Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure-based design of small-molecule inhibitors of EBNA1 DNA binding blocks Epstein-Barr virus latent infection and tumor growth

EBNA1 inhibitors developed by fragment-based drug design block EBNA1 DNA binding and EBV-dependent tumor cell growth in vivo. No holds Barr-ed for cancer treatment Persistent latent infection with Epstein-Barr virus contributes to tumorigenesis in a variety of cancer types. The only viral protein that is consistently expressed in these tumors is Epstein-Barr nuclear antigen 1, which performs a variety of biological functions. To target this antigen, Messick et al. designed a series of chemical inhibitors and then tested them in vitro and in multiple mouse models of Epstein-Barr virus–associated cancer. The authors characterized the inhibitors using a variety of assays, examined the mechanism by which they work, and demonstrated preclinical effectiveness and suitable pharmacological properties, paving the way for further therapeutic development. Epstein-Barr virus (EBV) is a DNA tumor virus responsible for 1 to 2% of human cancers including subtypes of Burkitt’s lymphoma, Hodgkin’s lymphoma, gastric carcinoma, and nasopharyngeal carcinoma (NPC). Persistent latent infection drives EBV-associated tumorigenesis. Epstein-Barr nuclear antigen 1 (EBNA1) is the only viral protein consistently expressed in all EBV-associated tumors and is therefore an attractive target for therapeutic intervention. It is a multifunctional DNA binding protein critical for viral replication, genome maintenance, viral gene expression, and host cell survival. Using a fragment-based approach and x-ray crystallography, we identify a 2,3-disubstituted benzoic acid series that selectively inhibits the DNA binding activity of EBNA1. We characterize these inhibitors biochemically and in cell-based assays, including chromatin immunoprecipitation and DNA replication assays. In addition, we demonstrate the potency of EBNA1 inhibitors to suppress tumor growth in several EBV-dependent xenograft models, including patient-derived xenografts for NPC. These inhibitors selectively block EBV gene transcription and alter the cellular transforming growth factor–β (TGF-β) signaling pathway in NPC tumor xenografts. These EBNA1-specific inhibitors show favorable pharmacological properties and have the potential to be further developed for the treatment of EBV-associated malignancies.
Gain-Framed Messaging for Promoting Adult Sport: Examining the Effects of Efficacy-Enhancing Information

Sport is a potential venue for more middle-aged adults to engage in sufficient physical activity for health benefits. Little is known about whether messaging interventions can motivate sport activity. This experiment tested the impact of gain-framed messaging (i.e., information about the benefits of doing adult sport) based on the inclusion (or lack thereof) of efficacy-enhancing information. Adults (30–69 years-old) were randomly assigned to experimental (a 4-min online video of “Gain-framed messages alone,” or “Gain-framed plus efficacy-enhancing messages”) or control conditions. Participants (N = 232; 62.5% female) completed baseline measures for intentions, barrier and scheduling self-efficacy, outcomes expectancies (OEs), sport behavior and moderate-to-vigorous physical activity, received their condition 1-week later, reported measures immediately after, and 1-month later. Results showed no differences between the experimental conditions, indicating there was no advantage of supplemental efficacy-enhancing information compared to gain-framed messages alone. When the two messaging groups were collapsed, they showed significant increases for OEs related to travel, social affiliation, and stress relief immediately following experimental exposure, compared to the control group. Overall, there were few benefits attributed to messaging and no effects on self-reported sport registration or sport behavior. Discussion focuses on future messaging considerations that may more effectively motivate adult sport participation.
A memetic algorithm using local structural information for detecting community structure in complex networks

Community detection has received a great deal of attention in recent years. Modularity is the most used and best known quality function for measuring the quality of a partition of a network. Based on the optimization of modularity, we proposed a memetic algorithm with a local search operator to detect community structure. The local search operator uses a quality function of local community tightness based on structural similarity. In addition, the tactics of vertex mover is used for reassigning vertices to neighboring communities to improve the partition result. Experiments on real-world networks and computer-generated networks show the effectiveness of our algorithm.
Natural antisense transcripts in diseases: From modes of action to targeted therapies

Antisense transcription is a widespread phenomenon in mammalian genomes, leading to production of RNAs molecules referred to as natural antisense transcripts (NATs). NATs apply diverse transcriptional and post‐transcriptional regulatory mechanisms to carry out a wide variety of biological roles that are important for the normal functioning of living cells, but their dysfunctions can be associated with human diseases. In this review, we attempt to provide a molecular basis for the involvement of NATs in the etiology of human disorders such as cancers and neurodegenerative and cardiovascular diseases. We also discuss the pros and cons of oligonucleotide‐based therapies targeted against NATs, and we comment on state‐of‐the‐art progress in this promising area of clinical research. WIREs RNA 2018, 9:e1461. doi: 10.1002/wrna.1461
Handling preprocessor-conditioned declarations

