cssi/SciSciGPT-SciSciNet huggingface dataset:
A 60 Kilodalton Oncofetal Protein as Tumor Marker

A 60 kd oncofetal protein, shown previously to be present in tumor cytosol and rat embryonic tissue, has been measured by a specific assay in the blood plasma of 42 cancer patients with tumors at 12 different sites. All 42 cancer patient plasmas were positive, while the plasmas from 20 normal controls were negative. Similar results were obtained with the experimental animal system. The protein is present in low concentrations in cancer patient plasma, amounting to less than 0.008% of the total plasma protein. Following complete surgical removal of the tumor, the plasma concentration of the factor drops rapidly. In the rate the biological half-life was 8 days.
A Product Report: Osto-Zyme as an Odor Eliminator

A product, Osto-Zyme, was clinically tested at our Institute for the elimination of odor. Thirteen clients were chosen by enterostomal therapists to be tested. The product was sprayed directly into pouch, peristomal skin, and into night drainage units. This product proved very effective in the elimination of odor. The manufacturer of Osto-Zyme is the Richard C. Shelton Co., Wright Brothers Branch, Dayton, Ohio.
Bounding Laconic Proof Systems by Solving CSPs in Parallel

We show that the basic semidefinite programming relaxation value of any constraint satisfaction problem can be computed in NC; that is, in parallel polylogarithmic time and polynomial work. As a complexity-theoretic consequence we get that \MIPone[k,c,s] \subseteq \PSPACE provided s/c \leq (.62-o(1))k/2^k, resolving a question of Austrin, H\aa stad, and Pass. Here \MIPone[k,c,s] is the class of languages decidable with completeness c and soundness s by an interactive proof system with k provers, each constrained to communicate just 1 bit.
The Theory of Entrepreneurial Rent

The theory of entrepreneurial rent argues that the source of competitive advantage lies in the firm’s value creation and appropriation mechanism enhanced by the entrepreneurial behavior and sustained by the entrepreneurial incentives. Entrepreneurial logic emphasizes the firm’s proactive exploitation of environmental uncertainty and complexity, recognition of complex stimuli, organizational agility, managerial promotional focus, and distant searches for superior opportunities. The entrepreneurial incentives enhance management learning, which further enhances the management logics and situation awareness, enabling the firm to seize cognitively distant opportunities that provide the value creation engine its fuel. It is the firm’s superior execution of the business model that exploits the value opportunities in a timely manner, sustained by the entrepreneurial incentives, which creates and sustains the competitive advantage. It is the management’s desire to earn an entrepreneurial surplus that drives the firm’s superior performance. Entrepreneurial incentives enhance the firm’s alertness, agility, absorptive capacity, proactive adaptation, and opportunity focus. The firm’s entrepreneurial behavior is a source of competitive advantage that provides the firm an entrepreneurial rent and the management an entrepreneurial surplus. Several research propositions are developed herein.
Portfolio Construction by Mitigating Error Amplification: The Bounded-Noise Portfolio

We address the problem of poor portfolio performance when a minimum-variance portfolio is constructed using the sample estimates. Estimation errors are mostly blamed for the poor portfolio performa...
Discontinuity Stresses in Beams on Elastic Foundations

Method of analysis is presented and applied to beams or cylinders subjected to axisymmetrical loading; solution of basic cases which provide numerical constants required in proposed method; several examples are included.
Towards a more molecular taxonomy of disease

Disease taxonomies have been designed for many applications, but they tend not to fully incorporate the growing amount of molecular-level knowledge of disease processes, inhibiting research efforts. Understanding the degree to which we can infer disease relationships from molecular data alone may yield insights into how to ultimately construct more modern taxonomies that integrate both physiological and molecular information. We introduce a new technique we call Parent Promotion to infer hierarchical relationships between disease terms using disease-gene data. We compare this technique with both an established ontology inference method (CliXO) and a minimum weight spanning tree approach. Because there is no gold standard molecular disease taxonomy available, we compare our inferred hierarchies to both the Medical Subject Headings (MeSH) category C forest of diseases and to subnetworks of the Disease Ontology (DO). This comparison provides insights about the inference algorithms, choices of evaluation metrics, and the existing molecular content of various subnetworks of MeSH and the DO. Our results suggest that the Parent Promotion method performs well in most cases. Performance across MeSH trees is also correlated between inference methods. Specifically, inferred relationships are more consistent with those in smaller MeSH disease trees than larger ones, but there are some notable exceptions that may correlate with higher molecular content in MeSH. Our experiments provide insights about learning relationships between diseases from disease genes alone. Future work should explore the prospect of disease term discovery from molecular data and how best to integrate molecular data with anatomical and clinical knowledge. This study nonetheless suggests that disease gene information has the potential to form an important part of the foundation for future representations of the disease landscape.
Optimized surface code communication in superconducting quantum computers

