cssi/SciSciGPT-SciSciNet huggingface dataset:
Maternal and fetal health effects of working during pregnancy

We provide some of the first empirical evidence of maternal and fetal health effects of working during pregnancy by using a unique dataset from the New Jersey Department of Health that includes information not only on pregnancy and birth outcomes but also on maternal employment. We match the mother’s occupation with the Metabolic Equivalent of Task, provided by the Census Occupational Classification System and used as a measure for the strenuousness of the work activities performed. Focusing on an empirical setting where laws regarding reasonable accommodations for pregnant women are already in place, we still find consistent and robust evidence that working in a relatively more strenuous job during pregnancy raises the likelihood of an adverse birth outcome, specifically fetal macrosomia, by about 1.5 percentage points. While there are no statistically or economically significant effects on other birth outcomes, our finding of a significant increase in fetal macrosomia nevertheless highlights a possible deficiency of existing accommodation laws intended to protect pregnant workers. In addition, our study indicates an under-studied link between gestational diabetes (a known risk factor for fetal macrosomia) and intensive physical activities at work during pregnancy, potentially mediated by disrupted sleep due to greater work intensity.
Glyphosate: Its Environmental Persistence and Impact on Crop Health and Nutrition

Glyphosate-based herbicide products are the most widely used broad-spectrum herbicides in the world for postemergent weed control. There are ever-increasing concerns that glyphosate, if not used judiciously, may cause adverse nontarget impacts in agroecosystems. The purpose of this brief review is to present and discuss the state of knowledge with respect to its persistence in the environment, possible effects on crop health, and impacts on crop nutrition.
Effects of metallicity on high-mass X-ray binary formation

The heating of the intergalactic medium in the early, metal-poor Universe may have been partly due to radiation from high mass X-ray binaries (HMXBs). Previous investigations on the effect of metallicity have used galaxies of different types. To isolate the effects of metallicity on the production of HMXBs, we study a sample consisting only of 46 blue compact dwarf galaxies (BCDs) covering metallicity in the range 12+log(O/H) of 7.15 to 8.66. To test the hypothesis of metallicity dependence in the X-ray luminosity function (XLF), we fix the XLF form to that found for near-solar metallicity galaxies and use a Bayesian method to constrain the XLF normalization as a function of star formation rate (SFR) for three different metallicity ranges in our sample. We find an increase by a factor of 4.45 $\pm$ 2.04 in the XLF normalization between the metallicity ranges 7.1-7.7 and 8.2-8.66 at a statistical significance of 99.79 per cent. Our results suggest that HMXB production is enhanced at low metallicity, and consequently that HMXBs may have contributed significantly to the reheating of the early Universe.
Two Deletion Correcting Codes From Indicator Vectors

Construction of capacity achieving deletion correcting codes has been a baffling challenge for decades. A recent breakthrough by Brakensiek et al ., alongside novel applications in DNA storage, have reignited the interest in this longstanding open problem. In spite of recent advances, the amount of redundancy in existing codes is still orders of magnitude away from being optimal. In this paper, a novel approach for constructing binary two-deletion correcting codes is proposed. By this approach, parity symbols are computed from indicator vectors (i.e., vectors that indicate the positions of certain patterns) of the encoded message, rather than from the message itself. Most interestingly, the parity symbols and the proof of correctness are a direct generalization of their counterparts in the Varshamov-Tenengolts construction. Our techniques require $7\log ({n})+ {o}(\log ({n}))$ redundant bits to encode an n-bit message, which is closer to optimal than previous constructions. Moreover, the encoding and decoding algorithms have ${O}({n})$ time complexity.
Inside money, business cycle, and bank capital requirements

Abstract A search theoretical model is constructed to study bank capital requirements in a perspective of inside money. In the model bank liabilities, backed by bank assets, are useful for exchange, while bank capital is not. When the supply of bank liabilities is not sufficiently large for the trading demand, banks do not issue bank capital in competitive equilibrium. This equilibrium allocation can be sub-optimal when the bank assets are exposed to the aggregate risk. Specifically, a pecuniary externality is generated because banks do not internalize the impact of issuing inside money on the asset prices in general equilibrium. Imposing a capital requirement can improve welfare by raising the prices of bank assets in both states.
Demographic Determinants of Savings: Estimating and Interpreting the Aggregate Association in Asia

Life cycle savings in one possible explanation for the recent increase in savings and economic growth in Asia. The association between age composition of a nation's population and its savings rate, observed within 16 Asian countries from 1952 to 1992, is reestimated here to be less than quarter the size reported in a study by Higgins and Williamson (1997) which assumed lagged savings is exogenous. Specification tests as well as common sense imply, moreover, that lagged savings is likely to be endogenous, and when estimated accordingly there remains no significant dependence of current savings on age composition, measured in several ways. Research should consider lifetime savings of a cohort as a substitute for children, and model the causes for the decline in fertility which changes the age composition and could thereby account for the common trends in demographic transition and savings in Asia.
Electron Emission From Disordered Tetrahedral Carbon

