cssi/SciSciGPT-SciSciNet huggingface dataset:
Evaluation and Diagnosis of the Traumatized Dentition.

Abstract Traumatic dental injuries comprise a number of the dental emergency patients who are often seen after hours or on an unscheduled basis in a dental practice environment. Although there are a variety of traumatic dental injuries that can occur, each with their own recommended treatment protocols, the initial evaluation and diagnosis of the traumatized dentition make up a critical aspect of the management of these cases. This article will highlight the key components of a thorough and efficient examination process of the traumatized dentition to include (1) documenting an accurate history of the events causing the injury, (2) performing a systematic clinical examination to include the use of clinical photographs and pulp sensibility tests, (3) obtaining appropriate radiographic images and scans, (4) understanding some considerations unique to evaluating young patients with traumatic injuries, and (5) recognizing the importance of having accurate and thorough documentation of these types of cases. Once the evaluation and diagnosis phase has been completed, the necessary treatment protocols can be initiated in an appropriate manner.
The 5-Way Scale

In this paper, we discuss coin-weighing problems that use a 5-way scale which has five different possible outcomes: MUCH LESS, LESS, EQUAL, MORE, and MUCH MORE. The 5-way scale provides more information than the regular 3-way scale. We study the problem of finding two fake coins from a pile of identically looking coins in a minimal number of weighings using a 5-way scale. We discuss similarities and differences between the 5-way and 3-way scale. We introduce a strategy for a 5-way scale that can find both counterfeit coins among $2^k$ coins in $k+1$ weighings, which is better than any strategy for a 3-way scale.
Student and instructor framing in upper-division physics

Upper-division physics students spend much of their time solving problems. In addition to their basic skills and background, their epistemic framing can form an important part of their ability to learn physics from these problems. Encouraging students to move toward productive framing may help them solve problems. Thus, an instructor should understand the specifics of how students have framed a problem and understand how her interaction with the students will impact that framing. In this study, we investigate epistemic framing of students in problem solving situations where math is applied to physics. To analyze the frames and changes in frames, we develop and use a two axis framework involving conceptual and algorithmic physics and math. We examine student and instructor framing and the interactions of these frames over a range of problems in an upper-division electromagnetic field course. Within interactions, students and instructors generally follow each others' leads in framing.Upper-division physics students spend much of their time solving problems. In addition to their basic skills and background, their epistemic framing can form an important part of their ability to learn physics from these problems. Encouraging students to move toward productive framing may help them solve problems. Thus, an instructor should understand the specifics of how students have framed a problem and understand how her interaction with the students will impact that framing. In this study, we investigate epistemic framing of students in problem solving situations where math is applied to physics. To analyze the frames and changes in frames, we develop and use a two axis framework involving conceptual and algorithmic physics and math. We examine student and instructor framing and the interactions of these frames over a range of problems in an upper-division electromagnetic field course. Within interactions, students and instructors generally follow each others' leads in framing.
The determinants of international migration: Unbundling the role of economic, political and social institutions

International migrants may relocate because of economic, political and social factors in their origin or destination countries. Using global bilateral migration flows from 103 countries over the period 1990–2000, we explore whether emigrants self‐select based on economic, political and social institutions. Our study adds social dimension as a potential determinant of migration and separates the pull and push effects of political, economic and social institutions. Our results indicate that economic, political and social institutions are significant pull factors of migration; economic freedom has the most substantial pull effect followed by the political institutions; social institutions have the weakest pull effect on migration. Moreover, economic and social institutions are significant push factors of migration, while political institutions do not show any push effect. Furthermore, educated migrants are more sensitive to the destination economic, political and social institutions than less‐educated migrants, and less‐educated migrants are more sensitive to the social institutions at the origin.
Tight Information Theoretic Converse Results for Some Pliable Index Coding Problems