Quantum computing (QC) is at the cusp of a revolution. Machines with 100 quantum bits (qubits) are anticipated to be operational by 2020 [30, 73], and several-hundred-qubit machines are around the corner. Machines of this scale have the capacity to demonstrate quantum supremacy, the tipping point where QC is faster than the fastest classical alternative for a particular problem. Because error correction techniques will be central to QC and will be the most expensive component of quantum computation, choosing the lowest-overhead error correction scheme is critical to overall QC success. This paper evaluates two established quantum error correction codes-planar and double-defect surface codes-using a set of compilation, scheduling and network simulation tools. In considering scalable methods for optimizing both codes, we do so in the context of a full microarchitectural and compiler analysis. Contrary to previous predictions, we find that the simpler planar codes are sometimes more favorable for implementation on superconducting quantum computers, especially under conditions of high communication congestion.CCS CONCEPTS• Hardware $\rightarrow$ Quantum computation; Quantum error correction and fault tolerance;
In situ stress measurements during electrochemical cycling of lithium-rich cathodes

Abstract Layered lithium transition metal oxides (Li1+xM1-xO2, M = Ni, Mn, Co) are attractive cathode materials for lithium-ion batteries due to their high reversible capacity. However, they suffer from structural changes that lead to substantial voltage fade. In this study, we use stress as a novel way to track irreversible changes in Li1.2Mn0.55Ni0.125Co0.125O2 (LR-NMC) cathodes. A unique and unpredicted stress signature is observed during the first delithiation. Initially, a tensile stress is observed, consistent with volume contraction from lithium removal, however, the stress reverses and becomes compressive with continued charging beyond 4 V vs Li/Li+, indicating volume expansion; this phenomenon is present in the first cycle only. This irreversible stress during delithiation is likely to be at least partially due to oxygen loss and the resulting cation rearrangement. Raman spectroscopy provides evidence of the layered-to-spinel phase transition after cycling in the LR-NMC films, as well as recovery of the original spectra upon re-annealing in an oxygen environment.
The Gulf oil spill, miscarriage, and infertility: the GROWH study

To examine whether reported exposure to the Gulf oil spill (2010) was related to reproductive reported miscarriage or infertility. 1524 women aged 18–45 recruited through prenatal and Women, Infant, and Children (WIC) clinics, and community events were interviewed about their experience of the oil spill and reproductive history. 1434 women had information on outcomes of at least one pregnancy, and 633 on a pregnancy both before and after the spill. Generalized estimating equations were used to examine the relationship between contact with oil and economic and social consequences of the spill with postponement of pregnancy, miscarriage, and infertility (time to pregnancy >12 months or reported fertility issues), with adjustment for age, race, BMI, smoking, and socioeconomic status. Results were compared for pregnancies occurring prior to and after the oil spill. 77 (5.1%) women reported postponing pregnancy due to the oil spill, which was more common in those with high contact with oil or overall high exposure (aOR 2.92, 95% CI 1.31–6.51). An increased risk of miscarriage was found with any exposure to the oil spill (aOR, 1.54, 95% CI 1.17–2.02). Fertility issues were more common in the overall most highly exposed women (aOR 1.88, 1.19–2.95), when the data were limited to those with pregnancies before and after. However, no particular aspect of oil spill exposure was strongly associated with the outcomes, and effects were almost as strong for pregnancies prior to the oil spill. The oil spill appears to have affected reproductive decision-making. The evidence is not strong that exposure to the oil spill was associated with miscarriage or infertility.
Polyamine-Mediated Stoichiometric Assembly of Ribonucleoproteins for Enhanced mRNA Delivery.

Messenger RNA (mRNA) represents a promising class of nucleic acid drugs. Although numerous carriers have been developed for mRNA delivery, the inefficient mRNA expression inside cells remains a major challenge. Inspired by the dependence of mRNA on 3'-terminal polyadenosine nucleotides (poly A) and poly A binding proteins (PABPs) for optimal expression, we complexed synthetic mRNA containing a poly A tail with PABPs in a stoichiometric manner and stabilized the ribonucleoproteins (RNPs) with a family of polypeptides bearing different arrangements of cationic side groups. We found that the molecular structure of these polypeptides modulates the degree of PABP-mediated enhancement of mRNA expression. This strategy elicits an up to 20-fold increase in mRNA expression in vitro and an approximately fourfold increase in mice. These findings suggest a set of new design principles for gene delivery by the synergistic co-assembly of mRNA with helper proteins.
Impact of international trade on unemployment under oligopoly

