Basal delamination during mouse gastrulation primes pluripotent cells for differentiation

The blueprint of the mammalian body plan is laid out during gastrulation, when a trilaminar embryo is formed. This process entails a burst of proliferation, the ingression of embryonic epiblast cells at the primitive streak, and their priming toward primitive streak fates. How these different events are coordinated remains unknown. Here, we developed and characterized a 3D culture of self-renewing mouse embryonic cells that captures the main transcriptional and architectural features of the early gastrulating mouse epiblast. Using this system in combination with microfabrication and in vivo experiments, we found that proliferation-induced crowding triggers delamination of cells that express high levels of the apical polarity protein aPKC. Upon delamination, cells become more sensitive to Wnt signaling and upregulate the expression of primitive streak markers such as Brachyury. This mechanistic coupling between ingression and differentiation ensures that the right cell types become specified at the right place during embryonic development.

Investigation of Rare Protein Conformational Transitions via Dissipation-Corrected Targeted Molecular Dynamics

To sample rare events, dissipation-corrected targeted molecular dynamics (dcTMD) applies a constant velocity constraint along a one-dimensional reaction coordinate s, which drives an atomistic system from an initial state into a target state. Employing a cumulant approximation of Jarzynski's identity, the free energy ΔG(s) is calculated from the mean external work and dissipated work of the process. By calculating the friction coefficient Γ(s) from the dissipated work, in a second step, the equilibrium dynamics of the process can be studied by propagating a Langevin equation. While so far dcTMD has been mostly applied to study the unbinding of protein-ligand complexes, here its applicability to rare conformational transitions within a protein and the prediction of their kinetics are investigated. As this typically requires the introduction of multiple collective variables {xj} = x, a theoretical framework is outlined to calculate the associated free energy ΔG(x) and friction Γ(x) from dcTMD simulations along coordinate s. Adopting the α-β transition of alanine dipeptide as well as the open-closed transition of T4 lysozyme as representative examples, the virtues and shortcomings of dcTMD to predict protein conformational transitions and the related kinetics are studied.

The Onset of Molecule-Spanning Dynamics in Heat Shock Protein Hsp90

Protein dynamics have been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how fast (local) fluctuations can lead to slow global (allosteric) changes. Here, fast molecule-spanning dynamics on the 100 to 200 ns time scale in the heat shock protein 90 (Hsp90) are shown. Global real-space movements are assigned to dynamic modes on this time scale, which is possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom molecular dynamics (MD) simulations. The time scale of these dynamic modes depends on the conformational state of the Hsp90 dimer. In addition, the dynamic modes are affected to various degrees by Sba1, a co-chaperone of Hsp90, depending on the location within Hsp90, which is in very good agreement with MD simulations. Altogether, this data is best described by fast molecule-spanning dynamics, which precede larger conformational changes in Hsp90 and might be the molecular basis for allostery. This integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein.

Craniectomy size and decompression of the temporal base using the altered posterior question-mark incision for decompressive hemicraniectomy

The altered posterior question-mark incision for decompressive hemicraniectomy (DHC) was proposed to reduce the risk of intraoperative injury of the superficial temporal artery (STA) and demonstrated a reduced rate of wound-healing disorders after cranioplasty. However, decompression size during DHC is essential and it remains unclear if the new incision type allows for an equally effective decompression. Therefore, this study evaluated the efficacy of the altered posterior question-mark incision for craniectomy size and decompression of the temporal base and assessed intraoperative complications compared to a modified standard reversed question-mark incision. The authors retrospectively identified 69 patients who underwent DHC from 2019 to 2022. Decompression and preservation of the STA was assessed on postoperative CT scans and CT or MR angiography. Forty-two patients underwent DHC with the standard reversed and 27 patients with the altered posterior question-mark incision. The distance of the margin of the craniectomy to the temporal base was 6.9 mm in the modified standard reversed and 7.2 mm in the altered posterior question-mark group (p = 0.77). There was no difference between the craniectomy sizes of 158.8 mm and 158.2 mm, respectively (p = 0.45), and there was no difference in the rate of accidental opening of the mastoid air cells. In both groups, no transverse/sigmoid sinus was injured. Twenty-four out of 42 patients in the modified standard and 22/27 patients in the altered posterior question-mark group had a postoperative angiography, and the STA was preserved in all cases in both groups. Twelve (29%) and 5 (19%) patients underwent revision due to wound-healing disorders after DHC, respectively (p = 0.34). There was no difference in duration of surgery. Thus, the altered posterior question-mark incision demonstrated technically equivalent and allows for an equally effective craniectomy size and decompression of the temporal base without increasing risks of intraoperative complications. Previously described reduction in wound-healing complications and cranioplasty failures needs to be confirmed in prospective studies to demonstrate the superiority of the altered posterior question-mark incision.

