cssi/SciSciGPT-SciSciNet huggingface dataset:
Lymphadenopathy in Homosexual Men. Morbid Anatomy With Clinical and Immunologic Correlations

This is a study of the histopathologic features of lymph nodes removed from eleven young, homosexual men, all of whom presented with lymph node enlargement of more than 3 months' duration, accompanied, in the majority of cases, by fever and weight loss. Reactive follicular and sinusoidal hyperplasia were the main findings in 10 patients. The medullary sinuses were packed with monomorphic round sinusoidal cells associated with neutrophils. In one of the 10 patients, granulomas packed the subcapsular sinuses, especially. Another patient presented with lymphoid-depleted nodes with absent germinal centers and a prominent vascular skeleton; this latter feature made it difficult to distinguish this picture from the nodal form of Kaposi's sarcoma. All patients had a history of sexual promiscuity and used "recreational" drugs. Nine of 10 patients had deficient cellular immunity and inverted T-cell helper/suppressor ratios. The only patient with normal cellular immunity had nodal granulomas. Humoral immunity was normal in all patients.
Entropy for Strongly Coupled Oscillators

This paper examines an approach to determining the entropy of coupled oscillators that does not rely on the assumption of weak coupling. The results of this approach are compared to the results for a weakly coupled system. It is shown that the results from each methodology agree in the case of weak coupling, and that a correction term is required for moderate to strong coupling. The correction term is shown to be related to the mixed energy term from the coupling spring as well as the geometry and stiffness of the system. Numerical simulations are performed for a symmetric system of identical coupled oscillators and an asymmetric system of nonidentical oscillators to demonstrate these findings.Copyright © 2016 by ASME
Malignant Peripheral Primitive Neuroectodermal Tumor of the Uterus

Peripheral primitive neuroectodermal tumor (PPNET) is rare, occurring most often in young adults. Approximately 50 cases have been reported, with only four cases involving the female genital tract. We report the fifth patient. The term "PPNET" should be used only for tumors with neuroectodermal elements exclusively that occur at sites outside the central and sympathetic nervous system. The pathologic differential diagnoses include rhabdomyosarcoma, immature malignant teratoma, small cell carcinoma of the cervix, and ganglioneuroma. Therapy for this tumor has varied, and no effective regimen has been established.
Infinite random matrix theory, tridiagonal bordered Toeplitz matrices, and the moment problem

Abstract The four major asymptotic level density laws of random matrix theory may all be showcased through their Jacobi parameter representation as having a bordered Toeplitz form. We compare and contrast these laws, completing and exploring their representations in one place. Inspired by the bordered Toeplitz form, we propose an algorithm for the finite moment problem by proposing a solution whose density has a bordered Toeplitz form.
Behavioral finance: A panel discussion

Abstract This panel discussion on behavioral finance took place on November 19, 2016 during the annual meeting of the Southern Finance Association held at Sandestin, Florida. The panel provides an overview of behavioral finance and discusses different types of behavioral biases; how they influence private and institutional wealth management clients and professionals, corporate decision making, and traders; generational and gender differences in asset allocation and estate planning; as well as identifying future areas of research. The content of this panel discussion is partly based on Baker et al. (2017).
New general continuous-time state-task network formulation for short-term scheduling of multipurpose batch plants

A new continuous-time MILP model for the short-term scheduling of multipurpose batch plants is presented. The proposed model relies on the state-task network (STN) approach and addresses the general problem of batch scheduling, accounting for resource (utility) constraints, variable batch sizes and processing times, various storage policies (UIS, FIS, NIS, ZW), batch mixing/splitting, and sequence-dependent changeover times. The key features of the proposed model are the following: (a) a continuous-time representation is used, common for all units; (b) assignment constraints are expressed using binary variables that are defined only for tasks, not for units; (c) start times of tasks are eliminated, so that time-matching constraints are used only for the finish times of tasks; and (d) a new class of valid inequalities that improves the LP relaxation is added to the MILP formulation. Compared to other general continuous time STN formulations, the proposed model is faster. Compared to event-driven formulations, it is more general, as it accounts for resources other than equipment and gives solutions in comparable computational times. The application of the model is illustrated through four example problems.
Deriving genomic diagnoses without revealing patient genomes.