ABSTRACTBy studying a two-sector general equilibrium model in which firms engage in oligopolistic competition and unemployment is a result of the existence of efficiency wages, we derive the following results analytically. A country's comparative advantage in producing manufactured goods increases with the level of efficiencies in the labor market. The opening of international trade leads to the equalization of wage rates even though countries differ in their factor endowments and labor market efficiencies. If countries have the same level of labor market efficiencies but differ in their endowments of labor and land, the opening to international trade leads to an increase in the wage rate in both countries.
Corporate litigation and debt

Abstract This study examines the effect of litigation risk and litigation costs on firms’ credit ratings and debt financing. The results show that litigation affects a firm's creditworthiness and debt costs in two stages. Before a lawsuit filing, firms at higher risk of litigation have lower credit ratings, are more likely to be rated speculative grade, pay higher yields on loans and bonds, and are less likely to rely on debt financing. At the time of the lawsuit resolution, settlement costs have an additional effect on firm credit quality. Companies facing larger settlement disbursements in relation to their available cash experience a decline in credit ratings and an increase in yield spread. The results are robust to endogeneity concerns and different proxies of litigation risk.
Enforcing calibration in ensemble postprocessing

Desirable attributes of probability forecasts are maximal sharpness, consistent with calibration ("reliability"). The usual procedure of optimizing ensemble-postprocessing parameters by minimizing a proper scoring rule such as the continuous ranked probability score or the "ignorance" (i.e., negative log-likelihood) does not guarantee the necessary calibration condition, potentially compromising the value of the resulting forecasts to users. The calibration condition can be enforced by including a miscalibration penalty in the loss function to be minimized in parameter estimation. The procedure is illustrated using ensemble forecasts for minimum temperatures and windspeeds, postprocessed using member-by-member algorithms.
Firm Growth and Corruption: Empirical Evidence from Vietnam

This paper tests whether firm growth reduces corruption, using data from over 10,000 Vietnamese firms. We employ instrumental variables based on growth in a firm's industry in other provinces within Vietnam and in China. We find that firm growth reduces bribes as a share of revenues. We propose a mechanism for this effect whereby government officials’ decisions about bribes are modulated by inter-jurisdictional competition. This mechanism also implies that growth reduces bribery more for more mobile firms; consistent with this prediction, we find a larger effect for firms with transferable rights to their land or operations in multiple provinces. This article is protected by copyright. All rights reserved.
Regulatory challenges for road vehicle automation: Lessons from the California experience

Abstract The development of automated driving systems presents a special challenge to the existing regulatory regimes for road vehicles. Automated driving systems shift the responsibility for roadway and traffic monitoring, decision making, and vehicle control from the driver to the automated driving system, which will necessitate a shift of the responsibility for traffic law violations and crashes from the individual driver toward the manufacturers of the automated driving systems. However, the opportunities for automated driving systems to improve transportation system performance and safety are great, and rapid innovation needs to be balanced against the risks to public safety from poorly engineered or hastily released systems competing for first-on-the-market status. Furthermore, in the absence of clearly defined standards and testing procedures, it is not yet clear how safety can be certified by the developer, a regulatory agency, or a third party. This paper describes California’s pioneering efforts to grapple with these challenges after its legislature passed a law requiring the development of regulations to govern the testing and operation of automated vehicles on public roads, and it provides recommendations regarding what should and should not be incorporated into such regulations.
The Wisdom of Twitter Crowds: Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds

With the rise of social media, investors have a new tool for measuring sentiment in real time. However, the nature of these data sources raises serious questions about its quality. Because anyone on social media can participate in a conversation about markets—whether the individual is informed or not—these data may have very little information about future asset prices. In this article, the authors show that this is not the case. They analyze a recurring event that has a high impact on asset prices—Federal Open Market Committee (FOMC) meetings—and exploit a new dataset of tweets referencing the Federal Reserve. The authors show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset allocation strategy outperforms several benchmarks—including a strategy that buys and holds a market index, as well as a comparable dynamic asset allocation strategy that does not use Twitter information.
A New Keynesian Model with Robots: Implications for Business Cycles and Monetary Policy