Anisotropic Friction in a Ligand-Protein Complex

The effect of an externally applied directional force on molecular friction is so far poorly understood. Here, we study the force-driven dissociation of the ligand-protein complex biotin-streptavidin and identify anisotropic friction as a not yet described type of molecular friction. Using AFM-based stereographic single molecule force spectroscopy and targeted molecular dynamics simulations, we find that the rupture force and friction for biotin-streptavidin vary with the pulling angle. This observation holds true for friction extracted from Kramers' rate expression and by dissipation-corrected targeted molecular dynamics simulations based on Jarzynski's identity. We rule out ligand solvation and protein-internal friction as sources of the angle-dependent friction. Instead, we observe a heterogeneity in free energy barriers along an experimentally uncontrolled orientation parameter, which increases the rupture force variance and therefore the overall friction. We anticipate that anisotropic friction needs to be accounted for in a complete understanding of friction in biomolecular dynamics and anisotropic mechanical environments.

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Path separation of dissipation-corrected targeted molecular dynamics simulations of protein-ligand unbinding

Protein-ligand (un)binding simulations are a recent focus of biased molecular dynamics simulations. Such binding and unbinding can occur via different pathways in and out of a binding site. Here, we present a theoretical framework on how to compute kinetics along separate paths and on how to combine the path-specific rates into global binding and unbinding rates for comparison with experimental results. Using dissipation-corrected targeted molecular dynamics in combination with temperature-boosted Langevin equation simulations [S. Wolf et al., Nat. Commun. 11, 2918 (2020)] applied to a two-dimensional model and the trypsin-benzamidine complex as test systems, we assess the robustness of the procedure and discuss the aspects of its practical applicability to predict multisecond kinetics of complex biomolecular systems.

Pre-stroke socioeconomic status predicts upper limb motor recovery after inpatient neurorehabilitation

BACKGROUND

Lower socioeconomic status (SES) is associated with higher mortality rates and the likelihood of receiving less evidence-based treatment after stroke. In contrast, little is known about the impact of SES on recovery after discharge from inpatient rehabilitation. The aim of this study was to investigate the influence of SES on long-term recovery after stroke.

PATIENTS AND METHODS

In a prospective, observational, multicentre study, inpatients were recruited towards the end of rehabilitation. The 12-month follow-up focussed on upper limb motor recovery, measured by the Fugl-Meyer score. A clinically relevant improvement of ≥5.25 points was considered recovery. Patient-centric measures such as the Patient-reported Outcomes Measurement Information System-Physical Health (PROMIS-10 PH) provided secondary outcomes. Information on schooling, vocational training, income and occupational status pre-stroke entered a multidimensional SES index. Multivariate logistic regression models calculating odds ratios (ORs) and corresponding confidence intervals (CIs) were applied. SES was added to an initial model including age, sex and baseline neurological deficit. Additional exploratory analyses examined the association between SES and outpatient treatment.

