cssi/SciSciGPT-SciSciNet huggingface dataset:
A systematic overview of the literature regarding group prenatal care for high-risk pregnant women.

Group prenatal care (GPC) models have been gaining popularity in recent years. Studies of high-risk groups have shown improved outcomes. Our objective was to review and summarize outcomes for women in GPC for women with specific high-risk conditions. A systematic literature review of Ovid, PubMed, and Google Scholar was performed to identify studies reporting the effects of group prenatal care in high-risk populations. Studies were included if they reported on pregnancy outcome results for women using GPC. We also contacted providers known to be utilizing GPC for specific high-risk women. Descriptive results were compiled and summarized by high-risk population. We identified 37 reports for inclusion (8 randomized trials, 23 nonrandomized studies, 6 reports of group outcomes without controls). Preterm birth was found to be decreased among low-income and African American women. Attendance at prenatal visits was shown to increase among women in GPC in the following groups: Opioid Addiction, Adolescents, and Low-Income. Improved weight trajectories and compliance with the IOM’s weight recommendations were found in adolescents. Increased rates of breastfeeding were found in adolescents and African Americans. Increased satisfaction with care was found in adolescents and African Americans. Pregnancy knowledge was increased among adolescents, as was uptake of LARC. Improved psychological outcomes were found among adolescents and low-income women. Studies in women with diabetes demonstrated that fewer women required treatment with medication when exposed to GPC, and for those requiring treatment with insulin, GPC individuals required less than half the dose. Among women with tobacco use, those who had continued to smoke after finding out they were pregnant were 5 times more likely to quit later in pregnancy if they were engaged in GPC. Several groups of high-risk pregnant women may have benefits from engaging in group prenatal care. Because there is a paucity of high-quality, well-controlled studies, more trials in high-risk women are needed to determine whether it improves outcomes and costs of pregnancy-related care.
Seasonal Synchronization of a Simple Stochastic Dynamical Model Capturing El Niño Diversity

AbstractRecently, a simple stochastic dynamical model was developed that automatically captures the diversity and intermittency of El Nino–Southern Oscillation (ENSO) in nature, where state-dependent stochastic wind bursts and nonlinear advection of sea surface temperature (SST) are coupled to simple ocean–atmosphere processes that are otherwise deterministic, linear, and stable. In the present article, it is further shown that the model can reproduce qualitatively the ENSO synchronization (or phase locking) to the seasonal cycle in nature. This goal is achieved by incorporating a cloud radiative feedback that is derived naturally from the model’s atmosphere dynamics with no ad hoc assumptions and accounts in simple fashion for the marked seasonal variations of convective activity and cloud cover in the eastern Pacific. In particular, the weak convective response to SSTs in boreal fall favors the eastern Pacific warming that triggers El Nino events while the increased convective activity and cloud cover d...
ADVANCING SUTURE TECHNOLOGIES IN TOTAL JOINT ARTHROPLASTY: STITCHES IN TIME

Wound closure is not often an area that is discussed by orthopaedic surgeons. We commonly attend meetings and talks related to different types of implants and surgical technique but in reality the wound and how we close it maybe one of the most important aspects of a surgical procedure. The avoidance of wound complications such as unplanned clinic visits, readmissions, reoperations and deep infections are very important as we move into a world of bundled payments and public reporting of complications. Not to mention the poor results for the patients when wound complications occur. Often there is little thought about how wound closure and surgical dressings could affect surgical site healing. We all have a common belief that blisters, drainage and surgical site infections are rare. In the literature it has been shown that the method of wound closure can influence skin and soft tissue perfusion. Which closure techniques can achieve physiologic blood flow, which may improve wound healing? This talk will cover topics related to reducing the dead space, avoidance of hematoma formation and what the literature says regarding different types of wound closure materials.
Needs and Challenges of Post-Acute Brain Injury Patients in Understanding Personal Recovery

