aicardi and Arnold. Our lower bound also implies that $$\Omega (n^{3/2})$$ facial electrical transformations are required to reduce any plane graph with treewidth $$\Omega (\sqrt{n})$$ to a single vertex, matching known upper bounds for rectangular and cylindrical grid graphs. More generally, we prove that transforming one immersion of k circles with at most n self-crossings into another requires $$\Theta (n^{3/2} + nk + k^2)$$ homotopy moves in the worst case. Finally, we prove that transforming one non-contractible closed curve to another on any orientable surface requires $$\Omega (n^2)$$ homotopy moves in the worst case; this lower bound is tight if the curve is homotopic to a simple closed curve.
The Macroeconomics of Shadow Banking

We propose a dynamic macroeconomic model of nancial intermediation in an environment where households demand liquid assets. In contrast to the literature, intermediaries can issue equity without any friction. In normal times, intermediaries maximize liquidity creation by levering up the collateral value of their assets, a process we call shadow banking. A rise in uncertainty causes investors to demand liquidity in bad states. Intermediaries respond by simultaneously deleveraging and substituting toward safe liabilities; shadow banking eectively shuts down, prices fall and investment plummets. The model generates amplication via endogenous collateral runs, as well as ight to quality eects. The model’s macroeconomic eects are consistent with a slow economic recovery even when|and especially when|intermediary capital is high. We analyze the impact of several policy interventions in the areas of unconventional monetary policy and regulatory reform.
The New Market for Federal Judicial Law Clerks

Introduction 448 I. Framework 453 A. Time of Hiring 454 B. Nature of the Hiring Process 455 II. Evidence on Hiring in the Third Rather Than the Second Year of Law School 457 III. Adherence to Start Dates 459 A. Historical Experience with Start Dates in the Law Clerk Market and Beyond 459 B. Evidence on Adherence to Start Dates in the Present Law Clerk Market 461 C. Stabilization of the Present Law Clerk Market in a Pattern of Mixed Adherence and Nonadherence 469 IV. “Exploding Offers” and Compressed Market Timing 476 A. Exploding Offers 479 B. Market Compression in General 481 V. Conclusion 483 Appendix 484
Social desirability bias and polling errors in the 2016 presidential election

Abstract Social scientists have observed that socially desirable responding (SDR) often biases unincentivized surveys. Nonetheless, media, campaigns, and markets all employ unincentivized polls to make predictions about electoral outcomes. During the 2016 presidential campaign, we conducted three list experiments to test the effect SDR has on polls of agreement with presidential candidates. We elicit a subject’s agreement with either Hillary Clinton or Donald Trump using explicit questioning or an implicit elicitation that allows subjects to conceal their individual responses. We find evidence that explicit polling overstates agreement with Clinton relative to Trump. Subgroup analysis by party identification shows that SDR significantly diminishes explicit statements of agreement with the opposing party’s candidate driven largely by Democrats who are significantly less likely to explicitly state agreement with Trump. We measure economic policy preferences and find no evidence that ideological agreement drives SDR. We find suggestive evidence that local voting patterns predict SDR.
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions

We provide new results for noise-tolerant and sample-efficient learning algorithms under $s$-concave distributions. The new class of $s$-concave distributions is a broad and natural generalization of log-concavity, and includes many important additional distributions, e.g., the Pareto distribution and $t$ distribution. This class has been studied in the context of efficient sampling, integration, and optimization, but much remains unknown about the geometry of this class of distributions and their applications in the context of learning. The challenge is that unlike the commonly used distributions in learning (uniform or more generally log-concave distributions), this broader class is not closed under the marginalization operator and many such distributions are fat-tailed. In this work, we introduce new convex geometry tools to study the properties of $s$-concave distributions and use these properties to provide bounds on quantities of interest to learning including the probability of disagreement between two halfspaces, disagreement outside a band, and the disagreement coefficient. We use these results to significantly generalize prior results for margin-based active learning, disagreement-based active learning, and passive learning of intersections of halfspaces. Our analysis of geometric properties of $s$-concave distributions might be of independent interest to optimization more broadly.
Decision fatigue and heuristic analyst forecasts