RESULTS

One hundred and seventy-six patients were enrolled of whom 98 had SES and long-term recovery data. Model comparisons showed the SES-model superior to the initial model (Akaike information criterion (AIC): 123 vs. 120, Pseudo R²: 0.09 vs. 0.13). The likelihood of motor recovery (OR = 17.12, 95%CI = 1.31; 224.18) and PROMIS-10 PH improvement (OR = 20.76, 95%CI = 1.28; 337.11) were significantly increased with higher SES, along with more frequent use of outpatient therapy (p = .02).

CONCLUSIONS

Higher pre-stroke SES is associated with better long-term recovery after discharge from rehabilitation. Understanding these factors can improve outpatient long-term stroke care and lead to better recovery.KEY MESSAGEHigher pre-stroke socioeconomic status (SES) is associated with better long-term recovery after discharge from rehabilitation both in terms of motor function and self-reported health status.Higher SES is associated with significantly higher utilization of outpatient therapies.Discharge management of rehabilitation clinics should identify and address socioeconomic factors in order to detect individual needs and to improve outpatient recovery. Article registration: clinicaltrials.gov NCT04119479.

COGNITION: a prospective precision oncology trial for patients with early breast cancer at high risk following neoadjuvant chemotherapy

BACKGROUND

COGNITION (Comprehensive assessment of clinical features, genomics and further molecular markers to identify patients with early breast cancer for enrolment on marker driven trials) is a diagnostic registry trial that employs genomic and transcriptomic profiling to identify biomarkers in patients with early breast cancer with a high risk for relapse after standard neoadjuvant chemotherapy (NACT) to guide genomics-driven targeted post-neoadjuvant therapy.

PATIENTS AND METHODS

At National Center for Tumor Diseases Heidelberg patients were biopsied before starting NACT, and for patients with residual tumors after NACT additional biopsy material was collected. Whole-genome/exome and transcriptome sequencing were applied on tumor and corresponding blood samples.

RESULTS

In the pilot phase 255 patients were enrolled, among which 213 were assessable: thereof 48.8% were identified to be at a high risk for relapse following NACT; 86.4% of 81 patients discussed in the molecular tumor board were eligible for a targeted therapy within the interventional multiarm phase II trial COGNITION-GUIDE (Genomics-guided targeted post neoadjuvant therapy in patients with early breast cancer) starting enrolment in Q4/2022. An in-depth longitudinal analysis at baseline and in residual tumor tissue of 16 patients revealed some cases with clonal evolution but largely stable genetic alterations, suggesting restricted selective pressure of broad-acting cytotoxic neoadjuvant chemotherapies.

CONCLUSIONS

While most precision oncology initiatives focus on metastatic disease, the presented concept offers the opportunity to empower novel therapy options for patients with high-risk early breast cancer in the post-neoadjuvant setting within a biomarker-driven trial and provides the basis to test the value of precision oncology in a curative setting with the overarching goal to increase cure rates.

Energy Transport and Its Function in Heptahelical Transmembrane Proteins

Photoproteins such as bacteriorhodopsin (bR) and rhodopsin (Rho) need to effectively dissipate photoinduced excess energy to prevent themselves from damage. Another well-studied seven transmembrane (TM) helices protein is the β₂ adrenergic receptor (β₂AR), a G protein-coupled receptor for which energy dissipation paths have been linked with allosteric communication. To study the vibrational energy transport in the active and inactive states of these proteins, a master equation approach [J. Chem. Phys.2020, 152, 045103] is employed, which uses scaling rules that allow us to calculate energy transport rates solely based on the protein structure. Despite their overall structural similarity, the three 7TM proteins reveal quite different strategies to redistribute excess energy. While bR quickly removes the energy using the TM7 helix as a "lightning rod", Rho exhibits a rather poor energy dissipation, which might eventually require the hydrolysis of the Schiff base between the protein and the retinal chromophore to prevent overheating. Heating the ligand adrenaline of β₂AR, the resulting energy transport network of the protein is found to change significantly upon switching from the active state to the inactive state. While the energy flow may highlight aspects of the inter-residue couplings of β₂AR, it seems not particularly suited to explain allosteric phenomena.

