cssi/SciSciGPT-SciSciNet huggingface dataset:
Private Set Intersection with Linear Communication from General Assumptions

This work presents a hashing-based algorithm for Private Set Intersection (PSI) in the honest-but-curious setting. The protocol is generic, modular and provides both asymptotic and concrete efficiency improvements over existing PSI protocols. If each player has m elements, our scheme requires only $O(m \secpar)$ communication between the parties, where $\secpar$ is a security parameter. Our protocol builds on the hashing-based PSI protocol of Pinkas et al. (USENIX 2014, USENIX 2015), but we replace one of the sub-protocols (handling the cuckoo "stash'') with a special-purpose PSI protocol that is optimized for comparing sets of unbalanced size. This brings the asymptotic communication complexity of the overall protocol down from $omega(m \secpar)$ to $O(m\secpar)$, and provides concrete performance improvements (10-15% reduction in communication costs) over Kolesnikov et al. (CCS 2016) under real-world parameter choices. Our protocol is simple, generic and benefits from the permutation-hashing optimizations of Pinkas et al. (USENIX 2015) and the Batched, Relaxed Oblivious Pseudo Random Functions of Kolesnikov et al. (CCS 2016).
A Rose by Any Other Name or an Insane Root? Adventures in Name Resolution.

Namespaces are fundamental to computing systems. Each namespace maps the names that clients use to retrieve resources to the actual resources themselves. However, the indirection that namespaces provide introduces avenues of attack through the name resolution process. Adversaries can trick programs into accessing unintended resources by changing the binding between names and resources and by using names whose target resources are ambiguous. In this paper, we explore whether a unified system approach may be found to prevent many name resolution attacks. For this, we examine attacks on various namespaces and use these to derive invariants to defend against these attacks. Four prior techniques are identified that enforce aspects of name resolution, so we explore how these techniques address the proposed invariants. We find that each of these techniques are incomplete in themselves, but a combination could provide effective enforcement of the invariants. We implement a prototype system that can implement these techniques for the Linux file system namespace, and show that invariant rules specific to each, individual program system call can be enforced with a small overhead (less than 3%), indicating that fine-grained name resolution enforcement may be practical.
Seeing Transparency More Clearly

In recent years, transparency has been proposed as the solution to, and the cause of, a remarkable range of public problems. The proliferation of seemingly contradictory claims about transparency becomes less puzzling, this essay argues, when one appreciates that transparency is not, in itself, a coherent normative ideal. Nor does it have a straightforward instrumental relationship to any primary goals of governance. To gain greater purchase on how transparency policies operate, scholars must move beyond abstract assumptions and drill down to the specific legal, institutional, historical, political, and cultural contexts in which these policies are crafted and implemented. The field of transparency studies, in other words, is due for a "sociological turn."
Using Implementation Fidelity to Aid in Interpreting Program Impacts: A Brief Review:

Poor program implementation constitutes one explanation for null results in trials of educational interventions. For this reason, researchers often collect data about implementation fidelity when c...
Auxin‐induced actin cytoskeleton rearrangements require AUX1

The actin cytoskeleton is required for cell expansion and implicated in cellular responses to the phytohormone auxin. However, the mechanisms that coordinate auxin signaling, cytoskeletal remodeling and cell expansion are poorly understood. Previous studies examined long-term actin cytoskeleton responses to auxin, but plants respond to auxin within minutes. Before this work, an extracellular auxin receptor - rather than the auxin transporter AUXIN RESISTANT 1 (AUX1) - was considered to precede auxin-induced cytoskeleton reorganization. In order to correlate actin array organization and dynamics with degree of cell expansion, quantitative imaging tools established baseline actin organization and illuminated individual filament behaviors in root epidermal cells under control conditions and after indole-3-acetic acid (IAA) application. We evaluated aux1 mutant actin organization responses to IAA and the membrane-permeable auxin 1-naphthylacetic acid (NAA). Cell length predicted actin organization and dynamics in control roots; short-term IAA treatments stimulated denser and more parallel, longitudinal arrays by inducing filament unbundling within minutes. Although AUX1 is necessary for full actin rearrangements in response to auxin, cytoplasmic auxin (i.e. NAA) stimulated a lesser response. Actin filaments became more 'organized' after IAA stopped elongation, refuting the hypothesis that 'more organized' actin arrays universally correlate with rapid growth. Short-term actin cytoskeleton response to auxin requires AUX1 and/or cytoplasmic auxin.
Decentralized Stochastic Control in Partially Nested Information Structures

Abstract In this paper, we analyze a network of agents in a partially nested information structure with a common ancestor. We present the prescription approach applied to different permutations of agents and a structural result for optimal prescriptions of control strategies. We demonstrate the proposed approach through an example that aims at establishing time-invariant domains of the prescriptions without assuming a Linear Quadratic Gaussian problem.
Purinergic signaling in cochlear supporting cells reduces hair cell excitability by increasing the extracellular space.

