cssi/SciSciGPT-SciSciNet huggingface dataset:
A Standard Method for Determining the Minimal Clinically Important Difference for Rehabilitation Measures.

Abstract The minimal clinically important difference (MCID) is receiving increasing interest and importance in medical practice and research. The MCID is the smallest improvement in scores in the domain of interest that patients perceive as beneficial. In clinical trials, comparing the proportion of individuals between treatment and control groups who obtain a MCID may be more informative than comparisons of mean change between groups because a statistically significant mean difference does not necessarily represent a difference that is perceived as meaningful by treatment recipients. The MCID may also be useful in advancing personalized medicine by characterizing those who are most likely to benefit from a treatment. In clinical practice, the MCID can be used to identify if a participant is experiencing a meaningful change in status. A variety of methods have been used to determine the MCID with no clear agreement on the most appropriate approach. Two major sets of methods are either (1) distribution-based, that is, referencing the MCID to a measure of variability or effect size in the measure of interest or (2) anchor-based, that is, referencing the MCID to an external assessment of change in the condition, ability, or activity represented by the measure of interest. In prior literature, using multiple methods to “triangulate” on the value of the MCID has been proposed. In this commentary, we describe a systematic approach to triangulate on the MCID using both distribution-based and anchor-based methods. Adaptation of a systematic approach for obtaining the MCID in rehabilitation would facilitate communication and comparison of results among rehabilitation researchers and providers.
Alternative transcription cycle for bacterial RNA polymerase.

RNA polymerases (RNAPs) transcribe genes through a cycle of recruitment to promoter DNA, initiation, elongation, and termination. After termination, RNAP is thought to initiate the next round of transcription by detaching from DNA and rebinding a new promoter. Here we use single-molecule fluorescence microscopy to observe individual RNAP molecules after transcript release at a terminator. Following termination, RNAP almost always remains bound to DNA and sometimes exhibits one-dimensional sliding over thousands of basepairs. Unexpectedly, the DNA-bound RNAP often restarts transcription, usually in reverse direction, thus producing an antisense transcript. Furthermore, we report evidence of this secondary initiation in live cells, using genome-wide RNA sequencing. These findings reveal an alternative transcription cycle that allows RNAP to reinitiate without dissociating from DNA, which is likely to have important implications for gene regulation. In the canonical bacterial transcription, both nascent transcript and polymerase dissociate from template DNA. By employing multi-color single-molecule fluorescence imaging, here the authors show that RNA polymerases remain bound to DNA after the transcript release.
Interaction between an inclined gravity current and a pycnocline in a two-layer stratification

A series of laboratory experiments were conducted to investigate the characteristics of a dense gravity current flowing down an inclined slope into a quiescent two-layer stratification. The presence of the pycnocline causes the gravity current to split and intrude into the ambient at two distinct levels of neutral buoyancy, as opposed to the classical description of gravity currents in stratified media as being either a pure underflow or interflow. The splitting behaviour is observed to be dependent on the Richardson number (. Additionally, we have also identified two different types of waves that form on the pycnocline in response to the intrusion of the gravity current. An underflow-dominated regime causes a pycnocline displacement where the speed of the wave crest is locked to the gravity current, whereas an interflow-dominated regime launches an internal wave that moves much faster than the gravity current head or interfacial intrusion.
Parallel distributed networks dissociate episodic and social functions within the individual.

Two distributed, interdigitated networks exist within the bounds of the canonical default network. Here we used repeated scanning of individuals, across three independent samples, to provide eviden...
Array‐based iterative measurements of SmKS travel times and their constraints on outermost core structure

Vigorous convection in Earths outer core led to the suggestion that it is chemically homogeneous. However, there is increasing seismic evidence for structural complexities close to the outer cores upper and lower boundaries. Both body waves and normal mode data have been used to estimate a P-wave velocity, Vp, at the top of the outer core (the E layer), which is lower than that in the Preliminary Reference Earth Model. However, these low Vp models do not agree on the form of this velocity anomaly. One reason for this is the difficulty in retrieving and measuring SmKS arrival times. To address this issue, we propose a novel approach using data from seismic arrays to iteratively measure SmKS-SKKS differential travel times. This approach extracts individual SmKS signal from mixed waveforms of the SmKS series, allowing us to reliably measure differential travel times. We successfully use this method to measure SmKS time delays from earthquakes in the Fiji-Tonga and Vanuatu subduction zones. SmKS time delays are measured by waveform cross-correlation (CC) between SmKS and SKKS and the CC coefficient allows us to access measurement quality. We also apply this iterative scheme to synthetic SmKS seismograms to investigate the 3D mantle structures effects. The mantle structure corrections are not negligible for our data and neglecting them could bias the Vp estimation of uppermost outer core. After mantle structure corrections, we can still see substantial time delays of S3KS, S4KS and S5KS, supporting a low Vp at the top of Earths outer core.
Implementation of the perfect plasticity approximation with biogeochemical compartments in R