Nonequilibrium Modeling of the Elementary Step in PDZ3 Allosteric Communication

While allostery is of paramount importance for protein signaling and regulation, the underlying dynamical process of allosteric communication is not well understood. The PDZ3 domain represents a prime example of an allosteric single-domain protein, as it features a well-established long-range coupling between the C-terminal α₃-helix and ligand binding. In an intriguing experiment, Hamm and co-workers employed photoswitching of the α₃-helix to initiate a conformational change of PDZ3 that propagates from the C-terminus to the bound ligand within 200 ns. Performing extensive nonequilibrium molecular dynamics simulations, the modeling of the experiment reproduces the measured time scales and reveals a detailed picture of the allosteric communication in PDZ3. In particular, a correlation analysis identifies a network of contacts connecting the α₃-helix and the core of the protein, which move in a concerted manner. Representing a one-step process and involving direct α₃-ligand contacts, this cooperative transition is considered as the elementary step in the propagation of conformational change.

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intraligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.

Cooperative Protein Allosteric Transition Mediated by a Fluctuating Transmission Network

Allosteric communication between distant protein sites represents a key mechanism of biomolecular regulation and signal transduction. Compared to other processes such as protein folding, however, the dynamical evolution of allosteric transitions is still not well understood. As an example of allosteric coupling between distant protein regions, we consider the global open-closed motion of the two domains of T4 lysozyme, which is triggered by local motion in the hinge region. Combining extensive molecular dynamics simulations with a correlation analysis of interresidue contacts, we identify a network of interresidue distances that move in a concerted manner. The cooperative process originates from a cogwheel-like motion of the hydrophobic core in the hinge region, which constitutes an evolutionary conserved and flexible transmission network. Through rigid contacts and the protein backbone, the small local changes of the hydrophobic core are passed on to the distant terminal domains and lead to the emergence of a rare global conformational transition. As in an Ising-type model, the cooperativity of the allosteric transition can be explained via the interaction of local fluctuations.

Molecular Origin of Driving-Dependent Friction in Fluids

The friction coefficient of fluids may become a function of the velocity at increased external driving. This non-Newtonian behavior is of general theoretical interest and of great practical importance, for example, for the design of lubricants. Although the effect has been observed in large-scale atomistic simulations of bulk liquids, its theoretical formulation and microscopic origin are not well understood. Here, we use dissipation-corrected targeted molecular dynamics, which pulls apart two tagged liquid molecules in the presence of surrounding molecules, and analyze this nonequilibrium process via a generalized Langevin equation. The approach is based on a second-order cumulant expansion of Jarzynski's identity, which is shown to be valid for fluids and therefore allows for an exact computation of the friction profile as well of the underlying memory kernel. We show that velocity-dependent friction in fluids results from an intricate interplay of near-order structural effects and the non-Markovian behavior of the friction memory kernel. For complex fluids such as the model lubricant C₄₀H₈₂, the memory kernel exhibits a stretched-exponential long-time decay, which reflects the multitude of timescales of the system.

Temporal control of the integrated stress response by a stochastic molecular switch

Stress granules (SGs) are formed in the cytosol as an acute response to environmental cues and activation of the integrated stress response (ISR), a central signaling pathway controlling protein synthesis. Using chronic virus infection as stress model, we previously uncovered a unique temporal control of the ISR resulting in recurrent phases of SG assembly and disassembly. Here, we elucidate the molecular network generating this fluctuating stress response by integrating quantitative experiments with mathematical modeling and find that the ISR operates as a stochastic switch. Key elements controlling this switch are the cooperative activation of the stress-sensing kinase PKR, the ultrasensitive response of SG formation to the phosphorylation of the translation initiation factor eIF2α, and negative feedback via GADD34, a stress-induced subunit of protein phosphatase 1. We identify GADD34 messenger RNA levels as the molecular memory of the ISR that plays a central role in cell adaptation to acute and chronic stress.