Abstract Psychological evidence indicates that decision quality declines after an extensive session of decision-making, a phenomenon known as decision fatigue. We study whether decision fatigue affects analysts’ judgments. Analysts cover multiple firms and often issue several forecasts in a single day. We find that forecast accuracy declines over the course of a day as the number of forecasts the analyst has already issued increases. Also consistent with decision fatigue, we find that the more forecasts an analyst issues, the higher the likelihood the analyst resorts to more heuristic decisions by herding more closely with the consensus forecast, self-herding (i.e., reissuing their own previous outstanding forecasts), and issuing a rounded forecast. Finally, we find that the stock market understands these effects and discounts for analyst decision fatigue.
Healing the Rupture: A Jungian Perspective in African Religion/Mythology

In a letter dated March 25, 1998 from the Pontifical Council for Inter-religious Dialogue, Vatican City, to the Presidents of the Episcopal Conferences of Africa and of Madagascar, the pastoral began by saying that, “[t]he secretariat for Non-Christians has become more and more convinced of the importance of giving greater pastoral attention to the traditional religion (incorrectly called animism) in Africa and Madagascar. This conviction is reinforced by the expressed wishes of many Bishops of Africa and Madagascar which our Secretariat receives, together with the experience gained by other heralds of Gospel in this continent” (Arinze 1998:1).
Machine Learning for Drug Overdose Surveillance

ABSTRACTWe describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan (Herlands, McFowland, Wilson, & Neill, 2017) enables early detection of emerging patterns in spatio-temporal data, accounting for both the complex, correlated nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan (Neill, 2017) to 8 year...
A MULTIWAVELENGTH VIEW OF A MASS OUTFLOW FROM THE GALACTIC CENTER

The Galactic center (GC) lobe is a degree-tall shell of gas that spans the central degree of our Galaxy. It has been cited as evidence for a mass outflow from our GC region, which has inspired diverse models for its origin. However, most work has focused on the morphology of the GC lobe, which has made it difficult to draw strong conclusions about its nature. Here, I present a coherent, multiwavelength analysis of new and archival observations of the GC lobe. New radio continuum observations show that the entire structure has a similar spectral index, indicating that it has a common origin. The radio continuum emission shows that the GC lobe has a magnetized layer with a diameter of 110 pc and an equipartition field strength ranging from 40 to 100 μG. I show that optical and radio recombination line emission are associated with the GC lobe and are consistent with being located in the GC region. The recombination line emission traces an ionized shell nested within the radio continuum with diameter of 80 pc and height 165 pc. Mid-infrared maps at 8 and 15 μm show that the GC lobe has a third layer of warm dust and polycyclic aromatic hydrocarbon-emission that surrounds the radio continuum shell with a diameter of 130 pc. Assuming adiabatic expansion of the gas in the GC lobe, its formation required an energy input of about 5 × 1052 ergs. I compare the physical conditions of the GC lobe to several models and find best agreement with the canonical starburst outflow model. The formation of the GC lobe is consistent with the currently observed pressure and star formation rate in the central tens of parsecs of our Galaxy. Outflows of this scale are more typical of dwarf galaxies and would not be easily detected in nearby spiral galaxies. Thus, the existence of such an outflow in our own Galaxy may indicate that it is a relatively common phenomenon in the nuclei of spiral galaxies.
Simulating and quantifying legacy topographic data uncertainty: an initial step to advancing topographic change analyses

