cssi/SciSciGPT-SciSciNet huggingface dataset:
Detection of a small shift in a broad distribution

Abstract Statistical methods for the extraction of a small shift in broad data distributions are examined by means of Monte Carlo simulations. This work was originally motivated by the CERN neutrino beam to Gran Sasso (CNGS) experiment for which the OPERA detector collaboration reported a time shift in a broad distribution with an accuracy of ±7.8 ns, while the fluctuation of the average time turns with ±23.8 ns out to be much larger. Although the physical result of a big shift has been withdrawn, statistical methods that make an identification in a broad distribution with such a small error possible remain of interest.
Approximately Maximizing the Broker's Profit in a Two-sided Market

We study how to maximize the broker's (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers. Each seller has a single item to sell and holds a private value on her item, and each buyer has a valuation function over the bundles of the sellers' items. We consider the Bayesian setting where the agents' values are independently drawn from prior distributions, and aim at designing dominant-strategy incentive-compatible (DSIC) mechanisms that are approximately optimal. Production-cost markets, where each item has a publicly-known cost to be produced, provide a platform for us to study two-sided markets. Briefly, we show how to covert a mechanism for production-cost markets into a mechanism for the broker, whenever the former satisfies cost-monotonicity. This reduction holds even when buyers have general combinatorial valuation functions. When the buyers' valuations are additive, we generalize an existing mechanism to production-cost markets in an approximation-preserving way. We then show that the resulting mechanism is cost-monotone and thus can be converted into an 8-approximation mechanism for two-sided markets.
A Risk Assessment Framework for Airport Development Projects

This paper develops a risk assessment framework for airport development projects. It discusses the major types of inherent development risk, including political risk, environmental risk, financial ...
Is it time to change the reference genome

The use of the human reference genome has shaped methods and data across modern genomics. This has offered many benefits while creating a few constraints. In the following opinion, we outline the history, properties, and pitfalls of the current human reference genome. In a few illustrative analyses, we focus on its use for variant-calling, highlighting its nearness to a ‘type specimen’. We suggest that switching to a consensus reference would offer important advantages over the continued use of the current reference with few disadvantages.
Prospects for transcranial temporal interference stimulation in humans: A computational study.

Abstract Transcranial alternating current stimulation (tACS) is a noninvasive method used to modulate activity of superficial brain regions. Deeper and more steerable stimulation could potentially be achieved using transcranial temporal interference stimulation (tTIS): two high-frequency alternating fields interact to produce a wave with an envelope frequency in the range thought to modulate neural activity. Promising initial results have been reported for experiments with mice. In this study we aim to better understand the electric fields produced with tTIS and examine its prospects in humans through simulations with murine and human head models. A murine head finite element model was used to simulate previously published experiments of tTIS in mice. With a total current of 0.776 mA, tTIS electric field strengths up to 383 V/m were reached in the modeled mouse brain, affirming experimental results indicating that suprathreshold stimulation is possible in mice. Using a detailed anisotropic human head model, tTIS was simulated with systematically varied electrode configurations and input currents to investigate how these parameters influence the electric fields. An exhaustive search with 88 electrode locations covering the entire head (146M current patterns) was employed to optimize tTIS for target field strength and focality. In all analyses, we investigated maximal effects and effects along the predominant orientation of local neurons. Our results showed that it was possible to steer the peak tTIS field by manipulating the relative strength of the two input fields. Deep brain areas received field strengths similar to conventional tACS, but with less stimulation in superficial areas. Maximum field strengths in the human model were much lower than in the murine model, too low to expect direct stimulation effects. While field strengths from tACS were slightly higher, our results suggest that tTIS is capable of producing more focal fields and allows for better steerability. Finally, we present optimal four-electrode current patterns to maximize tTIS in regions of the pallidum (0.37 V/m), hippocampus (0.24 V/m) and motor cortex (0.57 V/m).
Postoperative Atrial Fibrillation and Flutter in Liver Transplantation: An Important Predictor of Early and Late Morbidity and Mortality.