Many software systems are developed with configurable functionality, and for multiple hardware platforms and operating systems. This can lead to thousands of possible configurations, requiring each configuration-dependent programming entity or variable to have different types. Such configuration-dependent variables are often declared inside preprocessor conditionals (e.g., C language). Preprocessor-conditioned declarations may be a source of problems. Commonly used configurations are type-checked by repeated compilation. Rarely used configurations are unlikely to be recently type checked, and in such configurations a variable may have a type not compatible to its use or it may contains uses of variables never defined. This paper proposes an approach to identify all possible types each variable declared in a software system can assume, and under which conditions. Inconsistent variable usages can then be detected for all possible configurations. Impacts of preprocessor-conditioned declaration in 17 different open source software systems are also reported.
Applying Meyer's taxonomy to object-oriented software systems

Inheritance is one of the core concepts in object-orientation. There has been much discussion and disagreement as to the correct uses of the inheritance mechanism in the literature. The classification of B. Meyer (1996) seems to be the most comprehensive for the identification of appropriate uses of inheritance. While it seems that this taxonomy has been validated by its author, in the design and development of software, it does not seem to have been validated against existing systems. We attempt, in the first instance, to move towards this validation by attempting to define a method whereby the inheritance relationships in software systems can be classified into individual categories in Meyer's taxonomy. Subsequently it also provides some insight on how inheritance is used in such systems.
Contributions of regulated transcription and mRNA decay to the dynamics of gene expression

Organisms have acquired sophisticated regulatory networks that control gene expression in response to cellular perturbations. Understanding of the mechanisms underlying the coordinated changes in gene expression in response to external and internal stimuli is a fundamental issue in biology. Recent advances in high‐throughput technologies have enabled the measurement of diverse biological information, including gene expression levels, kinetics of gene expression, and interactions among gene expression regulatory molecules. By coupling these technologies with quantitative modeling, we can now uncover the biological roles and mechanisms of gene regulation at the system level. This review consists of two parts. First, we focus on the methods using uridine analogs that measure synthesis and decay rates of RNAs, which demonstrate how cells dynamically change the regulation of gene expression in response to both internal and external cues. Second, we discuss the underlying mechanisms of these changes in kinetics, including the functions of transcription factors and RNA‐binding proteins. Overall, this review will help to clarify a system‐level view of gene expression programs in cells.
Molecular Gas Clumps from the Destruction of Icy Bodies in the β Pictoris Debris Disk

One-Sided Story from Disk In young analogs of the solar system, the ongoing erosion of comets and nascent planets produces dusty debris that is eventually expelled by the host star. Gas should also be released in this process when volatile ices sublimate, but it is detected less often. Using the Atacama Large Millimeter/Submillimeter Array, Dent et al. (p. 1490, published online 6 March; see the Perspective by Brandeker) mapped a highly asymmetric disk of dust and carbon monoxide orbiting the planet-hosting star, β Pictoris. The distribution of gas and dust is consistent with two proposed scenarios: In one, an outward-migrating planet has resonantly trapped dust-yielding bodies in two clumps opposite the star. In another, the entire debris mass is the result of a single recent collision of Mars-sized bodies. An asymmetric disk of dust and carbon monoxide indicates a recent large-scale collision or shepherding by an unseen planet. [Also see Perspective by Brandeker] Many stars are surrounded by disks of dusty debris formed in the collisions of asteroids, comets, and dwarf planets, but is gas also released in such events? Observations at submillimeter wavelengths of the archetypal debris disk around β Pictoris show that 0.3% of a Moon mass of carbon monoxide orbits in its debris belt. The gas distribution is highly asymmetric, with 30% found in a single clump 85 astronomical units from the star, in a plane closely aligned with the orbit of the inner planet, β Pictoris b. This gas clump delineates a region of enhanced collisions, either from a mean motion resonance with an unseen giant planet or from the remnants of a collision of Mars-mass planets.
Counting Bugs is Harder Than You Think

