Structural basis for the homotypic fusion of chlamydial inclusions by the SNARE-like protein IncA.

Many intracellular bacteria, including Chlamydia, establish a parasitic membrane-bound organelle inside the host cell that is essential for the bacteria\u27s survival. Chlamydia trachomatis forms inclusions that are decorated with poorly characterized membrane proteins known as Incs. The prototypical Inc, called IncA, enhances Chlamydia pathogenicity by promoting the homotypic fusion of inclusions and shares structural and functional similarity to eukaryotic SNAREs. Here, we present the atomic structure of the cytoplasmic domain of IncA, which reveals a non-canonical four-helix bundle. Structure-based mutagenesis, molecular dynamics simulation, and functional cellular assays identify an intramolecular clamp that is essential for IncA-mediated homotypic membrane fusion during infection

Longitudinal Beam Dynamics and Coherent Synchrotron Radiation at cSTART

The compact STorage ring for Accelerator Research and Technology (cSTART) project aims to store electron bunches of LWFA-like beams in a very large momentum acceptance storage ring. The project will be realized at the Karlsruhe Institute of Technology (KIT, Germany). Initially, the Ferninfrarot Linac- Und Test-Experiment (FLUTE), a source of ultra-short bunches, will serve as an injector for cSTART to benchmark and emulate laser-wakefield accelerator-like beams. In a second stage a laser-plasma accelerator will be used as an injector, which is being developed as part of the ATHENA project in collaboration with DESY and Helmholtz Institute Jena (HIJ). With an energy of 50 MeV and damping times of several seconds, the electron beam does not reach equilibrium emittance. Furthermore, the critical frequency of synchrotron radiation is 50 THz and in the same order as the bunch spectrum, which implies that the entire bunch radiates coherently. We perform longitudinal particle tracking simulations to investigate the evolution of the bunch length and spectrum as well as the emitted coherent synchrotron radiation. Finally, different options for the RF system are discussed

Acquisition of suppressive function by conventional T cells limits antitumor immunity upon Treg depletion

Regulatory T (Treg) cells contribute to immune homeostasis but suppress immune responses to cancer. Strategies to disrupt Treg cell–mediated cancer immunosuppression have been met with limited clinical success, but the underlying mechanisms for treatment failure are poorly understood. By modeling Treg cell–targeted immunotherapy in mice, we find that CD4+ Foxp3− conventional T (Tconv) cells acquire suppressive function upon depletion of Foxp3+ Treg cells, limiting therapeutic efficacy. Foxp3− Tconv cells within tumors adopt a Treg cell–like transcriptional profile upon ablation of Treg cells and acquire the ability to suppress T cell activation and proliferation ex vivo. Suppressive activity is enriched among CD4+ Tconv cells marked by expression of C-C motif receptor 8 (CCR8), which are found in mouse and human tumors. Upon Treg cell depletion, CCR8+ Tconv cells undergo systemic and intratumoral activation and expansion, and mediate IL-10–dependent suppression of antitumor immunity. Consequently, conditional deletion of Il10 within T cells augments antitumor immunity upon Treg cell depletion in mice, and antibody blockade of IL-10 signaling synergizes with Treg cell depletion to overcome treatment resistance. These findings reveal a secondary layer of immunosuppression by Tconv cells released upon therapeutic Treg cell depletion and suggest that broader consideration of suppressive function within the T cell lineage is required for development of effective Treg cell–targeted therapies

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Vibrational Density of States of Strongly H‑Bonded Interfacial Water: Insights from Inelastic Neutron Scattering and Theory

The molecular scale interaction between water and an oxide surface depends on the strength of the surface hydrogen bonds (H-bonds) through a subtle interplay among surface structure, surface atom polarity, and orientation of sorbed species. Tin oxide (SnO₂) in the rutile structure is an important catalytic and gas-sensing material, and its surface properties have been the subject of intense scrutiny. Here we show that the vibrational dynamics of H₂O and OH sorbed on SnO₂ nanoparticles, probed with inelastic neutron scattering and analyzed with ab initio molecular dynamics, reveals very strong surface H-bonds, with a formation enthalpy twice that of liquid water. This unusually strong interaction results in (i) decoupling of the hydrated surface from additional water layers due to an epitaxial screening layer of H₂O and OH species, (ii) high energy of OH wagging modes that provides an experimental indicator of surface H-bond strengths, and (iii) high proton exchange rates at the interface. H-bonding energetics and interfacial structures also control the average degree of dissociation of sorbed water. The close agreement in the vibrational density of states measured experimentally and generated in silico provides validation of the theory, while the atomistic simulations provide atomic/molecular-level details of individual species contributions to the observed spectrum. Together, these integrated studies provide definitive insights into the role of H-bonds in controlling the structure, dynamics, and reactivity of metal oxide/water interfaces

Structure and Stability of SnO₂ Nanocrystals and Surface-Bound Water Species

The structure of SnO₂ nanoparticles (avg. 5 nm) with a few layers of water on the surface has been elucidated by atomic pair distribution function (PDF) methods using in situ neutron total scattering data and molecular dynamics (MD) simulations. Analysis of PDF, neutron prompt gamma, and thermogravimetric data, coupled with MD-generated surface D₂O/OD configurations demonstrates that the minimum concentration of OD groups required to prevent rapid growth of nanoparticles during thermal dehydration corresponds to ∼0.7 monolayer coverage. Surface hydration layers not only stabilize the SnO₂ nanoparticles but also induce particle-size-dependent structural modifications and are likely to promote interfacial reactions through hydrogen bonds between adjacent particles. Upon heating/dehydration under vacuum above 250 °C, nanoparticles start to grow with low activation energies, rapid increase of nanoparticle size, and a reduction in the <i>a</i> lattice dimension. This study underscores the value of neutron diffraction and prompt-gamma analysis, coupled with molecular modeling, in elucidating the influence of surface hydration on the structure and metastable persistence of oxide nanomaterials

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

When coupled with spatio-temporal context, location-based data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as well as for external applications related to social wellbeing, its inherent massive volume make such ‘Big Data’ sets complex to process. A significant number of studies involving such mobile phone data have been presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner, high performance computing environments, scalable data analytics, innovative data fusion with other sources–all finally linked into the applications ready for operational mode. In this chapter, we provide a broad overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular networks