ElectronJield emission tests have been performed onJilms grown by a modiied microwave plasma assisted chemical vapor depm ition diumond process. This modificution includes the addition of N2 and 02 during the growth stage. Characterization of these films shows the presence of a disordered tetrahedral carbon structure. Raman spectroscopy indicates a disturbance in the cubic symmetry of the lattice andX-ray diffraction indicates a disordered tetrahedral structure. Field emission testing indicate that current densities of 0. 5mA/cm2 can be obtainedfor applied,fields of 5-8 V/pm. The results are explained in terms ofa change in the band structure and the formation oj electronic states in the band gap.
Detecting floodwater on roadways from image data with handcrafted features and deep transfer learning

Detecting roadway segments inundated due to floodwater has important applications for vehicle routing and traffic management decisions. This paper proposes a set of algorithms to automatically detect floodwater that may be present in an image captured by mobile phones or other types of optical cameras. For this purpose, image classification and flood area segmentation methods are developed. For the classification task, we used Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and pre-trained deep neural network (VGG-16) as feature extractors and trained logistic regression, k-nearest neighbors, and decision tree classifiers on the extracted features. Pre-trained VGG-16 network with logistic regression classifier outperformed all other methods. For the flood area segmentation task, we investigated superpixel based methods and Fully Convolutional Neural Network (FCN). Similar to the classification task, we trained logistic regression and k-nearest neighbors classifiers on the superpixel areas and compared that with an end-to-end trained FCN. Conditional Random Fields (CRF) method was applied after both segmentation methods to post-process coarse segmentation results. FCN offered the highest scores in all metrics; it was followed by superpixel-based logistic regression and then superpixel-based KNN.
Causal inference and American political development: the case of the gag rule

We investigate the “gag rule”, a parliamentary device that from 1836 to 1844 barred the US House of Representatives from receiving petitions concerning the abolition of slavery. In the mid-1830s, the gag rule emerged as a partisan strategy to keep slavery off the congressional agenda, amid growing abolitionist agitation in the North. Very quickly, however, the strategy backfired, as the gag rule was framed successfully as a mechanism that encroached on white northerners’ rights of petition. By 1844, popular pressure had become so great that many northern Democrats, an important bloc of prior gag rule supporters, yielded to electoral pressure, broke party ranks, and voted to rescind the rule, thereby sealing its fate. More generally, the politics of the gag rule provide an interesting causal-inference case study of the interplay between social movement development and congressional politics before the Civil War.
A rigorous exploration of anal HPV genotypes using a next-generation sequencing (NGS) approach in HIV-infected men who have sex with men at risk for developing anal cancer.

Background There are no HPV-based measures for managing anal cancer (AC) in HIV-infected (HIV+) men who have sex with men (MSM) because of the high positivity of high-risk (HR)-HPVs. As next-generation sequencing (NGS) is able to describe the composition of HPVs as percent (%) reads rather than positive vs negative results, we used NGS approach to detect HPVs in anal samples of HIV+ MSM to test its ability to differentiate those who are diagnosed with atypical squamous cells of unknown significance or greater (ASCUS+) from those who are free of such lesions and to understand the burden of HPV infections in relation to HPV vaccines. Methods Study included 81 HIV+ MSM characterized for demographics, patient-reported outcome measures, HIV related laboratory measures and anal cytology. We summarized NGS HPV data using % read cut points (>0%->30%) and tested the relationship between % reads of HR-HPVs and risk of ASCUS+ using logistic regression. Results Forty-six HPVs were detected at the >0% read cut point. The prevalence of any HR-HPVs varied from 100% to 40% with >0% to >30% reads while ≥99% were infected with HR-HPVs included or not included in the 9 valent HPV vaccine at the >0% read cut point. MSM with >30% HR-HPV reads were 4.5 times more likely to be diagnosed with ASCUS+ compared to ≤30% reads (P = .033). Conclusion NGS-based approach is more accurate than PCR-based HPV testing for identifying HIV+ MSM at risk for developing AC. We raise the concern regarding the efficacy of current HPV vaccines for preventing AC in this high-risk population.
Bayesian inference for finite populations under spatial process settings