Predicting Ion Channel Conductance via Dissipation-Corrected Targeted Molecular Dynamics and Langevin Equation Simulations

Ion channels are important proteins for physiological information transfer and functional control. To predict the microscopic origins of their voltage-conductance characteristics, here we applied dissipation-corrected targeted molecular dynamics in combination with Langevin equation simulations to potassium diffusion through the gramicidin A channel as a test system. Performing a nonequilibrium principal component analysis on backbone dihedral angles, we find coupled protein-ion dynamics to occur during ion transfer. The dissipation-corrected free energy profiles correspond well to predictions from other biased simulation methods. The incorporation of an external electric field in Langevin simulations enables the prediction of macroscopic observables in the form of I-V characteristics.

finDr: A web server for in silico D-peptide ligand identification

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.

[Methods for analysing spatial data : Using the example of skin cancer prevalence in Germany]

BACKGROUND

Studies show a spatial variation in skin cancer frequencies, but the causes have not been fully understood. When analysing spatial patterns and correlations, different approaches need to be considered, as the data have special features due to their spatial structure.

AIM OF THE STUDY

Why the spatial consideration of skin cancer frequencies is important at all, and which methods of analysis are useful is considered in more detail in this article. The article provides an introduction and overview of statistical methods that are important for the spatial analysis of skin cancer frequencies.

MATERIALS AND METHODS

At first it is shown which descriptive methods, such as statistical smoothing, can be applied. Next, spatial cluster and regression analyses will be discussed. Testing for spatial autocorrelation will be considered.

RESULTS

In particular, the spatial dependence of the data on neighbouring regions, which if ignored can lead to biased estimates, is of particular importance in the analysis of spatial data and requires special spatial analysis methods.

DISCUSSION

This article provides an introduction and overview of statistical methods relevant to the spatial analysis of skin cancers.

Control Strategies for Reactive Extrusion of Polypropylene by Peroxide Degradation A Brief Review and an Experimental Study

Industry 4.0 and digitalization are widely argued for the future success of numerous industrial solutions. Big data management might lead to the assumption that every issue can be solved numerically without any physical background. To some extent, this strategy will help within the plastics industry in general and in the extrusion technology in particular. However, a deep process knowledge together with process-relevant sensors, as well as the right process arrangements within the processing chain combined with smart data mining methods will be still the key success of industry 4.0. This presentation illustrates, based on a brief review on existing control strategies (Part 1), including sensory and predictive control models for reactive extrusion applied at a real-life on-site best practice project (Part 2), possibilities in combination of process tasks with digitalization approaches for PP-Polymer production. Specifically, rheological research conducted with a novel, patented multi-point rheometer (part 3), will provide a deeper insight into dynamic processes such as reactive extrusion. With those results and derivations thereof, improvements in predictive process control in addition to artificial control systems are made and might even lead to further interesting opportunities.

Effectiveness and safety analysis of home-treatment models in child and adolescent psychiatric care

Background

The Austrian child and adolescent psychiatric care landscape is characterised by substantial deficits on the intramural and extramural level. To overcome these supply shortfalls alternative outreach approaches, such as home-treatment (HT) can be introduced, which shall facilitate the reduction of hospitalisations. Thus, we aimed to systematically characterise international HT models considering their efficacy and safety in the area of child and adolescent psychiatry.

Methods

A systematic literature search in six databases, as well as an extended hand search, was conducted. Taking into account the predefined inclusion criteria, six HT studies were eligible for the qualitative synthesis; comprising two randomised controlled trials (RCTs), two non-randomised controlled trials (NRCTs) and two observational studies.