Rapid technological advances, sustained funding, and a greater recognition of the value of topographic data have helped develop an increasing archive of topographic data sources. Advances in basic and applied research related to Earth surface changes require researchers to integrate recent high-resolution topography (HRT) data with the legacy datasets. Several technical challenges and data uncertainty issues persist to date when integrating legacy datasets with more recent HRT data. The disparate data sources required to extend the topographic record back in time are often stored in formats that are not readily compatible with more recent HRT data. Legacy data may also contain unknown error or unreported error that make accounting for data uncertainty difficult. There are also cases of known deficiencies in legacy datasets, which can significantly bias results. Finally, scientists are faced with the daunting challenge of definitively deriving the extent to which a landform or landscape has or will continue to change in response natural and/or anthropogenic processes. Here, we examine the question: how do we evaluate and portray data uncertainty from the varied topographic legacy sources and combine this uncertainty with current spatial data collection techniques to detect meaningful topographic changes? We view topographic uncertainty as a stochastic process that takes into consideration spatial and temporal variations from a numerical simulation and physical modeling experiment. The numerical simulation incorporates numerous topographic data sources typically found across a range of legacy data to present high-resolution data, while the physical model focuses on more recent HRT data acquisition techniques. Elevation uncertainties observed from anchor points in the digital terrain models are modeled using “states” in a stochastic estimator. Stochastic estimators trace the temporal evolution of the uncertainties and are natively capable of incorporating sensor measurements observed at various times in history. The geometric relationship between the anchor point and the sensor measurement can be approximated via spatial correlation even when a sensor does not directly observe an anchor point. Findings from a numerical simulation indicate the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator largely as a result of the low density of data added from these sources. The estimator provides a history of DEM estimation as well as the uncertainties and cross correlations observed on anchor points. Our work provides preliminary evidence that our approach is valid for integrating legacy data with HRT and warrants further exploration and field validation.
Dismantling Knowledge Boundaries at NASA: The Critical Role of Professional Identity in Open Innovation:

Using a longitudinal in-depth field study at NASA, I investigate how the open, or peer-production, innovation model affects RD yet it challenged not only the knowledge-work boundaries but also the professional identity of the R&D professionals. This led to divergent reactions from R&D professionals, as adopting the open model required them to go through a multifaceted transformation. Only R&D professionals who underwent identity refocusing work dismantled their boundaries, truly adopting the knowledge from outside and sharing their internal knowledge. Others who did not go through that identity work failed to incorporate the solutions the open model produced. Adopting open innovation without a change in R&D professionals' identity resulted in no real change in the R&D process. This paper reveals how such processes unfold and illustrates the critical role of professional identity work in changing knowledge-work boundaries and shifting the locus of innovation.
Implementing clinical practice guidelines for chronic obstructive pulmonary disease in an EHR system

The use of clinical practice guidelines to improve quality of care has been a vividly discussed topic. Clinical practice guidelines (CPG) aim to improve the health of patients by guiding individual care in clinical settings. CPGs bring potential benefits for patients by improving clinical decision making, improving efficiency and enhancing patient care, while essentially optimizing financial value. Chronic conditions like heart disease, stroke, and chronic obstructive pulmonary disease (COPD), plague the US healthcare system causing several million dollars in healthcare related cost. This paper demonstrates the development of a CPG into an open-source EHR system to effectively manage COPD patients. The CPG is incorporated using the open web app standard, which allows it to be used with any web browser based EHR system, once data from the EHR system can be fed into the app. As a result, the CPG helps create a more effective and efficient decision-making process.
Energy-Transport Sector Interdependence in Extreme Events: the Case of a Hurricane Event in Boston

This paper examines the governance challenges of intersectoral critical infrastructure interdependencies. Between 2015 and 2017, the US Department of Homeland Security (DHS) supported a team of researchers at Northeastern University and The Ohio State University to evaluate critical infrastructure interdependencies between the energy and transportation sectors in the context of a potential hurricane event in the Boston metro region. The paper reports on 2 years of semi-structured interviews with key stakeholders and infrastructure operators. The results provide insights gained from the interviews and a workshop convened jointly by Northeastern University, the Boston Mayor’s Office of Emergency Management, and the Massachusetts Emergency Management Agency. Research relating to the resilience of interdependent critical infrastructure has proliferated in recent years. Nearly all of this research involves analysis of secondary data or abstracted operational models, rather than in-depth interviews of on-the-ground infrastructure owners and operators. This paper identifies a number of important constraints that undermine critical infrastructure resilience. At its core, infrastructure resilience is less a question of managerial desire or skill, or technical resources or design, but is a larger institutional challenge. The institutional environment in which energy and transportation infrastructure operates ultimately informs and defines resilience efforts.
Weakly Supervised Learning of Single-Cell Feature Embeddings