We develop a Bayesian model–based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction from the small area estimation setting is that we analyze finite populations referenced by their geographic coordinates. Specifically, we consider a two‐stage sampling design in which the primary units are geographic regions, the secondary units are point‐referenced locations, and the measured values are assumed to be a partial realization of a spatial process. Estimation of finite population quantities from geostatistical models does not account for sampling designs, which can impair inferential performance, whereas design‐based estimates ignore the spatial dependence in the finite population. We demonstrate by using simulation experiments that process‐based finite population sampling models improve model fit and inference over models that fail to account for spatial correlation. Furthermore, the process‐based models offer richer inference with spatially interpolated maps over the entire region. We reinforce these improvements and demonstrate scalable inference for groundwater nitrate levels in the population of California Central Valley wells by offering estimates of mean nitrate levels and their spatially interpolated maps.
Automatic transversality in contact homology II: filtrations and computations

This paper is the sequel to the previous paper [Ne15], which showed that sufficient regularity exists to define cylindrical contact homology in dimension three for nondegenerate dynamically separated contact forms, a subclass of dynamically convex contact forms. The Reeb orbits of these so-called dynamically separated contact forms satisfy a uniform growth condition on their Conley-Zehnder indices with respect to a free homotopy class; see Definition 1.7. {Given a contact form which is dynamically separated up to large action, we demonstrate a filtration by action on the chain complex and show how to obtain the desired cylindrical contact homology by taking direct limits.} We give a direct proof of invariance of cylindrical contact homology within the class of dynamically separated contact forms, {and elucidate the independence of the filtered cylindrical contact homology with respect to the choice of the dynamically separated contact form and almost complex structure.} We also show that these regularity results are compatible with geometric methods of computing cylindrical contact homology of prequantization bundles, proving a conjecture of Eliashberg [El07] in dimension three.
Muscle structure governs joint function: linking natural variation in medial gastrocnemius structure with isokinetic plantar flexor function.

Despite the robust findings linking plantar flexor muscle structure to gross function within athletes, the elderly and patients following Achilles tendon ruptures, the link between natural variation in plantar flexor structure and function in healthy adults is unclear. In this study, we determined the relationship between medial gastrocnemius structure and peak torque and total work about the ankle during maximal effort contractions. We measured resting fascicle length and pennation angle using ultrasound in healthy adults (N=12). Subjects performed maximal effort isometric and isokinetic contractions on a dynamometer. We found that longer fascicles were positively correlated with higher peak torque and total work (R2>0.41, P 0.296, P<0.067). These correlations were not significant in isometric conditions. We further explored this relationship using a simple computational model to simulate isokinetic contractions. These simulations confirmed that longer fascicle lengths generate more joint torque and work throughout a greater range of motion. This study provides evidence that ankle function is strongly influenced by muscle structure in healthy adults.
Prognosis and Long-Term Outcome of Women With Idiopathic Recurrent Vulvovaginal Candidiasis Caused by Candida albicans.

Objectives This study evaluated use of long-term fluconazole beyond an initial 6-month course of weekly fluconazole in premenopausal patients with idiopathic recurrent vulvovaginal candidiasis (RVVC) due to Candida albicans. Materials and methods A retrospective chart review was performed of women seen in Wayne State University Vaginitis Clinic with culture-confirmed idiopathic RVVC due to Candida albicans during a 10-year period (January 2006 to December 2015). Only patients without risk factors for secondary VVC and who initiated a 6-month course of weekly fluconazole therapy were selected. Data included long-term use of fluconazole therapy, treatment efficacy, and development of fluconazole resistance. Questionnaires were mailed to evaluate patient's experience after fluconazole therapy. Results Of 883 patients with RVVC based on clinical records, 191 with culture positive idiopathic RVVC due to C. albicans were started on the maintenance fluconazole regimen, and 147 (77.0%) completed 6 months of therapy. Of these, 107 (72.8%) continued or received maintenance past 6 months. The most common reason for additional fluconazole therapy was culture-confirmed VVC recurrence (55.1%), unconfirmed but possible VVC recurrence (16.8%), and patient preference (10.3%). The mean duration of fluconazole maintenance was 35.7 (range = 7-288) months. Fluconazole resistance emerged in 7.5% completing 6-month therapy. Upon questionnaire follow-up, 93.6% of 51 respondents reported benefit during maintenance regimen; however, 80.9% described relapse after discontinuing weekly therapy. Conclusions Fluconazole suppression therapy was highly effective in preventing VVC symptoms but was rarely curative and VVC relapse occurred frequently after discontinuation of maintenance therapy. The development of drug resistance in C. albicans isolates after long-term fluconazole maintenance therapy although uncommon is a previously unrecognized complication.
Acceleration of the solar wind.