Recovery from brain injury is a complex and longitudinal process consisting of clinical recovery, illness management, and personal recovery. Lack of initiatives to create awareness about expected recovery and tools for helping patients understand their progress make the personal recovery journey challenging and frustrating. In this paper, we discuss results from interviews with five post-acute brain injury patients about their needs and challenges in understanding their personal recovery.
Constant approximation for k-median and k-means with outliers via iterative rounding

In this paper, we present a new iterative rounding framework for many clustering problems. Using this, we obtain an (α1 + є ≤ 7.081 + є)-approximation algorithm for k-median with outliers, greatly improving upon the large implicit constant approximation ratio of Chen. For k-means with outliers, we give an (α2+є ≤ 53.002 + є)-approximation, which is the first O(1)-approximation for this problem. The iterative algorithm framework is very versatile; we show how it can be used to give α1- and (α1 + є)-approximation algorithms for matroid and knapsack median problems respectively, improving upon the previous best approximations ratios of 8 due to Swamy and 17.46 due to Byrka et al. The natural LP relaxation for the k-median/k-means with outliers problem has an unbounded integrality gap. In spite of this negative result, our iterative rounding framework shows that we can round an LP solution to an almost-integral solution of small cost, in which we have at most two fractionally open facilities. Thus, the LP integrality gap arises due to the gap between almost-integral and fully-integral solutions. Then, using a pre-processing procedure, we show how to convert an almost-integral solution to a fully-integral solution losing only a constant-factor in the approximation ratio. By further using a sparsification technique, the additive factor loss incurred by the conversion can be reduced to any є > 0.
Preserving Graph Utility in Anonymized Social Networks? A Study on the Persistent Homology

Following the trend of privacy preserving online social network publishing, various anonymization mechanisms have been designed and employed. Many differential privacybased mechanisms claim that they can preserve the utility as well as guarantee the privacy. Their utility analysis are always based on some specifically chosen metrics.This paper aims to find a novel angle that describing the network in multiple scales. Persistent homology is such a high level metric that it reveals the parameterized topological features with various scales and it is applicable for read-world applications. In this paper, four differential privacy mechanisms employing different models are analyzed under the traditional graph metrics and the persistent homology. The evaluation results demonstrate that all algorithms can partially or conditionally preserve certain traditional graph utilities, but none of them are suitable for all metrics. Furthermore, none of the existing mechanisms can fully preserve the persistent homology, especially in high dimensions, which implies that the true graph utility is lost.
Reading, Writing, and English Course Pathways when Developmental Education is Optional: Course Enrollment and Success for Underprepared First-time-in-College Students

Academic underpreparedness is an issue for many first-time-in-college students, particularly those entering community colleges. Whereas many underprepared students enroll in developmental education...
Staged endovascular repair of an abdominal aortic aneurysm adjacent to a chronic high-flow iliocaval traumatic arteriovenous fistula

Abstract Large-vessel chronic traumatic arteriovenous fistulas are a rare complication after trauma. Delayed presentation can consist of one or more features of high-output cardiac failure, pulsatile abdominal mass, bruit, limb ischemia, and venous congestion. We describe a patient with a complex iliocaval fistula secondary to a remote gunshot wound associated with a large 8.5-cm aortic aneurysm. Informed consent of the patient was obtained for publication of the case.
Economic Fundamentals, Capital Expenditures and Asset Dispositions

The real estate literature recognizes the real option to invest in capital expenditures (CAPEX) or sell a property but treats these options as independent. We show that these real options are interconnected. We provide empirical evidence that, consistent with the real option framework, CAPEX increases in income growth expectations but declines in their volatility; that CAPEX are partially capitalized into property market values; and that CAPEX significantly reduce the subsequent likelihood of sale. We also present evidence that, controlling for market timing, past property performance influences CAPEX but not disposition choices, consistent with a value-add investment strategy.
MRAttractor: Detecting communities from large-scale graphs