Postoperative atrial fibrillation/flutter (POAF) is the most common perioperative arrhythmia and may be particularly problematic after liver transplantation (LT). This study is a single-center retrospective analysis of POAF to determine its incidence following LT, to identify risk factors, to assess its impact on clinical outcomes, and to summarize management strategies. The records of all patients who underwent LT between 2010 and 2018 were reviewed. Extracted data included pre-LT demographics and cardiac evaluation, in-hospital post-LT cardiac events, early and late complications, and survival. Among 1011 patients, the incidence of post-LT POAF was 10%. Using binary logistic regression, pre-LT history of atrial fibrillation was the strongest predictor of POAF (odds ratio [OR], 6.72; 95% confidence interval [CI], 2.00-22.57; P < 0.001), followed by history of coronary artery disease (CAD; OR, 2.52; 95% CI, 1.10-5.81; P = 0.03). Cardiac stress testing abnormality and CAD on cardiac catheterization were also associated with higher risk. Median time to POAF onset after LT was 3 days with 72% of cases resolving within 48 hours. POAF patients had greater hospital length of stay, death during the LT admission, and 90-day and 1-year mortality. POAF was an independent risk factor for post-LT mortality (OR, 2.0; 95% CI, 1.3-3.0; P < 0.01). Amiodarone was administered to 73% of POAF patients with no evidence of increased serum alanine aminotransferase levels. POAF occurred in 10% of post-LT patients with early onset and rapid resolution in most affected patients. POAF patients, however, had significant morbidity and mortality, suggesting that POAF is an important marker for worse early and late post-LT outcomes.
Is Compassion a Good Career Move?: Nonprofit Earnings Differentials from Job Changes

We explore the nonprofit earnings penalty. To separate the influence of demand and supply, we leverage workers who change employers in administrative tax data. The average nonprofit worker earns 5.5 percent less than the average for-profit worker. Supply-side factors (worker selection) contribute 80 percent of the nonprofit differential. The remaining 20 percent is from demand (a nonprofit penalty). Within-worker nonprofit variation generates several insights about the influence of nonprofits on the labor market. Nonprofits compress the wage distribution and reduce inequality among earners. Nonprofit penalties are much more pronounced in classic charities than in "commercial" nonprofits, which sometimes exhibit nonprofit premia.
Sets of p-restriction and p-spectral synthesis

In this paper we investigate the restriction problem. More precisely, we give sufficient conditions for the failure of a set $E$ in $\mathbb{R}^n$ to have the $p$-restriction property. We also extend the concept of spectral synthesis to $L^p(\mathbb{R}^n)$ for sets of $p$-restriction when $p > 1$. We use our results to show that there are $p$-values for which the unit sphere is a set of $p$-spectral synthesis in $\mathbb{R}^n$ when $n \geq 3$.
The Real Effects of Financial Statement Recognition: Evidence from Corporate Credit Ratings

We examine whether the recognition versus disclosure of identical accounting information affects the credit rating process and ultimately corporate credit ratings. The primary input into corporate ...
Activity of a ubiquitin ligase adaptor is regulated by disordered insertions in its arrestin domain.

Alpha-arrestins (ARTs) mediate selective ubiquitin-mediated endocytosis. Here, we show that the ARTs in Saccharomyces cerevisiae contain multiple disordered loops inserted within its arrestin domai...
Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles

This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.
Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

Abstract The goal of this study is to show emerging applications of deep learning technology in cancer imaging. Deep learning technology is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Applications of deep learning technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase their survival. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that the sharp increase of the studies of deep learning technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep learning technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new technology, in accordance with Amara's law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new technology can also generate socioeconomic benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare sector among different regions.
HIV-1 Coreceptor Usage and Variable Loop Contact Impact V3 Loop Broadly Neutralizing Antibody Susceptibility.