In this review, we discuss critically recent research on the acceleration of the solar wind, giving emphasis to high-speed solar wind streams emanating from solar coronal holes. We first explain why thermally driven wind models constrained by solar and interplanetary observations encounter substantial difficulties in explaining high speed streams. Then, through a general discussion of energy addition to the solar wind above the coronal base, we indicate a possible resolution of these difficulties. Finally, we consider the question of what role MHD waves might play in transporting energy through the solar atmosphere and depositing it in the solar wind, and we conclude by examining, in a simple way, the specific mechanism of solar wind acceleration by Alfven waves and the related problem of accelerating massive stellar winds with Alfven waves.
An accelerated linear method for optimizing non-linear wavefunctions in variational Monte Carlo

Although the linear method is one of the most robust algorithms for optimizing nonlinearly parametrized wavefunctions in variational Monte Carlo, it suffers from a memory bottleneck due to the fact that at each optimization step, a generalized eigenvalue problem is solved in which the Hamiltonian and overlap matrices are stored in memory. Here, we demonstrate that by applying the Jacobi-Davidson algorithm, one can solve the generalized eigenvalue problem iteratively without having to build and store the matrices in question. The resulting direct linear method greatly lowers the cost and improves the scaling of the algorithm with respect to the number of parameters. To further improve the efficiency of optimization for wavefunctions with a large number of parameters, we use the first order method AMSGrad far from the minimum as it is very inexpensive and only switch to the direct linear method near the end of the optimization where methods such as AMSGrad have long convergence tails. We apply this improved optimizer to wavefunctions with real and orbital space Jastrow factors applied to a symmetry-projected generalized Hartree-Fock reference. Systems addressed include atomic systems such as beryllium and neon, molecular systems such as the carbon dimer and iron(ii) porphyrin, and model systems such as the Hubbard model and hydrogen chains.
Random matrices with prescribed eigenvalues and expectation values for random quantum states

Given a collection $\{\lambda_1, \dots, \lambda_n\} $ of real numbers, there is a canonical probability distribution on the set of real symmetric or complex Hermitian matrices with eigenvalues $\lambda_1,\ldots,\lambda_n$. In this paper, we study various features of random matrices with this distribution. Our main results show that under mild conditions, when $n$ is large, linear functionals of the entries of such random matrices have approximately Gaussian joint distributions. The results take the form of upper bounds on distances between multivariate distributions, which allows us also to consider the case when the number of linear functionals grows with $n$. In the context of quantum mechanics, these results can be viewed as describing the joint probability distribution of the expectation values of a family of observables on a quantum system in a random mixed state. Other applications are given to spectral distributions of submatrices, the classical invariant ensembles, and to a probabilistic counterpart of the Schur--Horn theorem, relating eigenvalues and diagonal entries of Hermitian matrices.
An explicit CM type norm formula and effective nonvanishing of class group L-functions for CM fields

We show that the central value of class group L-functions of general CM fields can be expressed in terms of derivatives of real-analytic Hilbert Eisenstein series at CM points. Using this in conjunction with an explicit CM type norm formula established in Section 3, following an idea of Iwaniec and Kowalski (2004), we obtain a conditional explicit lower bound for class numbers of CM fields under the assumption ζ_K(1/2)≪F log D_(K∕F) (note that GRH implies ζ_K(1/2) ≤ 0). Some results in the proof lead to an effective nonvanishing result for class group L-functions of general CM fields, generalizing the only known ineffective results. Moreover, combining the CM type norm formula with Barquero-Sanchez and Masri’s work (2016), we shall deduce an explicit Chowla–Selberg formula for all abelian CM fields.
PopSyCLE: A New Population Synthesis Code for Compact Object Microlensing Events

We present a new Milky Way microlensing simulation code, dubbed PopSyCLE (Population Synthesis for Compact object Lensing Events). PopSyCLE is the first resolved microlensing simulation to include a compact object distribution derived from numerical supernovae explosion models and both astrometric and photometric microlensing effects. We demonstrate the capabilities of PopSyCLE by investigating the optimal way to find black holes (BHs) with microlensing. Candidate BHs have typically been selected from wide-field photometric microlensing surveys, such as OGLE, by selecting events with long Einstein crossing times ($t_E>120$ days). These events can be selected at closest approach and monitored astrometrically in order to constrain the mass of each lens; PopSyCLE predicts a BH detection rate of ~40% for such a program. We find that the detection rate can be enhanced to ~85% by selecting events with both $t_E>120$ days and a microlensing parallax of $\pi_E<0.08$. Unfortunately, such a selection criterion cannot be applied during the event as $\pi_E$ requires both pre- and post-peak photometry. However, historical microlensing events from photometric surveys can be revisited using this new selection criteria in order to statistically constrain the abundance of BHs in the Milky Way. The future WFIRST microlensing survey provides both precise photometry and astrometry and will yield individual masses of $\mathcal{O}(100-1000) black holes, which is at least an order of magnitude more than is possible with individual candidate follow-up with current facilities. The resulting sample of BH masses from WFIRST will begin to constrain the shape of the black hole present-day mass function, BH multiplicity, and BH kick velocity distributions.
Glioblastoma in the Cerebellopontine Angle in a Patient with Neurofibromatosis Type I: A Case Report and Review of the Literature