Results

The following four effectiveness measures were investigated: change in symptoms, hospitalisation, treatment satisfaction and need for further treatment. A benefit of HT models compared to stationary care could be identified considering patient symptoms, espeicially psychopathological symptoms; however, solely in the longer-term treatment. Studies have also shown that HT is associated with fewer days of hospitalisation, but with a higher need for further treatments. Inconclusive evidence was identified considering treatment satisfaction. Safety endpoints were not evaluated by any included study.

Conclusions

The results are limited by a moderate to high risk of bias due to several factors (e.g. small sample sizes, various assessment instruments). However, the evidence indicates that in long-term, HT can lead to an improvement in psychopathological symptoms and supports the transition from inpatient to outpatient care. Therefore, HT may be considered as an alternative or supplement to the stationary child and adolescent psychiatric care. However, further evidence is needed considering safety outcomes.

Key messages

In the long-term, home-treatment can lead to a bigger improvement in psychopathological symptoms for the children and adolescent compared to stationary care. Further evidence is needed especially regarding possible adverse events of home-treatment in the field of child and adolescent psychiatry.

The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.

Current approaches to fate mapping and lineage tracing using image data

Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.

Data-Driven Langevin Modeling of Nonequilibrium Processes

Given nonstationary data from molecular dynamics simulations, a Markovian Langevin model is constructed that aims to reproduce the time evolution of the underlying process. While at equilibrium the free energy landscape is sampled, nonequilibrium processes can be associated with a biased energy landscape, which accounts for finite sampling effects and external driving. When the data-driven Langevin equation (dLE) approach [Phys. Rev. Lett. 2015, 115, 050602] is extended to the modeling of nonequilibrium processes, an efficient way to calculate multidimensional Langevin fields is outlined. The dLE is shown to correctly account for various nonequilibrium processes, including the enforced dissociation of sodium chloride in water, the pressure-jump induced nucleation of a liquid of hard spheres, and the conformational dynamics of a helical peptide sampled from nonstationary short trajectories.

Field-effect silicon-plasmonic photodetector for coherent T-wave reception

Plasmonic internal photoemission detectors (PIPED) have recently been shown to combine compact footprint and high bandwidth with monolithic co-integration into silicon photonic circuits, thereby opening an attractive route towards optoelectronic generation and detection of waveforms in the sub-THz and THz frequency range, so-called T-waves. In this paper, we further expand the PIPED concept by introducing a metal-oxide-semiconductor (MOS) interface with an additional gate electrode that allows to control the carrier dynamics in the device and the degree of internal photoemission at the metal-semiconductor interfaces. We experimentally study the behavior of dedicated field-effect (FE-)PIPED test structures and develop a physical understanding of the underlying principles. We find that the THz down-conversion efficiency of FE-PIPED can be significantly increased when applying a gate potential. Building upon the improved understanding of the device physics, we further perform simulations and show that the gate field increases the carrier density in the conductive channel below the gate oxide to the extent that the device dynamics are determined by ultra-fast dielectric relaxation rather than by the carrier transit time. In this regime, the bandwidth can be increased to more than 1 THz. We believe that our experiments open a new path towards understanding the principles of internal photoemission in plasmonic structures, leading to PIPED-based optoelectronic signal processing systems with unprecedented bandwidth and efficiency.

Agent-based modeling: Population limits and large timescales

Modeling, simulation, and analysis of interacting agent systems is a broad field of research, with existing approaches reaching from informal descriptions of interaction dynamics to more formal, mathematical models. In this paper, we study agent-based models (ABMs) given as continuous-time stochastic processes and their pathwise approximation by ordinary and stochastic differential equations (SDEs) for medium to large populations. By means of an appropriately adapted transfer operator approach, we study the behavior of the ABM process on long time scales. We show that, under certain conditions, the transfer operator approach allows us to bridge the gap between the pathwise results for large populations on finite timescales, i.e., the SDE limit model, and approaches built to study dynamical behavior on long time scales like large deviation theory. The latter provides a rigorous analysis of rare events including the associated asymptotic rates on timescales that scale exponentially with the population size. We demonstrate that it is possible to reveal metastable structures and timescales of rare events of the ABM process by finite-length trajectories of the SDE process for large enough populations. This approach has the potential to drastically reduce computational effort for the analysis of ABMs.