Patient genomes are interpretable only in the context of other genomes; however, genome sharing enables discrimination. Thousands of monogenic diseases have yielded definitive genomic diagnoses and potential gene therapy targets. Here we show how to provide such diagnoses while preserving participant privacy through the use of secure multiparty computation. In multiple real scenarios (small patient cohorts, trio analysis, two-hospital collaboration), we used our methods to identify the causal variant and discover previously unrecognized disease genes and variants while keeping up to 99.7% of all participants’ most sensitive genomic information private.
A primary method for the complex calibration of a hydrophone from 1 Hz to 2 kHz

A primary calibration method is demonstrated to obtain the magnitude and phase of the complex sensitivity for a hydrophone at frequencies between 1 Hz and 2 kHz. The measurement is performed in a coupler reciprocity chamber ('coupler'); a closed test chamber where time harmonic oscillations in pressure can be achieved and the reciprocity conditions required for a primary calibration can be realized. Relevant theory is reviewed and the reciprocity parameter updated for the complex measurement. Systematic errors and corrections for magnitude are reviewed and more added for phase. The combined expanded uncertainties of the magnitude and phase of the complex sensitivity at 1 Hz were 0.1 dB re 1 V and ±, respectively. Complex sensitivity, sensitivity magnitude, and phase measurements are presented on an example primary reference hydrophone.
MOS models for LIM transient simulations

This paper presents an approach for the transient simulation of circuits through the latency insertion model using advanced models for MOS transistors. By taking into account the dynamic charge storage effects in short-channel devices a more accurate simulation of high-speed digital and analog circuits via the latency insertion method can be performed. The approach makes use of the SPICE LEVEL 3 transistor model for MOSFETs. In addition the use of the latency insertion method allows better convergence and higher computational speed for the simulation. Several computer simulations are performed to validate the method. Results show improvement in accuracy by using the high-level models.
Lowering of the complexity of quantum chemistry methods by choice of representation.

The complexity of the standard hierarchy of quantum chemistry methods is not invariant to the choice of representation. This work explores how the scaling of common quantum chemistry methods can be reduced using real-space, momentum-space, and time-dependent intermediate representations without introducing approximations. We find the scalings of exact Gaussian basis Hartree–Fock theory, second-order Moller-Plesset perturbation theory, and coupled cluster theory (specifically, linearized coupled cluster doubles and the distinguishable cluster approximation with doubles) to be O(N3), O(N3), and O(N5), respectively, where N denotes the system size. These scalings are not asymptotic and hold over all ranges of N.
Decision Tree Complexity and Betti Numbers

We show that any algebraic computation tree or any fixed-degree algebraic tree for solving the membership question of a compact setS?Rnmust have height greater than?(log(si(S)))?cnfor eachi, wheresi(S) is theith Betti number. This generalizes a well-known result by Ben-Or who proved this lower bound for the casei=0, and a recent result by Bjorner and Lovasz who proved this lower bound for allifor linear decision trees.
Fourier transform refractometry

We describe instrumentation that has been constructed at the National Institute of Standards and Technology (NIST) for measurement of the index of refraction of solid materials in the spectral region from 0.5 micrometer to 1000 micrometer using Fourier-transform based spectrophotometers and etalon samples. Preliminary index of refraction measurements have been performed on fused silica and arsenic trisulfide glass samples from 0.5 micrometer to 12 micrometer, and the results are compared to tabulated values on these materials.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Modeling Developmental Class Provides Insights into Individual Contributions to Infant Survival in Callitrichids

Cooperative breeders live in social groups in which individuals in an age–sex class vary in reproductive development due to reproductive dominance by a few individuals in each group. Among callitrichids, adult males have been implicated in driving group reproductive output, but uneven sampling efforts, the underlying effects of group size, and pseudoreplication at the group and species levels are confounding variables in these analyses. We examined the drivers of group reproductive output in callitrichids by 1) conducting a meta-analysis of published studies of callitrichid group composition; 2) assigning developmental class based on reproductive morphology; and 3) using multivariate modeling to test whether the proportion of individuals of each developmental class predicts the presence and the number of surviving offspring among free-ranging Weddell’s saddleback tamarins (Leontocebus weddelli) and emperor tamarins (Saguinus imperator) in Peru. The meta-analysis revealed that the number of adult females and group size, but not the number of adult males, are significantly correlated with group reproductive output. Statistical models of the new dataset revealed that the proportion of primary breeding males, primary breeding females, and group size predicted whether groups had surviving infants, and that only the proportion of primary breeding females and group size predicted the number of surviving infants. Thus, primary breeding males appear to be necessary for groups to raise any infants, but a higher proportion of primary breeding females and a larger group size increase group reproductive output overall.
Refined and Microlocal Kakeya-Nikodym Bounds of Eigenfunctions in Higher Dimensions