Introduction Glioblastoma multiforme (GBM) is the most common primary brain malignancy in adults and is typically in the supratentorial cerebral hemispheres. It has been reported to occur in the posterior fossa at the cerebellopontine angle (CPA), but the incidence is extremely rare. Case Report We report a case of a patient with a history of neurofibromatosis type I (NFI) diagnosed with a GBM arising in the CPA after presenting with facial numbness and pain. Patients with NFI are known to have an increased risk of developing both benign and malignant tumors, including a propensity for brainstem gliomas. However, there is no known association between NFI and tumors of the CPA. We believe this is the first reported case of a GBM of the CPA in a patient with NFI. Conclusion Although rare, GBM should be included in the differential diagnosis of a patient with a CPA tumor, especially in patients with increased risk of malignant pathology.
Characterization of a reproducible model of fracture healing in mice using an open femoral osteotomy.

Abstract Purpose The classic fracture model, described by Bonnarens and Einhorn in 1984, enlists a blunt guillotine to generate a closed fracture in a pre-stabilized rodent femur. However, in less experienced hands, this technique yields considerable variability in fracture pattern and requires highly-specialized equipment. This study describes a reproducible and low-cost model of mouse fracture healing using an open femoral osteotomy. Methods Femur fractures were produced in skeletally mature male and female mice using an open femoral osteotomy after intramedullary stabilization. Mice were recovered for up to 28 days prior to analysis with microradiographs, histomorphometry, a novel μCT methodology, and biomechanical torsion testing at weekly intervals. Results Eight mice were excluded due to complications (8/193, 4.1%), including unacceptable fracture pattern (2/193, 1.0%). Microradiographs showed progression of the fracture site to mineralized callus by 14 days and remodelling 28 days after surgery. Histomorphometry from 14 to 28 days revealed decreased cartilage area and maintained bone area. μCT analysis demonstrated a reduction in mineral surface from 14 to 28 days, stable mineral volume, decreased strut number, and increased strut thickness. Torsion testing at 21 days showed that fractured femurs had 61% of the ultimate torque, 63% of the stiffness, and similar twist to failure when compared to unfractured contralateral femurs. Conclusions The fracture model described herein, an open femoral osteotomy, demonstrated healing comparable to that reported using closed techniques. This simple model could be used in future research with improved reliability and reduced costs compared to the current options.
Maternal risk factors associated with inadequate testing and loss to follow-up in infants with perinatal hepatitis C virus exposure.

Objectives Inadequate testing (IT) and follow-up in infants with perinatal hepatitis C virus (HCV) exposure are challenging. We sought to identify maternal clinical and demographic risk factors that are associated with inadequate testing (IT) and follow-up of perinatally HCV-exposed infants. Methods In a retrospective cohort study spanning a period of 23 years, medical records of HCV-infected women and their perinatally exposed infants were reviewed for maternal characteristics that could be associated with their infants' IT and loss to follow-up. Results A total of 27% (108/407) of HCV-exposed infants were adequately tested (AT) for HCV perinatal transmission. Among AT infants, HCV transmission rate was 11% (12/108). History of maternal intravenous drug use (IVDU) was significantly higher in IT vs. AT infants [88% (193/218) vs. 76% (70/92); p = 0.005]. The percentage of mothers on methadone maintenance treatment during pregnancy was higher in AT vs. IT infants [53% (35/66) vs. 34% (65/186); p = 0.010]. The percentage of mothers with HCV medical care was higher among AT than IT infants [54% (56/102) vs. 41% (106/255); p = 0.022]. Conclusions Infants born to HCV-infected mothers have suboptimal testing, possibly leading to an underestimation of the rate of HCV vertical transmission. Infants of mothers receiving HCV medical care and methadone treatment have improved testing. Infants of HCV-positive mothers with history of IVDU have lower rates of testing. Screening HCV-infected pregnant women for history of IVDU and linking them to drug treatment programs, and HCV medical care may improve testing and follow-up in their infants.
A note on the rationing of divisible and indivisible goods in a general network.