ABSTRACT In clinical trials, HIV-1 broadly neutralizing antibodies (bnAbs) effectively lower plasma viremia and delay virus reemergence. The presence of less neutralization-susceptible strains prior to treatment decreases the efficacy of these antibody-based treatments, but neutralization sensitivity often cannot be predicted by sequence analysis alone. We found that phenotypically confirmed CXCR4-utilizing strains are less neutralization sensitive, especially to variable loop 3 (V3 loop)-directed bnAbs, than exclusively CCR5-utilizing strains in some, but not all, cases. Homology modeling suggested that the primary V3 loop bnAb epitope is equally accessible among CCR5- and CXCR4-using strains, although variants that exclusively use CXCR4 have V3 loop protrusions that interfere with CCR5 receptor interactions. Homology modeling also showed that among some, but not all, envelopes with decreased neutralization sensitivity, V1 loop orientation interfered with V3 loop-directed bnAb binding. Thus, there are likely different structural reasons for the coreceptor usage restriction and the different bnAb susceptibilities. Importantly, we show that individuals harboring envelopes with higher likelihood of using CXCR4 or greater predicted V1 loop interference have faster virus rebound and a lower maximum decrease in plasma viremia, respectively, after treatment with a V3 loop bnAb. Knowledge of receptor usage and homology models may be useful in developing future algorithms that predict treatment efficacy with V3 loop bnAbs. IMPORTANCE The efficacy of HIV-1 broadly neutralizing antibody (bnAb) therapies may be compromised by the preexistence of less susceptible variants. Sequence-based methods are needed to predict pretreatment variants’ neutralization sensitivities. HIV-1 strains that exclusively use the CXCR4 receptor rather than the CCR5 receptor are less neutralization susceptible, especially to variable loop 3 (V3 loop) bnAbs in some, but not all, instances. While the inability to utilize the CCR5 receptor maps to a predicted protrusion in the envelope V3 loop, this viral determinant does not directly influence V3 loop bnAb sensitivity. Homology modeling predicts that contact between the envelope V1 loop and the antibody impacts V3 loop bnAb susceptibility in some cases. Among pretreatment envelopes, increased probability of using CXCR4 and greater predicted V1 interference are associated with faster virus rebound and a smaller decrease in the plasma virus level, respectively, after V3 loop bnAb treatment. Receptor usage information and homology models may be useful for predicting V3 loop bnAb therapy efficacy.
Dynamic predictive accuracy of electrocardiographic biomarkers of sudden cardiac death within a survival framework: the Atherosclerosis Risk in Communities (ARIC) study.

The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63–1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37–2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526–0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303–0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526–0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515–0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668–0.753 for ECG recorded within 10 years before SCD). Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.
Strategic Tax Planning for State Tax Amnesties: Evidence from Eligibility Period Restrictions

Tax amnesty programs have exploded in popularity among cash-strapped states since the beginning of the Great Recession. Though many scholars have been interested in the long-term tax compliance eff...
A baseline Antarctic GIA correction for space gravimetry

Abstract Within the past decade, newly collected GPS data and geochronological constraints have resulted in refinement of glacial isostatic adjustment (GIA) models for Antarctica. These are critical to estimating and understanding ice mass changes at present-day. A correction needs to be made when using space gravity for ice mass balance assessments due to the fact that any vertical movements of the solid Earth masquerade as changes in ice mass, and these must be carefully removed. The main upshot of the new Antarctic GIA models is a downward revision of negative ice mass trends deduced from the Gravity Recovery and Climate Experiment (GRACE), resulting from a reduced GIA correction. This revision places GRACE inferred trend in mass balance within the one sigma uncertainty of mass balance deduced by altimetry. Because uncertainties in Holocene ice history and the low viscosity rheology beneath the West Antarctic Ice Sheet (WAIS) continue to vex further improvement in predictions of present-day GIA gravity rate, more emphasis has been given to regional-scale GIA models. Here we use a Bayesian method to explore the gravimetric GIA trend over Antarctica, both with and without the impact of a late Pleistocene Antarctic ice loads, along with the contribution of oceanic loads. We call this model without loads associated with Antarctica a baseline for regional GIA models to build upon. We consider variations of the radial mantle viscosity profile and the volume of continental-scale ice sheets during the last glacial cycle. The modeled baseline GIA is mainly controlled by the lower mantle viscosity and continental levering caused by ocean loading. We find that the predicted baseline GIA correction weakly depends on the ice history. This correction averages to +28.4 [16.5–41.9, 95% confidence] Gt/yr, of which +23.6±5.3 are contributed by East Antarctica. In contrast, with late-Pleistocene Antarctic-proximal ice included, the total modeled mass trend due to GIA is +73.7 [30.1–114.7] Gt/yr. A baseline GIA correction of 28.4 Gt/yr is of order 50% of the mean net mass trend measured during the period 1992-2017. The statistical analysis provides tools for properly synthesizing any regional Antarctic GIA model with a self-consistent far-field component. This may prove important for accounting for both global and regional 3-D variations in mantle viscosity.
BAlGaN alloys nearly lattice-matched to AlN for efficient UV LEDs