Software Assurance Metrics and Tool Evaluation (SAMATE) is a broad, inclusive project at the U.S. National Institute of Standards and Technology (NIST) with the goal of improving software assurance by developing materials, specifications, and methods to test tools and techniques and measure their effectiveness. We review some SAMATE sub-projects: web application security scanners, malware research protocol, electronic voting systems, the SAMATE Reference Dataset, a public repository of thousands of example programs with known weaknesses, and the Static Analysis Tool Exposition (SATE). Along the way we list over two dozen possible research questions, which are also collaboration opportunities. Software metrics are incomplete without metrics of what is variously called bugs, flaws, or faults. We detail numerous critical research problems related to such metrics. For instance, is a warning from a source code scanner a real bug, a false positive, or something else? If a numeric overflow leads to buffer overflow, which leads to command injection, what is the error? How many bugs are there if two sources call two sinks: 1, 2, or 4? Where is a missing feature? We conclude with a list of concepts which may be a useful basis of bug metrics.
Volumetric additive manufacturing via tomographic reconstruction

Fabrication goes for a quick spin Most 3D printing techniques involve adding material layer by layer. This sets some limitations on the types of applications for which 3D printing is suitable, such as printing around a preexisting object. Kelly et al. present a different method for manufacturing by rotating a photopolymer in a dynamically evolving light field (see the Perspective by Hart and Rao). This allowed them to print entire complex objects through one complete revolution, circumventing the need for layering. The method may be particularly useful for high-viscosity photopolymers and multimaterial fabrication. Science, this issue p. 1075; see also p. 1042 Exposing a rotating photopolymer to a dynamically evolving light pattern achieves 3D printing without layer-by-layer assembly. Additive manufacturing promises enormous geometrical freedom and the potential to combine materials for complex functions. The speed, geometry, and surface quality limitations of additive processes are linked to their reliance on material layering. We demonstrated concurrent printing of all points within a three-dimensional object by illuminating a rotating volume of photosensitive material with a dynamically evolving light pattern. We printed features as small as 0.3 millimeters in engineering acrylate polymers and printed soft structures with exceptionally smooth surfaces into a gelatin methacrylate hydrogel. Our process enables us to construct components that encase other preexisting solid objects, allowing for multimaterial fabrication. We developed models to describe speed and spatial resolution capabilities and demonstrated printing times of 30 to 120 seconds for diverse centimeter-scale objects.
InputTracer: A Data-Flow Analysis Tool for Manual Program Comprehension of x86 Binaries

Third-party security analysis of closed-source programs has become an important part of a defense-in-depth approach to software security for many companies. In the absence of efficient tools, the analysis has generally been performed through manual reverse engineering of the machine code. As reverse engineering is an extremely time-consuming and costly task, much research has been performed to develop more powerful methods for analysis of program binaries. One such popular method is dynamic taint analysis (DTA), which is a type of runtime data-flow analysis, where certain input data is marked as tainted. By tracking the flow of tainted data, DTA can, for instance, be used to determine which computations in a program are affected by a certain part of the input. In this paper we present Input Tracer, a tool that utilizes DTA for aiding in manual program comprehension and analysis of unmodified x86 executables running in Linux. A brief overview of dynamic taint analysis is given, followed by a description of the tool and its implementation. We also demonstrate the tool's ability to provide exact information on the origin of tainted data through a detailed use case, where the tool is used to find the root cause of a memory corruption bug.
Proteum/FL: A tool for localizing faults using mutation analysis

Fault diagnosis is the process of analyzing programs with the aim of identifying the code fragments that are faulty. It has been identified as one of the most expensive and time consuming tasks of software development. Even worst, this activity is usually accomplished based on manual analysis. To this end, automatic or semi-automatic fault diagnosis approaches are useful in assisting software developers. Hence, they can play an essential role in decreasing the overall development cost. This paper presents Proteum/FL, a mutation analysis tool for diagnosing previously detected faults. Given an ANSI-C program and a set of test cases, Proteum/FL returns a list of program statements ranked according to their likelihood of being faulty. The tool differs from the rest of the mutation analysis and fault diagnosis tools by employing mutation analysis as a means of diagnosing program faults. It therefore demonstrates the effective use of mutation in supporting both testing and debugging activities.
Flowgen: Flowchart-Based Documentation Framework for C++

We present the Flowgen framework, which generates flowcharts from annotated C++ source code. It generates a set of interconnected high-level UML activity diagrams, one for each function or method in the C++ sources. It provides a simple and visual overview of complex implementations of numerical algorithms. Flowgen is complementary to the widely-used Doxygen documentation tool. The ultimate aim is to render complex C++ codes accessible, and to enhance collaboration between programmers and algorithm or science specialists. We describe the tool and a proof-of-concept application to the VINCIA plug-in for simulating collisions at CERN's Large Hadron Collider.
Accelerating CNN computation: quantisation tuning and network resizing