We study the problem of learning representations for single cells in microscopy images to discover biological relationships between their experimental conditions. Many new applications in drug discovery and functional genomics require capturing the morphology of individual cells as comprehensively as possible. Deep convolutional neural networks (CNNs) can learn powerful visual representations, but require ground truth for training; this is rarely available in biomedical profiling experiments. While we do not know which experimental treatments produce cells that look alike, we do know that cells exposed to the same experimental treatment should generally look similar. Thus, we explore training CNNs using a weakly supervised approach that uses this information for feature learning. In addition, the training stage is regularized to control for unwanted variations using mixup or RNNs. We conduct experiments on two different datasets; the proposed approach yields single-cell embeddings that are more accurate than the widely adopted classical features, and are competitive with previously proposed transfer learning approaches.
Global connectivity of the fronto-parietal cognitive control network is related to depression symptoms in the general population

We all vary in our mental health, even among people not meeting diagnostic criteria for mental illness. Understanding this individual variability may reveal factors driving the risk for mental illness, as well as factors driving subclinical problems that still adversely affect quality of life. To better understand the large-scale brain network mechanisms underlying this variability, we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. One such system is the fronto-parietal cognitive control network (FPN). Changes in FPN connectivity may impact mental health by disrupting the ability to regulate symptoms in a goal-directed manner. Here we test the hypothesis that FPN dysconnectivity relates to mental health symptoms even among individuals who do not meet formal diagnostic criteria but may exhibit meaningful symptom variation. We found that depression symptoms severity negatively correlated with between-network global connectivity (BGC) of the FPN. This suggests that decreased connectivity between the FPN and the rest of the brain is related to increased depression symptoms in the general population. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both health and disease.
Connectomics of the zebrafish’s lateral-line neuromast reveals wiring and miswiring in a simple microcircuit

The lateral-line neuromast of the zebrafish displays a restricted, consistent pattern of innervation that facilitates the comparison of microcircuits across individuals, developmental stages, and genotypes. We used serial blockface scanning electron microscopy to determine from multiple specimens the neuromast connectome, a comprehensive set of connections between hair cells and afferent and efferent nerve fibers. This analysis delineated a complex but consistent wiring pattern with three striking characteristics: each nerve terminal is highly specific in receiving innervation from hair cells of a single directional sensitivity; the innervation is redundant; and the terminals manifest a hierarchy of dominance. Mutation of the canonical planar-cell-polarity gene vangl2, which decouples the asymmetric phenotypes of sibling hair-cell pairs, results in randomly positioned, randomly oriented sibling cells that nonetheless retain specific wiring. Because larvae that overexpress Notch exhibit uniformly oriented, uniformly innervating hair-cell siblings, wiring specificity is mediated by the Notch signaling pathway.
Sampling for Approximate Bipartite Network Projection

Bipartite networks manifest as a stream of edges that represent transactions, e.g., purchases by retail customers. Many machine learning applications employ neighborhood-based measures to characterize the similarity among the nodes, such as the pairwise number of common neighbors (CN) and related metrics. While the number of node pairs that share neighbors is potentially enormous, only a relatively small proportion of them have many common neighbors. This motivates finding a weighted sampling approach to preferentially sample these node pairs. This paper presents a new sampling algorithm that provides a fixed size unbiased estimate of the similarity matrix resulting from a bipartite graph stream projection. The algorithm has two components. First, it maintains a reservoir of sampled bipartite edges with sampling weights that favor selection of high similarity nodes. Second, arriving edges generate a stream of \textsl{similarity updates} based on their adjacency with the current sample. These updates are aggregated in a second reservoir sample-based stream aggregator to yield the final unbiased estimate. Experiments on real world graphs show that a 10% sample at each stage yields estimates of high similarity edges with weighted relative errors of about 1%.
Theory of Mind: a Hidden Factor in Reading Comprehension?