The lattice mismatch between AlGaN and AlN substrates limits the design and efficiency of UV light-emitting diodes, but it can be mitigated by the coincorporation of boron. We employ hybrid density functional theory to investigate the thermodynamic, structural, and electronic properties of BAlGaN alloys. We show that BAlGaN can lattice match AlN with bandgaps that match AlGaN of the same gallium content. We predict that BAlGaN emits transverse-electric polarization for a gallium content of ∼45% or more. Our results indicate that BAlGaN alloys are promising materials for higher efficiency UV optoelectronic devices on bulk AlN substrates.
Microtubule binding kinetics of membrane-bound kinesin-1 predicts high motor copy numbers on intracellular cargo

Bidirectional vesicle transport along microtubules is necessary for cell viability and function, particularly in neurons. When multiple motors are attached to a vesicle, the distance a vesicle travels before dissociating is determined by the race between detachment of the bound motors and attachment of the unbound motors. Motor detachment rate constants (koff) can be measured via single-molecule experiments, but motor reattachment rate constants (kon) are generally unknown, as they involve diffusion through the bilayer, geometrical considerations of the motor tether length, and the intrinsic microtubule binding rate of the motor. To understand the attachment dynamics of motors bound to fluid lipid bilayers, we quantified the microtubule accumulation rate of fluorescently labeled kinesin-1 motors in a 2-dimensional (2D) system where motors were linked to a supported lipid bilayer. From the first-order accumulation rate at varying motor densities, we extrapolated a koff that matched single-molecule measurements and measured a 2D kon for membrane-bound kinesin-1 motors binding to the microtubule. This kon is consistent with kinesin-1 being able to reach roughly 20 tubulin subunits when attaching to a microtubule. By incorporating cholesterol to reduce membrane diffusivity, we demonstrate that this kon is not limited by the motor diffusion rate, but instead is determined by the intrinsic motor binding rate. For intracellular vesicle trafficking, this 2D kon predicts that long-range transport of 100-nm-diameter vesicles requires 35 kinesin-1 motors, suggesting that teamwork between different motor classes and motor clustering may play significant roles in long-range vesicle transport.
Retrieving Top Weighted Triangles in Graphs

Pattern counting in graphs is a fundamental primitive for many network analysis tasks, and there are several methods for scaling subgraph counting to large graphs. Many real-world networks have a notion of strength of connection between nodes, which is often modeled by a weighted graph, but existing scalable algorithms for pattern mining are designed for unweighted graphs. Here, we develop deterministic and random sampling algorithms that enable the fast discovery of the 3-cliques (triangles) of largest weight, as measured by the generalized mean of the triangle's edge weights. For example, one of our proposed algorithms can find the top-1000 weighted triangles of a weighted graph with billions of edges in thirty seconds on a commodity server, which is orders of magnitude faster than existing "fast" enumeration schemes. Our methods open the door towards scalable pattern mining in weighted graphs.
Oral Cancer: Genetics and the Role of Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the leading cancers in the world. OSCC patients are managed with surgery and/or chemoradiation. Prognoses and survival rates are dismal, however, and have not improved for more than 20 years. Recently, the concept of precision medicine was introduced, and the introduction of targeted therapeutics demonstrated promising outcomes. This article reviews the current understanding of initiation, progression, and metastasis of OSCC from both genetic and epigenetic perspectives. In addition, the applications and integration of omics technologies in biomarker discovery and drug development for treating OSCC are reviewed.
The politics of fear and relationship with the effective level of crime and socioeconomic issues: Empirical analysis of a case study

Abstract. The goal of this study is to analyze if a perceived risk of crime and social issues is supported by effective levels of empirical data. This study focuses on a vital case study, Italy that is one of the largest economies in Europe with socioeconomic issues also with a high flows of immigration from Africa and Middle East. Descriptive statistics and non-parametric analyses are applied on data from EUROSTAT and UNODC over a period from 2000 to 2019. Although levels of crime and immigration are declining over time, perceived risk of these social issues is high. In general, the perceived risk of crime seems to be overestimated. The only factor of concern in Italian economy for population is the increasing level of unemployment that can be a source of social issues. These results seem to be due to misleading information diffused by some political parties with media that induce people to overestimate perceived risk of crime and immigration: the politics of fear . The findings of this paper can provide a preliminary analysis and encourage the development of more in-depth studies to better understand the sources of fear of crime due to social issues and their possible interrelationships with social and political contexts in order to support appropriate policies in society. Keywords. Violent crime, Homicide, Sexual violence, Fear of crime, Perceived risk, Vulnerability, Immigration, Poverty, Europe, Political party, Politics of fear. JEL. I30, D63, F60.
Lineage tracing on transcriptional landscapes links state to fate during differentiation