This paper studies the Pliable Index CODing problem (PICOD), which models content-type distribution networks. In the PICOD $({t})$ problem there are ${m}$ messages, ${n}$ users and each user has a distinct message side information set, as in the classical Index Coding problem (IC). Differently from IC, where each user has a pre-specified set of messages to decode, in the PICOD $({t})$ a user is “pliable” and is satisfied if it can decode any ${t}$ messages that are not in its side information set. The goal is to find a code with the shortest length that satisfies all the users. This flexibility in determining the desired message sets makes the PICOD $({t})$ behave quite differently compared to the IC, and its analysis even more challenging. This paper mainly focuses on the complete – ${S}$ PICOD $({t})$ with ${m}$ messages, where the set ${S}\subset [{m}]$ contains the sizes of the side information sets, and the number of users is ${n}=\sum _{s\in {S}}\binom {m} {s}$ , with no two users having the same side information set. Capacity results are shown for: (i) the consecutive complete– ${S}$ PICOD $({t})$ , where ${S}=[{s}_{\text {min}}:{s}_{\text {max}}]$ for some $0 \leqslant {s}_{\text {min}}\leqslant {s}_{\text {max}} \leqslant {m}-{t}$ , and (ii) the complement-consecutive complete– ${S}$ PICOD $({t})$ , where ${S}=[0: {m}-{t}]\backslash [{s}_{\text {min}}:{s}_{\text {max}}]$ , for some $0 . The novel converse proof is inspired by combinatorial design techniques and the key insight is to consider all messages that a user can eventually decode successfully, even those in excess of the ${t}$ required ones. This allows one to circumvent the need to consider all possible desired message set assignments at the users in order to find the one that leads to the shortest code length. The core of the novel proof is to solve the critical complete– ${S}$ PICOD $({t})$ with ${m} = 2{s}+{t}$ messages and ${S}=\{{s}\}$ , by showing the existence of a user who can decode ${s}+{t}$ messages regardless of the desired message set assignment. All other tight converse results for the complete– ${S}$ PICOD $({t})$ can be deduced from this critical case. The converse results show the information theoretic optimality of simple linear coding schemes. By similar reasoning, all complete– ${S}$ PICOD $({t})$ where the number of messages is ${m}\leqslant 5$ can be fully characterized. In addition, tight converse results are also shown for the PICOD(1) with circular-arc network topology hypergraph.
Prime twists of elliptic curves

For certain elliptic curves $E/\mathbb{Q}$ with $E(\mathbb{Q})[2]=\mathbb{Z}/2 \mathbb{Z}$, we prove a criterion for prime twists of $E$ to have analytic rank 0 or 1, based on a mod 4 congruence of 2-adic logarithms of Heegner points. As an application, we prove new cases of Silverman's conjecture that there exists a positive proposition of prime twists of $E$ of rank zero (resp. positive rank).
Experimenting with Constraint Programming on GPU.

The focus of my PhD thesis is on exploring parallel approaches to efficiently solve problems modeled by constraints and presenting a new proposal. Current solvers are very advanced; they are carefully designed to effectively manage the high-level problems' description and include refined strategies to avoid useless work. Despite this, finding a solution can take an unacceptable amount of time. Parallelization can mitigate this problem when the instance of the problem modeled is large, as it happens in real world problems. It is done by propagating constraints in parallel and concurrently exploring different parts of the search space. I am developing on a constraint solver that exploits the many cores available on Graphics Processing Units (GPU) to speed up the search.
ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology

Neurons perform computations by integrating inputs from thousands of synapses – mostly in the dendritic tree – to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neuron with a substance that allows subsequent staining, reconstruct the three-dimensional architecture of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic process, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.
Somatic selection distinguishes oncogenes and tumor suppressor genes.