The study of matching theory has gained importance recently with applications in Kidney Exchange, House Allocation, School Choice etc. The general theme of these problems is to allocate goods in a fair manner amongst participating agents. The agents generally have a unit supply/demand of a good that they want to exchange with other agents. On the other hand, Bochet et al. study a more general version of the problem where they allow for agents to have arbitrary number of divisible goods to be rationed to other agents in the network. In this current work, our main focus is on non-bipartite networks where agents have arbitrary units of a homogeneous indivisible good that they want to exchange with their neighbors. Our aim is to develop mechanisms that would identify a fair and strategyproof allocation for the agents in the network. Thus, we generalize the kidney exchange problem to that of a network with arbitrary capacity of available goods. Our main idea is that this problem and a couple of other related versions of non-bipartite fair allocation problem can be suitably transformed to one of fair allocations on bipartite networks for which we know of well studied fair allocation mechanisms.
Simple post-translational circadian clock models from selective sequestration

It is possible that there are post-translational circadian oscillators that continue functioning in the absence of negative feedback transcriptional repression in many cell types from diverse organisms. Apart from the KaiABC system from cyanobacteria, the molecular components and interactions required to create in-vitro ("test-tube") circadian oscillations in different cell types are currently unknown. Inspired by the KaiABC system, I provide "proof-of-principle" mathematical models that a protein with 2 (or more) modification sites which selectively sequesters an effector/cofactor molecule can function as a circadian time-keeper. The 2-site mechanism can be implemented using two relatively simple coupled non-linear ODEs in terms of site occupancy; the models do not require overly special fine-tuning of parameters for generating stable limit cycle oscillations.
An “On The Fly” Framework for Efficiently Generating Synthetic Big Data Sets

Collecting, analyzing and gaining insight from large volumes of data is now the norm in an ever increasing number of industries. Data analytics techniques, such as machine learning, are powerful tools used to analyze these large volumes of data. Synthetic data sets are routinely relied upon to train and develop such data analytics methods for several reasons: to generate larger data sets than are available, to generate diverse data sets, to preserve anonymity in data sets with sensitive information, etc. Processing, transmitting and storing data is a key issue faced when handling large data sets. This paper presents an “On the fly” framework for generating big synthetic data sets, suitable for these data analytics methods, that is both computationally efficient and applicable to a diverse set of problems. An example application of the proposed framework is presented along with a mathematical analysis of its computational efficiency, demonstrating its effectiveness. Empirical results indicate that the proposed data generation framework provides a reduction in computational time of $\approx$33% when compared to the alternative approach of generating the data set in full.
Snow-powered research on utility-scale wind turbine flows

This paper provides a review of the general experimental methodology of snow-powered flow visualization and super-large-scale particle image velocimetry (SLPIV), the corresponding field deployments and major scientific findings from our work on a 2.5 MW utility-scale wind turbine at the Eolos field station. The field measurements were conducted to investigate the incoming flow in the induction zone and the near-wake flows from different perspectives. It has been shown that these snow-powered measurements can provide sufficient spatiotemporal resolution and fields of view to characterize both qualitatively and quantitatively the incoming flow, all the major coherent structures generated by the turbine (e.g., blade, nacelle and tower vortices, etc.) as well as the development and interaction of these structures in the near wake. Our work has further revealed several interesting behaviors of near-wake flows (e.g., wake contraction, dynamic wake modulation, meandering and deflection of the nacelle wake, etc.), and their connections with constantly-changing inflows and turbine operation, which are uniquely associated with utility-scale turbines. These findings have demonstrated that the near wake flows, though highly complex, can be predicted with substantial statistical confidence using supervisory control and data acquisition (SCADA) and structural response information readily available from current utility-scale turbines. Such knowledge can be potentially incorporated into wake development models and turbine controllers for wind farm optimization in the future.
Genome elimination mediated by gene expression from a selfish chromosome

Numerous plants and animals harbor selfish B chromosomes that "drive" or transmit themselves at super-Mendelian frequencies, despite long-term fitness costs to the organism. Currently, it is unknown how B chromosome drive is mediated, and whether B-gene expression plays a role. We used modern sequencing technologies to analyze the fine-scale sequence composition and expression of paternal sex ratio (PSR), a B chromosome in the jewel wasp Nasonia vitripennis. PSR causes female-to-male conversion by destroying the sperm's hereditary material in young embryos to drive. Using RNA interference, we demonstrate that testis-specific expression of a PSR-linked gene, named haploidizer, facilitates this genome elimination-and-sex conversion effect. haploidizer encodes a putative protein with a DNA binding domain, suggesting a functional link with the sperm-derived chromatin.
Coagulation of Grains in Static and Collapsing Protostellar Clouds