Modeling forest ecosystems is a landmark challenge in science, due to the complexity of the processes involved and their importance in predicting future planetary conditions. While there are a number of open‐source forest biogeochemistry models, few papers exist detailing the software development approach used to develop these models. This has left many forest biogeochemistry models large, opaque and/or difficult to use, typically implemented in compiled languages for speed. Here, we present a forest biogeochemistry model from the SORTIE‐PPA class of models, PPA‐SiBGC. Our model is based on the perfect plasticity approximation with simple biogeochemistry compartments and uses empirical vegetation dynamics rather than detailed prognostic processes to drive the estimation of carbon and nitrogen fluxes. This allows our model to be used with traditional forest inventory data, making it widely applicable and simple to parameterize. We detail the conceptual design of the model as well as the software implementation in the R language for statistical computing. Our aim is to provide a useful tool for the biogeochemistry modeling community that demonstrates the importance of vegetation dynamics in biogeochemical models.
A Framework for Generating Synthetic Distribution Feeders using OpenStreetMap

This work proposes a framework to generate synthetic distribution feeders mapped to real geo-spatial topologies using available OpenStreetMap data. The synthetic power networks can facilitate power systems research and development by providing thousands of realistic use cases. The location of substations is taken from recent efforts to develop synthetic transmission test cases, with underlying real and reactive power in the distribution network assigned using population information gathered from United States 2010 Census block data. The methods illustrate how to create individual synthetic distribution feeders, and groups of feeders across entire ZIP Code, with minimal input data for any location in the United States. The framework also has the capability to output data in OpenDSS format to allow further simulation and analysis.
Search and rescue in the face of uncertain threats

Abstract We consider a search problem in which one or more targets must be rescued by a search party, or Searcher. The targets may be survivors of some natural disaster, or prisoners held by an adversary. The targets are hidden among a finite set of locations, but when a location is searched, there is a known probability that the search will come to an end, perhaps because the Searcher becomes trapped herself, or is captured by the adversary. If this happens before all the targets have been recovered, then the rescue attempt is deemed a failure. The objective is to find the search that maximizes the probability of recovering all the targets. We present and solve a game theoretic model for this problem, by placing it in a more general framework that encompasses another game previously introduced by the author. We also consider an extension to the game in which the targets are hidden on the vertices of a graph. In the case that there is only one target, we give a solution of the game played on a tree.
Quantification of mitochondrial membrane potential in the isolated rat lung using rhodamine 6G.

A novel experimental and computational approach for estimating mitochondrial membrane potential (Δψm) in intact functioning lungs is presented. The isolated rat lung inlet-outlet concentrations of ...
A gene-diet interaction-based score predicts response to dietary fat in the Women's Health Initiative

Background Although diet response prediction for cardiometabolic risk factors (CRFs) has been demonstrated using single genetic variants and main-effect genetic risk scores, little investigation has gone into the development of genome-wide diet response scores. Objective We sought to leverage the multistudy setup of the Women's Health Initiative cohort to generate and test genetic scores for the response of 6 CRFs (BMI, systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting glucose) to dietary fat. Methods A genome-wide interaction study was undertaken for each CRF in women (n ∼ 9000) not participating in the dietary modification (DM) trial, which focused on the reduction of dietary fat. Genetic scores based on these analyses were developed using a pruning-and-thresholding approach and tested for the prediction of 1-y CRF changes as well as long-term chronic disease development in DM trial participants (n ∼ 5000). Results Only 1 of these genetic scores, for LDL cholesterol, predicted changes in the associated CRF. This 1760-variant score explained 3.7% (95% CI: 0.09, 11.9) of the variance in 1-y LDL cholesterol changes in the intervention arm but was unassociated with changes in the control arm. In contrast, a main-effect genetic risk score for LDL cholesterol was not useful for predicting dietary fat response. Further investigation of this score with respect to downstream disease outcomes revealed suggestive differential associations across DM trial arms, especially with respect to coronary heart disease and stroke subtypes. Conclusions These results lay the foundation for the combination of many genome-wide gene-diet interactions for diet response prediction while highlighting the need for further research and larger samples in order to achieve robust biomarkers for use in personalized nutrition.
Joint Time and Energy-Optimal Control of Connected Automated Vehicles at Signal-Free Intersections with Speed-Dependent Safety Guarantees