Motivation Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. Results We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. Availability and implementation An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust. Supplementary information Supplementary data are available at Bioinformatics online.
The diminishing hedging role of crude oil: Evidence from time varying financialization

Abstract Using daily data from 1999 to 2019, we document a diminishing hedging role that crude oil plays for the stock market as a result of growing financialization. With interest rates driven near zero after the crisis of 2007-2009 and the extreme volatility of oil prices, vector autoregressions (VARs) suggest larger roles of oil prices in explaining stock returns during the 2007-2009 crisis and afterwards. We also find increased co-movement between stock and oil during the post-crisis period. Our results indicate that more short positions in crude oil are required to hedge long positions in equity markets during the post-crisis period, i.e., it costs more to use crude oil investments as a hedging tool compared to pre-crisis period. Therefore, investors holding a mixed portfolio of stocks and oil enjoy lower diversification benefits.
More Logarithmic-factor Speedups for 3SUM, (median,+)-convolution, and Some Geometric 3SUM-hard Problems

This article presents an algorithm that solves the 3SUM problem for n real numbers in O((n2/ log2n)(log log n)O(1)) time, improving previous solutions by about a logarithmic factor. Our framework for shaving off two logarithmic factors can be applied to other problems, such as (median,+)-convolution/matrix multiplication and algebraic generalizations of 3SUM. This work also obtains the first subquadratic results on some 3SUM-hard problems in computational geometry, for example, deciding whether (the interiors of) a constant number of simple polygons have a common intersection.
Treewidth and gonality of glued grid graphs

Abstract We compute the treewidth of a family of graphs we refer to as the glued grids, consisting of the stacked prism graphs and the toroidal grids. Our main technique is constructing strict brambles of large orders. We discuss connections to divisorial graph theory coming from tropical geometry, and use our results to compute the divisorial gonality of these graphs.
Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases.

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.
Disrupting the transmembrane domain-mediated oligomerization of protein tyrosine phosphatase receptor J inhibits EGFR-driven cancer cell phenotypes

Receptor protein tyrosine phosphatases (RPTPs) play critical regulatory roles in mammalian signal transduction. However, the structural basis for the regulation of their catalytic activity is not fully understood, and RPTPs are generally not therapeutically targetable. This knowledge gap is partially due to the lack of known natural ligands or selective agonists of RPTPs. Contrary to what is known from structure-function studies of receptor tyrosine kinases (RTKs), RPTP activities have been reported to be suppressed by dimerization, which may prevent RPTPs from accessing their RTK substrates. We report here that homodimerization of protein tyrosine phosphatase receptor J (PTPRJ, also known as DEP-1) is regulated by specific transmembrane (TM) residues. We found that disrupting these interactions destabilizes homodimerization of full-length PTPRJ in cells, reduces the phosphorylation of the known PTPRJ substrate epidermal growth factor receptor (EGFR) and of other downstream signaling effectors, antagonizes EGFR-driven cell phenotypes, and promotes substrate access. We demonstrate these observations in human cancer cells using mutational studies and identified a peptide that binds to the PTPRJ TM domain and represents the first example of an allosteric agonist of RPTPs. The results of our study provide fundamental structural and functional insights into how PTPRJ activity is tuned by TM interactions in cells. Our findings also open up opportunities for developing peptide-based agents that could be used as tools to probe RPTPs' signaling mechanisms or to manage cancers driven by RTK signaling.
Distance measures for tumor evolutionary trees.

MOTIVATION There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
A Phage-Assisted Continuous Selection Approach for Deep Mutational Scanning of Protein–Protein Interactions

Protein-protein interactions (PPIs) are critical for organizing molecules in a cell and mediating signaling pathways. Dysregulation of PPIs is often a key driver of disease. To better understand the biophysical basis of such disease processes-and to potentially target them-it is critical to understand the molecular determinants of PPIs. Deep mutational scanning (DMS) facilitates the acquisition of large amounts of biochemical data by coupling selection with high throughput sequencing (HTS). The challenging and labor-intensive design and optimization of a relevant selection platform for DMS, however, limits the use of powerful directed evolution and selection approaches. To address this limitation, we designed a versatile new phage-assisted continuous selection (PACS) system using our previously reported proximity-dependent split RNA polymerase (RNAP) biosensors, with the aim of greatly simplifying and streamlining the design of a new selection platform for PPIs. After characterization and validation using the model KRAS/RAF PPI, we generated a library of RAF variants and subjected them to PACS and DMS. Our HTS data revealed positions along the binding interface that are both tolerant and intolerant to mutations, as well as which substitutions are tolerated at each position. Critically, the "functional scores" obtained from enrichment data through continuous selection for individual variants correlated with KD values measured in vitro, indicating that biochemical data can be extrapolated from sequencing using our new system. Due to the plug and play nature of RNAP biosensors, this method can likely be extended to a variety of other PPIs. More broadly, this, and other methods under development support the continued development of evolutionary and high-throughput approaches to address biochemical problems, moving toward a more comprehensive understanding of sequence-function relationships in proteins.
Raman Spectra of Polyisoprene Rubbers