We prove a Kakeya–Nikodym bound on eigenfunctions and quasimodes, which sharpens a result of the authors (Blair and Sogge in Anal PDE 8:747–764, 2015) and extends it to higher dimensions. As in the prior work, the key intermediate step is to prove a microlocal version of these estimates, which involves a phase space decomposition of these modes that is essentially invariant under the bicharacteristic/geodesic flow. In a companion paper (Blair and Sogge in J Differ Geom, 2015), it will be seen that these sharpened estimates yield improved L q (M) bounds on eigenfunctions in the presence of nonpositive curvature when $${2 < q < \frac{2(d+1)}{d-1}}$$ .
Trigger events and poverty transitions among young adults in Finland after leaving the parental home.

Abstract Studies have shown that leaving the parental home is associated with an increase in the risk of poverty. However, less is known about poverty dynamics after leaving the nest. Based on Finnish population register data, this research answers the question of how employment and demographic events affect poverty entries and exits among 18- to 24-year-olds in Finland. The follow-up of individuals starts when young adults move away from the parental home. In addition to descriptive methods, discrete-time event-history models are estimated. This study illustrates that changes in employment are typical and are, therefore, often associated with poverty transitions among young adults. Although demographic events are less common, when experienced, they often lead to poverty transitions. This study found that the effects of demographic events vary by gender. This study also illustrates that a move back to the parental home is more likely for some poor population subgroups than for others.
Expression of Nuclear Lamin Proteins in Endothelial Cells is Sensitive to Cell Passage and Fluid Shear Stress

Vascular cells are regulated by continuous hemodynamic forces in vivo, and mechanical forces such as shear stress are proposed to involve in the progression of cardiovascular diseases such as atherosclerosis. Lamin A/C makes up the nuclear lamina, which structurally supports the nucleus while also functionally participates in chromatin organization and gene transcription. Diseases caused by lamin or other nuclear proteins are called laminopathies. One example, Hutchinson Gilford Progeria Syndrome (HGPS) where young patients show signs of accelerated aging, is caused by de novo mutations on the lamin A/C gene. Vasculature of HGPS patients shares many similarities with people of advanced age, suggesting a role for lamin in vascular aging. In this study, we examined how arterial shear stress affects lamin A/C expression in bovine aortic endothelial cells at different population doubling levels (PDL). We also used fluorescence image analysis to examine nuclear shape changes with shear stress and PDL. Our results suggest that laminar shear stress downregulated lamin A/C expression in low PDL cells, but the effect was reversed in high PDL cells. Nuclear shape changes were more prominent after shear stress in low PDL cells. Moreover, lamin A/C accumulated more at the nuclear periphery after exposure to shear stress. Overall, our results indicate that both shear stress and cell passage can have an impact on lamin expressions at transcriptional and translational levels, as we continue to understand the effect of shear stress on endothelial lamina as part of the vascular aging process.
Evolving Understanding of Antarctic Ice-Sheet Physics and Ambiguity in Probabilistic Sea-Level Projections

Mechanisms such as ice-shelf hydrofracturing and ice-cliff collapse may rapidly increase discharge from marine-based ice sheets. Here, we link a probabilistic framework for sea-level projections to a small ensemble of Antarctic ice-sheet (AIS) simulations incorporating these physical processes to explore their influence on global-mean sea-level (GMSL) and relative sea-level (RSL). We compare the new projections to past results using expert assessment and structured expert elicitation about AIS changes. Under high greenhouse gas emissions (Representative Concentration Pathway [RCP] 8.5), median projected 21st century GMSL rise increases from 79 to 146 cm. Without protective measures, revised median RSL projections would by 2100 submerge land currently home to 153 million people, an increase of 44 million. The use of a physical model, rather than simple parameterizations assuming constant acceleration of ice loss, increases forcing sensitivity: overlap between the central 90% of simulations for 2100 for RCP 8.5 (93-243 cm) and RCP 2.6 (26-98 cm) is minimal. By 2300, the gap between median GMSL estimates for RCP 8.5 and RCP 2.6 reaches >10 m, with median RSL projections for RCP 8.5 jeopardizing land now occupied by 950 million people (versus 167 million for RCP 2.6). The minimal correlation between the contribution of AIS to GMSL by 2050 and that in 2100 and beyond implies current sea-level observations cannot exclude future extreme outcomes. The sensitivity of post-2050 projections to deeply uncertain physics highlights the need for robust decision and adaptive management frameworks.
Assessing the Impact of CEO Activism