We extend earlier work establishing a framework for optimally controlling Connected Automated Vehicles (CAVs) crossing a signal-free intersection by jointly optimizing energy and travel time. We derive explicit optimal control solutions in a decentralized manner that guarantee both a speed-dependent rear-end safety constraint and a time-dependent lateral collision constraint, in addition to lower/upper bounds on speed and acceleration. Extensive simulation examples are included to illustrate this framework.
Long-term Dietary Intervention Reveals Resilience of the Gut Microbiota Despite Changes in Diet and Weight

BACKGROUND With the rising rates of obesity and associated metabolic disorders, there is a growing need for effective long-term weight-loss strategies, coupled with an understanding of how they interface with human physiology. Interest is growing in the potential role of gut microbes as they pertain to responses to different weight-loss diets; however, the ways that diet, the gut microbiota, and long-term weight loss influence one another is not well understood. OBJECTIVES Our primary objective was to determine if baseline microbiota composition or diversity was associated with weight-loss success. A secondary objective was to track the longitudinal associations of changes to lower-carbohydrate or lower-fat diets and concomitant weight loss with the composition and diversity of the gut microbiota. METHODS We used 16S ribosomal RNA gene amplicon sequencing to profile microbiota composition over a 12-mo period in 49 participants as part of a larger randomized dietary intervention study of participants consuming either a healthy low-carbohydrate or a healthy low-fat diet. RESULTS While baseline microbiota composition was not predictive of weight loss, each diet resulted in substantial changes in the microbiota 3-mo after the start of the intervention; some of these changes were diet specific (14 taxonomic changes specific to the healthy low-carbohydrate diet, 12 taxonomic changes specific to the healthy low-fat diet) and others tracked with weight loss (7 taxonomic changes in both diets). After these initial shifts, the microbiota returned near its original baseline state for the remainder of the intervention, despite participants maintaining their diet and weight loss for the entire study. CONCLUSIONS These results suggest a resilience to perturbation of the microbiota's starting profile. When considering the established contribution of obesity-associated microbiotas to weight gain in animal models, microbiota resilience may need to be overcome for long-term alterations to human physiology. This trial was registered at clinicaltrials.gov as NCT01826591.
Shared-memory Parallel Maximal Clique Enumeration from Static and Dynamic Graphs

Maximal Clique Enumeration (MCE) is a fundamental graph mining problem and is useful as a primitive in identifying dense structures in a graph. Due to the high computational cost of MCE, parallel methods are imperative for dealing with large graphs. We present shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm, (2) the algorithms have a provably small parallel depth, showing they can scale to a large number of processors, and (3) our implementations on a multicore machine show good speedup and scaling behavior with increasing number of cores and are substantially faster than prior shared-memory parallel algorithms for MCE; for instance, on certain input graphs, while prior works either ran out of memory or did not complete in five hours, our implementation finished within a minute using 32 cores. We also present work-efficient parallel algorithms for maintaining the set of all maximal cliques in a dynamic graph that is changing through the addition of edges.
The Market For Bitcoin Transactions

Abstract Transaction fees in the bitcoin system work differently from those in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees and transaction volume in the bitcoin system increase whenever the network is congested, and our VAR results confirm that is indeed the case. To account for the empirical findings, we build a model where users and miners together determine transaction fees and transaction volume. Even though the mechanism of fluctuating transaction fees in bitcoin introduces an extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fixed transaction fee rate. However, this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.
Identification of universal and cell-type specific p53 DNA binding