Detecting groups of users, who have similar opinions, interests, or social behavior, has become an important task for many applications. A recent study showed that dynamic distance based Attractor, a community detection algorithm, outperformed other community detection algorithms such as Spectral clustering, Louvain and Infomap, achieving higher Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). However, Attractor often takes long time to detect communities, requiring many iterations. To overcome the drawback and handle large-scale graphs, in this paper we propose MRAttractor, an advanced version of Attractor to be runnable on a MapReduce framework. In particular, we (i) apply a sliding window technique to reduce the running time, keeping the same community detection quality; (ii) design and implement the Attractor algorithm for a MapReduce framework; and (iii) evaluate MRAttractor's performance on synthetic and real-world datasets. Experimental results show that our algorithm significantly reduced running time and was able to handle large-scale graphs.
MELK expression correlates with tumor mitotic activity but is not required for cancer growth

The Maternal Embryonic Leucine Zipper Kinase (MELK) has been identified as a promising therapeutic target in multiple cancer types. MELK over-expression is associated with aggressive disease, and MELK has been implicated in numerous cancer-related processes, including chemotherapy resistance, stem cell renewal, and tumor growth. Previously, we established that triple-negative breast cancer cell lines harboring CRISPR/Cas9-induced null mutations in MELK proliferate at wild-type levels in vitro ( Lin et al., 2017 ). Here, we generate several additional knockout clones of MELK and demonstrate that across cancer types, cells lacking MELK exhibit wild-type growth in vitro, under environmental stress, in the presence of cytotoxic chemotherapies, and in vivo. By combining our MELK-knockout clones with a recently described, highly specific MELK inhibitor, we further demonstrate that the acute inhibition of MELK results in no specific anti-proliferative phenotype. Analysis of gene expression data from cohorts of cancer patients identifies MELK expression as a correlate of tumor mitotic activity, explaining its association with poor clinical prognosis. In total, our results demonstrate the power of CRISPR/Cas9-based genetic approaches to investigate cancer drug targets, and call into question the rationale for treating patients with anti-MELK monotherapies.
Contrastive Training for Models of Information Cascades

This paper proposes a model of information cascades as directed spanning trees (DSTs) over observed documents. In addition, we propose a contrastive training procedure that exploits partial temporal ordering of node infections in lieu of labeled training links. This combination of model and unsupervised training makes it possible to improve on models that use infection times alone and to exploit arbitrary features of the nodes and of the text content of messages in information cascades. With only basic node and time lag features similar to previous models, the DST model achieves performance with unsupervised training comparable to strong baselines on a blog network inference task. Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.
Economics media library

The Economics Media Library includes a variety of television, movies, comedy specials, or news clips that can be used for teaching economics courses. The clips are sorted by topics, courses, or sources and can be searched by tags, categories or search feature.
Disentangling the impacts of industrial and global diversification on firm risk

Abstract We examine the impact of corporate diversification on firm risk exposure from 1998 to 2016. We find that both global and industrial diversification mitigate idiosyncratic and world market risk while having a negligible impact on U.S. market risk, but the effects vary before, during, and after the financial crisis of 2007–2009. Before the crisis, only global diversification mitigates idiosyncratic risk, but it increases firms' exposure to world market risk. During the crisis, industrial diversification increases idiosyncratic risk, but both types of diversification increase exposure to U.S. market risk. After the crisis, both types of diversification increase firms' exposure to U.S. market risk but have negligible impact on idiosyncratic and world market risk. Our findings remain robust after we control for the potential endogeneity of the diversification decision through various self-selection models.
Corporate social responsibility versus corporate shareholder responsibility: A family firm perspective

Abstract Recent literature suggests that some socially responsible corporate actions benefit shareholders while others do not. We study differences in policy toward corporate social responsibility (CSR) between family and non-family firms, using environmental performance as the proxy for CSR. We show that family firms are more responsible to shareholders than non-family firms in making environmental investments. When shareholder interests and societal interests coincide, i.e., when it comes to alleviating environmental concerns that have potential to harm society and elevate the firm's risk exposure, family firms do at least as well as non-family firms in protecting shareholder interests. However, when shareholder and societal interests diverge, i.e., when it comes to making environmental investments that might benefit society but do not benefit shareholders, family firms protect shareholder interests by undertaking a significantly lower level of such investments than non-family firms. Our findings suggest that lack of diversification by controlling families creates strong incentives for them to act in the financial interest of all shareholders, which more than overcomes any noneconomic benefits families may derive from engaging in social causes that do not benefit non-controlling shareholders.
Fast classification of MPI applications using Lamport’s logical clocks