Abstract The Raman spectra of cis-1,4-, trans-1,4-, and 3,4-polyisoprene are presented, and the frequencies are classified by configurational structure type. The stretching frequency of the carbon-carbon double bond vibrations are used to describe structure content. Only Raman bands characteristic of total 1,4 content and total vinyl content can be observed. Tentative values of structure content determined by both peak height and peak area are presented for two mixed structure rubbers.
Wealth taxation in the United States

The paper analyzes fiscal effects of a Swiss-type tax on household wealth, with $120,000 exemption and marginal tax rates running from 0.05 to 0.3 percent on $2.4 million or more of wealth. It also considers a wealth tax proposed by Senator Elizabeth Warren with a $50 million exemption, a 2 percent tax on wealth above that and a 1 percent surcharge on wealth above $1 billion. Based on the 2016 Survey of Consumer Finances, the Swiss tax would yield $189.3 billion and the Warren tax $303.4 billion. Only 0.07 percent of households would pay the Warren tax, compared to 44.3 percent for the Swiss tax. The Swiss tax would have a very small effect on income inequality, lowering the post-tax Gini coefficient by 0.004 Gini points. The effect of the Swiss tax and Warren tax on wealth inequality is miniscule, lowering the Gini coefficient by at most 0.0005 Gini points.
Control of Astrocyte Quiescence and Activation in a Synthetic Brain Hydrogel

Bioengineers have designed numerous instructive brain extracellular matrix (ECM) environments with tailored and tunable protein compositions and biomechanical properties in vitro to study astrocyte reactivity during trauma and inflammation. However, a major limitation of both protein-based and synthetic model microenvironments is that astrocytes within fail to retain their characteristic stellate morphology and quiescent state without becoming activated under "normal" culture conditions. Here, a synthetic hydrogel is introduced, which for the first time demonstrates maintenance of astrocyte quiescence and activation on demand. With this synthetic brain hydrogel, the brain-specific integrin-binding and matrix metalloprotease-degradable domains of proteins are shown to control astrocyte star-shaped morphologies, and an ECM condition that maintains astrocyte quiescence with minimal activation can be achieved. In addition, activation can be induced in a dose-dependent manner via both defined cytokine cocktails and low molecular weight hyaluronic acid. This synthetic brain hydrogel is envisioned as a new tool to study the physiological role of astrocytes in health and disease.
Gromov-Wasserstein Factorization Models for Graph Clustering

We propose a new nonlinear factorization model for graphs that are with topological structures, and optionally, node attributes. This model is based on a pseudometric called Gromov-Wasserstein (GW) discrepancy, which compares graphs in a relational way. It estimates observed graphs as GW barycenters constructed by a set of atoms with different weights. By minimizing the GW discrepancy between each observed graph and its GW barycenter-based estimation, we learn the atoms and their weights associated with the observed graphs. The model achieves a novel and flexible factorization mechanism under GW discrepancy, in which both the observed graphs and the learnable atoms can be unaligned and with different sizes. We design an effective approximate algorithm for learning this Gromov-Wasserstein factorization (GWF) model, unrolling loopy computations as stacked modules and computing gradients with backpropagation. The stacked modules can be with two different architectures, which correspond to the proximal point algorithm (PPA) and Bregman alternating direction method of multipliers (BADMM), respectively. Experiments show that our model obtains encouraging results on clustering graphs.
Underwater Fish Detection Using Deep Learning for Water Power Applications