The tumor suppressor p53 is a major regulator of the DNA damage response and has been suggested to selectively bind and activate cell-type specific gene expression programs. However recent studies and meta-analyses of genomic data propose largely uniform, and condition independent p53 binding and thus question the selective and cell-type dependent function of p53. To systematically assess the cell-type specificity of p53, we measured its association with DNA in 12 p53 wild-type cancer cell lines, from a range of epithelial linages, in response to ionizing radiation. We found that the majority of bound sites were occupied across all cell lines, however we also identified a subset of binding sites that were specific to one or a few cell lines. Unlike the shared p53-bound genome, which was not dependent on chromatin accessibility, the association of p53 with these atypical binding sites was well explained by chromatin accessibility and could be modulated by forcing cell state changes such as the epithelial-to-mesenchymal transition. Our study reconciles previous conflicting views in the p53 field, by demonstrating that although the majority of p53 DNA binding is conserved across cell types, there is a small set of cell line specific binding sites that depend on cell state.
On the monotonicity and discrete maximum principle of the finite difference implementation of $C^0$-$Q^2$ finite element method

We show that the fourth order accurate finite difference implementation of continuous finite element method with tensor product of quadratic polynomial basis is monotone thus satisfies the discrete maximum principle for solving a scalar variable coefficient equation $$-\nabla \cdot (a\nabla u)+cu=f$$ under a suitable mesh constraint.
Whirl Flutter Investigation of Hingeless Proprotors

Hingeless proprotor whirl flutter in cruise flight is investigated using comprehensive rotorcraft analysis codes CAMRAD II and RCAS. Generic hingeless proprotor designs include conventional soft-in...
A Bayesian Nonparametric Approach for Evaluating the Causal Effect of Treatment in Randomized Trials with Semi-Competing Risks

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting.
Characterization of Hard Piezoelectric Lead-Free Ceramics

K 4 CuNb 8 O 23 doped K 0.45 Na 0.55 NbO 3 (KNN-KCN) ferroelectric ceramics were found to exhibit asymmetrical polarization hysteresis loops, related to the development of an internal bias field. The internal bias field is believed to be the result of defect dipoles of acceptor ions and oxygen vacancies, which lead to piezoelectric "hardening" effect, by stabilizing and pinning of the domain wall motion. The dielectric loss for the hard lead-free piezoelectric ceramic was found to be 0.6%, with mechanical quality factors Q on the order of >1500. Furthermore, the piezoelectric properties were found to decrease and the coercive field increased, when compared with the undoped material, exhibiting a typical characteristic of "hard" behavior. The temperature usage range was limited by the polymorphic phase transition temperature, being 188°C. The full set of material constants was determined for the KNN-KCN materials. Compared with conventional hard PZT ceramics, the lead-free possessed lower dielectric and piezoelectric properties; however, comparable values of mechanical Q, dielectric loss, and coercive fields were obtained, making acceptor modified KNN based lead-free piezoelectric material promising for high-power applications, where lead-free materials are desirable.
Exchange-Rate Risk Mitigation with Price-Level-Adjusting Mortgages:The Case of the Mexican UDI

This article is the winner of the Real Estate Finance manuscript prize (sponsored by the Fannie Mae Foundation) presented at the 2002 American Real Estate Society Annual Meeting.In 1995, Mexico int...
How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

Endowing robots with the capability of assessing risk and making risk-aware decisions is widely considered a key step toward ensuring safety for robots operating under uncertainty. But, how should a robot quantify risk? A natural and common approach is to consider the framework whereby costs are assigned to stochastic outcomes–an assignment captured by a cost random variable. Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping from the cost random variable to a real number. Yet, the question of what constitutes a “good” risk metric has received little attention within the robotics community. The goal of this paper is to explore and partially address this question by advocating axioms that risk metrics in robotics applications should satisfy in order to be employed as rational assessments of risk. We provide instantiations of the class of risk metrics that satisfy these axioms (referred to as distortion risk metrics) and also discuss pitfalls of commonly used risk metrics in robotics that do not satisfy these axioms. Our hope is that the ideas presented here will lead to a foundational framework for quantifying risk (and hence safety) in robotics applications.
Multiparty Generation of an RSA Modulus

We present a new multiparty protocol for the distributed generation of biprime RSA moduli, with security against any subset of maliciously colluding parties assuming oblivious transfer and the hardness of factoring.
Rise and Shine: The Effect of School Start Times on Academic Performance from Childhood through Puberty