Theory of mind is the understanding that other people have mental states that drive their actions and that those mental states can be different from one’s own. Without understanding theory of mind and being able to take others’ perspectives, it could be difficult for children to read and understand narrative texts. This paper posits that children’s understanding of others’ minds may be a potential missing piece in current accounts of reading comprehension. Indeed, the typical progression of children’s theory of mind abilities across childhood is closely aligned with the development of narrative processing skills. Furthermore, emerging evidence shows that both narrative processing and theory of mind are predictive of children’s reading comprehension, both concurrently and longitudinally. We present a possible explanation for why such a link exists and propose a causal framework of this relation in which increased ToM leads to increased understanding of and inferencing about characters’ mental states. Understanding characters’ mental states then leads to better reading comprehension. The framework makes novel, testable predictions and provides directions for future research.
Pilot Randomized Trial of a Family Management Efficacy Intervention for Caregivers of African American Adolescents with Disruptive Behaviors

Caregivers of adolescents diagnosed with Oppositional Defiant Disorder and/or Conduct Disorder (ODD/CD) experience unique challenges when interacting with child service systems involved in their adolescents’ care. Absent from the literature are interventions to improve these interactions, which in the long term may improve adolescent behavioral health outcomes. To examine feasibility/acceptability of Family Management Efficacy (FAME) intervention content, structure, delivery, and appropriateness of selected measures for caregivers of African American adolescents with ODD/CD. Secondary aim was to explore changes in FAME caregivers’ interaction self-efficacy, stress, quality of life, and family functioning scores relative to caregivers receiving treatment as usual (TAU). A pilot two-group randomized trial was conducted with caregivers of African American adolescents (ages 12–18 years) diagnosed with ODD/CD receiving FAME (n = 11) or TAU (n = 9). Feasibility outcomes of enrollment/attrition, measurement completion, session attendance, and homework completion were assessed using tracking logs and field notes, and acceptability through caregiver satisfaction scores and interviews. Preliminary outcomes were assessed at baseline, post- and 2-months post intervention. FAME was highly acceptable and met a priori thresholds for feasibility in enrollment (56%), attrition (35%), caregiver attendance (55%), and homework completion (50%), with lower than anticipated kin attendance (42%) and measurement completion (55%). Preliminary outcomes suggest FAME may benefit caregivers in areas of family communication, cohesion, and quality of life, but lacked observed benefit for self-efficacy and problem solving indicating need for refinement. Results inform changes to FAME content, measurement, and delivery schedule in preparation for a fully powered randomized controlled trial.
Schools as moderators of genetic associations with life course attainments: Evidence from the WLS and add health

Genetic variants identified in genome-wide association studies of educational attainment have been linked with a range of positive life course development outcomes. However, it remains unclear whether school environments may moderate these genetic associations. We analyze data from two biosocial surveys that contain both genetic data and follow students from secondary school through mid- to late life. We test if the magnitudes of the associations with educational and occupational attainments varied across the secondary schools that participants attended or with characteristics of those schools. Although we find little evidence that genetic associations with educational and occupational attainment varied across schools or with school characteristics, genetic associations with any postsecondary education and college completion were moderated by school-level socioeconomic status. Along similar lines, we observe substantial between-school variation in the average level of educational attainment students achieved for a fixed genotype. These findings emphasize the importance of social context in the interpretation of genetic associations. Specifically, our results suggest that though existing measures of individual genetic endowment have a linear relationship with years of schooling that is relatively consistent across school environments, school context is crucial in connecting an individual's genotype to his or her likelihood of crossing meaningful educational thresholds.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse

We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.
Risk of Bleomycin-Related Pulmonary Toxicities and Operative Morbidity After Postchemotherapy Retroperitoneal Lymph Node Dissection in Patients With Good-Risk Germ Cell Tumors

PurposeThree cycles of bleomycin, etoposide, and cisplatin (BEP × 3) or four cycles of etoposide and cisplatin (EP × 4) are first-line chemotherapy regimens for men with International Germ Cell Cancer Collaborative Group (IGCCCG) good-risk germ cell tumors (GCTs). We determined whether inclusion of bleomycin affected pulmonary and operative morbidity after postchemotherapy retroperitoneal lymph node dissection (PC-RPLND).Patients and MethodsWe queried our database to identify IGCCCG good-risk patients who received BEP × 3 or EP × 4 induction chemotherapy before PC-RPLND from 2006 to 2016. Patients who received combination regimens were excluded. The primary outcomes of interest were pulmonary morbidity (prolonged intubation, reintubation, supplemental oxygen use, intensive care unit stay) and operative morbidity (operative time, length of stay, concomitant procedures, estimated blood loss).ResultsWe analyzed 234 patients (191 BEP × 3 v 43 EP × 4). All patients were extubated immediately after the operatio...
Meta-Learning MCMC Proposals

Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for training learning agents that can generalize to unseen environments, we propose a meta-learning approach to building effective and generalizable MCMC proposals. We parametrize the proposal as a neural network to provide fast approximations to block Gibbs conditionals. The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required. We explore several applications including open-universe Gaussian mixture models, in which our learned proposals outperform a hand-tuned sampler, and a real-world named entity recognition task, in which our sampler yields higher final F1 scores than classical single-site Gibbs sampling.
The Effects of Contract Framing on Misconduct and Entitlement

ABSTRACT This study examines the effects of incentive contract framing on misreporting and entitlement. I conduct a 2 × 2 between-subjects experiment, manipulating incentive contract framing (Bonus...
Reflection by and transmission through an ENZ interface

We have studied the reflection and transmission of traveling and evanescent plane waves, incident upon an ENZ material. The Fresnel reflection and transmission coefficients were obtained in the ENZ limit. For a p polarized incident wave, the transmission coefficient vanishes, except very close to normal incidence. The reflection coefficient is -1 for both traveling and evanescent waves. It is shown, however, that there is a finite electric field in the ENZ material, even though the transmission coefficient is zero. This field is either linearly polarized or circularly polarized. The magnetic field in the medium for p polarized illumination is zero, and therefore there can be no energy flow through the material. For s polarization, the magnetic field in the medium is circularly polarized, and energy can flow through the material, parallel to the interface.
Online Worker Fraud and Evolving Threats to the Integrity of MTurk Data: A Discussion of Virtual Private Servers and the Limitations of IP-Based Screening Procedures

ABSTRACT We identify a pervasive, yet previously undocumented threat to the reliability of MTurk data—and discuss how this issue is symptomatic of opportunities and incentives that facilitate fraud...
Evaluating the Impact of Intensity Normalization on MR Image Synthesis.

Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled—i.e., normalized—both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.
Spinning constraints on chaotic large c CFTs

We study out-of-time ordered four-point functions in two dimensional conformal field theories by suitably analytically continuing the Euclidean correlator. For large central charge theories with a sparse spectrum, chaotic dynamics is revealed in an exponential decay; this is seen directly in the contribution of the vacuum block to the correlation function. However, contributions from individual non-vacuum blocks with large spin and small twist dominate over the vacuum block. We argue, based on holographic intuition, that suitable summations over such intermediate states in the block decomposition of the correlator should be sub-dominant, and attempt to use this criterion to constrain the OPE data with partial success. Along the way we also discuss the relation between the spinning Virasoro blocks and the on-shell worldline action of spinning particles in an asymptotically AdS spacetime.
Transthyretin stimulates tumor growth through regulation of tumor, immune, and endothelial cells

Early detection of lung cancer offers an important opportunity to decrease mortality while it is still treatable and curable. Thirteen secretory proteins that are Stat3 downstream gene products were identified as a panel of biomarkers for lung cancer detection in human sera. This panel of biomarkers potentially differentiates different types of lung cancer for classification. Among them, the transthyretin (TTR) concentration was highly increased in human serum of lung cancer patients. TTR concentration was also induced in the serum, bronchoalveolar lavage fluid, alveolar type II epithelial cells, and alveolar myeloid cells of the CCSP-rtTA/(tetO)7-Stat3C lung tumor mouse model. Recombinant TTR stimulated lung tumor cell proliferation and growth, which were mediated by activation of mitogenic and oncogenic molecules. TTR possesses cytokine functions to stimulate myeloid cell differentiation, which are known to play roles in tumor environment. Further analyses showed that TTR treatment enhanced the reactive oxygen species production in myeloid cells and enabled them to become functional myeloid-derived suppressive cells. TTR demonstrated a great influence on a wide spectrum of endothelial cell functions to control tumor and immune cell migration and infiltration. TTR-treated endothelial cells suppressed T cell proliferation. Taken together, these 13 Stat3 downstream inducible secretory protein biomarkers potentially can be used for lung cancer diagnosis, classification, and as clinical targets for lung cancer personalized treatment if their expression levels are increased in a given lung cancer patient in the blood.
The Prevalence of Cognitive Impairment Among Adults With Incident Heart Failure: The "Reasons for Geographic and Racial Differences in Stroke" (REGARDS) Study.