Clean energy from oceans and rivers is becoming a reality with the development of new technologies like tidal and instream turbines that generate electricity from naturally flowing water. These new technologies are being monitored for effects on fish and other wildlife using underwater video. Methods for automated analysis of underwater video are needed to lower the costs of analysis and improve accuracy. A deep learning model, YOLO, was trained to recognize fish in underwater video using three very different datasets recorded at real-world water power sites. Training and testing with examples from all three datasets resulted in a mean average precision (mAP) score of 0.5392. To test how well a model could generalize to new datasets, the model was trained using examples from only two of the datasets and then tested on examples from all three datasets. The resulting model could not recognize fish in the dataset that was not part of the training set. The mAP scores on the other two datasets that were included in the training set were higher than the scores achieved by the model trained on all three datasets. These results indicate that different methods are needed in order to produce a trained model that can generalize to new data sets such as those encountered in real world applications.
The Intrinsically Disordered Region of ExbD Is Required for Signal Transduction

The TonB system actively transports vital nutrients across the unenergized outer membranes of the majority of Gram-negative bacteria. In this system, integral membrane proteins ExbB, ExbD, and TonB work together to transduce the proton motive force (PMF) of the inner membrane to customized active transporters in the outer membrane by direct and cyclic binding of TonB to the transporters. A PMF-dependent TonB-ExbD interaction is prevented by 10-residue deletions within a periplasmic disordered domain of ExbD adjacent to the cytoplasmic membrane. Here, we explored the function of the ExbD disordered domain in more detail. In vivo photo-cross-linking through sequential pBpa substitutions in the ExbD disordered domain captured five different ExbD complexes, some of which had been previously detected using in vivo formaldehyde cross-linking, a technique that lacks the residue-specific information that can be achieved through photo-cross-linking: two ExbB-ExbD heterodimers (one of which had not been detected previously), previously detected ExbD homodimers, previously detected PMF-dependent ExbD-TonB heterodimers, and for the first time, a predicted, ExbD-TonB PMF-independent interaction. The fact that multiple complexes were captured by the same pBpa substitution indicated the dynamic nature of ExbD interactions as the energy transduction cycle proceeded in vivo In this study, we also discovered that a conserved motif-V45, V47, L49, and P50-within the disordered domain was required for signal transduction to TonB and to the C-terminal domain of ExbD and was the source of motif essentiality.IMPORTANCE The TonB system is a virulence factor for Gram-negative pathogens. The mechanism by which cytoplasmic membrane proteins of the TonB system transduce an electrochemical gradient into mechanical energy is a long-standing mystery. TonB, ExbB, and ExbD primary amino acid sequences are characterized by regions of predicted intrinsic disorder, consistent with a proposed multiplicity of protein-protein contacts as TonB proceeds through an energy transduction cycle, a complex process that has yet to be recapitulated in vitro This study validates a region of intrinsic disorder near the ExbD transmembrane domain and identifies an essential conserved motif embedded within it that transduces signals to distal regions of ExbD suggested to configure TonB for productive interaction with outer membrane transporters.
Scalable and Robust Community Detection With Randomized Sketching

This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a reduced graph sketch constructed using random sampling. Then, the clusters of the full graph are inferred based on the clusters extracted from the sketch using a correlation-based retrieval step. Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced. A new random degree-based node sampling algorithm is presented which significantly improves upon the performance of the clustering algorithm even when clusters are unbalanced. This framework improves the phase transitions for matrix-decomposition-based clustering with regard to computational complexity and minimum cluster size, which are shown to be nearly dimension-free in the low inter-cluster connectivity regime. A third sampling technique is shown to improve balance by randomly sampling nodes based on spatial distribution. We provide analysis and numerical results using a convex clustering algorithm based on matrix completion.
Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science.