Abstract We present a novel trace-based analysis tool that rapidly classifies an MPI application as bandwidth-bound, latency-bound, load-imbalance-bound, or computation-bound for different interconnection networks. The tool uses an extension of Lamport’s logical clock to track application progress in the trace replay. It has two unique features. First, it can predict application performance for many latency and bandwidth parameters from a single replay of the trace. Second, it infers the performance characteristics of an application and classifies the application using the predicted performance trend for a range of network configurations instead of using the predicted performance for a particular network configuration. We describe the techniques used in the tool and its design and implementation, and report our performance study of the tool and our experience with classifying twelve applications and mini-apps from the DOE DesignForward project as well as the NAS Parallel Benchmarks.
Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants.

Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naieve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj The proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.
Regulation, Public Attitudes, and Private Governance

Corporate adoption of politically contestable practices (e.g., sustainable forestry; cage-free eggs) are increasingly common. In two studies, we empirically explore the relationship between corporate practices and subsequent public support for legislation mandating such practices. One hypothesis is that public support for new legislation decreases following corporate action because the private sector is perceived to be adequately managing the problem, thus obviating the need for a legislative response. A competing hypothesis is that public support for new legislation increases because people are prompted to recognize the issue in question as one in need of regulation. Our results suggest that announced changes to corporate practices can increase public support for legislation, but the effects differ depending on the political orientation of the perceiver. Legislators might fruitfully integrate corporate endorsements into public information efforts.
Modern Machine Learning as a Benchmark for Fitting Neural Responses

Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.
Consequences of Immigrating During a Recession: Evidence from the US Refugee Resettlement Program

I examine long-term employment and wage consequences for refugees who immigrate to the United States under different business cycle conditions. It is difficult to causally identify the relationship between initial economic conditions and subsequent outcomes for most immigrants because they can choose when and where they immigrate. However, refugees offer a unique opportunity to empirically measure these outcomes because their dates of arrival and states of placement are exogenously chosen through the US Refugee Resettlement Program. For every one percentage point increase in the national unemployment rate at arrival, refugees on average experience a 2.99% reduction in wages five years later and a 1.8 percentage point reduction in employment four years later. Estimates using state unemployment rate at arrival show less persistence suggesting mobility or differential economic improvement across states may be important in mitigating these effects. I also divide the sample across gender and educational attainment. I find no evidence of wage scarring for uneducated males but observe a 4.85% reduction in wages five years later for high school-educated males and a 5.29% reduction in wages four years later for college-educated.
Revisiting the functional significance of binocular cues for perceiving motion-in-depth

Binocular differencing of spatial cues required for perceiving depth relationships is associated with decreased sensitivity to the corresponding retinal image displacements. However, binocular summation of contrast signals increases sensitivity. Here, we investigated this divergence in sensitivity by making direct neural measurements of responses to suprathreshold motion in human adults and 5-month-old infants using steady-state visually evoked potentials. Interocular differences in retinal image motion generated suppressed response functions and correspondingly elevated perceptual thresholds compared to motion matched between the two eyes. This suppression was of equal strength for horizontal and vertical motion and therefore not specific to the perception of motion-in-depth. Suppression is strongly dependent on the presence of spatial references in the image and highly immature in infants. Suppression appears to be the manifestation of a succession of spatial and interocular opponency operations that occur at an intermediate processing stage either before or in parallel with the extraction of motion-in-depth.
Building a functional connectome of the Drosophila central complex