We simulate collisional evolution of grains in dense turbulent molecular cloud cores (or Bok globules) in static equilibrium and free-fall collapse, assuming spherical symmetry. Relative velocities are due to thermal motions, differential settling, and turbulence, with the latter dominant for sonic turbulence with an assumed Kolmogorov spectrum. Realistic criteria are used to determine outcomes of collisions (coagulation vs. destruction) as functions of particle size and velocity. Results are presented for a variety of cloud parameters (radial density profile, turbulent velocity) and particle properties (density, impact strength). Results are sensitive to the assumed mechanical properties (density and impact strength) of grain aggregates. Particle growth is enhanced if aggregates have low density or fractal structures. On a timescale of a few Myr, an initial population of 0.1 micrometers grains may produce dense compact particles approximately 1 micrometer in size, or fluffy aggregates approximately 100 micrometers. For impact strengths less than or equal to 10(exp 6) ergs/g, a steady state is reached between coagulation of small grains and collisional disruption of larger aggregates. Formation of macroscopic aggregates requires high mechanical strengths and low aggregate densities. We assume sonic turbulence during collapse, with varied eddy size scales determining the dissipation rate or turbulence strength. The degree of collisional evolution during collapse is sensitive to the assumed small-scale structure (inner sc ale) of the turbulence. Weak turbulence results in few collisions and preserves the precollapse particle size distribution with little change. Strong turbulence tends to produce net destruction, rather than particle growth, during infall, unless inpact strengths are greater than 10(exp 6)ergs/g.
Climate Change, Insurability of Large-scale Disasters and the Emerging Liability Challenge

This Article focuses on the interaction between uncertainty and insurability in the context of some of the risks associated with climate change. It discusses the evolution of insured losses due to weather-related disasters over the past decade and the key drivers of the sharp increases in both economic and insured catastrophe losses over the past twenty years. In particular, the authors examine the impact of development in hazard-prone areas and of global warming on the potential for catastrophic losses in the future. In this context, the authors discuss the implications for insurance risk capital and the capacity of the insurance industry to handle large-scale events. A key question that needs to be addressed is which factors determine the insurability of a risk and the extent of coverage offered by the private sector to provide protection against extreme events when there is significant uncertainty surrounding the probability and consequences of a catastrophic loss. The authors further discuss the concepts of insurability by focusing on coverage for natural hazards, such as earthquakes, hurricanes, and floods. The Article also focuses on the liability issues associated with global climate change and possible implications for insurers, including issuers of Directors’ and Officers’ policies, given the difficulty in identifying potential defendants, tracing harm to their actions, and apportioning damages among them. The Article concludes by suggesting ways that insurers can help mitigate future damages from global climate change by providing premium reductions and rate credits to companies investing in risk-reducing measures.
Chapter 3 – Conservation Laws

In this chapter, we develop the conservation laws that govern many fluid flows. First, we discuss the variation in hydrostatic pressure within a static fluid, which allows us to develop relationships for the forces that act on fluid elements. Buoyancy is addressed because many implantable cardiovascular devices will experience buoyancy forces when in use. The conservation of mass, the conservation of momentum (with and without acceleration), the conservation of energy, and the Navier–Stokes equations are derived in this chapter. The Bernoulli principle is discussed as a means to obtain some meaningful data regarding the pressure distribution, the height difference, and the velocity within a fluid, if certain assumptions are met and are appropriate. These laws are fundamental to biofluid mechanics principles and will be referred to throughout the remaining chapters in the textbook.
The growth of relative wealth and the Kelly criterion

We propose an evolutionary framework for optimal portfolio growth theory in which investors subject to environmental pressures allocate their wealth between two assets. By considering both absolute wealth and relative wealth between investors, we show that different investor behaviors survive in different environments. When investors maximize their relative wealth, the Kelly criterion is optimal only under certain conditions, which are identified. The initial relative wealth plays a critical role in determining the deviation of optimal behavior from the Kelly criterion regardless of whether the investor is myopic across a single time period or maximizing wealth over an infinite horizon. We relate these results to population genetics, and discuss testable consequences of these findings using experimental evolution.
Statistical Consistency of Set-Membership Estimator for Linear Systems

Suppose we can choose from a set of linear autonomous systems with bounded process noise, the dynamics of each system are unknown, and we would like to design a stabilizing policy. The underlying question is how to estimate the dynamics of each system given that measurements of each system will be nonsequential. Though seemingly straightforward, existing proof techniques for proving statistical consistency of system identification procedures fail when measurements are nonsequential. Here, we prove that the set-membership estimator is statistically consistent even when measurements are nonsequential. We numerically illustrate its strong consistency.
Was There Really a Hawthorne Effect at the Hawthorne Plant? An Analysis of the Original Illumination Experiments †

The "Hawthorne effect" draws its name from a landmark set of studies conducted at the Hawthorne plant in the 1920s. The data from the first and most influential of these studies, the "Illumination Experiment/' were never formally analyzed and were thought to have been destroyed. Our research has uncovered these data. Existing descriptions of supposedly remarkable data patterns prove to be entirely fictional. We do find more subtle manifestations of possible Hawthorne effects. We also propose a new means of test ing for Hawthorne effects based on excess responsiveness to experi menter-induced variations relative to naturally occurring variation. (JEL C90, J24, J28, M12, M54, N32)
Spatially heterogeneous dynamics of cells in a growing tumor spheroid: comparison between theory and experiments.