CEO activism refers to corporate leaders speaking out on social and environmental policy issues not directly related to their company’s core business, which distinguishes it from nonmarket strategy...
An information theory approach to hypothesis testing in criminological research

This research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way. Using data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage. Models including place-based measures of land use emerged as the best models among the set of tested models. These were superior to models that included measures of age and socioeconomic status. The best models for commercial and street robbery include three measures of ordinary businesses, liquor stores, and spatial lag. Models based on information theory offer a useful alternative to significance testing when a strong theoretical framework guides the selection of model sets. Theoretically relevant ‘ordinary businesses’ have a greater influence on robbery than socioeconomic variables and most measures of discretionary businesses.
Carpooling and the Economics of Self-Driving Cars

We study the interplay between autonomous transportation, carpooling, and road pricing. We discuss how improvements in these technologies, and interactions among them, will affect transportation markets. Our main results show how to achieve socially efficient outcomes in such markets, taking into account the costs of driving, road capacity, and commuter preferences. An important component of the efficient outcome is the socially optimal matching of carpooling riders. Our approach shows how to set road prices and how to share the costs of driving and tolls among carpooling riders in a way that implements the efficient outcome.
The science and application of IPS e.Max dental ceramic

The aim of this paper is to report the state of current literature and recommendations for the lithium disilicate glass-ceramic IPS e.Max. The materials science, mechanical and optical properties were reviewed. Additionally an assessment was conducted of current implementation recommendations and clinical outcomes. This paper provides a brief historical overview, summary of the findings the findings of current literature, and clinical recommendation for the use of IPS e.Max CAD in dental applications.
Role of Imaging in Acute Ischemic Stroke

Rapid multimodal imaging is essential in the workup and management of acute ischemic stroke. Early parenchymal findings on noncontrast computed tomography or standard magnetic resonance imaging are used to triage patients for intravenous thrombolysis and to provide insight on prognosis. In the wake of recent endovascular stroke trials, advanced techniques including perfusion imaging and noninvasive vascular imaging are becoming important tools to guide potential endovascular treatment or expand therapy windows. Advanced imaging is also important in pediatric ischemic stroke which requires a slightly different workflow and treatment approach. Here, we will discuss key imaging findings in acute ischemic stroke, as well as the present and future of neuroimaging in light of recent and ongoing clinical trials.
Back to the future: the nature of regulatory capital requirements

This paper compares the current regulatory capital requirements under the Dodd–Frank Act and the 10-% leverage ratio, as proposed by the US Treasury and the US House of Representatives’ Financial CHOICE Act (FCA). We find that the majority of US banks would not qualify for an “off-ramp” option—where regulatory relief is offered to qualifying banks organizations (QBOs)—unless considerable amounts of capital are added and that large banks are much closer to the proposed leverage threshold and, therefore, are more likely to stand to gain from regulatory relief. The paper identifies an important moral hazard problem that arises due to the QBO optionality, where banks are likely to increase the riskiness of their asset portfolio and qualify for the FCA “off-ramp” relief with unintended effects on financial stability.
FarmCPUpp: Efficient large‐scale genomewide association studies

Genomewide association studies (GWAS) are computationally demanding analyses that use large sample sizes and dense marker sets to discover associations between quantitative trait variation and genetic variants. FarmCPU is a powerful new method for performing GWAS. However, its performance is hampered by details of its implementation and its reliance on the R programming language. In this paper, we present an efficient implementation of FarmCPU, called FarmCPUpp, that retains the R user interface but improves memory management and speed through the use of C++ code and parallel computing.
Moral Character in Negotiation

In this article, we discuss the role of moral character in negotiation and identify open questions and promising directions for future scholars to explore. We advance research in this area by introducing a dyadic model of moral character in negotiation, which highlights the joint influence of each party’s moral character on negotiation attitudes, motives, and behaviors. We discuss the implications of our model and conclude that personality science, and especially the study of moral character, has great potential to enhance research and practice in negotiations. Our hope is that this work will accelerate theoretical development and empirical studies that address the question of how moral character influences negotiation processes and outcomes—from pre-negotiation (e.g., planning, selecting negotiating partners) to actual bargaining (e.g., bargaining tactics, concessions) and finally, post-negotiation (e.g., deal implementation, long-term consequences, relationship building and maintenance, reputations)—and...
Scorpins in the DNA Damage Response