A challenge in biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Here, we used expressed DNA barcodes to clonally trace transcriptomes over time and applied this to study fate determination in hematopoiesis. We identified states of primed fate potential and located them on a continuous transcriptional landscape. We identified two routes of monocyte differentiation that leave an imprint on mature cells. Analysis of sister cells also revealed cells to have intrinsic fate biases not detectable by single-cell RNA sequencing. Finally, we benchmarked computational methods of dynamic inference from single-cell snapshots, showing that fate choice occurs earlier than is detected by state-of the-art algorithms and that cells progress steadily through pseudotime with precise and consistent dynamics.
One Visit-one Brace: Patient and Parent Satisfaction After Treatment for Pediatric Distal Radius Buckle Fractures*

Purpose: Multiple previously published studies have shown the efficacy of brace treatment for distal radius buckle fractures. There is also literature to support the fact that these inherently stable injuries do not require additional radiographic imaging. However, no study has looked at a treatment protocol combining both of these aspects. We aim to determine if the treatment of pediatric buckle fractures of the distal radius, with a removable brace and no further …
Copy-number variation contributes 9% of pathogenicity in the inherited retinal degenerations.

Current sequencing strategies can genetically solve 55–60% of inherited retinal degeneration (IRD) cases, despite recent progress in sequencing. This can partially be attributed to elusive pathogenic variants (PVs) in known IRD genes, including copy-number variations (CNVs), which have been shown as major contributors to unsolved IRD cases. Five hundred IRD patients were analyzed with targeted next-generation sequencing (NGS). The NGS data were used to detect CNVs with ExomeDepth and gCNV and the results were compared with CNV detection with a single-nucleotide polymorphism (SNP) array. Likely causal CNV predictions were validated by quantitative polymerase chain reaction (qPCR). Likely disease-causing single-nucleotide variants (SNVs) and small indels were found in 55.6% of subjects. PVs in USH2A (11.6%), RPGR (4%), and EYS (4%) were the most common. Likely causal CNVs were found in an additional 8.8% of patients. Of the three CNV detection methods, gCNV showed the highest accuracy. Approximately 30% of unsolved subjects had a single likely PV in a recessive IRD gene. CNV detection using NGS-based algorithms is a reliable method that greatly increases the genetic diagnostic rate of IRDs. Experimentally validating CNVs helps estimate the rate at which IRDs might be solved by a CNV plus a more elusive variant.
Evaluating the Performance of the WHO International Reference Standard for Osteoporosis Diagnosis in Postmenopausal Women of Varied Polygenic Score and Race

Background: Whether the bone mineral density (BMD) T-score performs differently in osteoporosis classification in women of different genetic profiling and race background remains unclear. Methods: The genomic data in the Women’s Health Initiative study was analyzed (n = 2417). The polygenic score (PGS) was calculated from 63 BMD-associated single nucleotide polymorphisms (SNPs) for each participant. The World Health Organization′s (WHO) definition of osteoporosis (BMD T-score ≤ −2.5) was used to estimate the cumulative incidence of fracture. Results: T-score classification significantly underestimated the risk of major osteoporotic fracture (MOF) in the WHI study. An enormous underestimation was observed in African American women (POR: 0.52, 95% CI: 0.30–0.83) and in women with low PGS (predicted/observed ratio [POR]: 0.43, 95% CI: 0.28–0.64). Compared to Caucasian women, African American, African Indian, and Hispanic women respectively had a 59%, 41%, and 55% lower hazard of MOF after the T-score was adjusted for. The results were similar when used for any fractures. Conclusions: Our study suggested the BMD T-score performance varies significantly by race in postmenopausal women.
Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion

This paper studies the effects of various types of online advertisements on purchase conversion by capturing the dynamic interactions among advertisement clicks themselves. It is motivated by the observation that certain advertisement clicks may not result in immediate purchases, but they stimulate subsequent clicks on other advertisements, which then lead to purchases. We develop a novel model based on mutually exciting point processes, which consider advertisement clicks and purchases as dependent random events in continuous time. We incorporate individual random effects to account for consumer heterogeneity and cast the model in the Bayesian hierarchical framework. We construct conversion probability to properly evaluate the conversion effects of online advertisements. We develop simulation algorithms for mutually exciting point processes to compute the conversion probability and for out-of-sample prediction. Model comparison results show the proposed model outperforms the benchmark models that ignore exciting effects among advertisement clicks. Using a proprietary data set, we find that display advertisements have relatively low direct effect on purchase conversion, but they are more likely to stimulate subsequent visits through other advertisement formats. We show that the commonly used measure of conversion rate is biased in favor of search advertisements and underestimates the conversion effect of display advertisements the most. Our model also furnishes a useful tool to predict future purchases and advertisement clicks for the purpose of targeted marketing and customer relationship management. This paper was accepted by Eric Bradlow, special issue on business analytics.
One-Stop Microfluidic Assembly of Human Brain Organoids To Model Prenatal Cannabis Exposure