As the brain develops, billions of cells respond to genetic and environmental cues to form the trillions of connections that make up its neural networks. However, before these brain circuits can respond to real life stimuli, their connections are refined by bursts of electrical activity. For example, sensory cells in the ear produce bursts of spontaneous electrical activity that mimic those made by sounds. This activity allows the neural network in the hearing system to ‘practice’ responding to sounds. However, the origin of these electrical bursts is unusual as they do not start in the sensory cells themselves, but are initiated by the non-sensory cells around them. Past research has shown that as the ear develops these non-sensory cells, or supporting cells, release regular doses of a molecule called ATP. The supporting cells then detect their own ATP release using specialized receptor proteins on their surface. This self-stimulation causes the supporting cells to release potassium ions that interact with the sensory cells and trigger bursts of electrical activity. However, the identity of this ATP-detecting receptor was not known, and without this information it was unclear how the electrical activity starts and why it happens in rhythmic bursts. To fill this knowledge gap, Babola et al. measured electrical activity in ear cells isolated from mice, and examined nerve cell activity in live mice during this critical stage of development. This revealed that the bursts of activity in the ear depend on a receptor called P2RY1 which can be found on the supporting cells located next to sensory cells. When P2RY1 is activated it triggers the release of calcium ions inside the supporting cells. This opens channels in the cell membrane, allowing the potassium ions to flow out and electrically activate the sensory cells. But, when the potassium ions leave the supporting cells, water is drawn out with them, causing the cells to shrink and the space around the cells to get bigger. As a result, the released potassium ions disperse more quickly, moving away from the sensory cells and stopping the burst in electrical activity. Conversely, when P2RY1 is inhibited, this causes the supporting cells to swell, trapping potassium ions near the sensory cells and making them fire continuously. This indicates that bursts in electrical activity are controlled by the rhythmic swelling and shrinking of supporting cells. Although supporting cells cannot detect sound themselves, they seem to play a crucial role in developing the hearing system. A better understanding of these cells could therefore aid research into hearing problems without a known cause such as hypersensitivity to sound, tinnitus, and complex auditory processing disorders in children.
Is the recent increase in national homicide abnormal? Testing the application of fan charts in monitoring national homicide trends over time

Abstract Purpose The goal of this study is to compare the increase in the 2015 national homicide rate to the historical data series and other violent crime rate changes. Methods We use ARIMA models and a one-step ahead forecasting technique to predict national homicide, rape, robbery, and aggravated assault rates in the United States. Annual Uniform Crime Report data published by the Federal Bureau of Investigation are used in our analysis. Results The 2015 homicide rate increased above the 90% prediction interval for our model, but not more conservative intervals. Predictions intervals for other national level crime rates consistently produced correct coverage using our forecasting approach. Conclusions Our findings provide weak evidence that the national homicide rate spiked in 2015, though data for 2016–2018 do not show a continued anomalous increase in the U.S. homicide rate.
Effect of Noise on Analog Circuit Testing

The effect of test environment noise (tester noise) on test waveforms is considered. We show that tests generated ignoring the tester noise characteristics are prone to failure when actually applied to the circuit-under-test (CUT). The failure may result in the good circuit being declared faulty or the faulty circuit being declared good. This failure is independent of the fault model and nature of the test, i.e., AC or DC, time domain or frequency domain. We characterize the total noise at the primary outputs (PO‘s) of the circuit using second order statistics. We use the noise power spectrum and root mean square (RMS) values to make decisions about the test waveforms and recommend more noise-robust tests. For non-linear circuits we use the Central Limit Theorem of statistics to approximate narrow band noise at a primary input (PI) by a sum of sinusoidal distributions, and we use Monte-Carlo simulations to determine the noise at the PO‘s in the time domain. Results of experiments on an instrumentation amplifier, a biquadratic filter, and a Gilbert multiplier are presented, which prove that valid tests in a noise-free environment are invalid when tester noise is considered.
Social network of extreme tweeters: a case study