ABSTRACT Background Cognitive impairment (CI) is estimated to be present in 25%–80% of heart failure (HF) patients, but its prevalence at diagnosis is unclear. To improve our understanding of cognition in HF, we determined the prevalence of CI among adults with incident HF in the REGARDS study. Methods and Results REGARDS is a longitudinal cohort study of adults ≥45 years of age recruited in the years 2003–2007. Incident HF was expert adjudicated. Cognitive function was assessed with the Six-Item Screener. The prevalence of CI among those with incident HF was compared with the prevalence of CI among an age-, sex-, and race-matched cohort without HF. The 436 participants with incident HF had a mean age of 70.3 years (SD 8.9), 47% were female, and 39% were black. Old age, black race, female sex, less education, and anticoagulation use were associated with CI. The prevalence of CI among participants with incident HF (14.9% [95% CI 11.7%–18.6%]) was similar to the non-HF matched cohort (13.4% [11.6%–15.4%]; P Conclusions A total of 14.9% of the adults with incident HF had CI, suggesting that the majority of cognitive decline occurs after HF diagnosis. Increased awareness of CI among newly diagnosed patients and ways to mitigate it in the context of HF management are warranted.
Reputational dynamics in financial networks during a crisis

Abstract Firm reputation plays a vital role in financial networks, and it is especially impactful in times of market stress or a financial crisis. This paper uses a novel theoretical model to study reputational dynamics in financial networks, taking into account that firms begin with incomplete information, learn about others over time, and update their connections as their beliefs evolve. In our model, stronger firms develop high reputations and remain in the network, while weaker firms will eventually drop in reputation and get shut out. We show that more information revelation during crisis generally increases network fragility and harms social welfare. It is thus crucial to maintain anonymity among firms during a crisis. Certain network structures, such as core-periphery networks, are more systemically resilient against negative informational effects.
On weighted $L^{2}$ cohomology

Consider a complete orientable manifold with countably many components of bounded dimension. Suppose that its rational homology is infinitely generated in some degree. Then there is no choice of weight function for which the natural map from weighted L^2 cohomology to de Rham cohomology is surjective in that degree.
Optimal Transmission Radius for Flooding in Large Scale Sensor Networks

One of the principal characteristics of large scale wireless sensor networks is their distributed, multi-hop nature. Due to this characteristic, applications such as query propagation rely regularly on network-wide flooding for information dissemination. If the transmission radius is not set optimally, the flooded packet may be holding the transmission medium for longer periods than are necessary, reducing overall network throughput. We analyze the impact of the transmission radius on the average settling time--the time at which all nodes in the network finish transmitting the flooded packet. Our analytical model takes into account the behavior of the underlying contention-based MAC protocol, as well as edge effects and the size of the network. We show that for large wireless networks there exists an intermediate transmission radius which minimizes the settling time, corresponding to an optimal tradeoff between reception and contention times. We also explain how physical propagation models affect small wireless networks and why there is no intermediate optimal transmission radius observed in these cases. The mathematical analysis is supported and validated through extensive simulations.
Hidden Schooling: Repeated Grades and the Returns to Education and Experience

Over the past four decades, nearly 25% of all American public school students repeated at least one grade in primary or secondary school, and ninth grade repeating increased four-fold. Despite its prevalence, few economists have attempted to account for grade repeating when estimating the returns to education and experience. I show that 10% of the increase in ninth grade repeating was caused by changes in compulsory schooling laws (CSLs). Because CSLs increase both grade repeating and educational attainment, compulsory education-based IV estimates of the returns to education are positively biased by up to 38%. Additionally, grade repeating causes endogenous measurement error in labor market experience. Solely through this measurement error, I show that the residual black-white wage gap is overstated by 10%, the wage return to a high school diploma is overstated by 11% relative to dropouts, and the labor supply gap between dropouts and high school graduates is overstated by 23%. Controlling for age instead of experience reduces this bias, suggesting age should be a standard control variable for reduced-form analysis, not experience.
Computational Translation of Genomic Responses From Experimental Model Systems to Humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.