Some of the most evocative discoveries in neuroscience have been those of internal representations, such as neural activity patterns that represent which direction an animal is facing and its place in its surroundings. Understanding how neurons connect to one another to form ‘circuits’ is crucial to understanding how these circuits maintain such representations. Many of the design principles that underlie circuit function in the brains of fruit flies apply to other animals. However, fly brains are easier to study because genetic tools can be used on them to selectively activate and image the activity of specific types of neurons. By activating one type of neuron and imaging the activity of another that may be connected to it, we obtain what is called a functional ‘connectome’: a map of neural connectivity that identifies different pathways that information can flow along. A region of the fly brain called the central complex is involved in many important behaviors, including navigation and sleep. Researchers know about the types of neurons in the region and about how the activity of some of them changes during different behaviors. However, obtaining the connectome of the central complex would make it easier to understand how the central complex works. A technique called optogenetics allows specific types of neurons to be activated one at a time by shining light onto them. By imaging the activity of neurons that might be connected to an optogenetically activated neuron, Franconville et al. have now built an extensive – albeit still incomplete – map of the connections within the central complex of fruit flies. The map reveals two key bottlenecks in the central complex circuit. Firstly, a neuron type in a substructure called the protocerebral bridge controls a lot of the information flowing through the circuit. Secondly, the circuit appears to have very few true ‘output’ neuron types – Franconville et al. identified only one. These results suggest that however complicated the computations performed by the central complex circuit might be, the output of the circuit, which likely guides the fly’s actions, may be much simpler. Franconville et al. have compiled the mapping results into an interactive website that makes the neuroscientific data both freely available and easily explorable. As researchers perform more such experiments, the new data can be added to the map. This information can be used to constrain theories and inspire new ideas about how the central complex does what it does.
Real-Time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy:

Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency comm...
Acute sleep deprivation and culpable motor vehicle crash involvement.

Study objectives To quantify the relationship between acute sleep deprivation and culpable involvement in motor vehicle crashes. Methods Participants were 6845 drivers involved in a representative sample of crashes investigated by the US Department of Transportation in years 2005-2007. A modified case-control study design was used to compare self-reported hours of sleep in the 24 hr before crashing between drivers deemed culpable versus nonculpable. Analyses controlled for fatigue-related, driver-related, and environmental factors. Specific errors that led to crashes were also examined. Results Drivers who reported having slept for 6, 5, 4, and less than 4 hr in the 24 hr before crashing had 1.3 (95% confidence interval [CI] = 1.04 to 1.7), 1.9 (1.1 to 3.2), 2.9 (1.4 to 6.2), and 15.1 (4.2 to 54.4) times the odds, respectively, of having been culpable for their crashes, compared with drivers who reported 7-9 hr of sleep. Drivers who had slept less than 4 hr had 3.4 (95% CI = 2.1 to 5.6) times the increase in odds of culpable involvement in single-vehicle crashes compared with multiple-vehicle crashes. Recent change in sleep schedule, typically feeling drowsy upon waking, and driving for 3+ hr were also associated with culpability (all p ≤ 0.013). Assuming nonculpable drivers comprised a representative sample of all drivers present where crashes occurred, these odds ratios approximate incidence rate ratios for culpable crash involvement per unit of time driving. Conclusions Driving after having slept less than 7 hr in a 24 hr period is associated with elevated risk of culpable crash involvement. Risk is greatest for drivers who have slept less than 4 hr and is manifested disproportionately in single-vehicle crashes.
Selective attention, filtering, and the development of working memory.

Selective attention is fundamental for learning across many situations, yet it exhibits protracted development, with young children often failing to filter out distractors. In this research, we examine links between selective attention and working memory (WM) capacity across development. One possibility is that WM is resource-limited, with development resulting in an increase in the amount of resources available for processing information. However, it is also possible that development results in greater efficiency of using available resources. In the current research, we explore the latter possibility by examining the developmental trajectory of selectivity and filtering in relation to WM capacity. We report that filtering efficiency of adults (N = 30), 7-year-olds (N = 29), and 4-year-olds (N = 28) was uniquely predictive of WM capacity. We also report that filtering efficiency continues to develop after 7 years of age, whereas WM capacity may reach an asymptote around 7 years of age. The latter finding suggests that selective attention plays a critical role in developmental and individual differences in visual working memory capacity.
Obtaining History with a Language Barrier in the Emergency Department: Perhaps not a Barrier After All.