This paper examines the effects of labor-replacing capital, referred to as robots, on business cycle dynamics using a New Keynesian model with a role for both traditional and robot capital. This study finds that shocks to the price of robots have effects on wages, output, and employment that are distinct from shocks to the price of traditional capital. Further, the inclusion of robots alters the response of employment and labor’s share to total factor productivity and monetary policy shocks. The presence of robots also weakens the correlation between human labor and output and the correlation between human labor and labor’s share. The paper finds that monetary policymakers would need to place a greater emphasis on output stabilization if their objective is to minimize a weighted average of output and inflation volatility. Moreover, if policymakers have an employment stabilization objective apart from their output stabilization objective, they would have to further focus on output stabilization due to the deterioration of the output-employment correlation.
Remnance of form

Through several vignettes - Disappearing, Showing Fear, Many Faces, and Dream of Flying, a transforming shadow deconstructs the notion of materialistic view of the physical world as well as offers to become a surrogate for channelling the images and imaginations about one's own self.
Asymmetric cointegration and the J-curve: evidence from commodity trade between Turkey and EU

In this paper, we assess the asymmetric effects of exchange rate changes on the trade balance of 57 industries that trade between Turkey and EU. Since the analysis introduces nonlinear adjustment of the exchange rate, we find results that are different than previous research. More precisely, we find short-run asymmetry effects in all industries, short run adjustment asymmetry in 24 industries, short-run impact asymmetry in 17 industries, and long-run asymmetry effects in 23 industries. Small and large industries seem to be subject to the same asymmetry effects.
Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq.

Summary Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues such as adult mammalian brains is challenging. Here, we integrate sucrose-gradient-assisted purification of nuclei with droplet microfluidics to develop a highly scalable single-nucleus RNA-seq approach (sNucDrop-seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ∼18,000 nuclei isolated from cortical tissues of adult mice, we demonstrate that sNucDrop-seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity but also enables in-depth analysis of transient transcriptional states driven by neuronal activity, at single-cell resolution, in vivo.
Disease implications of animal social network structure: A synthesis across social systems

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures.
The Economic Effects of Providing Legal Status to DREAMers

This study quantifies the economic effects of two major immigration policies aimed at legalizing undocumented individuals that entered the United States as children and completed high school: Deferred Action for Childhood Arrivals (DACA) and the DREAM Act. The former offers only temporary legal status to eligible individuals, whereas the latter provides a track to legal permanent residence. Our analysis is based on a general equilibrium model that allows for shifts in participation between work, college, and non-employment. The model is calibrated to account for productivity differences across workers of different skills and documentation status, and a rich pattern of complementarities across different types of workers. We estimate that DACA increased gross domestic product (GDP) by almost 0.02% (about $3.5 billion), or $7,454 per legalized worker. Passing the DREAM Act would increase GDP by around 0.08% (or $15.2 billion), which amounts to an average of $15,371 for each legalized worker. The larger effects of the DREAM Act stem from the expected larger take-up and the increased incentive to attend college among DREAMers with a high school degree. We also find substantial wage increases for individuals obtaining legal status, particularly those that increase their educational attainment. Because of the small size of the DREAMer population, and their skill distribution, legalization entails negligible effects on the wages of US-born workers.
Dependency analysis and improved parameter estimation for dynamic composite load modeling

Dynamic load modeling by fitting the input-output measurements during fault events is crucial for power system dynamic studies. The WECC composite load model (CMPLDW) has been developed recently to better represent fault-induced delayed-voltage recovery (FIDVR) events, which are of increasing concern to electric utilities. However, the model nonlinearity and large number of parameters of the CMPLDW model pose severe identifiability issues and performance degradation for the measurement based load modeling approach using the classical nonlinear least-squares (NLS) objective. This paper will first present a general framework that can effectively analyze and visualize the parameter dependency of complex dynamic load models with large numbers of parameters under FIDVR. Furthermore, we propose to improve the parameter estimation performance by regularizing the NLS error objective using a priori information about parameter values. Effectiveness of the proposed dependency analysis and parameter estimation scheme is validated using both synthetic and real measurement data during faults. Albeit focused on CMPLDW, the proposed approaches can be readily used for dynamic composite load modeling in general.
Two-stage Residual Inclusion Estimation: A Practitioners Guide to Stata Implementation:

Empirical econometric research often requires implementation of nonlinear models whose regressors include one or more endogenous variables—regressors that are correlated with the unobserved random ...
Assimilation of Big Data Innovation: Investigating the Roles of IT, Social Media, and Relational Capital