Prenatal cannabis exposure (PCE) influences human brain development, but it is challenging to model PCE using animals and current cell culture techniques. Here, we developed a one-stop microfluidic platform to assemble and culture human cerebral organoids from human embryonic stem cells (hESC) to investigate the effect of PCE on early human brain development. By incorporating perfusable culture chambers, air-liquid interface, and one-stop protocol, this microfluidic platform can simplify the fabrication procedure and produce a large number of organoids (169 organoids per 3.5 cm × 3.5 cm device area) without fusion, as compared with conventional fabrication methods. These one-stop microfluidic assembled cerebral organoids not only recapitulate early human brain structure, biology, and electrophysiology but also have minimal size variation and hypoxia. Under on-chip exposure to the psychoactive cannabinoid, Δ-9-tetrahydrocannabinol (THC), cerebral organoids exhibited reduced neuronal maturation, downregulation of cannabinoid receptor type 1 (CB1) receptors, and impaired neurite outgrowth. Moreover, transient on-chip THC treatment also decreased spontaneous firing in these organoids. This one-stop microfluidic technique enables a simple, scalable, and repeatable organoid culture method that can be used not only for human brain organoids but also for many other human organoids including liver, kidney, retina, and tumor organoids. This technology could be widely used in modeling brain and other organ development, developmental disorders, developmental pharmacology and toxicology, and drug screening.
Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses.

Over the past 20 years, several coronaviruses have crossed the species barrier into humans, causing outbreaks of severe, and often fatal, respiratory illness. Since SARS-CoV was first identified in animal markets, global viromics projects have discovered thousands of coronavirus sequences in diverse animals and geographic regions. Unfortunately, there are few tools available to functionally test these viruses for their ability to infect humans, which has severely hampered efforts to predict the next zoonotic viral outbreak. Here, we developed an approach to rapidly screen lineage B betacoronaviruses, such as SARS-CoV and the recent SARS-CoV-2, for receptor usage and their ability to infect cell types from different species. We show that host protease processing during viral entry is a significant barrier for several lineage B viruses and that bypassing this barrier allows several lineage B viruses to enter human cells through an unknown receptor. We also demonstrate how different lineage B viruses can recombine to gain entry into human cells, and confirm that human ACE2 is the receptor for the recently emerging SARS-CoV-2. This study describes the development of an approach to rapidly screen lineage B betacoronaviruses, such as SARS-CoV and the recently emerged SARS-CoV-2, for receptor usage and their ability to infect cell types from different species. Using it, they confirm human ACE2 as the receptor for SARs-CoV-2 and show that host protease processing during viral entry is a significant barrier for viral entry.
Exploring Integrative Analysis Using the BioMedical Evidence Graph.

PURPOSEThe analysis of cancer biology data involves extremely heterogeneous data sets, including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenet...
Unusual dislocation behavior in high-entropy alloys

Abstract This Viewpoint article articulates ten unusual features associated with dislocations and their slip processes in high-entropy alloys (HEAs). Attention will be directed towards the composition undulation almost inevitable in these highly concentrated solutions, even when the alloy is a single-phase and nominally random solution. This inhomogeneity is often exacerbated by lattice distortion and local chemical order arising from the interaction among multiple principal elements. As a consequence, dislocation motion faces a rugged atomic and energy landscape, presenting new twists in defect energetics and properties, as well as cocktail strengthening as a new form of solid solution hardening beyond the conventional mechanism. Our systematic account from this standpoint may help answer the continual query in the community as to what is special about these HEAs that makes them different from traditional solid solutions.
Farvasi: A Response to Ecological Concerns