We analyze the effect of school start time on academic performance. Sleep patterns are determined in part by sunrise times, which vary across time zones. Because school start times do not fully reflect this difference, we instrument for the hours of sunlight before school with the time zone boundary in Florida. We find that moving start times one hour later relative to sunrise increases test scores by 0.08 and 0.06 standard deviations for adolescents in math and reading, respectively. In math, the effect is larger for older children and co-varies with entry into an important pubertal stage. School districts can improve performance while maintaining the current distribution of start times by moving classes earlier for younger children and later for older children. [ABSTRACT FROM AUTHOR]
Fiscal Deficits and Inflation

Abstract Macroeconomic theory postulates that persistent fiscal deficits are inflationary. Yet empirical research has had limited success in uncovering this relationship. This paper reexamines the issue in light of broader data and a new modeling approach that incorporates two key features of the theory. Unlike previous studies, we model inflation as non-linearly related to fiscal deficits through the inflation tax base and estimate this relationship as intrinsically dynamic, using panel techniques that explicitly distinguish between short- and long-run effects of fiscal deficits. Results spanning 107 countries over 1960–2001 show a strong positive association between deficits and inflation among high-inflation and developing country groups, but not among low-inflation advanced economies.
2 – The Vitamins

Publisher Summary This chapter provides an overview of the chemical characteristics, functions, and the deficiency syndromes of several water-soluble and fat-soluble vitamins found in fishes. Typical avitaminosis symptoms of Chastek-type paralysis, cataracts, convulsions, scoliosis, anemia, slime patch disease, clubbed gills, poor growth anorexia, and increasing mortality were reported wherever fish were concentrated and intensive fish cultural practices were used. Hypervitaminosis D and A were reported when seal and whale liver were used as one of the fresh meat components in salmon diets. Some other forms such as animal protein factors and vitamin P are yet to be defined and isolated. Unknown factors exist which influence osmoregulation and cell membrane permeability. These may be especially important in fish during transition from fresh water to a saltwater environment. Most of the water-soluble vitamins act as coenzymes for enzyme functions, which often require trace amounts of certain mineral ions for activation. Several types of nutritional anemia occur which may be due to several nutritional deficiencies. Unfortunately, most anemia are multiple deficiencies of essential nutrients and the fish clinician must carefully observe the characteristics of the anemia involved to obtain clues as to the nutritional cause.
Ion-Acoustic Double Layers

It is found, by means of numerical simulations, that an ion-acoustic instability (${v}_{d}l{v}_{\mathrm{te}}$) results in the formation of double layers in a sufficiently long system. Anomalous resistivity generated by the ion-acoustic instability causes the buildup of a dc potential which in turn accelerates electrons further to enhance the original instability leading to the formation of double layers.
The Second War and Postmodern Memory

We never discussed the Second World War much when I was growing up. I don't feel much like discussing it now. It seems presumptuous to interpret, much less give literary interpretations of, the Systematic Extermination Process or the dropping of the H-Bomb, the two poles of the Second War. When Stanley Diamond asked me to speak on "Poetry after the Holocaust," my first reaction was to wonder what qualifications I had to speak?as if the topic of the war made me question my standing, made me wonder what I might say that could bear the weight of this subject matter. Diamond reassured me that the audience would be small: "For many, the Holocaust is too far in the past to matter; for most of the rest, it's too painful to bring to mind." My father-in-law, who left Berlin as a teenager on a youth ally ah and spent the war in Palestine, had a different reaction: all these Holocaust conferences are a fad. This reaction is as disturbing as it is right. The Holocaust has come to stand for a kind of Secular Satanism?everyone's against it, anyone can work up a feverish moral fervor denouncing the Nazi Monster. Yet I've been struck by just the opposite: that the psychological effects of the Second War are still largely repressed and that we are
Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

In this study, a new approach based on adaptive dynamic programming (ADP) is proposed to control permanent magnet synchronous motors (PMSMs). The objective of this study is to control the torque and consequently the speed of a PMSM when an unknown load torque is applied to it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilised to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field oriented control (FOC) and direct torque control based on space vector modulation. Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modelled dynamics. Although the performance of FOC method is comparable with ADP under nominal conditions, the torque and speed response of ADP is better than FOC under realistic scenarios, that is, when parameter uncertainties exist.
Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series