The research-grade Autism Diagnostic Observational Schedule (ADOS) is a broadly used instrument that informs and steers much of the science of autism. Despite its broad use, little is known about t...
Learning from, understanding, and supporting DevOps artifacts for docker

With the growing use of DevOps tools and frameworks, there is an increased need for tools and techniques that support more than code. The current state-of-the-art in static developer assistance for tools like Docker is limited to shallow syntactic validation. We identify three core challenges in the realm of learning from, understanding, and supporting developers writing DevOps artifacts: (i) nested languages in DevOps artifacts, (ii) rule mining, and (iii) the lack of semantic rule-based analysis. To address these challenges we introduce a toolset, binnacle, that enabled us to ingest 900,000 GitHub repositories. Focusing on Docker, we extracted approximately 178,000 unique Dockerfiles, and also identified a Gold Set of Dockerfiles written by Docker experts. We addressed challenge (i) by reducing the number of effectively uninterpretable nodes in our ASTs by over 80% via a technique we call phased parsing. To address challenge (ii), we introduced a novel rule-mining technique capable of recovering two-thirds of the rules in a benchmark we curated. Through this automated mining, we were able to recover 16 new rules that were not found during manual rule collection. To address challenge (iii), we manually collected a set of rules for Dockerfiles from commits to the files in the Gold Set. These rules encapsulate best practices, avoid docker build failures, and improve image size and build latency. We created an analyzer that used these rules, and found that, on average, Dockerfiles on GitHub violated the rules five times more frequently than the Dockerfiles in our Gold Set. We also found that industrial Dockerfiles fared no better than those sourced from GitHub. The learned rules and analyzer in binnacle can be used to aid developers in the IDE when creating Dockerfiles, and in a post-hoc fashion to identify issues in, and to improve, existing Dockerfiles.
Crazy Sequential Representations of Numbers for Small Bases

Throughout history, recreational mathematics has always played a prominent role in advancing research. Following in this tradition, in this paper we extend some recent work with crazy sequential representations of numbers- equations made of sequences of one through nine (or nine through one) that evaluate to a number. All previous work on this type of puzzle has focused only on base ten numbers and whether a solution existed. We generalize this concept and examine how this extends to arbitrary bases, the ranges of possible numbers, the combinatorial challenge of finding the numbers, efficient algorithms, and some interesting patterns across any base. For the analysis, we focus on bases three through ten. Further, we outline several interesting mathematical and algorithmic complexity problems related to this area that have yet to be considered.
Risk factors which predict biopsy upgrading over time in active surveillance for prostate cancer.

290Background: Active surveillance (AS) is recognized as the preferred treatment for low risk prostate cancer (PCa). No validated clinical tools currently exist to standardize the frequency of biop...
Principles of Information Storage in Small-Molecule Mixtures

Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this idea have used DNA, but nature also uses a wide diversity of smaller non-polymeric molecules to preserve, process, and transmit information. In this paper, we present a general framework for quantifying chemical memory, which is not limited to polymers and extends to mixtures of molecules of all types. We show that the theoretical limit for molecular information is two orders of magnitude denser by mass than DNA, although this comes with different practical constraints on total capacity. We experimentally demonstrate kilobyte-scale information storage in mixtures of small synthetic molecules, and we consider some of the new perspectives that will be necessary to harness the information capacity available from the vast non-genomic chemical space.
Mirrored continuum and molecular scale simulations of deflagration in a nano-slab of HMX

Abstract We have developed a continuum modeling approach, grounded in classical physical chemistry, based on the following assumptions: (1) that the states in the material can be represented by local stationary averages of the pressure (stress), temperature, and mass fractions computed from atomistic simulation, (2) and that the mixture has well-defined molecular components, each with a complete equation of state. The continuum model, “Gibbs formulation”, applies to near-atomic length and time scales, which we identify as the scales where the high frequency, high energy phonons equilibrate in molecular mixtures, (about six atomic radii and six to ten vibrational periods). Phase changes and chemical changes due to reaction are not in (asymptotically, long-time) equilibrium, and changes are assumed to occur on much longer time scales than those required for stress and temperature equilibration. Recently in the Journal of Chemical Physics, J. Chem. Phys. 144, 184111 (2016), we carried out both atomistic molecular dynamics (MD) simulations and “mirrored” continuum simulations to model, thermal ignition of a nano-sized cube of explosive RDX. The NVE ensemble simulations of a constant volume explosions of RDX were performed using reactive molecular dynamics (RMD), that use ReaxFF as model chemical changes in the MD simulation. The MD simulation was regarded as the exact molecular system. The continuum simulation was regarded as an interpretation and measurement of the average chemical changes between a set of identified chemical components of that molecular system. In this work, we extend these ideas to include spatial averaging to study wave propagation and spatially distributed transport, combined with chemical reaction and compare continuum based simulations with recent RMD simulations of a sustained spatially distributed deflagration in a nano-scale slab of HMX carried out by Joshi and Chaudhuri, similarly done in RDX. [3] Both atomistic and continuum simulations show a hot spot ignition followed by a structured deflagration that propagates through the HMX slab and are compared with good to excellent agreement.
A differential-algebraic criterion for obtaining a small maximal Cohen-Macaulay module