The DNA Damage Response (DDR) is a complex signaling network that comes into play when cells experience genotoxic stress. Upon DNA damage, cellular signaling pathways are rewired to slow down cell cycle progression and allow recovery. However, when the damage is beyond repair, cells activate complex and still not fully understood mechanisms, leading to a complete proliferative arrest or cell death. Several conventional and novel anti-neoplastic treatments rely on causing DNA damage or on the inhibition of the DDR in cancer cells. However, the identification of molecular determinants directing cancer cells toward recovery or death upon DNA damage is still far from complete, and it is object of intense investigation. SPRY-containing RAN binding Proteins (Scorpins) RANBP9 and RANBP10 are evolutionarily conserved and ubiquitously expressed proteins whose biological functions are still debated. RANBP9 has been previously implicated in cell proliferation, survival, apoptosis and migration. Recent studies also showed that RANBP9 is involved in the Ataxia Telangiectasia Mutated (ATM) signaling upon DNA damage. Accordingly, cells lacking RANBP9 show increased sensitivity to genotoxic treatment. Although there is no published evidence, extensive protein similarities suggest that RANBP10 might have partially overlapping functions with RANBP9. Like RANBP9, RANBP10 bears sites putative target of PIK-kinases and high throughput studies found RANBP10 to be phosphorylated following genotoxic stress. Therefore, this second Scorpin might be another overlooked player of the DDR alone or in combination with RANBP9. This review focuses on the relatively unknown role played by RANBP9 and RANBP10 in responding to genotoxic stress.
Rhodiola rosea Improves Lifespan, Locomotion, and Neurodegeneration in a Drosophila melanogaster Model of Huntington’s Disease

Huntington's disease (HD) is a dominant, late-onset disease characterized by choreiform movements, cognitive decline, and personality disturbance. It is caused by a polyglutamine repeat expansion in the Huntington's disease gene encoding for the Huntingtin protein (Htt) which functions as a scaffold for selective macroautophagy. Mutant Htt (mHtt) disrupts vesicle trafficking and prevents autophagosome fusion with lysosomes, thus deregulating autophagy in neuronal cells, leading to cell death. Autophagy has been described as a therapeutic target for HD, owing to the key role Htt plays in the cellular process. Rhodiola rosea, a plant extract used in traditional medicine in Europe and Asia, has been shown to attenuate aging in the fly and other model species. It has also been shown to inhibit the mTOR pathway and induce autophagy in bladder cancer cell lines. We hypothesized that R. rosea, by inducing autophagy, may improve the phenotype of a Huntington's disease model of the fly. Flies expressing HttQ93 which exhibit decreased lifespan, impaired locomotion, and increased neurodegeneration were supplemented with R. rosea extract, and assays testing lifespan, locomotion, and pseudopupil degeneration provided quantitative measures of improvement. Based on our observations, R. rosea may be further evaluated as a potential therapy for Huntington's disease.
Learning to Become a Taste Expert

Evidence suggests that consumers seek to become more expert about hedonic products to enhance their enjoyment of future consumption occasions. Current approaches to becoming expert center on cultivating an analytic mindset. In the present research the authors explore the benefit to enthusiasts of moving beyond analytics to cultivate a holistic style of processing. In the taste context the authors define holistic processing as non-verbal, imagery-based, and involving narrative processing. The authors conduct qualitative interviews with taste experts (Master Sommeliers) to operationalize the holistic approach to hedonic learning, and then test it against traditional analytic methods in a series of experiments across a range of hedonic products. The results suggest that hedonic learning follows a sequence of stages whose order matters, and that the holistic stage is facilitated by attending to experience as a narrative event and by employing visual imagery. The results of this multi-method investigation have implications for both managers and academics interested in how consumers learn to become expert in hedonic product categories.
Solving zero-sum games using best-response oracles with applications to search games

How to Choose Between Exponentially Many StrategiesIn “Solving Zero-Sum Games Using Best-Response Oracles with Applications to Search Games,” L. Hellerstein, T. Lidbetter, and D. Pirutinsky conside...
New Distribution Record for Lucilia cuprina (Diptera: Calliphoridae) in Indiana, United States.