A Bayesian lattice filtering and smoothing approach is proposed for fast and accurate modeling and inference in multivariate non‐stationary time series. This approach offers computational feasibility and interpretable time‐frequency analysis in the multivariate context. The proposed framework allows us to obtain posterior estimates of the time‐varying spectral densities of individual time series components, as well as posterior measurements of the time‐frequency relationships across multiple components, such as time‐varying coherence and partial coherence. The proposed formulation considers multivariate dynamic linear models (MDLMs) on the forward and backward time‐varying partial autocorrelation coefficients (TV‐VPARCOR). Computationally expensive schemes for posterior inference on the multivariate dynamic PARCOR model are avoided using approximations in the MDLM context. Approximate inference on the corresponding time‐varying vector autoregressive (TV‐VAR) coefficients is obtained via Whittle's algorithm. A key aspect of the proposed TV‐VPARCOR representations is that they are of lower dimension, and therefore more efficient, than TV‐VAR representations. The performance of the TV‐VPARCOR models is illustrated in simulation studies and in the analysis of multivariate non‐stationary temporal data arising in neuroscience and environmental applications. Model performance is evaluated using goodness‐of‐fit measurements in the time‐frequency domain and also by assessing the quality of short‐term forecasting.
A simple generalization of El-Gamal cryptosystem to non-abelian groups.

In this paper we propose the group of unitriangular matrices over a finite field as a non-abelian group and composition of inner, diagonal and central automorphisms as a group of automorphisms for the MOR cryptosystem.
Detecting transcriptomic structural variants in heterogeneous contexts via the Multiple Compatible Arrangements Problem

Transcriptomic structural variants (TSVs)—large-scale transcriptome sequence change due to structural variation - are common in cancer. TSV detection from high-throughput sequencing data is a computationally challenging problem. Among all the confounding factors, sample heterogeneity, where each sample contains multiple distinct alleles, poses a critical obstacle to accurate TSV prediction. To improve TSV detection in heterogeneous RNA-seq samples, we introduce the Multiple Compatible Arrangements Problem (MCAP), which seeks k genome arrangements that maximize the number of reads that are concordant with at least one arrangement. This models a heterogeneous or diploid sample. We prove that MCAP is NP-complete and provide a $$\frac{1}{4}$$-approximation algorithm for $$k=1$$ and a $$\frac{3}{4}$$-approximation algorithm for the diploid case ($$k=2$$) assuming an oracle for $$k=1$$. Combining these, we obtain a $$\frac{3}{16}$$-approximation algorithm for MCAP when $$k=2$$ (without an oracle). We also present an integer linear programming formulation for general k. We characterize the conflict structures in the graph that require $$k>1$$ alleles to satisfy read concordancy and show that such structures are prevalent. We show that the solution to MCAP accurately addresses sample heterogeneity during TSV detection. Our algorithms have improved performance on TCGA cancer samples and cancer cell line samples compared to a TSV calling tool, SQUID. The software is available at https://github.com/Kingsford-Group/diploidsquid.
Machine learning guided adjuvant treatment of head and neck cancer.

6567Background: A combined analysis of the EORTC 22931 and RTOG 95-01 trials confirmed that patients (pts) with head and neck squamous cell carcinoma (HNSCC) and positive margins or extracapsular e...
On Fujita's freeness conjecture in dimension 5

Abstract Let X be a smooth projective variety of dimension 5 and A an integral ample divisor on X. We show that | K X + k A | is basepoint-free for any integer k ≥ 6 .
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields. A key remaining challenge lies in developing programming paradigms for molecular computation that are well-aligned with the underlying chemical hardware and do not attempt to shoehorn ill-fitting electronics paradigms. We discover a surprisingly tight connection between a popular class of neural networks (binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates. The robustness of rate-independent chemical computation makes it a promising target for bioengineering implementation. We show how a BinaryConnect neural network trained in silico using well-founded deep learning optimization techniques, can be compiled to an equivalent chemical reaction network, providing a novel molecular programming paradigm. We illustrate such translation on the paradigmatic IRIS and MNIST datasets. Toward intended applications of chemical computation, we further use our method to generate a chemical reaction network that can discriminate between different virus types based on gene expression levels. Our work sets the stage for rich knowledge transfer between neural network and molecular programming communities.
Sparse representation for damage identification of structural systems

Identifying damage of structural systems is typically characterized as an inverse problem which might be ill-conditioned due to aleatory and epistemic uncertainties induced by measurement noise and...
Path integration error and adaptable search behaviors in a mantis shrimp