In a knowledge-based economy with increasing competition, firms are under pressure to enhance their innovation capability. Social capital theory advances the idea that social capital promotes firm-level innovation. However, little is known about the process by which social capital of a firm translates into innovation outcomes such as implementation of big data initiatives. Little research has been done on what role information technology plays, if any, in this translation process. In this paper, we investigate how relational capital, a form of social capital, in the presence of social media and IT platforms, impacts innovation in the form of assimilation of big data technologies. The direct and indirect effects of the three variables: social media, relational capital and IT platform is studied. Direct effects of the three variables on big data innovation is established. Social media and IT platforms are found to significantly add to the positive relationship between relational capital and big data innovation.
An improved combinatorial algorithm for Boolean matrix multiplication

Abstract We present a new combinatorial algorithm for triangle finding and Boolean matrix multiplication that runs in O ˆ ( n 3 / log 4 ⁡ n ) time, where the O ˆ notation suppresses poly(loglog) factors. This improves the previous best combinatorial algorithm by Chan that runs in O ˆ ( n 3 / log 3 ⁡ n ) time. Our algorithm generalizes the divide-and-conquer strategy of Chan's algorithm. Moreover, we propose a general framework for detecting triangles in graphs and computing Boolean matrix multiplication. Roughly speaking, if we can find the “easy parts” of a given instance efficiently, we can solve the whole problem faster than n 3 .
A Theory of Homeland Security

Homeland security is a recognized practice, profession and field without a unifying theory to guide its study and application. The one previous attempt by Bellavita [2] acknowledges its own shortcomings and may be considered incomplete at best. The failure may be attributed to the lack of an underlying correlating factor. This chapter demonstrates that “domestic catastrophic destruction” is the correlating factor that unites key historical homeland security incidents and this observation is leveraged to propose a theory of homeland security that is descriptive, prescriptive and predictive. The proposed theory is descriptive because it can differentiate between what is and what is not homeland security. The theory is prescriptive because it can suggest an optimum homeland security strategy. It is predictive because it renders homeland security into a technical problem and demonstrates how its effects may eventually be blunted through the technological evolution and revolution of the critical infrastructure. Accordingly, the proposed theory embodies a set of foundational principles to guide the study and application of the practice, profession and field of homeland security.
Higher Absolute Lymphocyte Counts Predict Lower Mortality from Early-Stage Triple-Negative Breast Cancer

Purpose: Tumor-infiltrating lymphocytes (TIL) in pretreatment biopsies are associated with improved survival in triple-negative breast cancer (TNBC). We investigated whether higher peripheral lymphocyte counts are associated with lower breast cancer-specific mortality (BCM) and overall mortality (OM) in TNBC.Experimental Design: Data on treatments and diagnostic tests from electronic medical records of two health care systems were linked with demographic, clinical, pathologic, and mortality data from the California Cancer Registry. Multivariable regression models adjusted for age, race/ethnicity, socioeconomic status, cancer stage, grade, neoadjuvant/adjuvant chemotherapy use, radiotherapy use, and germline BRCA1/2 mutations were used to evaluate associations between absolute lymphocyte count (ALC), BCM, and OM. For a subgroup with TIL data available, we explored the relationship between TILs and peripheral lymphocyte counts.Results: A total of 1,463 stage I-III TNBC patients were diagnosed from 2000 to 2014; 1,113 (76%) received neoadjuvant/adjuvant chemotherapy within 1 year of diagnosis. Of 759 patients with available ALC data, 481 (63.4%) were ever lymphopenic (minimum ALC <1.0 K/μL). On multivariable analysis, higher minimum ALC, but not absolute neutrophil count, predicted lower OM [HR = 0.23; 95% confidence interval (CI), 0.16-0.35] and BCM (HR = 0.19; CI, 0.11-0.34). Five-year probability of BCM was 15% for patients who were ever lymphopenic versus 4% for those who were not. An exploratory analysis (n = 70) showed a significant association between TILs and higher peripheral lymphocyte counts during neoadjuvant chemotherapy.Conclusions: Higher peripheral lymphocyte counts predicted lower mortality from early-stage, potentially curable TNBC, suggesting that immune function may enhance the effectiveness of early TNBC treatment. Clin Cancer Res; 24(12); 2851-8. ©2018 AACR.
The Catholic Tradition on the Due Use of Medical Remedies: The Charlie Gard Case:

The widely publicized British case of Charlie Gard became an international cause celebre when the treating physicians petitioned the British courts to prevent the parents from taking their dying child to America where a physician held out promise of an unproven experimental therapy. The case became more sensationalized when the press reported that Pope Francis had intervened in the case against the position of the Vatican’s Academy for Life on the appropriate response to a patient with a lethal genetic disorder for which there was no known treatment. A review of the centuries-long teaching of Catholic moral theology on care of the dying demonstrates that the pastoral concern of Pope Francis for the grieving parents did not signal a change in church teaching on the care of the dying patient or reveal a disagreement between Pope Francis and the Academy for Life’s position on the appropriate care of Charlie Gard.
Antagonism between the dynein and Ndc80 complexes at kinetochores controls the stability of kinetochore-microtubule attachments during mitosis