Determining range expansion for insect species is vital in order to evaluate their impact on new ecosystems and communities. This is particularly important for species which could be potentially harmful to humans or domestic animals. Lucilia cuprina Wiedemann (Diptera: Calliphoridae) can act as a facultative ectoparasite and has an extensive history as the primary inducer of sheep-strike in Australia, New Zealand, and Africa. We present here the first record of this species in Indiana, United States. Lucilia cuprina’s range expansion northward in the United States may be indicative of changing environmental conditions conducive to the proliferation of this species into historically cooler climates. The presence of this species could significantly impact forensic death investigations utilizing dipteran larvae to estimate a minimum postmortem interval. If range expansion of this species is not taken into account by a forensic entomologist (especillay if L. cuprina is not known previously in their region), and if this species is misidentified as the closely related species Lucilia sericata Meigen (Diptera: Calliphoridae), an inaccurate minimum postmortem interval (PMIMIN) estimation may be made. Therefore, the range expansion of this fly could have large impacts for many different entomological disciplines.
The apparent (gravitational) horizon in cosmology

In general relativity, a gravitational horizon (more commonly known as the “apparent horizon”) is an imaginary surface beyond which all null geodesics recede from the observer. The Universe has an apparent (gravitational) horizon, but unlike its counterpart in the Schwarzschild and Kerr metrics, it is not static. It may eventually turn into an event horizon—an asymptotically defined membrane that forever separates causally connected events from those that are not—depending on the equation of state of the cosmic fluid. In this paper, we examine how and why an apparent (gravitational) horizon is manifested in the Friedmann–Robertson–Walker metric, and why it is becoming so pivotal to our correct interpretation of the cosmological data. We discuss its observational signature and demonstrate how it alone defines the proper size of our visible Universe. In so doing, we affirm its physical reality and its impact on cosmological models.In general relativity, a gravitational horizon (more commonly known as the “apparent horizon”) is an imaginary surface beyond which all null geodesics recede from the observer. The Universe has an apparent (gravitational) horizon, but unlike its counterpart in the Schwarzschild and Kerr metrics, it is not static. It may eventually turn into an event horizon—an asymptotically defined membrane that forever separates causally connected events from those that are not—depending on the equation of state of the cosmic fluid. In this paper, we examine how and why an apparent (gravitational) horizon is manifested in the Friedmann–Robertson–Walker metric, and why it is becoming so pivotal to our correct interpretation of the cosmological data. We discuss its observational signature and demonstrate how it alone defines the proper size of our visible Universe. In so doing, we affirm its physical reality and its impact on cosmological models.
Infrastructure, Value Chains, and Economic Upgrades

Infrastructure development is critical to delivering growth, reducing poverty, and addressing broader development goals. This paper surveys the literature on the linkages between infrastructure investment and economic growth, discusses the role of infrastructure in participation in global value chains and supporting economic upgrades, highlights the challenges that the least developed countries face, and provides policy recommendations. It suggests that addressing the bottlenecks in infrastructure is a necessary condition to provide a window of opportunity for an economy to develop following its comparative advantage. With the right conditions, good infrastructure can support an economy, particularly a less developed economy, to reap the benefits of participation in global value chains to upgrade the economic structure.
General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multiple kernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.
A General Method for Amortizing Variational Filtering

We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step. By performing each inference optimization procedure with an iterative amortized inference model, we obtain a computationally efficient implementation of the algorithm, which we call amortized variational filtering. We present experiments demonstrating that this general-purpose method improves inference performance across several recent deep dynamical latent variable models.
Testing the deltaic origin of fan deposits at Bradbury Crater, Mars

Abstract The stratigraphic architectures of three fan-shaped deposits at Bradbury crater, Mars were investigated to test the hypothesis that the deposits were formed in standing bodies of water. Quantitative stratigraphic methods were applied to the northernmost deposit, which displays meter-thick layering. Planes fit to exposed strata display a constant dip throughout the section, with no obvious slope breaks and a mean dip of 6.1° to the northwest. We conclude that the stratal geometries of this outcrop are most consistent with formation as an alluvial/fluvial or debris flow fan. As this deposit lacks the diagnostic stratigraphic architecture of a delatic system, it presents no conclusive evidence for a sustained standing body of water at Bradbury crater.
An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping

Spectral mapping uses a deep neural network (DNN) to map directly from noisy speech to clean speech. Our previous study [1] found that the performance of spectral mapping improves greatly when using helpful cues from an acoustic model trained on clean speech. The mapper network learns to mimic the input favored by the spectral classifier and cleans the features accordingly. In this study, we explore two new innovations: we replace a DNN-based spectral mapper with a residual network that is more attuned to the goal of predicting clean speech. We also examine how integrating long term context in the mimic criterion (via wide-residual biL-STM networks) affects the performance of spectral mapping compared to DNNs. Our goal is to derive a model that can be used as a preprocessor for any recognition system; the features derived from our model are passed through the standard Kaldi ASR pipeline and achieve a WER of 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset using only feature adaptation.
True2F: Backdoor-Resistant Authentication Tokens

We present True2F, a system for second-factor authentication that provides the benefits of conventional authentication tokens in the face of phishing and software compromise, while also providing strong protection against token faults and backdoors. To do so, we develop new lightweight two-party protocols for generating cryptographic keys and ECDSA signatures, and we implement new privacy defenses to prevent cross-origin token-fingerprinting attacks. To facilitate real-world deployment, our system is backwards-compatible with today’s U2F-enabled web services and runs on commodity hardware tokens after a firmware modification. A True2F-protected authentication takes just 57ms to complete on the token, compared with 23ms for unprotected U2F.
Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module

Wireless communication plays a vital role in the promising performance of connected and automated vehicle (CAV) technology. This paper proposes a Vissim-based microscopic traffic simulation framework with an analytical dedicated short-range communication (DSRC) module for packet reception. Being derived from ns-2, a packet-level network simulator, the DSRC probability module takes into account the imperfect wireless communication that occurs in real-world deployment. Four managed lane deployment strategies are evaluated using the proposed framework. While the average packet reception rate is above 93% among all tested scenarios, the results reveal that the reliability of the vehicle-to-vehicle (V2V) communication can be influenced by the deployment strategies. Additionally, the proposed framework exhibits desirable scalability for traffic simulation and it is able to evaluate network-level deployment strategies in the near future for CAV technoloaies.
A Smoother State Space Multitaper Spectrogram

A recent work (Kim et al. 2018) has reported a novel statistical modeling framework, the State-Space Multitaper (SSMT) method, to estimate time-varying spectral representation of non-stationary time series data. It combines the strengths of the multitaper spectral (MT) analysis paradigm with that of state-space (SS) models. In this current work, we explore a variant of the original SSMT framework by imposing a smoothness promoting SS model to generate smoother estimates of power spectral densities for non-stationary data. Specifically, we assume that the continuous processes giving rise to observations in the frequencies of interest follow multiple independent Integrated Wiener Processes (IWP). We use both synthetic data and electroencephalography (EEG) data collected from a human subject under anesthesia to compare the IWP- SSMT with the SSMT method and demonstrate the former’s utility in yielding smoother descriptions of underlying processes. The original SSMT and IWP-SSMT can co-exist as a part of a model selection toolkit for nonstationary time series data.
Topological Methods for Visualization and Analysis of High Dimensional Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) techniques have been very powerful in analyzing heterogeneous cell population and identifying cell types. Visualizing scRNA-seq data can help researchers effectively extract meaningful biological information and make new discoveries. While commonly used scRNA-seq visualization methods, such as t-SNE, are useful in detecting cell clusters, they often tear apart the intrinsic continuous structure in gene expression profiles. Topological Data Analysis (TDA) approaches like Mapper capture the shape of data by representing data as topological networks. TDA approaches are robust to noise and different platforms, while preserving the locality and data continuity. Moreover, instead of analyzing the whole dataset, Mapper allows researchers to explore biological meanings of specific pathways and genes by using different filter functions. In this paper, we applied Mapper to visualize scRNA-seq data. Our method can not only capture the clustering structure of cells, but also preserve the continuous gene expression topologies of cells. We demonstrated that by combining with gene co-expression network analysis, our method can reveal differential expression patterns of gene co-expression modules along the Mapper visualization.
Screening, diagnosis, and long-term health outcomes in developing countries—The case of hypertension