Mantis shrimp of the species Neogonodactylus oerstedii occupy small burrows in shallow waters throughout the Caribbean. These animals use path integration, a vector-based navigation strategy, to return to their homes while foraging. Here we report that path integration in N. oerstedii is prone to error accumulated during outward foraging paths and we describe the search behavior that N. oerstedii employs after it fails to locate its home following the route provided by its path integrator. This search behavior forms continuously expanding, non-oriented loops that are centered near the point of search initiation. The radius of this search is scaled to the animal's positional uncertainty during path integration, improving the effectiveness of the search. The search behaviors exhibited by N. oerstedii bear a striking resemblance to search behaviors in other animals, offering potential avenues for the comparative examination of search behaviors and how they are optimized in disparate taxa.
Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization

Motivation Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy. Method We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods. Results On challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2-10%, and Q3 accuracy by more than 1-3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction. Availability and implementation A preliminary implementation in a new tool we call Nnessy is available free for non-commercial use at http://nnessy.cs.arizona.edu.
Worst-Case Sensitivity of DC Optimal Power Flow Problems

In this paper we consider the problem of analyzing the effect a change in the load vector can have on the optimal power generation in a DC power flow model. The methodology is based upon the recently introduced concept of the OPF operator. It is shown that for general network topologies computing the worst-case sensitivities is computationally intractable. However, we show that certain problems involving the OPF operator can be equivalently converted to a graphical discrete optimization problem. Using the discrete formulation, we provide a decomposition algorithm that reduces the computational cost of computing the worst-case sensitivity. A 27-bus numerical example is used to illustrate our results.
Short Stature is Progressive in Patients with Heterozygous NPR2 Mutations.

Background NPR2 encodes atrial natriuretic peptide receptor B (ANPRB), a regulator of skeletal growth. Biallelic loss-of-function mutations in NPR2 result in acromesomelic dysplasia Maroteaux type (AMDM; OMIM 602875), while heterozygous mutations may account for 2% to 6% of idiopathic short stature (ISS). Objective Describe the physical proportions and growth characteristics of an extended family with novel NPR2 mutations including members with AMDM, ISS, or normal stature. Design and participants We performed whole exome sequencing in 2 healthy parents and 2 children with AMDM. Detailed genotyping and phenotyping were performed on members of a multigenerational family in an academic medical center. We expressed mutant proteins in mammalian cells and characterized expression and function. Results The sisters with AMDM were compound heterozygotes for missense mutations in the NPR2 gene, a novel p.P93S (maternal) and the previously reported p.R989L (paternal). Both mutant ANPRB proteins were normally expressed in HEK293T cells and exhibited dominant negative effects on wild-type ANPRB catalytic activity. Heterozygous relatives had proportionate short stature (height z-scores -2.06 ± 0.97, median ± SD) compared with their wild-type siblings (-1.37 ± 0.59). Height z-scores progressively and significantly decreased as NPR2-heterozygous children matured, while remaining constant in their wild-type siblings. Conclusions Biallelic NPR2 mutations cause severe skeletal dysplasia (AMDM), whereas heterozygous mutations lead to a subtler phenotype characterized by progressive short stature with by increasing loss of height potential with age.
Separating Controller Design from Closed-Loop Design: A New Perspective on System-Level Controller Synthesis

We show that given a desired closed-loop response for a system, there exists an affine subspace of controllers that achieve this response. By leveraging the existence of this subspace, we are able to separate controller design from closed-loop design by first synthesizing the desired closed-loop response and then synthesizing a controller that achieves the desired response. This is a useful extension to the recently introduced System Level Synthesis framework, in which the controller and closed-loop response are jointly synthesized and we cannot enforce controller-specific constraints without subjecting the closed-loop map to the same constraints.We demonstrate the importance of separating controller design from closed-loop design with an example in which communication delay and locality constraints cause standard SLS to be infeasible. Using our new two-step procedure, we are able to synthesize a controller that obeys the constraints while only incurring a 3% increase in LQR cost compared to the optimal LQR controller.
Diagonalizing the Ricci Tensor

We show that a basis of a semisimple Lie algebra of compact type, for which any diagonal left-invariant metric has a diagonal Ricci tensor, is characterized by the Lie algebraic condition of being “nice”. Namely, the bracket of any two basis elements is a multiple of another basis element. This extends the work of Lauret and Will (Proc Am Math Soc 141(10):3651–3663, 2013) on nilpotent Lie algebras. The result follows from a more general characterization for diagonalizing the Ricci tensor for homogeneous spaces. Finally, we also study the Ricci flow behavior of diagonal metrics on cohomogeneity one manifolds.
Declarative Recursive Computation on an RDBMS: or, Why You Should Use a Database For Distributed Machine Learning

We explore the close relationship between the tensor-based computations performed during modern machine learning, and relational database computations. We consider how to make a very small set of changes to a modern RDBMS to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, DBMSbased learning allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.
Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition.