Chromosome alignment and segregation during mitosis require kinetochore-microtubule (kMT) attachments that are mediated by the molecular motor dynein and the kMT-binding complex Ndc80. The Rod-ZW10-Zwilch (RZZ) complex is central to this coordination as it has an important role in dynein recruitment and has recently been reported to have a key function in the regulation of stable kMT attachments in Caenorhabditis elegans besides its role in activating the spindle assembly checkpoint (SAC). However, the mechanism by which these protein complexes control kMT attachments to drive chromosome motility during early mitosis is still unclear. Here, using in vitro total internal reflection fluorescence microscopy, we observed that higher concentrations of Ndc80 inhibited dynein binding to MTs, providing evidence that Ndc80 and dynein antagonize each other's function. High-resolution microscopy and siRNA-mediated functional disruption revealed that severe defects in chromosome alignment induced by depletion of dynein or the dynein adapter Spindly are rescued by codepletion of the RZZ component Rod in human cells. Interestingly, rescue of the chromosome alignment defects was independent of Rod function in SAC activation and was accompanied by a remarkable restoration of stable kMT attachments. Furthermore, the chromosome alignment rescue depended on the plus-end-directed motility of centromere protein E (CENP-E) because cells codepleted of CENP-E, Rod, and dynein could not establish stable kMT attachments or align their chromosomes properly. Our findings support the idea that dynein may control the function of the Ndc80 complex in stabilizing kMT attachments directly by interfering with Ndc80-MT binding or indirectly by controlling the Rod-mediated inhibition of Ndc80.
A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain.

Summary Transcriptional activation domains are essential for gene regulation, but their intrinsic disorder and low primary sequence conservation have made it difficult to identify the amino acid composition features that underlie their activity. Here, we describe a rational mutagenesis scheme that deconvolves the function of four activation domain sequence features—acidity, hydrophobicity, intrinsic disorder, and short linear motifs—by quantifying the activity of thousands of variants in vivo and simulating their conformational ensembles using an all-atom Monte Carlo approach. Our results with a canonical activation domain from the Saccharomyces cerevisiae transcription factor Gcn4 reconcile existing observations into a unified model of its function: the intrinsic disorder and acidic residues keep two hydrophobic motifs from driving collapse. Instead, the most-active variants keep their aromatic residues exposed to the solvent. Our results illustrate how the function of intrinsically disordered proteins can be revealed by high-throughput rational mutagenesis.
Naor-Reingold Goes Public: The Complexity of Known-Key Security

We study the complexity of building secure block ciphers in the setting where the key is known to the attacker. In particular, we consider two security notions with useful implications, namely public-seed pseudorandom permutations (or psPRPs, for short) (Soni and Tessaro, EUROCRYPT ’17) and correlation-intractable ciphers (Knudsen and Rijmen, ASIACRYPT ’07; Mandal, Seurin, and Patarin, TCC ’12).
Sharon: Shared Online Event Sequence Aggregation

Streaming systems evaluate massive workloads of event sequence aggregation queries. State-of-the-art approaches suffer from long delays caused by not sharing intermediate results of similar queries and by constructing event sequences prior to their aggregation. To overcome these limitations, our Shared Online Event Sequence Aggregation (Sharon) approach shares intermediate aggregates among multiple queries while avoiding the expensive construction of event sequences. Our Sharon optimizer faces two challenges. One, a sharing decision is not always beneficial. Two, a sharing decision may exclude other sharing opportunities. To guide our Sharon optimizer, we compactly encode sharing candidates, their benefits, and conflicts among candidates into the Sharon graph. Based on the graph, we map our problem of finding an optimal sharing plan to the Maximum Weight Independent Set (MWIS) problem. We then use the guaranteed weight of a greedy algorithm for the MWIS problem to prune the search of our sharing plan finder without sacrificing its optimality. The Sharon optimizer is shown to produce sharing plans that achieve up to an 18-fold speed-up compared to state-of-the-art approaches.
PP2C phosphatases promote autophagy by dephosphorylation of the Atg1 complex