Farvasi is inspired by the most powerful spiritual symbol of Persian culture, Farvahar. The main purpose of this design was to bridge the contemporary world to cultural treasures, and importantly, to produce a zero waste and biodegradable dress. To address the goal, Nuno and 3D seamless felting techniques were utilized to reduce the energy consumption during the production using renewable fibers. Additionally, 2D and 3D CAD systems were applied to minimize the waste of paper and muslin in pattern making and fitting process to help with reducing environmental issues. The garment is intended to present a dramatic dress to the women who care about environmental issues in addition to the beauty aspect of clothes.
Temporal discounting correlates with directed exploration but not with random exploration

The explore-exploit dilemma describes the trade off that occurs any time we must choose between exploring unknown options and exploiting options we know well. Implicit in this trade off is how we value future rewards — exploiting is usually better in the short term, but in the longer term the benefits of exploration can be huge. Thus, in theory there should be a tight connection between how much people value future rewards, i.e. how much they discount future rewards relative to immediate rewards, and how likely they are to explore, with less ‘temporal discounting’ associated with more exploration. By measuring individual differences in temporal discounting and correlating them with explore-exploit behavior, we tested whether this theoretical prediction holds in practice. We used the 27-item Delay-Discounting Questionnaire to estimate temporal discounting and the Horizon Task to quantify two strategies of explore-exploit behavior: directed exploration, where information drives exploration by choice, and random exploration, where behavioral variability drives exploration by chance. We find a clear correlation between temporal discounting and directed exploration, with more temporal discounting leading to less directed exploration. Conversely, we find no relationship between temporal discounting and random exploration. Unexpectedly, we find that the relationship with directed exploration appears to be driven by a correlation between temporal discounting and uncertainty seeking at short time horizons, rather than information seeking at long horizons. Taken together our results suggest a nuanced relationship between temporal discounting and explore-exploit behavior that may be mediated by multiple factors.
Wiener analysis of nonlinear feedback in sensory systems.

Experimental studies of certain sensory systems (e.g., vertebrate retinal cells and auditory fibers) have yielded qualitative evidence of the presence of nonlinear feedback. However, no methods have been available to provide the tools for quantitative analysis of this nonlinear feedback mechanism and subsequent modeling of the overall dynamics of these sensory systems. Recent results offer the analytical means to relate Wiener kernel measurements with corresponding nonlinear feedback models and, thus, the ability to model the overall dynamics of such sensory systems. Furthermore, our analytical results offer an explanation for experimentally observed changes in the waveform of Wiener kernel estimates obtained for different white-noise input mean and/or power levels.
Quantitative sub-resolution blood velocity estimation using ultrasound localization microscopy ex-vivo and in-vivo

Super-resolution ultrasound imaging relies on the sub-wavelength localization of microbubble contrast agents. By tracking individual microbubbles, the velocity and flow within microvessels can be estimated. It has been shown that the average flow velocity, within a microvessel ranging from tens to hundreds of microns in diameter, can be measured. However, a 2D super-resolution image can only localize bubbles with sub-wavelength resolution in the imaging plane whereas the resolution in the elevation plane is limited by conventional beamwidth physics. Since ultrasound imaging integrates echoes over the elevation dimension, velocity estimates at a single location in the imaging plane include information throughout the imaging slice thickness. This slice thickness is typically a few orders or magnitude larger than the super-resolution limit. It is shown here that in order to estimate the velocity, a spatial integration over the elevation direction must be considered. This operation yields a multiplicative correction factor that compensates for the elevation integration. A correlation-based velocity estimation technique is then presented. Calibrated microtube phantom experiments are used to validate the proposed velocity estimation method and the proposed elevation integration correction factor. It is shown that velocity measurements are in excellent agreement with theoretical predictions within the considered range of flow rates (10 to 90 μl/min) in a microtube with a diameter of 200 μm. Then, the proposed technique is applied to two in-vivo mouse tail experiments imaged with a low frequency human clinical transducer (ATL L7-4) with human clinical concentrations of microbubbles. In the first experiment, a vein was visible with a diameter of 140 μm and a peak flow velocity of 0.8 mm s-1. In the second experiment, a vein was observed in the super-resolved image with a diameter of 120 μm and with maximum local velocity of ≈4.4 mm s-1. It is shown that the parabolic flow profiles within these micro-vessels are resolvable.
Angular momentum regulation may dictate the slip severity in young adults

