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Abstract We calculate the S -factor for proton–proton fusion using chiral effective field theory interactions and currents. By performing order-by-order calculations with a variety of chiral interactions that are regularized and calibrated in different ways, we assess the uncertainty in the S -factor from the truncation of the effective field theory expansion and from the sensitivity of the S -factor to the short-distance axial current determined from three- and four-nucleon observables. We find that S (0) = (4.100 ± 0.024(syst) ± 0.013(stat) ± 0.008( g A )) × 10 −23 MeV fm 2 , where the three uncertainties arise, respectively, from the truncation of the effective field theory expansion, use of the two-nucleon axial current fit to few-nucleon observables and variation of the axial coupling constant within the recommended range. The increased value of S (0) compared to previous calculations is mainly driven by an increase in the recommended value for the axial coupling constant and is in agreement with a recent analysis based on pionless effective field theory. 

Trajectory optimization o↵ers mature tools for motion planning in high-dimensional spaces under dynamic constraints. However, when facing complex configuration spaces, cluttered with obstacles, roboticists typically fall back to sampling-based planners that struggle in very high dimensions and with continuous di↵erential constraints. Indeed, obstacles are the source of many textbook examples of problematic nonconvexities in the trajectory-optimization prob- lem. Here we show that convex optimization can, in fact, be used to reliably plan trajectories around obstacles. Specifically, we consider planning problems with collision-avoidance constraints, as well as cost penalties and hard constraints on the shape, the duration, and the velocity of the trajectory. Combining the properties of B ́ezier curves with a recently-proposed framework for finding shortest paths in Graphs of Convex Sets (GCS), we formulate the planning problem as a compact mixed-integer optimization. In stark contrast with existing mixed-integer planners, the convex relaxation of our programs is very tight, and a cheap round- ing of its solution is typically sufficient to design globally-optimal trajectories. This reduces the mixed-integer program back to a simple convex optimization, and automatically provides optimality bounds for the planned trajectories. We name the proposed planner GCS, after its underlying optimization framework. We demonstrate GCS in simulation on a variety of robotic platforms, including a quadrotor flying through buildings and a dual-arm manipulator (with fourteen degrees of freedom) moving in a confined space. Using numerical experiments on a seven-degree-of-freedom manipulator, we show that GCS can outperform widely-used sampling-based planners by finding higher-quality trajectories in less time. 

Given a graph, the shortest-path problem requires finding a sequence of edges with minimum cumulative length that connects a source vertex to a target vertex. We consider a generalization of this classical problem in which the position of each vertex in the graph is a continuous decision variable, constrained to lie in a corresponding convex set. The length of an edge is then defined as a convex function of the positions of the vertices it connects. Problems of this form arise naturally in motion planning of autonomous vehicles, robot navigation, and even optimal control of hybrid dynamical systems. The price for such a wide applicability is the complexity of this problem, which is easily seen to be NP-hard. Our main contribution is a strong mixed-integer convex formulation based on perspective functions. This formulation has a very tight convex relaxation and makes it possible to efficiently find globally-optimal paths in large graphs and in high-dimensional spaces. 

Shear flow-induced migration is an important physiological phenomenon experienced by multiple cell types, including leukocytes and cancer cells. However, molecular mechanisms by which cells sense and directionally migrate in response to mechanical perturbation are not well understood. Dictyostelium discoideum social amoeba, a well-established model for studying amoeboid-type migration, also exhibits directional motility when exposed to shear flow, and this behavior is preceded by rapid and transient activation of the same signal transduction network that is activated by chemoattractants. The initial response, which can also be observed following brief 2 s stimulation with shear flow, requires an intact actin cytoskeleton; however, what aspect of the cytoskeletal network is responsible for sensing and/or transmitting the signal is unclear. We investigated the role of actin crosslinkers filamin and α-actinin by analyzing initial shear flow-stimulated responses in cells with or without these proteins. Both filamin and α-actinin showed rapid and transient relocalization from the cytosol to the cortex following shear flow stimulation. Using spatiotemporal analysis of Ras GTPase activation as a readout of signal transduction network activity, we demonstrated that lack of α-actinin did not reduce, and, in fact, slightly improved the response to acute mechanical stimulation compared to cells expressing α-actinin. In contrast, shear flow-induced Ras activation was significantly more robust in filamin-null cells rescued with filamin compared to cells expressing empty vector. Reduced responsiveness appeared to be specific to mechanical stimuli and was not due to a change in the basal activity since response to global stimulation with a chemoattractant and random migration was comparable between cells with or without filamin. Finally, while filamin-null cells rescued with filamin efficiently migrated upstream when presented with continuous flow, cells lacking filamin were defective in directional migration. Overall, our study suggests that filamin, but not α-actinin, is involved in sensing and/or transmitting mechanical stimuli that drive directed migration; however, other components of the actin cytoskeleton likely also contribute to the initial response since filamin-null cells were still able to activate the signal transduction network. These findings could have implications for our fundamental understanding of shear flow-induced migration of leukocytes, cancer cells and other amoeboid-type cells. 

Abstract The Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali del Gran Sasso (LNGS) is one of the largest underground physics laboratory, a very peculiar environment suited for experiments in Astroparticle Physics, Nuclear Physics and Fundamental Symmetries. The newly established Bellotti Ion Beam facility represents a major advance in the possibilities of studying nuclear processes in an underground environment. A workshop was organized at LNGS in the framework of the Nuclear Physics Mid Term Plan in Italy, an initiative of the Nuclear Physics Division of the Instituto Nazionale di Fisica Nucleare to discuss the opportunities that will be possible to study in the near future by employing state-of-the-art detection systems. In this report, a detailed discussion of the outcome of the workshop is presented. 

Policy responses to gun violence within K-12 school systems have not stopped the increasing frequency of their occurrence, but have instead increased racial and ethnic disparities in multiple forms of discipline. The crisis prevention policies that follow school shootings tend to exacerbate racial and ethnic discipline disparities (a) within schools as practitioners enact policies with discretion and bias, (b) between schools where policy is complicated by racial segregation, and (c) indirectly where academic consequences accrue to those who are not disciplined but attend schools with elevated school rates of discipline. Among the most promising policy alternatives to punitive disciplinary policy is restorative justice. 

Abstract The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?