Hypertension is a rapidly growing problem in developing countries. At the same time, due to its asymptomatic nature, the afflicted population is largely unaware of being hypertensive. Due to a lack of resources, routine medical exams are very rare in developing countries and many sick individuals remain undiagnosed. Using a large sample of hypertensive individuals from Indonesia, we show the importance of being diagnosed. Diagnosed individuals exhibit lower systolic and diastolic blood pressure, and overall lower probability of remaining hypertensive than undiagnosed individuals. We also show the main channels through which this is achieved: taking medication, routinely monitoring one’s blood pressure, and engaging in moderate physical activities. We also point to channels through which additional benefits could be realized, but that are currently ineffective: dietary changes and maintaining a healthy body weight. Combined, these results point to the importance of directing public policy towards addressing the under-diagnosis problem and educating the public of the benefits of adopting a healthy life-style.
Fast Learning Requires Good Memory: A Time-Space Lower Bound for Parity Learning

We prove that any algorithm for learning parities requires either a memory of quadratic size or an exponential number of samples. This proves a recent conjecture of Steinhardt et al. (2016) and shows that for some learning problems, a large storage space is crucial.More formally, in the problem of parity learning, an unknown string x ∈ {0,1}n was chosen uniformly at random. A learner tries to learn x from a stream of samples (a1, b1), (a2, b2) …, where each at is uniformly distributed over {0,1}n and bt is the inner product of at and x, modulo 2. We show that any algorithm for parity learning that uses less than n2/25 bits of memory requires an exponential number of samples.Previously, there was no non-trivial lower bound on the number of samples needed for any learning problem, even if the allowed memory size is O(n) (where n is the space needed to store one sample).We also give an application of our result in the field of bounded-storage cryptography. We show an encryption scheme that requires a private key of length n, as well as time complexity of n per encryption/decryption of each bit, and is provably and unconditionally secure as long as the attacker uses less than n2/25 memory bits and the scheme is used at most an exponential number of times. Previous works on bounded-storage cryptography assumed that the memory size used by the attacker is at most linear in the time needed for encryption/decryption.
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games

With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only access to agent actions. Although a recent work provides a powerful differentiable end-to-end learning frameworks which embed a game solver within a deep-learning framework, allowing unknown game parameters to be learned via backpropagation, this framework faces significant limitations when applied to boundedly rational human agents and large scale problems, leading to poor practicality. In this paper, we address these limitations and propose a framework that is applicable for more practical settings. First, seeking to learn the rationality of human agents in complex two-player zero-sum games, we draw upon well-known ideas in decision theory to obtain a concise and interpretable agent behavior model, and derive solvers and gradients for end-to-end learning. Second, to scale up to large, real-world scenarios, we propose an efficient first-order primal-dual method which exploits the structure of extensive-form games, yielding significantly faster computation for both game solving and gradient computation. When tested on randomly generated games, we report speedups of orders of magnitude over previous approaches. We also demonstrate the effectiveness of our model on both real-world one-player settings and synthetic data.
“Unmotivated bias” and partisan hostility: Empirical evidence

Abstract Extreme partisan animosity has been on the rise in the US and is prevalent around the world. This hostility is typically attributed to social group identity, motivated reasoning, or a combination thereof. In this paper, I empirically examine a novel contributing factor: the “unmotivated” cognitive bias of overprecision (overconfidence in precision of beliefs). Overprecision could cause partisan hostility indirectly via inflated confidence in one’s own ideology, partisan identity, or perceptions of social distance between the parties. Overprecision could also cause this hostility directly by causing excessively strong inferences from observed information that is either skewed against the out-party or simply misunderstood. Using a nationally representative sample, I find consistent support for direct effects of overprecision and mixed support for indirect effects. The point estimates imply a one standard deviation increase in a respondent’s overprecision predicts as much as a 0.71 standard deviation decline in relative out-party favorability.
A Systematic Review of miR-29 in Cancer.

MicroRNAs (miRNA) are small non-coding RNAs (∼22 nt in length) that are known as potent master regulators of eukaryotic gene expression. miRNAs have been shown to play a critical role in cancer pathogenesis, and the misregulation of miRNAs is a well-known feature of cancer. In recent years, miR-29 has emerged as a critical miRNA in various cancers, and it has been shown to regulate multiple oncogenic processes, including epigenetics, proteostasis, metabolism, proliferation, apoptosis, metastasis, fibrosis, angiogenesis, and immunomodulation. Although miR-29 has been thoroughly documented as a tumor suppressor in the majority of studies, some controversy remains with conflicting reports of miR-29 as an oncogene. In this review, we provide a systematic overview of miR-29’s functional role in various mechanisms of cancer and introspection on the contradictory roles of miR-29.