Oncogenic mutations in the kinase domain of the B-Raf protein have long been associated with cancers involving the MAPK pathway. One constitutive MAPK activating mutation in B-Raf, the V600E (valin...
Counting signed vexillary permutations

Abstract We show that the number of signed permutations avoiding 1234 equals the number of signed permutations avoiding 2143 (also called vexillary signed permutations), resolving a conjecture by Anderson and Fulton. The main tool that we use is the generating tree developed by West. Many further directions are mentioned in the end.
L1cam curbs the differentiation of adult-born hippocampal neurons

L1 is an immunoglobulin domain (Ig)-containing protein essential for a wide range of neurodevelopmental processes highly conserved across species from worms to humans. L1 can act as a cell adhesion molecule by binding to other Ig-containing proteins or as a ligand for certain tyrosine kinase receptors such as FGFRs and TRKs, which are required not only during neurodevelopment but also in hippocampal neurogenesis. Yet, the role of L1 itself in adult hippocampal neurogenesis remains unaddressed. Here, we used several Cre-driver lines in mice to conditionally delete a floxed allele of L1cam at different points along the differentiation lineage of new neurons and in surrounding neurons in the adult dentate gyrus of the hippocampus. We found that L1cam deletion in stem/progenitor cells increased: 1) the differentiation of progenitors into new neurons, 2) the complexity of dendritic arbors in immature neurons, and 3) anxiety-related behavior. In addition, deletion of L1cam in neurons leads to an earlier age-related decline in hippocampal neurogenesis. These data suggest that L1 is not only important for normal nervous system development, but also for maintaining certain neural processes in adulthood.
How to select observers

This paper develops a nested-set formalism for problems in which observers are part of the system. Whether observer selection effects arise in a variety of problems is shown to depend on how observers are selected.
Design of a Multi-Channel Photonic Crystal Dielectric Laser Accelerator

We propose a photonic crystal architecture for a dielectric laser accelerator that enables simutaneous acceleration of multiple electron beams. To achieve this, the band structure condition is discussed.
DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds

Planning in unstructured environments is challenging – it relies on sensing, perception, scene reconstruction, and reasoning about various uncertainties. We propose DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based planner for unstructured environments. Our algorithmic pipeline consists of: a deep Bayesian neural network which segments surfaces with uncertainty estimates; a flexible point cloud scene representation; a next-best-view planner which minimizes the uncertainty of scene semantics using sparse visual measurements; and a hypothesis-based path planner that proposes multiple kinematically feasible paths with evolving safety confidences given next-best-view measurements. Our pipeline iteratively decreases semantic uncertainty along planned paths, filtering out unsafe paths with high confidence. We show that our framework plans safe paths in real-world environments where existing path planners typically fail.
Minor Privacy Protection Through Real-time Video Processing at the Edge

The collection of a lot of personal information about individuals, including the minor members of a family, by closed-circuit television (CCTV) cameras creates a lot of privacy concerns. Particularly, revealing children’s identifications or activities may compromise their well-being. In this paper, we investigate lightweight solutions that are affordable to edge surveillance systems, which is made feasible and accurate to identify minors such that appropriate privacy-preserving measures can be applied accordingly. State of the art deep learning architectures are modified and re-purposed in a cascaded fashion to maximize the accuracy of our model. A pipeline extracts faces from the input frames and classifies each one to be of an adult or a child. Over 20,000 labeled sample points are used for classification. We explore the timing and resources needed for such a model to be used in the Edge-Fog architecture at the edge of the network, where we can achieve near real-time performance on the CPU. Quantitative experimental results show the superiority of our proposed model with an accuracy of 92.1% in classification compared to some other face recognition based child detection approaches.
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems

Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverage the characteristics of specific environment and operation context for more efficient control. In this work, we develop an online intermittent-control framework that combines formal verification with model-based optimization and deep reinforcement learning to opportunistically skip certain control computation and actuation to save actuation energy and computational resources without compromising system safety. Experiments on an adaptive cruise control system demonstrate that our approach can achieve significant energy and computation savings.