The number of posts made by a single user account on a social media platform Twitter in any given time interval is usually low. However, there is a subset of users whose volume of posts is much higher than the median. In this paper, we investigate the content diversity and the social neighborhood of these extreme users and others. We define a metric called "interest narrowness", and identify that a subset of extreme users, termed anomalous users, post with very low topic diversity. We show that anomalous groups have the strongest within-group interactions, compared to their interaction with others, and exhibit different information sharing behaviors with other anomalous users compared to non-anomalous extreme tweeters.
A Model Protometabolic Pathway across Protocell Membranes Assisted by Photocatalytic Minerals

Protocell analogs (lipid vesicles) to modern cell membranes have been postulated as compartments that may have been involved in primordial metabolism during the transition from geochemistry to biochemistry on early Earth. The transduction of light energy into chemical energy for metabolism was a key step in the transition from the earliest metabolisms to phototrophy. Photocatalytic minerals may have served the role of enzymes during these transitional stages. Here, we demonstrate a simple photoheterotrophic protometabolism promoted by photocatalytic minerals across a model protocell (vesicle) membrane. These minerals in the extra-vesicular medium utilized light energy to drive a coupled, multistep transmembrane electron transfer reaction (TMETR), while simultaneously generating a transmembrane pH gradient and reducing nicotinamide adenine dinucleotide (NAD+) to NADH within the vesicle. The proton gradient or chemiosmotic potential could have provided a basis for adenosine triphosphate (ATP) synthesis and ...
A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases

Abstract Molecular dynamics (MD) simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult because of the lack of accurate and transferrable interatomistic potential fields. As a result, this issue has become a major barrier to predicting the phase change of materials and their transport properties with atomistic-level modeling techniques. Recently, machine learning–based algorithms have emerged as promising tools to develop accurate potentials for MD simulations. In this work, we approach the problem of predicting the thermal conductivity of silicon in different phases by performing MD simulations with a deep neural network potential (NNP). This NNP is trained with ab initio data of silicon in the crystalline, liquid, and amorphous phases. The accuracy of our potential is first validated through reproducing the atomistic structures during the phase transition, where other empirical potentials usually fail. The thermal conductivity of different phases is then calculated, showing a good agreement with the experimental results and ab initio calculation results. Our work shows that a unified neural network–based potential can be a promising tool for studying phase change and thermal transport of materials with high accuracy.
A Waste-Efficient Algorithm for Single-Droplet Sample Preparation on Microfluidic Chips

We address the problem of designing micro-fluidic chips for sample preparation, which is a crucial step in many experimental processes in chemical and biological sciences. One of the objectives of sample preparation is to dilute the sample fluid, called reactant, using another fluid called buffer, to produce desired volumes of fluid with prespecified reactant concentrations. In the model we adopt, these fluids are manipulated in discrete volumes called droplets. The dilution process is represented by a mixing graph whose nodes represent 1-1 micro-mixers and edges represent channels for transporting fluids. In this work, we focus on designing such mixing graphs when the given sample (also referred to as the target) consists of a single-droplet, and the objective is to minimize total fluid waste. Our main contribution is an efficient algorithm called RPRIS that guarantees a better provable worst-case bound on waste and significantly outperforms state-of-the-art algorithms in experimental comparison.
Cold clouds as cosmic-ray detectors

Low energy cosmic-rays (CRs) are responsible for gas heating and ionization of interstellar clouds, which in turn introduces coupling to Galactic magnetic fields. So far the CR ionization rate (CRIR) has been estimated using indirect methods, such as its effect on the abundances of various rare molecular species. Here we show that the CRIR may be constrained from line emission of H2 rovibrational transitions, excited by CRs. We derive the required conditions for CRs to dominate line excitation, and show that CR-excited lines may be detected with the Very Large Telescope (VLT) over 8 hours integration. Our method, if successfully applied to a variety of clouds at different Galactic locations, will provide improved constraints on the spectrum of low energy CRs and their origins. Low energy cosmic rays in the interstellar medium are responsible for gas ionization of molecular clouds coupling them to large-scale Galactic magnetic fields, but to date, the cosmic-ray ionization rate is highly uncertain due to the difficulty of direct observation. The author proposes a method to derive the cosmic-ray ionization rate based on detecting radiative decay of H2 rovibrational levels excited by the cosmic rays, which could be detected with the Very Large Telescope.
The Smart Black Box: A Value-Driven High-Bandwidth Automotive Event Data Recorder