The interest in developing cognitive aware systems, specially for vision applications based on artificial neural networks, has grown exponentially in the last years. While high performance systems are key for the success of current Convolutional Neural Network (CNN) implementations, there is a trend to bring these capabilities to embedded real-time systems. This work contributes to tackle this challenge by exploring CNNs design space. Namely, it combines parameter quantisation techniques with a proposed set of CNN architectural transformations to reduce resource and execution time costs on Field Programmable Gate Array (FPGA) devices while maintaining high classification accuracy. An hardware mapping methodology is also proposed for deploying resource constrained CNNs into a reconfigurable platform for efficient algorithm acceleration. The proposed transformations reduce accuracy loss due to quantization by 44% in average. Also, analysis of the performance results obtained in a Central Processing Unit (CPU)+FPGA platform show up to 50% execution time reduction when compared with a state-of-the-art implementation.
HERO: an open-source research platform for HW/SW exploration of heterogeneous manycore systems

Heterogeneous systems on chip (HeSoCs) co-integrate a high-performance multicore host processor with programmable manycore accelerators (PMCAs) to combine "standard platform" software support (e.g. the Linux OS) with energy-efficient, domain-specific, highly parallel processing capabilities. In this work, we present HERO, a HeSoC platform that tackles this challenge in a novel way. HERO's host processor is an industry-standard ARM Cortex-A multicore complex, while its PMCA is a scalable, silicon-proven, open-source many-core processing engine, based on the extensible, open RISC-V ISA. We evaluate a prototype implementation of HERO, where the PMCA implemented on an FPGA fabric is coupled with a hard ARM Cortex-A host processor, and show that the run time overhead compared to manually written PMCA code operating on private physical memory is lower than 10 % for pivotal benchmarks and operating conditions.
The Effects of Indirect Exposure to September 11th–Related Trauma on Cardiovascular Reactivity

Direct and indirect exposure to trauma is related to cardiovascular reactivity to stress, which is a marker for the future development of cardiovascular disease. The current study compared the effects of indirect exposure and no exposure (assessed via self-reports) to the 9/11 terrorist attacks on cardiovascular reactivity among a sample of 31 New Yorkers. Cardiovascular responses, measured every 2 minutes during baseline, a semistructured interview in which they relived a personally relevant aspect of the terrorist attack, and recovery periods revealed that the indirect exposure group had significantly higher diastolic blood pressure levels at baseline (p < .01), task (p < .05), and recovery (p < .05) than the no exposure group. No differences in trauma symptoms were observed between the two groups. Results suggest that indirect exposure to a traumatic event may have an impact on physiological responses to stress.
Response to Comment on “Quantifying long-term scientific impact”

Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach.
Micro-differential evolution with vectorized random mutation factor

One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increases the stagnation risk. One approach to overcome the stagnation problem can be increasing the diversity of the population. To do so, a micro-differential evolution with vectorized random mutation factor (MDEVM) algorithm is proposed in this paper, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The proposed algorithm is tested on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013). Simulation results on the benchmark functions demonstrate that the proposed algorithm improves the convergence speed of its parent algorithm.
The Robustness and Evolvability of Transcription Factor Binding Sites

Robust to Change The variation in genetic sequences determining the binding of transcription factors is believed to be an important facet of evolution. However, the degree to which a genome is robust, that is, able to withstand changes and how robustness affects evolution is unclear. Payne and Wagner (p. 875) investigated the empirical support for mutational robustness by examining transcription factor (TF) binding in the mouse and yeast genomes. A network analysis of the degree of variation revealed that the sites with the highest affinity for TF binding exhibit the greatest tolerance for nucleotide substitutions, whereas low-affinity sites exhibit greater sensitivity to mutation. Thus, while mutational robustness and evolvability are antagonistic at the genotypic level, they are synergistic at the phenotypic level. Transcription factor binding sites form connected networks. Robustness, the maintenance of a character in the presence of genetic change, can help preserve adaptive traits but also may hinder evolvability, the ability to bring forth novel adaptations. We used genotype networks to analyze the binding site repertoires of 193 transcription factors from mice and yeast, providing empirical evidence that robustness and evolvability need not be conflicting properties. Network vertices represent binding sites where two sites are connected if they differ in a single nucleotide. We show that the binding sites of larger genotype networks are not only more robust, but the sequences adjacent to such networks can also bind more transcription factors, thus demonstrating that robustness can facilitate evolvability.