Macroautophagy is orchestrated by the Atg1-Atg13 complex in budding yeast. Under nutrient-rich conditions, Atg13 is maintained in a hyperphosphorylated state by the TORC1 kinase. After nutrient starvation, Atg13 is dephosphorylated, triggering Atg1 kinase activity and macroautophagy induction. The phosphatases that dephosphorylate Atg13 remain uncharacterized. Here, we show that two redundant PP2C phosphatases, Ptc2 and Ptc3, regulate macroautophagy by dephosphorylating Atg13 and Atg1. In the absence of these phosphatases, starvation-induced macroautophagy and the cytoplasm-to-vacuole targeting pathway are inhibited, and the recruitment of the essential autophagy machinery to the phagophore assembly site is impaired. Expressing a genomic ATG13-8SA allele lacking key TORC1 phosphorylation sites partially bypasses the macroautophagy defect in ptc2Δ ptc3Δ strains. Moreover, Ptc2 and Ptc3 interact with the Atg1-Atg13 complex. Taken together, these results suggest that PP2C-type phosphatases promote macroautophagy by regulating the Atg1 complex.
How to pay for saving biodiversity

The 1992 Convention on Biological Diversity (CBD) was one of the first international environmental agreements negotiated. In the same year, the Global Environment Facility (GEF) for funding biodiversity conservation in developing countries was launched. Yet 25 years later, biological populations and diversity continue to decline both on land ( 1 ) and in the oceans ( 2 ). The main reasons are chronic underfunding of global biodiversity conservation; the lack of incentives for global cooperation; and the failure to control habitat conversion, resource overexploitation, species invasions, and other drivers of biodiversity loss. Dinerstein et al. recently called for a global deal, complementing the 2015 Paris Climate Change Agreement, for conserving half of the terrestrial realm for biodiversity by 2050 ( 3 ). Here, we explore how such a deal might be implemented to overcome the funding problem in biodiversity protection.
Characteristics of Obstructive Sleep Apnea Across the Spectrum of Glucose Tolerance in Obese Adolescents

Background: It is not known if dysglycemia and sleep-disordered breathing are linked in adolescents, as in adults. Objective: To evaluate measures of sleep-disordered breathing across the spectrum of glucose tolerance in obese adolescents. We hypothesized that dysglycemia would be associated with sleep-disordered breathing. Participants/Methods: This was a prospective, cross-sectional clinical study that included 57 adolescents (BMI 38.98.4 kg/m2) aged 12-18 years (14.5±1.6) with normal glucose tolerance (NGT), or dysglycemia (impaired glucose tolerance [IGT] or type 2 diabetes [T2D]). Measures: Anthropometrics, overnight polysomnogram (PSG), and oral glucose tolerance tests (OGTT) were performed. Participant characteristics and outcome measures were compared by glucose tolerance status. Correlational analyses were conducted to assess the associations between variables of interest. Results: Participants with dysglycemia (n=21) were not different from those with NGT (n=36) for BMI, waist circumference, body fat, or sleep characteristics. Nocturnal oxygen desaturation was associated with higher BMI (r = -0.334, p = 0.012). The apnea-hypopnea index (AHI) was not associated with physical and metabolic parameters. Although participants with T2D tended to have higher AHIs (median 3.2, 2.2, and 1.6 events/hour for T2D, IGT, and NGT, respectively) there was not a linear relationship between measures of glycemia and AHI. Conclusion: Further study with a larger proportion of youth with prediabetes and T2D is necessary to determine whether evaluation for sleep-disordered breathing is uniformly warranted.
Lower objectively measured physical activity is linked with perceived risk of hypoglycemia in type 1 diabetes.

Abstract Aims Compare physical activity (PA) levels in adults with and without type 1 diabetes and identify diabetes-specific barriers to PA. Methods Forty-four individuals with type 1 diabetes and 77 non-diabetic controls in the Coronary Artery Calcification in Type 1 Diabetes study wore an accelerometer for 2 weeks. Moderate-to-vigorous physical activity (MVPA) was compared by diabetes status using multiple linear regression. The Barriers to Physical Activity in Type 1 Diabetes questionnaire measured diabetes-specific barriers to PA, and the Clarke hypoglycemia awareness questionnaire measured hypoglycemia frequency. Results Individuals with type 1 diabetes engaged in less MVPA, fewer bouts of MVPA, and spent less time in MVPA bouts per week than individuals without diabetes (all p 0.05). The most common diabetes-specific barrier to PA was risk of hypoglycemia. Individuals with diabetes reporting barriers spent less time in MVPA bouts per week than those not reporting barriers (p = 0.047). Conclusions Individuals with type 1 diabetes engage in less MVPA than those without diabetes despite similar self-reported levels, with the main barrier being perceived risk of hypoglycemia. Adults with type 1 diabetes require guidance to meet current PA guidelines and reduce cardiovascular risk.
SIR proteins create compact heterochromatin fibers.