We show how for a three-dimensional complete local ring in positive characteristic, the existence of an F-invariant, differentiable derivation implies Hochster's small MCM conjecture. As an application we show that any three-dimensional pseudo-graded ring in positive characteristic satisfies Hochster's small MCM conjecture.
Incentive Design for Temporal Logic Objectives

We study the problem of designing an optimal sequence of incentives that a principal should offer to an agent so that the agent’s optimal behavior under the incentives realizes the principal’s objective expressed as a temporal logic formula. We consider an agent with a finite decision horizon, and model its behavior as a Markov decision process (MDP). Under certain assumptions, we present a polynomial-time algorithm to synthesize an incentive sequence that minimizes the cost to the principal. We show that if the underlying MDP has only deterministic transitions, the principal can hide its objective from the agent and still realize the desired behavior through incentives. On the other hand, an MDP with stochastic transitions may require the principal to share its objective with the agent. Finally, we demonstrate the proposed method in motion planning examples where a principal changes the optimal trajectory of an agent by providing incentives.
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling.

Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further improves crowd counting accuracy. Our new network architecture performs favorably in comparison with the state-of-the-art. However, our labeling and upsampling techniques are generally applicable to existing crowd counting architectures.
Survival cluster analysis

Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions. An approach that addresses this need is likely to improve the characterization of individual outcomes by leveraging regularities in subpopulations, thus accounting for population-level heterogeneity. In this paper, we propose a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations) with distinct risk profiles. Experiments on real-world datasets show consistent improvements in predictive performance and interpretability relative to existing state-of-the-art survival analysis models.
A Binary Arginine Methylation Switch on Histone H3 Arginine 2 Regulates Its Interaction with WDR5.

Histone H3 arginine 2 (H3R2) is post-translationally modified in three different states by “writers” of the protein arginine methyltransferase (PRMT) family. H3R2 methylarginine isoforms include PR...
Necklaces and slimes

Abstract We show there is a bijection between the binary necklaces with n black beads and k white beads and certain ( n , k ) -codes when n is prime. The main idea is to come up with a new map on necklaces called slime migration.
Evolution of Gastrointestinal Transplantation at the University of Miami

EIGHTY-EIGHT intestinal and multivisceral transplants have been performed on 80 patients at the University of Miami between August 1994, when the program was established, and August 2000. They include 25 isolated intestinal, 26 liver intestinal transplants, and 37 multivisceral transplants. One intestinal recipient also received a pancreas graft. Kidney grafts were transplanted in one intestinal recipient while 6 adult and 6 children received multivisceral grafts. Two multivisceral recipients did not receive a liver graft. Forty-nine of the transplants (56%) were performed for children and 39 (44%) for adults. Two patients are excluded from this analysis because they were in extremes at the time and died of multiorgan failure within a few days after the transplant. Evolution during this period mainly involved ● surgical techniques, ● Immunoprophylaxis against CMV, and ● Immunosuppression and monitoring of rejection. We are presenting the main adjustments which took place and their effects.
Realistic anatomically detailed open-source spinal cord stimulation (RADO-SCS) model.

