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The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer’s, Parkinson’s, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.

Computational chemistry instructional activities are often based around students running chemical simulations via a graphical user interface (GUI). GUI-based activities offer many advantages, as they enable students to run chemical simulations with a few mouse clicks. Although these activities are excellent for introducing students to the capabilities of chemical simulations, the disadvantage is that the students’ experience is not representative of how professional computational chemists work. Just as it is important that students in an organic chemistry instructional lab gain hands-on experience with equipment commonly used by professional organic chemists, students of computational chemistry must gain hands-on experience with coding, as professional computational chemists do not rely on GUIs; we write code. Motivated by the need for instructional activities that provide hands-on experience with computer code, a pair of activities were created around a free lightweight (runs on standard laptops) open-source Lennard-Jones (LJ) fluid simulation code written in Python, a programming language that prioritizes readability. The first activity, aimed at undergraduate physical chemistry lab courses, involves students writing Python code in a Jupyter Notebook that is used to run LJ simulations and fit a van der Waals gas model to data produced by the LJ fluid simulations. The second is a jigsaw activity, aimed at advanced undergraduate or graduate students, where students are assigned different sections of the LJ fluid simulation code, and must demonstrate the functionality of their section to the class by both giving an oral presentation and sharing a Jupyter Notebook demonstration of their own design.

AbstractThe links between protests and state responses have taken on increased visibility in light of the Arab Spring movements. But we still have unanswered questions about the relationship between protest behaviors and responses by the state. We frame this in terms of concession and disruption costs. Costs are typically defined as government behaviors that impede dissidents’ capacity for collective action. We change this causal arrow and hypothesize how dissidents can generate costs that structure the government's response to a protest. By disaggregating costs along dimensions of concession and disruption we extend our understanding of protest behaviors and the conditions under which they are more (or less) effective. Utilizing a new cross-national protest-event data set, we test our theoretical expectations against protests from 1990 to 2014 and find that when protesters generate high concession costs, the state responds in a coercive manner. Conversely, high disruption costs encourage the state to accommodate demands. Our research provides substantial insights and inferences about the dynamics of government response to protest.

Listeria monocytogenes, one of the most important foodborne pathogens, can cause listeriosis, a lethal disease for humans. L. ivanovii, which is closely related to L. monocytogenes,is also widely distributed in nature and infects mainly warm-blooded ruminants, causing economic loss. Thus, there are high priority needs for methodologies for rapid, specific, cost-effective and accurate detection, characterization and subtyping of L. monocytogenesand L. ivanoviiin foods and environmental sources. In this review, we (A) described L. monocytogenesand L. ivanovii, world-wide incidence of listeriosis, and prevalence of various L. monocytogenesstrains in food and environmental sources; (B) comprehensively reviewed different types of traditional and newly developed methodologies, including culture-based, antigen/antibody-based, LOOP-mediated isothermal amplification, matrix-assisted laser desorption ionization-time of flight-mass spectrometry, DNA microarray, and genomic sequencing for detection and characterization of L. monocytogenesin foods and environmental sources; (C) comprehensively summarized different subtyping methodologies, including pulsed-field gel electrophoresis, multi-locus sequence typing, ribotyping, and phage-typing, and whole genomic sequencing etc. for subtyping of L. monocytogenesstrains from food and environmental sources; and (D) described the applications of these methodologies in detection and subtyping of L. monocytogenesin foods and food processing facilities.

Listeria monocytogenesis an important foodborne pathogen responsible for listeriosis, a fatal disease. It is widely distributed in various foods and environmental sources. In this review, we focused on addressing PCR-based technologies, including conventional PCR, qPCR and droplet digital PCR (ddPCR). Specifically, we described (a) conventional PCR and mono-, duplex- and multiplex-qPCR methodologies; (b) development and applications of gene HlyA-, Iap-, PrfA– and SsrA-based conventional and qPCR assays as well as PCR assays targeting newly identified gene targets for specific detection of L. monocytogenes; differentiation of viable from dead L. monocytogenesby qPCR in conjugation with propidium monoazide pretreatment; PCR-based serotype identification of L. monocytogenesisolates; PCR-based detection of L. ivanovii,infecting ruminants, differentiation of L. monocytogenesfrom other Listeriaspecies; and sigB-gene based PCR identification of Listeria spp; (c) applications of ddPCR in detection of L. monocytogenes; and (d) application of qPCR assays in detection and subtyping of L. monocytogenesin milk and dairy products; meats, meat products and meat-processing environment; and seafood, seafood products and processing environment. Our goal was to provide a relatively comprehensive overview of PCR-based methodologies available in detection, characterization and subtyping of various strains of L. monocytogenesin foods and environmental sources.

The article explores the ways to improve crumbling mass transit systems of the U.S., as of February 2016. Topics discussed include increased ridership and decreased investment in mass transit systems; consequences of poor public transit infrastructure; the U.S. National Transportation Safety Board (NTSB) comprehensive investigation; and responsibilities of engineers, department heads and budget officers.

Summary A patient with exocrine pancreatic insufficiency and hypoalbuminemic edema was shown also to have a protein-losing gastropathy secondary to giant hypertrophic gastritis (Ménétrier's disease). Despite continuation of excessive gastric protein loss, the manifestations of hypoproteinemia were reversed by oral pancreatic enzyme replacement therapy. We speculate that impaired intraluminal proteolysis exaggerated the consequences of gastric protein loss.