Autonomous vehicles require reliable and resilient sensor suites and ongoing validation through fleet-wide data collection. This paper proposes a Smart Black Box (SBB) to augment traditional low-bandwidth data logging with value-driven high-bandwidth data capture. The SBB caches short-term histories of data as buffers through a deterministic Mealy machine based on data value and similarity. Compression quality for each frame is determined by optimizing the trade-off between value and storage cost. With finite storage, prioritized data recording discards low-value buffers to make room for new data. This paper formulates SBB compression decision making as a constrained multi-objective optimization problem with novel value metrics and filtering. The SBB has been evaluated on a traffic simulator which generates trajectories containing events of interest (EOIs) and corresponding first-person view videos. SBB compression efficiency is assessed by comparing storage requirements with different compression quality levels and event capture ratios. Performance is evaluated by comparing results with a traditional first-in-first-out (FIFO) recording scheme. Deep learning performance on images recorded at different compression levels is evaluated to illustrate the reproducibility of SBB recorded data.
Reversible Enlargement of Photoswitchable Dielectric Properties by Plasmonic [60]Fullerenyl Core–Shell Nanoparticles on Graphene Nanosheets

Utilization of [60]fullerosome monolayer grafting approach on both sides of graphene nanosheets giving the corresponding sandwiched nanostructures was demonstrated as a key intermediate for the con...
Civil order, criminal justice, and ‘no justice no peace’

Two kinds of recurring events – police violence and political protests – highlight some of the tensions between criminal justice administration and the idea of civil peace. Maintaining civil order ...
Changes in accrual properties and operating environment: Implications for cash flow predictability

Abstract This paper reconciles conflicting evidence in prior literature on the relative ability of earnings and cash flows in predicting future cash flows. Further, we investigate the implications of temporal shifts in accrual properties and operating environment for cash flow predictability. Three key insights emerge. First, cash flows consistently outperform earnings in predicting future cash flows. Second, accruals and its components, including those capturing non-articulating events, have incremental (albeit small) predictive ability over cash flows. Third, earnings’ ability to predict future cash flows has increased over the period 1989–2015, due to changes in operating environment rather than accrual properties.
Birational Calabi-Yau manifolds have the same small quantum products

We show that any two birational projective Calabi-Yau manifolds have isomorphic small quantum cohomology algebras after a certain change of Novikov rings. The key tool used is a version of an algebra called symplectic cohomology, which is constructed using Hamiltonian Floer cohomology. Morally, the idea of the proof is to show that both small quantum products are identical deformations of symplectic cohomology of some common open affine subspace. Part of the proof uses the fact that subvarieties of positive codimension are stably displaceable.
A randomized controlled trial of permanent vs absorbable suture for uterosacral ligament suspension

Uterosacral ligament suspension (USLS) is a common procedure for apical pelvic organ prolapse. The procedure has been described using only permanent suture, only absorbable suture and a combination of permanent and absorbable suture. We hypothesized that the use of absorbable suture is not inferior to the use of permanent suture. All women undergoing USLS between October 2016 and November 2017 were approached. Subjects were randomized to permanent or absorbable suture. The primary outcome was POP-Q point C 12 months after surgery (non-inferiority limit = 2 cm). A composite outcome of success at 12 months was defined as no apical prolapse ≥ 1/2 TVL, no prolapse beyond the hymen, no prolapse retreatment and no bulge symptoms. Forty-four subjects with mean (SD) age 62.9 (12.0) years and body mass index 29.1 (5.4) kg/m2 were enrolled and underwent USLS. Fifteen (34.1%) had POP-Q stage II and 29 (65.9%) stage III prolapse. Twenty-two were randomized to permanent and 22 to absorbable suture. Forty (90.9%) completed the 12-month follow-up. Median (IQR) POP-Q point C at 12 months was −7 (−10, -6) for the permanent and − 7 (−9, -5.5) for the absorbable suture groups (p = 0.65, non-inferiority p < 0.0002). Four (20%) in the permanent and one (5%) in the absorbable suture group reported bulge symptoms (p = 0.34). Fifteen (75%) in the permanent and 18 (90%) in the absorbable suture groups met criteria for composite success (p = 0.41). Intervention-related adverse outcomes were uncommon and not different between groups. Absorbable suture for USLS is not inferior to permanent suture for apical anatomic outcomes.
Deep learning seismic substructure detection using the Frozen Gaussian approximation