Falls cause negative impacts on society and the economy. Slipping is a common initiating event for falling. Yet, individuals differ in their ability to recover from slips. Persons experiencing mild slips can accommodate the perturbation without falling, whereas severe slipping is associated with inadequate or slow pre- or post-slip control that make these individuals more prone to fall. Knowing the discrepancies between mild and severe slippers in kinematic and kinetic variables improves understanding of adverse control responsible for severe slipping. This study examined differences across these participants with respect to center of mass (COM) height, sagittal angular momentum (H), upper body kinematics, and the duration of single/double phase. Possible causality of such relationships was also studied by observing the time-lead of the deviations. Twenty healthy young adults performed walking trials in dry and slippery conditions. They were classified into mild and severe slippers based on their heel slipping speed. No inter-group differences were observed in the upper extremity kinematics. It was found that mild and severe slippers do not differ in the studied variables during normal gait; however, they do show significant differences through slipping. Compared to mild slippers, sever slippers lowered their COM height following a slip, presented higher H, and shortened their single support phase (p-value<0.05 for all). Based on the time-lead observed in H over all other variables suggests that failure to control angular momentum may influence slip severity.
Measuring justice in machine learning

How can we build more just machine learning systems? To answer this question, we need to know both what justice is and how to tell whether one system is more or less just than another. That is, we need both a definition and a measure of justice. Theories of distributive justice hold that justice can be measured (in part) in terms of the fair distribution of benefits and burdens across people in society. Recently, the field known as fair machine learning has turned to John Rawls's theory of distributive justice for inspiration and operationalization. However, philosophers known as capability theorists have long argued that Rawls's theory uses the wrong measure of justice, thereby encoding biases against people with disabilities. If these theorists are right, is it possible to operationalize Rawls's theory in machine learning systems without also encoding its biases? In this paper, I draw on examples from fair machine learning to suggest that the answer to this question is no: the capability theorists' arguments against Rawls's theory carry over into machine learning systems. But capability theorists don't only argue that Rawls's theory uses the wrong measure, they also offer an alternative measure. Which measure of justice is right? And has fair machine learning been using the wrong one?
Combinatorially refine a Zagier–Stanley result on products of permutations

Abstract In this paper, we enumerate the pairs of permutations that are long cycles and whose product has a given cycle-type. Our main result is a simple relation concerning the desired numbers for a few close cycle-types. The relation refines a formula for the number of pairs of long cycles whose product has k cycles independently obtained by Zagier and Stanley relying on group characters, and was previously obtained by Feray and Vassilieva by counting some coloured permutations first and then relying on some algebraic computations in the ring of symmetric functions. Our approach is based on the plane permutation framework (Chen and Reidys, 2016) which is totally combinatorial.
Relative scales of the GUT and twin sectors in an F-theory model

In this note we analyze the relative scales for the GUT and twin sectors in the F-theory model discussed in ref. [1]. There are a number of volume moduli in the model. The volume of the GUT surface in the visible sector (1) (with the Wilson line GUT breaking) defines the GUT scale MG∼ 2 × 1016GeV as the unification scale with precise gauge coupling unification of SU(3) × SU(2) × U(1)Y . We choose the GUT coupling constant, $$ {\alpha}_G^{-1} $$∼ 24. We are then free to choose the ratio αG(2)/αG(1) = m1/m2 with m1 and m2 independent volume moduli associated with the directions perpendicular to the two asymptotic GUT surfaces. We then analyze the effective field theory of the twin sector (2), which may lead to a SUSY breaking gaugino condensate. Of course, all these results are subject to the self-consistent stabilization of the moduli.
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation

Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity. In this study, we propose an adaptive boosting approach to learning locality sensitive hash codes, which represent audio spectra efficiently. We use the learned hash codes for single-channel speech denoising tasks as an alternative to a complex machine learning model, particularly to address the resource-constrained environments. Our adaptive boosting algorithm learns simple logistic regressors as the weak learners. Once trained, their binary classification results transform each spectrum of test noisy speech into a bit string. Simple bitwise operations calculate Hamming distance to find the $\mathcal{K}$-nearest matching frames in the dictionary of training noisy speech spectra, whose associated ideal binary masks are averaged to estimate the denoising mask for that test mixture. Our proposed learning algorithm differs from AdaBoost in the sense that the projections are trained to minimize the distances between the self-similarity matrix of the hash codes and that of the original spectra, rather than the misclassification rate. We evaluate our discriminative hash codes on the TIMIT corpus with various noise types, and show comparative performance to deep learning methods in terms of denoising performance and complexity.