Author(s): Litzau, Megan; Turner, Joseph; Pettit, Katie; Morgan, Zachary; Cooper, Dylan; Pettit, Katie | Abstract: Introduction: Patients with limited English proficiency may be at risk for incomplete history collection, potentially a patient safety issue. While federal law requires qualified medical interpreters be provided for these patients, little is known about the quality of information obtained in these encounters. Our study compared the medical histories obtained by physicians in the emergency department (ED) based on whether the patients primarily spoke English or Spanish. Methods: This was a prospective, observational study conducted at a single, urban, academic ED during a six-month time period. Resident and faculty physicians caring for adult patients with a chief complaint of chest or abdominal pain were eligible for participation. Patient encounters were directly observed by medical students who had been trained using simulated encounters. Observers documented which key historical data points were obtained by providers, including descriptions of pain (location, quality, severity, radiation, alleviating/aggravating factors), past medical/family/surgical history, and social history, in addition to the patient’s language in providing history. Providers, interpreters, and observers were blinded to the nature of the study. We used chi-square analyses to examine differences in whether specific elements were collected based on the primary language of the patient. Results: Encounters with 753 patients were observed: 105 Spanish speaking and 648 English speaking. Chi-square analyses found no statistically significant differences in any history questions between Spanish-speaking and English-speaking patients, with the exception that questions regarding alleviating factors were asked more often with Spanish-speaking patients (45%) than English-speaking patients (30%, p=.003). The average percentages of targeted history elements obtained in Spanish and English encounters were 60% and 57%, respectively. Conclusion: In this study at a large, urban, academic ED, the medical histories obtained by physicians were similar between English-speaking and Spanish-speaking patients. This suggests that the physicians sought to obtain medical histories at the same level of detail despite the language barrier. One limitation to consider is the Hawthorne effect; however, providers and observers were blinded to the nature of the study in an attempt to minimize the effect.
A Knowledge Representation of Cloud Data Controls for EU GDPR Compliance

The rollout of European Union's General Data Protection Regulation (EU GDPR) will have a far-reaching effect on Cloud data privacy and compliance for both the Cloud Service Providers (Data Providers) and the Consumers (Data Consumers). GDPR mandates that organizations collecting and processing information related to EU citizens adhere to its articles irrespective of where they are located or where the data is stored. This regulation is currently available only in the textual format and so requires significant manual effort to ensure its compliance. We have developed a semantically rich Ontology (or knowledge graph) to represent the rules and regulations mandated for both the cloud data consumers and providers by EU GDPR. In the Ontology, we have identified the different roles and their corresponding obligations under the GDPR articles. This knowledge graph, that is available in public domain, is machine processable and will help in automating EU GDPR regulation compliance.
Designed to Fail: Effects of the Default Option and Information Complexity on Student Loan Repayment

Abstract Within five years of leaving school, 25% of student loan borrowers default on required minimum payments. These defaults are costly: they add to interest and penalties on loans and lower credit scores, which limits access to future credit and can adversely affect job prospects. We ask why so few student loan borrowers enroll in Income Driven Repayment (IDR) plans, which insure against default caused by low earnings. To do so we run an incentivized laboratory experiment using a facsimile of the government’s Student Loan Exit Counseling website. We test the roles information complexity, uncertainty about earnings, and the default option play. We find that switching the default option from the Standard plan with fixed minimum payments to an IDR with income-contingent minimum payments, and providing good information about earnings, can dramatically decrease choice of the risky Standard plan.
Automated dispatch of helpdesk email tickets: Pushing the limits with ai

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.
Satellite Mapping of Land Degradation in Senegal, Uganda, Kenya, and Tanzania