Abstract We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging high-frequency data is advantageous since it can provide high resolution of substructures. However, generation of sufficient synthetic high-frequency data sets for training neural networks is computationally challenging. This bottleneck is overcome by a highly scalable computational platform built upon the FGA, which comes from the semiclassical theory and approximates the wavefields by a sum of fixed-width (frozen) Gaussian wave packets. Training data for deep neural networks is generated from a forward simulation of the elastic wave equation using the FGA. This data contains accurate traveltime information (from the ray path) but not exact amplitude information (with asymptotic errors not shrinking to zero even at extremely fine numerical resolution). Using this data we build convolutional neural network models using an open source API, GeoSeg, developed using Keras and Tensorflow. On a simple model, networks, despite only being trained on data generated by the FGA, can detect an interface with a high success rate from displacement data generated by the spectral element method. Benchmark tests are done for P-waves (acoustic) and P- and S-waves (elastic) generated using the FGA and a spectral element method. Further, results with a high accuracy are shown for more complicated geometries including a three-layered model, a sine interface, and a 2D-pocket model where the neural networks are trained by both clean and noisy data.
Aging induces aberrant state transition kinetics in murine muscle stem cells

Murine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unknown if the trajectory and dynamics of activation change with age. Here, we use timelapse imaging and single cell RNA-seq to measure activation trajectories and rates in young and aged MuSCs. We find that the activation trajectory is conserved in aged cells, and develop effective machine learning classifiers for cell age. Using cell behavior analysis and RNA velocity, we find that activation kinetics are delayed in aged MuSCs, suggesting that changes in stem cell dynamics may contribute to impaired stem cell function with age. Intriguingly, we also find that stem cell activation appears to be a random walk like process, with frequent reversals, rather than a continuous, linear progression. These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them.
Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise

Community detection tasks have received a lot of attention across statistics, machine learning, and information theory with work concentrating on providing theoretical guarantees for different methodological approaches to the stochastic block model. Recent work on community detection has focused on modeling the spectral embedding of a network using Gaussian mixture models (GMMs) in scaling regimes where the ability to detect community memberships improves with the size of the network. However, these regimes are not very realistic. This paper provides tractable methodology motivated by new theoretical results for networks with non-vanishing noise. We present a procedure for community detection using novel GMMs that incorporate truncation and shrinkage effects. We provide empirical validation of this new representation as well as experimental results using a large email dataset.
Conditioning of restricted Fourier matrices and super-resolution of MUSIC

This paper studies stable recovery of a collection of point sources from its noisy M+1 low-frequency Fourier coefficients. We focus on the super-resolution regime where the minimum separation of the point sources is below 1/M. We propose a separated clumps model where point sources are clustered in far apart sets, and prove an accurate lower bound of the Fourier matrix with nodes restricted to the source locations. This estimate gives rise to a theoretical analysis on the super-resolution limit of the MUSIC algorithm.
Lessons from archives: strategies for collecting sociocultural data in machine learning

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.
Active galactic nuclei as seen by the Spitzer Space Telescope

The Spitzer Space Telescope revolutionized studies of active galactic nuclei (AGNs). Its combined sensitivity and mapping speed at mid-infrared wavelengths revealed a substantial population of highly obscured AGNs. This population implies a higher radiative accretion efficiency, and thus possibly a higher spin, for black holes than indicated by surveys at optical or X-ray wavelengths. The unique mid-infrared spectrographic capability of Spitzer gave important insights into the distribution and nature of the dust surrounding AGNs, enabling the separation of AGN and starburst components, the detection of silicate features in emission from hot dust, and the identification of shocked gas associated with AGN activity. Spitzer’s sensitivity allowed almost complete identification of the host galaxies of samples of AGNs selected in the X-ray and radio. As we look forward to the James Webb Space Telescope, the lessons learned from Spitzer studies will inform observational programmes with new and upcoming infrared facilities. Spitzer revolutionized studies of active galactic nuclei through its high sensitivity and mapping speed at mid-infrared wavelengths. This Review summarizes key Spitzer insights on active galactic nuclei, including the properties of their dust and shocked gas.
Recent Developments in Spacetime-Symmetry Tests in Gravity

We summarize theoretical and experimental work on tests of CPT and local Lorentz symmetry in gravity. Recent developments include extending the effective field theory framework into the nonlinear regime of gravity.
Measuring human capital divergence in a growing economy

Abstract The stylized fact that the fraction of workers who are college graduates appears to increase more in US cities where the initial share is larger has attracted significant attention. Furthermore, more educated cities appear to grow faster. These two trends could portend the divergence of cities by skill, with low-skill workers segregated in slow-growing or declining cities. This paper compares measures of skill divergence and finds that relative measures, which have the property of scale invariance, show no divergence for the period from 1970 to 2010. In addition, the relation between skill intensity and city growth appears to be concave, so that differences in the growth rate of skill intensity across cities may diminish over time as the average college share of the country rises.
The human mitochondrial 12S rRNA m4C methyltransferase METTL15 is required for mitochondrial function

Mitochondrial DNA gene expression is coordinately regulated both pre- and post-transcriptionally, and its perturbation can lead to human pathologies. Mitochondrial rRNAs (mt-rRNAs) undergo a series of nucleotide modifications after release from polycistronic mitochondrial RNA precursors, which is essential for mitochondrial ribosomal biogenesis. Cytosine N4-methylation (m4C) at position 839 (m4C839) of the 12S small subunit mt-rRNA was identified decades ago; however, its biogenesis and function have not been elucidated in detail. Here, using several approaches, including immunofluorescence, RNA immunoprecipitation and methylation assays, and bisulfite mapping, we demonstrate that human methyltransferase-like 15 (METTL15), encoded by a nuclear gene, is responsible for 12S mt-rRNA methylation at m4C839 both in vivo and in vitro. We tracked the evolutionary history of RNA m4C methyltransferases and identified a difference in substrate preference between METTL15 and its bacterial ortholog rsmH. Additionally, unlike the very modest impact of a loss of m4C methylation in bacterial small subunit rRNA on the ribosome, we found that METTL15 depletion results in impaired translation of mitochondrial protein-coding mRNAs and decreases mitochondrial respiration capacity. Our findings reveal that human METTL15 is required for mitochondrial function, delineate the evolution of methyltransferase substrate specificities and modification patterns in rRNA, and highlight a differential impact of m4C methylation on prokaryotic ribosomes and eukaryotic mitochondrial ribosomes.
Developmental trajectory of prehematopoietic stem cell formation from endothelium

Abstract Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.
Harming the best: How schools affect the black-white achievement gap

Sizeable achievement differences by race appear in early grades, but substantial uncertainty exists about the impact of school quality on the black–white achievement gap and particularly about its evolution across different parts of the achievement distribution. Texas administrative data show that the overall growth in the achievement gap between third and eighth grades is larger for students with higher initial achievement and that specific teacher and peer characteristics explain a substantial share of the widening. The adverse effect of attending school with a high black enrollment share appears to be an important contributor to the larger growth in the achievement differential in the upper part of the test score distribution. This evidence reaffirms the major role played by peers and school quality, but also presents a policy dilemma. Teacher labor market complications, current housing patterns, legal limits to desegregation efforts, and uncertainty about the overall effects of specific desegregation programs indicate that effective policy responses will almost certainly involve a set of school improvements beyond simple changes in peer racial composition and the teacher experience distribution. © 2009 by the Association for Public Policy Analysis and Management. Perhaps no other social policy issue has been as important or as stubborn to deal with as racial gaps in economic outcomes. Black–white differences in academic attainment, occupation, and earnings, while showing some improvement over the past quarter century, remain large. Much of the policy effort aimed at reducing these gaps focuses on public elementary and secondary schools. This emphasis hinges on the widespread beliefs that school and peer characteristics disadvantage blacks relative to whites and that appropriate school interventions can raise achievement and improve future life outcomes. This paper investigates the first of these beliefs through an examination of the changes in the black-white achievement gap as students progress through school. 1 Our findings suggest that the increase in the achievement gap across grades is larger for blacks with higher initial achievement and that this is due primarily to stronger deleterious effects for initially high-achieving blacks of attending schools
Ensuring Data Quality: What Criteria do Monitors Use to Rate Interviewers?

Like most survey research organizations, Mathematica Policy Research monitors telephone interviewers to ensure that they collect high-quality data. Monitors identify problems with survey questions, recommend interviewing techniques, and retrain interviewers whose performance do not meet expectations. Because monitoring is a critical quality assurance tool, we are interested in understanding the behavior of monitors – specifically, their ability to provide effective and consistent feedback on interviewers’ performance. Mathematica implements several best practices to promote monitoring consistency:
Medical robotics and computer-integrated interventional medicine

Computer-Integrated Interventional Medicine (CIIM) promises to have a profound impact on health care in the next 20 years, much as and for many of the same reasons that the marriage of computers and information processing methods with other technology have had on manufacturing, transportation, and other sectors of our society. Our basic premise is that the steps of creating patient-specific computational models, using these models for planning, registering the models and plans with the actual patient in the operating room, and using this information with appropriate technology to assist in carrying out and monitoring the intervention are best viewed as part of a complete patient-specific intervention process that occurs over many time scales. Further, the information generated in computer-integrated interventions can be captured and analyzed statistically to improve treatment processes. This paper will explore these themes briefly, using examples drawn from our work at the Engineering Research Center for Computer-Integrated Surgical Systems and Technology (CISST ERC).
Fast, All-Optical, Zero to π Continuously Controllable Kerr Phase Gate

We demonstrate a fast Kerr phase gate in a room-temperature 85Rb vapor using a Raman gain method where the probe wave travels “superluminally”. Continuously variable, zero to π radian nonlinear Kerr phase shifts of the probe wave relative to a reference wave have been observed at 333 K. We show rapid manipulation of digitally encoded probe waves using a digitally encoded phase-control light field, demonstrating the capability of the system in information science and telecommunication applications.
Performance Characterization of Canonical Mobility Models in Drone Cellular Networks

In this paper, we characterize the performance of several canonical mobility models in a drone cellular network in which drone base stations (DBSs) serve a set of user equipment (UE) on the ground. In particular, we consider the following four mobility models: (i) straight line (SL), (ii) random stop (RS), (iii) random walk (RW), and (iv) random waypoint (RWP), among which the SL mobility model is inspired by the simulation models used by the third generation partnership project (3GPP) for the placement and trajectory of drones, while the other three are well-known canonical models (or their variants) that offer a useful balance between realism and tractability. Assuming the nearest-neighbor association policy, we consider two service models for the UEs: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). While the serving DBS follows the same mobility model as the other DBSs in the UIM, it is assumed to fly towards the UE of interest in the UDM and hover above its location after reaching there. The main contribution of this paper is a unified approach to characterize the point process of DBSs for all the mobility and service models. Using this, we provide exact mathematical expressions for the average received rate and the session rate as seen by the typical UE. Further, using tools from the calculus of variations, we concretely demonstrate that the simple SL mobility model provides a lower bound on the performance of other general mobility models (including the ones in which drones follow curved trajectories) as long as the movement of each drone in these models is independent and identically distributed (i.i.d.). To the best of our knowledge, this is the first work that provides a rigorous analysis of key canonical mobility models for an infinite drone cellular network and establishes useful connections between them.
Ribosome profiling in archaea reveals leaderless translation, novel translational initiation sites, and ribosome pausing at single codon resolution

High-throughput methods, such as ribosome profiling, have revealed the complexity of translation regulation in Bacteria and Eukarya with large-scale effects on cellular functions. In contrast, the translational landscape in Archaea remains mostly unexplored. Here, we developed ribosome profiling in a model archaeon, Haloferax volcanii, elucidating, for the first time, the translational landscape of a representative of the third domain of life. We determined the ribosome footprint of H. volcanii to be comparable in size to that of the Eukarya. We linked footprint lengths to initiating and elongating states of the ribosome on leadered transcripts, operons, and on leaderless transcripts, the latter representing 70% of H. volcanii transcriptome. We manipulated ribosome activity with translation inhibitors to reveal ribosome pausing at specific codons. Lastly, we found that the drug harringtonine arrested ribosomes at initiation sites in this archaeon. This drug treatment allowed us to confirm known translation initiation sites and also reveal putative novel initiation sites in intergenic regions and within genes. Ribosome profiling revealed an uncharacterized complexity of translation in this archaeon with bacteria-like, eukarya-like, and potentially novel translation mechanisms. These mechanisms are likely to be functionally essential and to contribute to an expanded proteome with regulatory roles in gene expression.
Medical Debt as a Cause of Consumer Bankruptcy

Associate Professor, Northeastern University School of Law. I am grateful to Professors Rashmi Dyal-Chand, Kristin Madison, and Daniel Medwed for their comments. Thanks also to Elliott Hibbler, NUSL Senior Law Librarian, for invaluable assistance. I am indebted to attorneys Susan Grossberg, Carl Davis, Ron Satija, Carl Bekofske, Marge Burks, and many other professionals who graciously shared their perspectives. Rachel Tritter (NUSL 2014) and Daniel DeBlander (NUSL 2013) adroitly organized data and provided advice. Thanks to the National Association of Consumer Bankruptcy Attorneys, the American Bankruptcy Institute, and the Boston Bar Association for publicizing the survey. Thanks also to NUSL student research assistants Evan Segal, Bradford Melson, Maja Jachimowicz, Lawrence Fleming, Hillary Knight, Patrick Mahoney, Garth Davis, Chrisiant Bracken, Max Ferullo and Daniela Mayer.
Implications of quantal response statistical equilibrium

Abstract This paper explores the foundations and properties of the quantal response statistical equilibrium (QRSE) model developed by Scharfenaker and Foley (2017). The QRSE model provides a behavioral foundation for the formation of aggregate economic outcomes in social systems characterized by negative feedbacks. It can approximate a wide range of commonly encountered theoretical distributions that have been identified as economic statistical equilibrium and displays qualitatively similar behavior to the Subbotin and Asymmetric Subbotin distributions that range from the Laplace to the Normal distribution in the limit. Asymmetry in the frequency distributions of economic outcomes can be understood as arising from the unfulfilled expectations of entropy-constrained decision makers. This paper demonstrates the logic of the QRSE model in an application to US stock market data dating back to 1926. The model provides a parsimonious explanation for the distribution of rates of return on private equities as well as a behavioral foundation for asset price fluctuations.
Regional differences in the abiotic environment contribute to genomic divergence within a wild tomato species

The wild currant tomato Solanum pimpinellifolium inhabits a wide range of abiotic habitats across its native range of Ecuador and Peru. Although it has served as a key genetic resource for the improvement of domestic cultivars, little is known about the genetic basis of traits underlying local adaptation in this species, nor what abiotic variables are most important for driving differentiation. Here we use redundancy analysis (RDA) and other multivariate statistical methods (structural equation modelling [SEM] and generalized dissimilarity modelling [GDM]) to quantify the relationship of genomic variation (6,830 single nucleotide polymorphisms [SNPs]) with climate and geography, among 140 wild accessions. RDA, SEM and GDM each identified environment as explaining more genomic variation than geography, suggesting that local adaptation to heterogeneous abiotic habitats may be an important source of genetic diversity in this species. Environmental factors describing temporal variation in precipitation and evaporative demand explained the most SNP variation among accessions, indicating that these forces may represent key selective agents. Lastly, by studying how SNP-environment associations vary throughout the genome (44,064 SNPs), we mapped the location and investigated the functions of loci putatively contributing to climatic adaptations. Together, our findings indicate an important role for selection imposed by the abiotic environment in driving genomic differentiation between populations.
Super-resolved multispectral lensless microscopy via angle-tilted, wavelength-multiplexed ptychographic modulation

We report an angle-tilted, wavelength-multiplexed ptychographic modulation approach for multispectral lensless on-chip microscopy. In this approach, we illuminate the specimen with lights at five wavelengths simultaneously. A prism is added at the illumination path for spectral dispersion. Thus, lightwaves at different wavelengths hit the specimen at slightly different incident angles, breaking the ambiguities in mixed-state ptychographic reconstruction. At the detection path, we place a thin diffuser between the specimen and the monochromatic image sensor for encoding the spectral information into 2D intensity measurements. By scanning the sample to different x-y positions, we acquire a sequence of monochromatic images for reconstructing the five complex object profiles at the five wavelengths. An up-sampling procedure is integrated into the recovery process to bypass the resolution limit imposed by the imager pixel size. We demonstrate a half-pitch resolution of 0.55 µm using an image sensor with 1.85 µm pixel size. We also demonstrate quantitative and high-quality multispectral reconstructions of stained tissue sections for digital pathology applications.