Heterochromatin is a silenced chromatin region essential for maintaining genomic stability and driving developmental processes. The complicated structure and dynamics of heterochromatin have rendered it difficult to characterize. In budding yeast, heterochromatin assembly requires the SIR proteins—Sir3, believed to be the primary structural component of SIR heterochromatin, and the Sir2–4 complex, responsible for the targeted recruitment of SIR proteins and the deacetylation of lysine 16 of histone H4. Previously, we found that Sir3 binds but does not compact nucleosomal arrays. Here we reconstitute chromatin fibers with the complete complement of SIR proteins and use sedimentation velocity, molecular modeling, and atomic force microscopy to characterize the stoichiometry and conformation of SIR chromatin fibers. In contrast to fibers with Sir3 alone, our results demonstrate that SIR arrays are highly compact. Strikingly, the condensed structure of SIR heterochromatin fibers requires both the integrity of H4K16 and an interaction between Sir3 and Sir4. We propose a model in which a dimer of Sir3 bridges and stabilizes two adjacent nucleosomes, while a Sir2–4 heterotetramer interacts with Sir3 associated with a nucleosomal trimer, driving fiber compaction.
Persistent disruption of lateral junctional complexes and actin cytoskeleton in parotid salivary glands following radiation treatment

Xerostomia and hyposalivation are debilitating side effects for patients treated with ionizing radiation for head and neck cancer. Despite technological advances, collateral damage to the salivary ...
Unbiased Learning-to-Rank with Biased Feedback.

Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a Propensity-Weighted Ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. In contrast to most conventional approaches to de-biasing the data using click models, this allows training of ranking functions even in settings where queries do not repeat. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.
LAMP: prompt layer 7 attack mitigation with programmable data planes

While there are various methods to detect application layer attacks or intrusion attempts on an individual end host, it is not efficient to provide all end hosts in the network with heavy-duty defense systems or software firewalls. In this work, we leverage a new concept of programmable data planes, to directly react on alerts raised by a victim and prevent further attacks on the whole network by blocking the attack at the network edge.
Insights into the Mutational Burden of Human Induced Pluripotent Stem Cells from an Integrative Multi-Omics Approach.

Summary To understand the mutational burden of human induced pluripotent stem cells (iPSCs), we sequenced genomes of 18 fibroblast-derived iPSC lines and identified different classes of somatic mutations based on structure, origin, and frequency. Copy-number alterations affected 295 kb in each sample and strongly impacted gene expression. UV-damage mutations were present in ∼45% of the iPSCs and accounted for most of the observed heterogeneity in mutation rates across lines. Subclonal mutations (not present in all iPSCs within a line) composed 10% of point mutations and, compared with clonal variants, showed an enrichment in active promoters and increased association with altered gene expression. Our study shows that, by combining WGS, transcriptome, and epigenome data, we can understand the mutational burden of each iPSC line on an individual basis and suggests that this information could be used to prioritize iPSC lines for models of specific human diseases and/or transplantation therapy.
Networks in Production: Asset Pricing Implications

This paper studies asset pricing in a multisector model in which sectors are connected to each other through an input-output network. Changes in the structure of the network are sources of systematic risk reflected in equilibrium asset prices. There are two key characteristics of the network that matter for asset prices: network concentration and network sparsity. Network concentration measures the degree to which equilibrium output is dominated by few large sectors while network sparsity measures the average input specialization of the economy. Furthermore, these two production-based asset pricing factors are determined by the structure of the network of production and can be computed from input-output data. By sorting stocks based on their exposure to the network factors, I find a return spread of 6% per year on portfolios sorted on sparsity-beta and ~4% per year on portfolios sorted on concentration-beta. These return gaps cannot be explained by standard asset pricing models such as the CAPM or the Fama French three-factor model. A calibrated model matches the network factor betas and return spreads alongside other asset pricing moments.
Anatomy of high-performance deep learning convolutions on SIMD architectures

Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image recognition, neural machine translation and speech recognition. The computationally expensive nature of a convolution operation has led to the proliferation of implementations including matrix-matrix multiplication formulation, and direct convolution primarily targeting GPUs. In this paper, we introduce direct convolution kernels for x86 architectures, in particular for Xeon and Xeon Phi systems, which are implemented via a dynamic compilation approach. Our JIT-based implementation shows close to theoretical peak performance, depending on the setting and the CPU architecture at hand. We additionally demonstrate how these JIT-optimized kernels can be integrated into a light-weight multi-node graph execution model. This illustrates that single- and multi-node runs yield high efficiencies and high image-throughputs when executing state-of-the-art image recognition tasks on CPUs.