Land degradation has been highlighted as a key development challenge by numerous international bodies, including the United Nations Convention to Combat Desertification or the UNCCD, the Convention on Biological Diversity, the United Nations Framework Convention on Climate Change, and is one of the so-called Sustainable Development Goals. The UNCCD aims to avoid, reduce, and reverse land degradation, especially desertification and deforestation, by providing support to sustainable land management. Sustainable land management implements practices that maintain vegetative cover, build soil organic matter, make efficient use of inputs, such as water, nutrients and pesticides, and minimize off-site impacts. In the context of Land Degradation Neutrality or Sustainable Development Goal 15.3, three indicators have been identified to quantify land degradation: trends in land cover; trends in carbon stocks above and below ground; and trends in land productivity.
Consumer Response to Chapter 11 Bankruptcy: Negative Demand Spillover to Competitors

We empirically study the effect of Chrysler’s Chapter 11 bankruptcy filing on the quantity sold by its competitors in the U.S. auto industry.
iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data

RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses. Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/ ) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.
R3-DLA (Reduce, Reuse, Recycle): A More Efficient Approach to Decoupled Look-Ahead Architectures

Modern societies have developed insatiable demands for more computation capabilities. Exploiting implicit parallelism to provide automatic performance improvement remains a central goal in engineering future general-purpose computing systems. One approach is to use a separate thread context to perform continuous look-ahead to improve the data and instruction supply to the main pipeline. Such a decoupled look-ahead (DLA) architecture can be quite effective in accelerating a broad range of applications in a relatively straightforward implementation. It also has broad design flexibility as the look-ahead agent need not be concerned with correctness constraints. In this paper, we explore a number of optimizations that make the look-ahead agent more efficient and yet extract more utility from it. With these optimizations, a DLA architecture can achieve an average speedup of 1.4 over a state-of-the-art microarchitecture for a broad set of benchmark suites, making it a powerful tool to enhance single-thread performance.
External Equity Financing Shocks, Financial Flows, and Asset Prices

We develop a dynamic model with time variation in external equity financing costs and show that variation in these costs is important for the model to quantitatively capture the joint dynamics of firms’ asset prices, real quantities, and financial flows in the U.S. economy. Growth firms and high investment firms are less risky in equilibrium, because they can substitute more easily debt financing for equity financing when it becomes more costly to raise external equity, which are high marginal utility states. Using a model-implied proxy of aggregate equity issuance cost shocks, we provide empirical support for the model’s economic mechanism. Received August 7, 2017; editorial decision September 24, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online
Biomarkers for Early Complications After Hematopoietic Stem Cell Transplantation

Advances in the field of omics have led to a significant expansion in biomarkers identified for complications after hematopoietic stem cell transplantation (HSCT). Biomarkers can offer an effective method for early identification of a specific disease and can be used to guide therapies. Ongoing investigations to discover biomarkers for acute graft-versus-host disease as well as other post-HSCT complications may improve early diagnosis, prognosis, and the development of new therapeutic targets. The authors review the most recent and validated diagnostic, prognostic, predictive, and response to treatment biomarkers for early complications following HSCT consistent with 2014 NIH consensus on biomarker criteria.
Consumer Spending During Unemployment: Positive and Normative Implications

Using de-identified bank account data, we show that spending drops sharply at the large and predictable decrease in income arising from the exhaustion of unemployment insurance (UI) benefits. We use the high-frequency response to a predictable income decline as a new test to distinguish between alternative consumption models. The sensitivity of spending to income we document is inconsistent with rational models of liquidity-constrained households, but is consistent with behavioral models with present-biased or myopic households. Depressed spending after exhaustion also implies that the consumption-smoothing gains from extending UI benefits are four times larger than from raising UI benefit levels.
The Right to Civil Counsel

The U.S. Constitution grants no categorical right to counsel in civil cases. Undaunted, the legal profession's renewed effort to improve access to justice for low-income unrepresented civil litigan...
Perspectives and preferences for a mobile health tool designed to facilitate HPV vaccination among young men who have sex with men

We sought to understand young men who have sex with men (YMSM) perspectives and preferred features for a mobile health (mHealth) tool designed to facilitate human papillomavirus (HPV) vaccination. ...
Using Models to Persuade