Objective: Computational current flow models of spinal cord stimulation (SCS) are widely used in device development, clinical trial design, and patient programming. Proprietary models of varied sophistication have been developed. An open-source model with state-of-the-art precision would serve as a standard for SCS simulation. Approach: We developed a sophisticated SCS modeling platform, named Realistic Anatomically Detailed Open-Source Spinal Cord Stimulation (RADO-SCS) model. This platform consists of realistic and detailed spinal cord and ancillary tissues anatomy derived based on prior imaging and cadaveric studies. In our finite element model of the T9-T11 spine levels, we represented the following tissues: vertebrae, intervertebral disc, epidural space, epidural space vasculature, dura mater, dural sac, intraforaminal tissue, cerebrospinal fluid (CSF), whitematter, spinal cord vasculature, Lissauer's tract, gray matter, dorsal and ventral roots and rootlets, dorsal root ganglion (DRG), sympathetic chain (trunk and ganglion), thoracic aorta and its branching, peripheral vasculature, and soft tissues (thorax). As an exemplary application to illustrate the model workflow, we simulated a bipolar SCS montage and calculated the corresponding activation thresholds for individual axons populating the spinal cord. Main Results: RADO-SCS provides state-of-the-art precision across 19 tissue compartments. The resulting model calculations of the electric fields generated in the white-matter and gray matter, and the axonal activation thresholds are broadly consistent with prior simulations. Significance: The RADO-SCS can be used to simulate any SCS approach with both unprecedented resolution (precision) and transparency (reproducibility). Freely-available online, the RADO-SCS will be updated continuously with version control.
Automated gene data integration with Databio.

Objective Although sequencing and other high-throughput data production technologies are increasingly affordable, data analysis and interpretation remains a significant factor in the cost of -omics studies. Despite the broad acceptance of findable, accessible, interoperable, and reusable (FAIR) data principles which focus on data discoverability and annotation, data integration remains a significant bottleneck in linking prior work in order to better understand novel research. Relevant and timely information discovery is difficult for increasingly multi-disciplinary projects when scientists cannot easily keep up with work across multiple fields. Computational tools are necessary to accurately describe data contents, and empower linkage to existing resources without prior knowledge of the various database resources. Results We developed the Databio tool, accessible at https://datab.io/, to automate data parsing, identifier detection, and streamline common tasks to provide a point-and-click approach to data manipulation and integration in life sciences research and translational medicine. Databio uses fast real-time data structures and a data warehouse of 137 million identifiers, with automated heuristics to describe data provenance without highly specialized knowledge or bioinformatics training.
Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals’ natural habitats. It has been unclear how to extend theoretical models to large or complex spaces. Complementarily, in autonomous systems technology, applications have been growing for distributed control methods that scale to large numbers of low-footprint mobile platforms. Animals and many-robot groups must solve common problems of navigating complex and uncertain environments. Here, we introduce the NeuroSwarms control framework to investigate whether adaptive, autonomous swarm control of minimal artificial agents can be achieved by direct analogy to neural circuits of rodent spatial cognition. NeuroSwarms analogizes agents to neurons and swarming groups to recurrent networks. We implemented neuron-like agent interactions in which mutually visible agents operate as if they were reciprocally connected place cells in an attractor network. We attributed a phase state to agents to enable patterns of oscillatory synchronization similar to hippocampal models of theta-rhythmic (5–12 Hz) sequence generation. We demonstrate that multi-agent swarming and reward-approach dynamics can be expressed as a mobile form of Hebbian learning and that NeuroSwarms supports a single-entity paradigm that directly informs theoretical models of animal cognition. We present emergent behaviors including phase-organized rings and trajectory sequences that interact with environmental cues and geometry in large, fragmented mazes. Thus, NeuroSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains.
Interconnected pay-as-bid auctions

Abstract I develop a framework to study common situations, in which substitute goods are sold in separate, good-specific multi-unit (pay-as-bid) auctions. I characterize bidding behavior and investigate auction design features that could increase revenues. The setting I develop gives rise to an essentially unique symmetric Nash equilibrium in which bidders shade their bids more strongly when goods are close substitutes. To increase revenues, the seller can offer total supply quantities that are (stochastically) unequal in size. This fosters more aggressive bidding and provides a rationale to hold separate, parallel auctions instead of selling all in one auction.