Collective cell movement, characterized by multiple cells that are in contact for substantial periods of time and undergo correlated motion, plays a central role in cancer and embryogenesis. Recent imaging experiments have provided time-dependent traces of individual cells, thus providing an unprecedented picture of tumor spheroid growth. By using simulations of a minimal cell model, we analyze the experimental data that map the movement of cells in a fibrosarcoma tumor spheroid embedded in a collagen matrix. Both simulations and experiments show that cells in the core of the spheroid exhibit subdiffusive glassy dynamics (mean square displacement, Δ(t) ≈ tα with α 1. The motion of most of the cells near the periphery is highly persistent and correlated directional motion due to cell doubling and apoptosis rates, thus explaining the observed superdiffusive behavior. The α values for cells in the core and periphery, extracted from simulations and experiments, are in near quantitative agreement with each other, which is surprising given that no parameter in the model was used to fit the measurements. The qualitatively different dynamics of cells in the core and periphery is captured by the fourth order susceptibility, introduced to characterize metastable states in glass forming systems. Analyses of the velocity autocorrelation of individual cells show remarkable spatial heterogeneity with no two cells exhibiting similar behavior. The prediction that α should depend on the location of the cells in the tumor is amenable to experimental testing. The highly heterogeneous dynamics of cells in the tumor spheroid provides a plausible mechanism for the origin of intratumor heterogeneity.
Exploring software reusability metrics with Q&A forum data

Abstract Question and answer (QA it can be used to explore the relationship between software reusability metrics and difficulties encountered by users, as well as predict the number of difficulties users will face in the future. Q&A forum data can help improve understanding of software reuse, and may be harnessed as an additional resource to evaluate software reusability metrics.
The gut microbiome’s role in the development, maintenance, and outcomes of sepsis

The gut microbiome regulates a number of homeostatic mechanisms in the healthy host including immune function and gut barrier protection. Loss of normal gut microbial structure and function has been associated with diseases as diverse as Clostridioides difficile infection, asthma, and epilepsy. Recent evidence has also demonstrated a link between the gut microbiome and sepsis. In this review, we focus on three key areas of the interaction between the gut microbiome and sepsis. First, prior to sepsis onset, gut microbiome alteration increases sepsis susceptibility through several mechanisms, including (a) allowing for expansion of pathogenic intestinal bacteria, (b) priming the immune system for a robust pro-inflammatory response, and (c) decreasing production of beneficial microbial products such as short-chain fatty acids. Second, once sepsis is established, gut microbiome disruption worsens and increases susceptibility to end-organ dysfunction. Third, there is limited evidence that microbiome-based therapeutics, including probiotics and selective digestive decontamination, may decrease sepsis risk and improve sepsis outcomes in select patient populations, but concerns about safety have limited uptake. Case reports of a different microbiome-based therapy, fecal microbiota transplantation, have shown correlation with gut microbial structure restoration and decreased inflammatory response, but these results require further validation. While much of the evidence linking the gut microbiome and sepsis has been established in pre-clinical studies, clinical evidence is lacking in many areas. To address this, we outline a potential research agenda for further investigating the interaction between the gut microbiome and sepsis.
Holographic Approaches to DIS on a Nucleus

We consider deep inelastic scattering (DIS) on a dense nucleus described as an extremal RN-AdS black hole with holographic quantum fermions in the bulk. We find that the R-ratio (the ratio of the structure function of the black hole to proton) exhibit shadowing for x 0.8 in a qualitative agreement with the experimental observation of the ratio for DIS on nucleus for all range of x. We also take the dilute limit of the black hole and show that its R-ratio exhibits EMC-like effect for 0.2 0.8, and no shadowing is observed in the dilute limit for both bottom-up (using Thomas-Fermi approximation for the nucleon distribution inside the dilute nucleus), and top-down (considering the dilute nucleus to be a Fermi gas in AdS) approaches.
Political Ideology and the Law Review Selection Process

We investigate the role that political ideology plays in the selection process for articles in law reviews. To do so, we match data on the political ideology of student editors from 15 top law reviews from 1990 to 2005 to data on the political ideology of the authors of accepted articles. We find that law reviews with a higher share of conservative editors accept a higher share of articles written by conservative authors. We then investigate potential explanations for this pattern. One possibility is that editors have a preference for publishing articles written by authors that share their ideology. Another possibility is that editors are objectively better at assessing the contribution of articles written by authors that share their ideology. We find evidence that the latter explanation drives the relationship between editor and author ideology.
Automatic Detection of Generated Text is Easiest when Humans are Fooled

Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their decisions. Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies---top-_k_, nucleus sampling, and untruncated random sampling---and show that improvements in decoding methods have primarily optimized for fooling humans. This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems. We also show that though both human and automatic detector performance improve with longer excerpt length, even multi-sentence excerpts can fool expert human raters over 30% of the time. Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems.