cssi/SciSciGPT-SciSciNet huggingface dataset:
Obfuscating Compute-and-Compare Programs under LWE

We show how to obfuscate a large and expressive class of programs, which we call compute-and-compare programs, under the learning-with-errors (LWE) assumption. Each such program CC[f,y] is parametrized by an arbitrary polynomial-time computable function f along with a target value y and we define CC[f,y](x) to output 1 if f(x)=y and 0 otherwise. In other words, the program performs an arbitrary {computation} f and then compares its output against a target y. Our obfuscator satisfies distributional virtual-black-box security, which guarantees that the obfuscated program does not reveal any partial information about the function f or the target value y, as long as they are chosen from some distribution where y has sufficient pseudo-entropy given f. We also extend our result to multi-bit compute-and-compare programs MBCC[f,y,z](x) which output a message z if f(x)=y.Compute-and-compare programs are powerful enough to capture many interesting obfuscation tasks as special cases. This includes obfuscating {conjunctions, and therefore we improve on the prior work of Brakerski et al. (ITCS 16) which constructed a conjunction obfuscator under a non-standard entropic ring-LWE assumption, while here we obfuscate a significantly broader class of programs under standard LWE. We show that our obfuscator has several interesting applications. For example, we can take any encryption scheme and publish an obfuscated plaintext equality tester that allows users to check whether a ciphertext decrypts to some target value y; as long as y has sufficient pseudo-entropy this will not harm semantic security. We can also use our obfuscator to generically upgrade attribute-based encryption to predicate encryption with one-sided attribute-hiding security, and to upgrade witness encryption to indistinguishability obfuscation which is secure for all null circuits. Furthermore, we show that our obfuscator gives new circular-security counter-examples for public-key bit encryption and for unbounded length key cycles.Our result uses the graph-induced multi-linear maps of Gentry, Gorbunov and Halevi (TCC 15), but only in a carefully restricted manner which is provably secure under LWE. Our technique is inspired by ideas introduced in a recent work of Goyal, Koppula and Waters (EUROCRYPT 17) in a seemingly unrelated context.
Serum pepsinogen 1 and anti-Helicobacter pylori IgG antibodies as predictors of gastric cancer risk in Finnish males.

BACKGROUND Serum pepsinogen 1 (SPG1) and anti-Helicobacter pylori serology have been used for gastric risk stratification in Asia. AIM To assess utility of these markers in a Western population. METHODS SPG1 measurements were available for 21 895 Finnish male smokers in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. We used Cox proportional hazards models adjusted for potential confounders to estimate gastric cancer hazard ratios (HR) and 95% confidence intervals (95% CI) for low SPG1 (<25 μg/L). In a subset (n = 3555) with anti-H. pylori serology, these markers jointly defined the following: Group A (H. pylori[-], SPG1[normal]; reference group), Group B (H. pylori[+], SPG1[normal]), Group C (H. pylori[+], SPG1[low]) and Group D (H. pylori[-], SPG1[low]). Odds ratios (ORs) and 95% CI were calculated using multivariate logistic regression. RESULTS There were 329 gastric cancers diagnosed an average of 13.9 years after baseline. Pre-diagnostic low SPG1 was significantly associated with increased gastric cancer risk (HR 2.68, 95% CI 1.99-3.61). Among subjects with both SPG1 and H. pylori serology, groups B, C and D had increased gastric cancer ORs (95% CI) of 1.79 (1.21-2.64), 3.85 (2.36-6.28) and 6.35 (2.20-18.34), respectively. CagA seropositives had significantly higher ORs than CagA seronegatives within group B (Pheterogeneity = 0.01). For groups B and C, repeat SPG1 level at 3 years did not further stratify gastric cancer risk. CONCLUSIONS Low SPG1 was associated with increased gastric cancer risk in our large Finnish cohort. A single measurement of SPG1 along with H. pylori whole cell and CagA serology provides potentially useful prediction of gastric cancer risk.
Kids Today: The Rise in Children's Academic Skills at Kindergarten Entry.

Private and public investments in early childhood education have expanded significantly in recent years. Despite this heightened investment, we have little empirical evidence on whether children to...
Significant Factors Related to Failed Pediatric Dental General Anesthesia Appointments at a Hospital-based Residency Program.

PURPOSE The purposes of this study were to: (1) evaluate the relationship between appointment failure and the factors of age, gender, race, insurance type, day of week, scheduled time of surgery, distance traveled, and weather; (2) investigate reasons for failure; and (3) explore the relationships between the factors and reasons for failure. METHODS Electronic medical records were accessed to obtain data for patients scheduled for dental care under general anesthesia from May 2012 to May 2015. Factors were analyzed for relation to appointment failure. RESULTS Data from 3,513 appointments for 2,874 children were analyzed. Bivariate associations showed statistically significant (P<0.05) relationships between failed appointment and race, insurance type, scheduled time of surgery, distance traveled, snowfall, and temperature. Multinomial regression analysis showed the following associations between factors and the reason for failure (P<0.05): (1) decreased temperature and increased snowfall were associated with weather as reason for failure; (2) the African American population showed an association with family barriers; (3) Hispanic families were less likely to give advanced notice; and (4) the "additional races" category showed an association with fasting violation. CONCLUSION Patients who have treatment under general anesthesia face specific barriers to care.
Complementary, Semiautomated Methods for Creating Multidimensional PEG-Based Biomaterials.

Tunable biomaterials that mimic selected features of the extracellular matrix (ECM) such as its stiffness, protein composition, and dimensionality are increasingly popular for studying how cells sense and respond to ECM cues. In the field, there exists a significant trade-off for how complex and how well these biomaterials represent the in vivo microenvironment versus how easy they are to make and how adaptable they are to automated fabrication techniques. To address this need to integrate more complex biomaterials design with high-throughput screening approaches, we present several methods to fabricate synthetic biomaterials in 96-well plates and demonstrate that they can be adapted to semiautomated liquid handling robotics. These platforms include (1) glass bottom plates with covalently attached ECM proteins and (2) hydrogels with tunable stiffness and protein composition with either cells seeded on the surface or (3) laden within the three-dimensional hydrogel matrix. This study includes proof-of-conce...
Complementarity for dark sector bound states

We explore the possibility that bound states involving dark matter particles could be detected by resonance searches at the LHC and the generic implications of such scenarios for indirect and direct detection. We demonstrate that resonance searches are complementary to monojet searches and can probe dark matter masses above 1 TeV with current LHC data. We argue that this parameter regime, in which the bound-state resonance channel is the most sensitive probe of the dark sector, arises most naturally in the context of nontrivial dark sectors with large couplings, nearly degenerate dark matter--like states, and multiple force carriers. If dark sector bound states are present and can be detected at the LHC, annihilation of dark matter particles in our galactic halo may occur either with a minimal Sommerfeld enhancement that may be appreciable or through radiative bound-state formation, leading to large signals in indirect searches. We calculate these complementary constraints, which favor either models in which the bound state--forming dark matter constitutes a small fraction of the total density or models in which the late-time annihilation is suppressed at low velocities or late times. We present concrete examples of models that satisfy all these constraints and in which the LHC resonance search is the most sensitive probe of the dark sector.
Integrated Model, Batch, and Domain Parallelism in Training Neural Networks

We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using minibatch stochastic gradient descent (SGD). Our goal is to find an efficient parallelization strategy for a fixed batch size using P processes. Our method is inspired by the communication-avoiding algorithms in numerical linear algebra. We see P processes as logically divided into a P_r x P_c grid where the P_r dimension is implicitly responsible for model/domain parallelism and the P_c dimension is implicitly responsible for batch parallelism. In practice, the integrated matrix-based parallel algorithm encapsulates these types of parallelism automatically. We analyze the communication complexity and analytically demonstrate that the lowest communication costs are often achieved neither with pure model nor with pure data parallelism. We also show how the domain parallel approach can help in extending the theoretical scaling limit of the typical batch parallel method.
Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets.

As we examine the space around us our eyes move in short steps, looking toward a new location about four times a second. Neurons in a region of the brain called the frontal eye field help initiate these eye movements, which are known as saccades. Each neuron contributes to a saccade with a specific direction and size. Before a saccade, the relevant neurons in the frontal eye field steadily increase their activity. When this activity reaches a critical threshold, the visual system issues a command to move the eyes in the appropriate direction. So a saccade that moves the eyes to the right requires a specific group of neurons to be strongly activated – but, at the same time, the neurons responsible for movement to the left need to be less active. Imagine that you have to move your eyes as quickly as possible to look at a spot of light that appears on a screen. Some of the time your eyes will start to move about 100 milliseconds after the light appears. But on other attempts, your eyes will not start moving until 300 milliseconds after the light came on. What causes this variability? To find out, Hauser et al. recorded from neurons in monkeys trained to perform such a task. When the spot of light appeared many different neurons were active, suggesting there is conflict between the plan that would move the eyes toward the target and plans to look at other locations. That is, when the target appears, the monkey is already thinking of looking somewhere. The time required to resolve this conflict depends on how far apart the target and the competing locations are from one another, and on how much the competing neurons have increased their activity before the target appears. Similar mechanisms are likely to operate when we sit at the dinner table and look for the salt shaker, for example, and so the results presented by Hauser et al. will help us to understand how we direct our attention to different points in space. Understanding how these processes work in more detail will help us to discern what happens when they go wrong, as occurs in attention deficit disorders like ADHD.
Field study of charitable giving reveals that reciprocity decays over time

We examine how reciprocity changes over time by studying a large quasiexperiment in the field. Specifically, we analyze administrative data from a university hospital system. The data include information about over 18,000 donation requests made by the hospital system via mail to a set of its former patients in the 4 months after their first hospital visit. We exploit quasiexperimental variation in the timing of solicitation mailings relative to patient hospital visits and find that an extra 30-day delay between the provision of medical care and a donation solicitation decreases the likelihood of a donation by 30%. Our findings have important implications for models of economic behavior, which currently fail to incorporate reciprocity’s sensitivity to time. The fact that reciprocal behavior decays rapidly as time passes also suggests the importance of capitalizing quickly on opportunities to benefit from a quid pro quo.
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation.

Author(s): Koanantakool, P; Ali, A; Azad, A; Buluc, A; Morozov, D; Oliker, L; Yelick, K; Oh, SY | Abstract: Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfortunately, most estimators are not scalable enough to handle the sizes of modern high-dimensional data sets (often on the order of terabytes), and assume Gaussian samples. To address these deficiencies, we introduce HP-CONCORD, a highly scalable optimization method for estimating a sparse inverse covariance matrix based on a regularized pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal gradient method uses a novel communication-avoiding linear algebra algorithm and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving parallel scalability on problems with up to ≈819 billion parameters (1.28 million dimensions); even on a single node, HP-CONCORD demonstrates scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to estimate the underlying dependency structure of the brain from fMRI data, and use the result to identify functional regions automatically. The results show good agreement with a clustering from the neuroscience literature.
Local Industrial Shocks and Infant Mortality

Local industrial development has the potential to improve health and well-being, while also damaging health through exposure to harmful pollution. It is an empirical question which of these effects dominate. Exploiting the quasi-experimental expansion of African large-scale gold mining, I find that local infant mortality rates decrease by more than 50% alongside rapid economic growth. The instantaneous reduction is comparable to overall gains in infant survival rates in the study countries from 1970 to today. The results are robust to migration. Local industrial development — despite risk of pollution — may be an effective tool to reduce infant mortality in developing countries.
Natural Language Processing for EHR-Based Computational Phenotyping

This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.
Group Resilience: The Place and Meaning of Relational Pauses:

Recent scholarship on resilience has shed light on the processes by which organizations absorb strain and maintain functioning in the face of adversity. These theories, however, often focus on the ...
A semi-nonparametric estimator of regression discontinuity design with discrete duration outcomes

Abstract We consider the regression discontinuity (RD) design with the duration outcome which has discrete support. The parameters of policy interest are treatment effects on unconditional (duration effect) and conditional (hazard effect) exiting probabilities for each discrete level. We propose a novel semi-nonparametric estimator which exploits a flexible separability structure of the underlying continuous-time duration process. Simultaneous inference over discrete levels is nonstandard since the asymptotic variance matrix is singular with unknown rank. The peculiarity is delivered by the nature of the RD estimand, and we provide solutions. Random censoring and competing risks can also be allowed in our framework.
Design of a Minimally Actuated Medical Exoskeleton With Mechanical Swing-Phase Gait Generation and Sit-Stand Assistance

Lower-extremity powered exoskeletons have traditionally used four to ten powered degrees of freedom to provide gait assistance for individuals with spinal cord injury (SCI). Systems with numerous high-impedance powered degrees of freedom commonly suffer from cumbersome walking dynamics and decreased utility due to added weight and increased control complexity. We propose a new approach to powered exoskeleton design that minimizes actuation and control complexity by embedding intelligence into the hardware. This paper describes a minimalistic system that uses a single motor for each exoskeleton leg in conjunction with a bio-inspired hip-knee coupling mechanism to enable users to walk, sit, and stand. Operating in concert with a custom orthotic knee joint, the exoskeleton hip joint has been designed to mimic the biarticular coupling of human leg muscles thus allowing a single actuator to power both hip and knee motions simultaneously. The implementation of this design resulted in a system that provides comparable performance to existing exoskeletons. This system has been tested on paraplegic subjects and has successfully enabled patients to stand up, sit down, and ambulate in numerous real world situations.Copyright © 2013 by ASME
Homogeneous universal $H$-fields

We consider derivations $\partial$ on Conway's field $\mathbf{No}$ of surreal numbers such that the ordered differential field $(\mathbf{No},\partial)$ has constant field $\mathbb{R}$ and is a model of the model companion of the theory of $H$-fields with small derivation. We show that this determines $(\mathbf{No},\partial)$ uniquely up to isomorphism, and that this structure is absolutely homogeneous universal for models of this theory with constant field $\mathbb{R}$.
Water and sanitation in Dhaka slums : access, quality, and informality in service provision

Slum populations are commonly characterized as having poorly developed water and sanitation systems and may access services through informal channels. However, there are limited representative prof...
Neutrino scattering and B anomalies from hidden sector portals

We examine current constraints on and the future sensitivity to the strength of couplings between quarks and neutrinos in the presence of a form factor generated from loop effects of hidden sector particles that interact with quarks via new interactions. We consider models associated with either vector or scalar interactions of quarks and leptons generated by hidden sector dynamics. We study constraints on these models using data from coherent elastic neutrino-nucleus scattering and solar neutrino experiments and demonstrate how these new interactions may be discovered by utilizing the recoil spectra. We show that our framework can be naturally extended to explain the lepton universality violating neutral current B decay anomalies, and that in a model framework the constraints from neutrino scattering can have implications for these anomalies.
Take Two! SAT Retaking and College Enrollment Gaps

Only half of SAT-takers retake the exam, with even lower retake rates among low-income students and underrepresented minority (URM) students. We exploit discontinuous jumps in retake probabilities at multiples of 100, driven by left-digit bias, to estimate retaking's causal effects. Retaking substantially improves SAT scores and increases four-year college enrollment rates, particularly for low-income and URM students. Eliminating disparities in retake rates could close up to 10 percent of the income-based gap and up to 7 percent of the race-based gap in four-year college enrollment rates of high school graduates.
Teaching Data Analytics Skills in Auditing Classes using Tableau

ABSTRACT We present a case for teaching data analytics skills in auditing classes using the data visualization software Tableau. We use the Tableau-embedded data file “US Superstore,” which we edit...
Direct RNA Sequencing of the Coding Complete Influenza A Virus Genome.

For the first time, a coding complete genome of an RNA virus has been sequenced in its original form. Previously, RNA was sequenced by the chemical degradation of radiolabeled RNA, a difficult method that produced only short sequences. Instead, RNA has usually been sequenced indirectly by copying it into cDNA, which is often amplified to dsDNA by PCR and subsequently analyzed using a variety of DNA sequencing methods. We designed an adapter to short highly conserved termini of the influenza A virus genome to target the (-) sense RNA into a protein nanopore on the Oxford Nanopore MinION sequencing platform. Utilizing this method with total RNA extracted from the allantoic fluid of influenza rA/Puerto Rico/8/1934 (H1N1) virus infected chicken eggs (EID50 6.8 × 109), we demonstrate successful sequencing of the coding complete influenza A virus genome with 100% nucleotide coverage, 99% consensus identity, and 99% of reads mapped to influenza A virus. By utilizing the same methodology one can redesign the adapter in order to expand the targets to include viral mRNA and (+) sense cRNA, which are essential to the viral life cycle, or other pathogens. This approach also has the potential to identify and quantify splice variants and base modifications, which are not practically measurable with current methods.
Learning Job Skills from Colleagues at Work: Evidence from a Field Experiment Using Teacher Performance Data

We study on-the-job learning among classroom teachers, especially learning skills from coworkers. Using data from a new field experiment, we document meaningful improvements in teacher job performance when high- and low-performing teachers working at the same school are paired and asked to work together on improving the low-performer’s skills. In particular, pairs are asked to focus on specific skills identified in the low-performer’s prior performance evaluations. In the classrooms of low-performing teachers treated by the intervention, students scored 0.12 standard deviations higher than students in control classrooms. These improvements in teacher performance persisted, and perhaps grew, in the year after treatment. Empirical tests suggest the improvements are likely the result of low-performing teachers learning skills from their partner.
The Seasonal Cycle in U.S. Manufacturing

Abstract This paper examines the seasonal cycle in the manufacturing sector of the U.S. economy. The most significant feature is a sharp slowdown in July. We argue that this event is not easily explained by technology or preference shifts but instead results from synergies across economic agents.
Phase behavior and morphology of multicomponent liquid mixtures

Multicomponent systems are ubiquitous in nature and industry. While the physics of few-component liquid mixtures (i.e., binary and ternary ones) is well-understood and routinely taught in undergraduate courses, the thermodynamic and kinetic properties of N-component mixtures with N > 3 have remained relatively unexplored. An example of such a mixture is provided by the intracellular fluid, in which protein-rich droplets phase separate into distinct membraneless organelles. In this work, we investigate equilibrium phase behavior and morphology of N-component liquid mixtures within the Flory-Huggins theory of regular solutions. In order to determine the number of coexisting phases and their compositions, we developed a new algorithm for constructing complete phase diagrams, based on numerical convexification of the discretized free energy landscape. Together with a Cahn-Hilliard approach for kinetics, we employ this method to study mixtures with N = 4 and 5 components. We report on both the coarsening behavior of such systems, as well as the resulting morphologies in three spatial dimensions. We discuss how the number of coexisting phases and their compositions can be extracted with Principal Component Analysis (PCA) and K-means clustering algorithms. Finally, we discuss how one can reverse engineer the interaction parameters and volume fractions of components in order to achieve a range of desired packing structures, such as nested "Russian dolls" and encapsulated Janus droplets.
Converting Fontan-Björk to 1.5- or 2-Ventricle Circulation.

Patients with tricuspid atresia and ventricular septal defect have in the past occasionally undergone a Fontan with "Bjork" modification to create a connection between the right atrium and the right ventricular outflow tract. Although rarely performed now, patients with this physiology often face severe complications requiring reintervention. We hypothesize that surgical conversion to a 2-ventricle or 1.5-ventricle circulation can improve hemodynamics, clinical status, and thus increase time to transplant. We present 2 successful cases to illustrate this idea.
A sequential density‐based empirical likelihood ratio test for treatment effects

In health-related experiments, treatment effects can be identified using paired data that consist of pre- and posttreatment measurements. In this framework, sequential testing strategies are widely accepted statistical tools in practice. Since performances of parametric sequential testing procedures vitally depend on the validity of the parametric assumptions regarding underlying data distributions, we focus on distribution-free mechanisms for sequentially evaluating treatment effects. In fixed sample size designs, the density-based empirical likelihood (DBEL) methods provide powerful nonparametric approximations to optimal Neyman-Pearson-type statistics. In this article, we extend the DBEL methodology to develop a novel sequential DBEL testing procedure for detecting treatment effects based on paired data. The asymptotic consistency of the proposed test is shown. An extensive Monte Carlo study confirms that the proposed test outperforms the conventional sequential Wilcoxon signed-rank test across a variety of alternatives. The excellent applicability of the proposed method is exemplified using the ventilator-associated pneumonia study that evaluates the effect of Chlorhexidine Gluconate treatment in reducing oral colonization by pathogens in ventilated patients.
Mean Service Metrics: Biased Quality Judgment and the Customer–Server Quality Gap

Problem definition: People often make service-quality judgments based on information about the quality of each server even though they care primarily about the quality each customer experiences. Wh...
Factor Market Activity and the Inverse Farm Size-Productivity Relationship in Tanzania

Although the inverse farm size-productivity relationship (IR) is sometimes used to motivate arguments in favour of smallholder-led agricultural development, it remains unclear what drives this rela...
What is relative plausibility

Allen and Pardo’s explanation of Relative Plausibility as a theory of evidence and proof in litigation is ambiguous and underspecified. Their account suggests at least three different interpretatio...
User-controllable learning of security and privacy policies

Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have successfully been used to refine models of user preferences, such as in recommender systems, they are generally configured as "black boxes" that take control over the entire policy and severely restrict the ways in which the user can manipulate it. This article presents an alternative approach, referred to as user-controllable policy learning. It involves the incremental manipulation of policies in a context where system and user refine a common policy model. The user regularly provides feedback on decisions made based on the current policy. This feedback is used to identify (learn) incremental policy improvements which are presented as suggestions to the user. The user, in turn, can review these suggestions and decide which, if any, to accept. The incremental nature of the suggestions enhances usability, and because the user and the system manipulate a common policy representation, the user retains control and can still make policy modifications by hand. Results obtained using a neighborhood search implementation of this approach are presented in the context of data derived from the deployment of a friend finder application, where users can share their locations with others, subject to privacy policies they refine over time. We present results showing policy accuracy, which averages 60% upon initial definition by our users climbing as high as 90% using our technique.
Competitive parallelism: getting your priorities right

Multi-threaded programs have traditionally fallen into one of two domains: cooperative and competitive. These two domains have traditionally remained mostly disjoint, with cooperative threading used for increasing throughput in compute-intensive applications such as scientific workloads and cooperative threading used for increasing responsiveness in interactive applications such as GUIs and games. As multicore hardware becomes increasingly mainstream, there is a need for bridging these two disjoint worlds, because many applications mix interaction and computation and would benefit from both cooperative and competitive threading. In this paper, we present techniques for programming and reasoning about parallel interactive applications that can use both cooperative and competitive threading. Our techniques enable the programmer to write rich parallel interactive programs by creating and synchronizing with threads as needed, and by assigning threads user-defined and partially ordered priorities. To ensure important responsiveness properties, we present a modal type system analogous to S4 modal logic that precludes low-priority threads from delaying high-priority threads, thereby statically preventing a crucial set of priority-inversion bugs. We then present a cost model that allows reasoning about responsiveness and completion time of well-typed programs. The cost model extends the traditional work-span model for cooperative threading to account for competitive scheduling decisions needed to ensure responsiveness. Finally, we show that our proposed techniques are realistic by implementing them as an extension to the Standard ML language.
Linear Multilevel Models

Multilevel models are regression models designed for observation units from different nested levels. Predictors at the various levels are combined into a single linear mixed model, in which some of the regression coefficients are random variables and others are fixed constants. The class of models is described, and examples from school effectiveness research and growth curve analysis are given. Keywords: linear models; mixed models; random coefficients; regression
The dark figure of sexual recidivism

Empirical studies of sexual offender recidivism have proliferated in recent decades. Virtually all of the studies define recidivism as a new legal charge or conviction for a sexual crime, and these studies tend to find recidivism rates of the order of 5-15% after 5 years and 10-25% after 10+ years. It is uncontroversial that such a definition of recidivism underestimates the true rate of sexual recidivism because most sexual crime is not reported to legal authorities, a principle known as the "dark figure of crime." To estimate the magnitude of the dark figure of sexual recidivism, this paper uses a probabilistic simulation approach in conjunction with the following: (i) victim self-report survey data about the rate of reporting sexual crime to legal authorities; (ii) offender self-report data about the number of victims per offender; and (iii) different assumptions about the chances of being convicted of a new sexual offense given that it is reported. Under any configuration of assumptions, the dark figure is substantial, and as a consequence the disparity between recidivism defined as a new legal charge or conviction for a sex crime and recidivism defined as actually committing a new sexual crime is large. These findings call into question the utility of recidivism studies that rely exclusively on official crime statistics to define sexual recidivism, and highlight the need for additional, long-term studies that use a variety of different measures to assess whether or not sexual recidivism has occurred.
Annual survey of organometallic metal cluster chemistry for the year 1997

Abstract The synthetic, mechanistic and structural chemistry of organometallic metal cluster compounds is reviewed for the year 1997.
Integrated 3D anatomical model for automatic myocardial segmentation in cardiac CT imagery

Segmentation of epicardial and endocardial boundaries is a critical step in diagnosing cardiovascular function in heart patients. The manual tracing of organ contours in Computed Tomography Angiography (CTA) slices is subjective, time-consuming and impractical in clinical setting. We propose a novel multi-dimensional automatic edge detection algorithm based on shape priors and principal component analysis. Inspired by the work of Tsai et al. [3] and Yezzi et al. [1], we have developed a highly customized parametric model for implicit representations of segmenting curves (3D) for Left Ventricle (LV), Right Ventricle (RV), and Epicardium (Epi) used simultaneously to achieve myocardial segmentation. We have extended the Chan-Vese [4] image modeling framework to segment four regions simultaneously with high level constraints enabling the modeling of complex cardiac anatomical structures to automatically guide the segmentation of endo/epicardial boundaries. Test results on 30 short-axis CTA datasets show robust segmentation with error (mean ± std mm) of (1.46 ± 0.41), (2.06 ± 0.65), (2.88 ± 0.59) for LV, RV and Epi respectively.
Teaching Across Cultures: Navigating Teacher Candidates’ Perceptions of Multicultural Education in China

Developing teaches’ cultural awareness and knowledge base of diversity is essential for developing teachers with multicultural perspective and culturally responsive pedagogy in China. The purpose of this qualitative study was to explore preservice Han teachers’ perspectives of multicultural education in order to enhance institutional quality and effectively prepare culturally responsive Han teachers for multicultural and multiethnic students in China. Following findings were revealed from this study: Puzzling attitudes towards multiculturalism and diversity, popularizing Western culture, unifying Chinese culture, blurring minority cultures and ethnic diversity, symbolized cultural representation and knowledge of minority groups, and inadequate academic preparation in teaching for diversity.
Function and clinical relevance of RHAMM isoforms in pancreatic tumor progression

The receptor for hyaluronic acid-mediated motility (RHAMM) is upregulated in various cancers. We previously screened genes upregulated in human hepatocellular carcinomas for their metastatic function in a mouse model of pancreatic neuroendocrine tumor (PNET) and identified that human RHAMMB promoted liver metastasis. It was unknown whether RHAMMB is upregulated in pancreatic cancer or contributes to its progression. In this study, we found that RHAMM protein was frequently upregulated in human PNETs. We investigated alternative splicing isoforms, RHAMMA and RHAMMB, by RNA-Seq analysis of primary PNETs and liver metastases. RHAMMB, but not RHAMMA, was significantly upregulated in liver metastases. RHAMMB was crucial for in vivo metastatic capacity of mouse and human PNETs. RHAMMA, carrying an extra 15-amino acid-stretch, did not promote metastasis in spontaneous and experimental metastasis mouse models. Moreover, RHAMMB was substantially higher than RHAMMA in pancreatic ductal adenocarcinoma (PDAC). RHAMMB, but not RHAMMA, correlated with both higher EGFR expression and poorer survival of PDAC patients. Knockdown of EGFR abolished RHAMMB-driven PNET metastasis. Altogether, our findings suggest a clinically relevant function of RHAMMB, but not RHAMMA, in promoting PNET metastasis in part through EGFR signaling. RHAMMB can thus serve as a prognostic factor for pancreatic cancer.
An online decision-theoretic pipeline for responder dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time of responders with a drastic reduction in computational time.
Beyond the seed: structural basis for supplementary microRNA targeting by human Argonaute2.

microRNAs (miRNAs) guide Argonaute proteins to mRNAs targeted for repression. Target recognition occurs primarily through the miRNA seed region, composed of guide (g) nucleotides g2-g8. However, nucleotides beyond the seed are also important for some known miRNA-target interactions. Here, we report the structure of human Argonaute2 (Ago2) engaged with a target RNA recognized through both miRNA seed and supplementary (g13-g16) regions. Ago2 creates a "supplementary chamber" that accommodates up to five miRNA-target base pairs. Seed and supplementary chambers are adjacent to each other and can be bridged by an unstructured target loop of 1-15 nucleotides. Opening of the supplementary chamber may be constrained by tension in the miRNA 3' tail, as increases in miRNA length stabilize supplementary interactions. Contrary to previous reports, we demonstrate that optimal supplementary interactions can increase target affinity > 20-fold. These results provide a mechanism for extended miRNA targeting, suggest a function for 3' isomiRs in tuning miRNA targeting specificity, and indicate that supplementary interactions may contribute more to target recognition than is widely appreciated.
CAT tails drive degradation of stalled polypeptides on and off the ribosome.

Stalled translation produces incomplete, ribosome-tethered polypeptides that the ribosome-associated quality control (RQC) pathway targets for degradation via the E3 ubiquitin ligase Ltn1. During this process, the protein Rqc2 and the large ribosomal subunit elongate stalled polypeptides with carboxy-terminal alanine and threonine residues (CAT tails). Failure to degrade CAT-tailed proteins disrupts global protein homeostasis, as CAT-tailed proteins can aggregate and sequester chaperones. Why cells employ such a potentially toxic process during RQC is unclear. Here, we developed quantitative techniques to assess how CAT tails affect stalled polypeptide degradation in Saccharomyces cerevisiae. We found that CAT tails enhance the efficiency of Ltn1 in targeting structured polypeptides, which are otherwise poor Ltn1 substrates. If Ltn1 fails to ubiquitylate those stalled polypeptides or becomes limiting, CAT tails act as degrons, marking proteins for proteasomal degradation off the ribosome. Thus, CAT tails functionalize the carboxy termini of stalled polypeptides to drive their degradation on and off the ribosome. Alanine and threonine residues added to the C terminus of stalled nascent chains (CAT tails) increase degradation of such polypeptides by promoting their ubiquitylation by Ltn1 or by acting as degrons off the ribosome.
Organolithium-mediated diversification of peptide thiazoles.

We report a one-step, racemization-free method for the diversification of peptide thiazoles via direct lithiation of the thiazole ring. The method is compatible with N-Boc, N-trityl, carboxylic ester, and carboxamide protecting groups and has been used to directly functionalize the thiazole ring of cyclopeptide natural products.
QuASAR: quantitative allele-specific analysis of reads

Motivation: Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele-specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available, it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. Results: We present QuASAR, quantitative allele-specific analysis of reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls, while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high-quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available. Availability and implementation: http://github.com/piquelab/QuASAR. Contact: ude.enyaw@aculf or ude.enyaw@euqipr Supplementary information: Supplementary Material is available at Bioinformatics online.
Synthesis of β,γ-unsaturated amino acid derivatives by alkyne carbometalation-palladium catalyzed coupling with 2-aza-π-allyl palladium complexes

Abstract Zirconium-catalyzed carboalumination of terminal or symmetrical internal alkynes followed by palladium-catalyzed coupling of the resulting vinylalane with Schiff base acetate 1 gives protected β,γ-unsaturated amino acid derivatives (vinylglycines) 6 .
Site-specific preparation of 3-fluoro-1-substituted-naphthalenes via a novel base-catalyzed cyclization reaction from (E)-monofluoroenynes.

A novel cyclization reaction for the preparation of 3-fluoro-1-substituted-naphthalenes is reported. (E)-Monofluoroenynes, which are prepared by Sonogashira coupling reaction from (Z)-1-bromo-1-flu...
Dynamics of Allosteric Transitions in Dynein

Summary Cytoplasmic dynein, whose motor domain belongs to the AAA+ family, walks on microtubules toward the minus end. Using the available structures in different nucleotide states, we performed simulations of a coarse-grained model to elucidate the dynamics of allosteric transitions. Binding of ATP closes the cleft between the AAA1 and AAA2 domains, triggering conformational changes in the rest of the motor domain, thus forming the pre-power stroke state. Interactions with the microtubule, modeled implicitly, enhance ADP release rate, and the formation of the post-power stroke state. The dynamics of the linker (LN), which reversibly changes from a straight to a bent state, is heterogeneous. Persistent interactions between the LN and the insert loops in the AAA2 domain prevent the formation of pre-power stroke state when ATP is bound to AAA3, thus locking dynein in a repressed non-functional state. Application of mechanical force to the LN restores motility in the repressed state.
Controlled Purification of Single-Walled Carbon Nanotube Films by Use of Selective Oxidation and Near-IR Spectroscopy

Electric-arc produced as-prepared single-walled carbon nanotube (AP-SWNT) films have been purified by a selective oxidation process. The purification is monitored by near-infrared (NIR) spectroscopy, which gives a quantitative estimate of relative purities. This technique also gives an estimate of the change in relative amounts of SWNTs and amorphous carbon impurities after each purification step, thereby providing accurate and selective control over the oxidation process. Oxidation was carried out by reaction in flowing oxygen at different temperatures and for different durations of time; the purity was evaluated after each oxidation step and compared to the starting as-prepared film. The purities and amounts of SWNTs and carbonaceous impurities were monitored as a function of oxidation temperature and time. Optimization of the process allowed a 3-fold improvement in the AP-SWNT purity with almost quantitative retention of the SWNT portion of the sample. A purification efficiency is defined, which is exp...
Carbocupration−Functionalization of Arynes: Rapid Access to Variably Ortho-Substituted ((E)-3-Phenylprop-1-enyl)silanes

A consecutive three-component coupling reaction involving a lithium di[3-(prop-1-enyltrimethylsilyl)]cuprate, variably substituted ortho-arynes, and a selection of common electrophiles is described. The method affords readily functionalized homobenzylic vinylsilanes with exceptional E-diastereoselectivity and allows for in situ incorporation of carbon- or heteroatom-based electrophiles into the arene.
Bridged to fused ring interchange. New methodology for the construction of fused cycloheptanes and cyclooctanes

Abstract The type two intramolecular Diels-Alder cycloaddition coupled with a bridged to fused interchange provides an efficient method for synthesis of fused 5,7 and 5,8 ring systems.