We present a framework where "model persuaders" influence receivers' beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers' prior beliefs. Model persuaders face a trade-off: better-fitting models induce less movement in receivers' beliefs. Consequently, a receiver exposed to the true model can be most misled by persuasion when that model fits poorly, competition between persuaders tends to neutralize the data by pushing toward better-fitting models, and a persuader facing multiple receivers is more effective when he can send tailored, private messages.
The Forewarning Effect of Critical Audit Matter Disclosures Involving Measurement Uncertainty

We present experimental evidence suggesting that critical audit matter (CAM) disclosures in the auditor's report involving areas of high measurement uncertainty forewarn users of misstatement risk. Specifically, in our first study with MBA students, financial analysts, and attorneys, we find that CAMs (i) lower premisstatement assessments of confidence in the financial statement area disclosed as a CAM, and (ii) lower assessments of auditor responsibility for a subsequently revealed misstatement in a CAM‐related area. In our second study with student participants proxying as mock jurors, we find that the responsibility‐mitigating effect of CAM disclosure is driven by CAM disclosures involving measurement uncertainty, as opposed to CAM disclosures involving categorical determinations. Combined, our findings help reconcile mixed evidence from prior research, supporting the view that the forewarning effect of CAM disclosures involving measurement uncertainty could mitigate perceived auditor responsibility for CAM‐related material misstatements. Valeur indicative de la communication de questions critiques de l'audit faisant intervenir l'incertitude relative a la mesure Les auteurs presentent des donnees experimentales selon lesquelles la communication dans le rapport d'audit de questions critiques de l'audit (QCA) faisant intervenir des sections dans lesquelles l'incertitude relative a la mesure est elevee est, pour les utilisateurs, indicative d'un risque d'anomalie. Plus precisement, les resultats d'une premiere etude, menee aupres d'etudiants MBA, d'analystes financiers et d'avocats, amenent les auteurs a constater que les QCA 1) diminuent l'evaluation, prealable a l'anomalie, de la confiance dans la section des etats financiers soulevant une QCA et 2) diminuent l'evaluation de la responsabilite de l'auditeur quant a une anomalie subsequemment mise au jour dans un domaine lie a la QCA. Les resultats d'une seconde etude aupres de participants etudiants jouant le role de jures, menent les auteurs a constater que l'effet attenuateur de responsabilite de la communication des QCA est attribuable au fait qu'elles se rapportent a une incertitude relative a la mesure plutot qu'a des decisions de classement. Ces resultats conjugues contribuent a rapprocher les resultats divergents des recherches anterieures, ce qui confirme le point de vue selon lequel la valeur indicative de la communication de QCA faisant intervenir l'incertitude relative a la mesure pourrait attenuer la responsabilite percue de l'auditeur en ce qui a trait aux anomalies significatives liees aux QCA.
Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C . elegans Reveals Temporal and Spatial Encoding

Animals must integrate the activity of multiple mechanoreceptors to navigate complex environments. In Caenorhabditis elegans, the general roles of the mechanosensory neurons have been defined, but most studies involve end-point or single-time-point measurements, and thus lack dynamic information. Here, we formulate a set of unbiased quantitative characterizations of the mechanosensory system by using reverse correlation analysis on behavior. We use a custom tracking, selective illumination, and optogenetics platform to compare two mechanosensory systems: the gentle-touch (TRNs) and harsh-touch (PVD) circuits. This method yields characteristic linear filters that allow for the prediction of behavioral responses. The resulting filters are consistent with previous findings and further provide new insights on the dynamics and spatial encoding of the systems. Our results suggest that the tiled network of the gentle-touch neurons has better resolution for spatial encoding than the harsh-touch neurons. Additionally, linear-nonlinear models can predict behavioral responses based only on sensory neuron activity. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that quantitatively characterize these systems. Furthermore, this platform can be extended to capture the behavioral dynamics induced by any neuron or other excitable cells in the animal.