Cerebrovascular Autoregulation Monitoring in the Management of Adult Severe Traumatic Brain Injury: A Delphi Consensus of Clinicians

BACKGROUND

Several methods have been proposed to measure cerebrovascular autoregulation (CA) in traumatic brain injury (TBI), but the lack of a gold standard and the absence of prospective clinical data on risks, impact on care and outcomes of implementation of CA-guided management lead to uncertainty.

AIM

To formulate statements using a Delphi consensus approach employing a group of expert clinicians, that reflect current knowledge of CA, aspects that can be implemented in TBI management and CA research priorities.

METHODS

A group of 25 international academic experts with clinical expertise in the management of adult severe TBI patients participated in this consensus process. Seventy-seven statements and multiple-choice questions were submitted to the group in two online surveys, followed by a face-to-face meeting and a third online survey. Participants received feedback on average scores and the rationale for resubmission or rephrasing of statements. Consensus on a statement was defined as agreement of more than 75% of participants.

RESULTS

Consensus amongst participants was achieved on the importance of CA status in adult severe TBI pathophysiology, the dynamic non-binary nature of CA impairment, its association with outcome and the inadvisability of employing universal and absolute cerebral perfusion pressure targets. Consensus could not be reached on the accuracy, reliability and validation of any current CA assessment method. There was also no consensus on how to implement CA information in clinical management protocols, reflecting insufficient clinical evidence.

CONCLUSION

The Delphi process resulted in 25 consensus statements addressing the pathophysiology of impaired CA, and its impact on cerebral perfusion pressure targets and outcome. A research agenda was proposed emphasizing the need for better validated CA assessment methods as well as the focused investigation of the application of CA-guided management in clinical care using prospective safety, feasibility and efficacy studies.

Hierarchical dynamics in allostery following ATP hydrolysis monitored by single molecule FRET measurements and MD simulations

We report on a study that combines advanced fluorescence methods with molecular dynamics (MD) simulations to cover timescales from nanoseconds to milliseconds for a large protein. This allows us to delineate how ATP hydrolysis in a protein causes allosteric changes at a distant protein binding site, using the chaperone Hsp90 as test system. The allosteric process occurs via hierarchical dynamics involving timescales from nano- to milliseconds and length scales from Ångstroms to several nanometers. We find that hydrolysis of one ATP is coupled to a conformational change of Arg380, which in turn passes structural information via the large M-domain α-helix to the whole protein. The resulting structural asymmetry in Hsp90 leads to the collapse of a central folding substrate binding site, causing the formation of a novel collapsed state (closed state B) that we characterise structurally. We presume that similar hierarchical mechanisms are fundamental for information transfer induced by ATP hydrolysis through many other proteins.

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera: A semi-quantitative, high throughput, microscopy-based assay expands existing approaches to measure SARS-CoV-2 specific antibody levels in human sera

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents challenges that demand immediate attention. Here, we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA, and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay that complements the portfolio of SARS-CoV-2 serological assays. Sensitive, specific and quantitative serological assays are urgently needed for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.

Incidence of presenting complaints and diagnoses in insured Australian dogs

Knowledge of the most common presenting complaints and diagnoses in companion animals is valuable in preparing veterinary students and veterinarians to manage the most frequently observed conditions in clinical practice. Pet insurance databases provide access to large sample populations and have been previously used to describe disease incidence in companion animals. The aim of this study was to determine the incidence of presenting complaints and diagnoses in insured Australian dogs through the use of a pet insurance database. Analysis of a de-identified dataset containing pet insurance claims associated with presenting complaints and diagnoses from 488,472 insured Australian dogs insured in the years 2016 and 2017, was performed. Annual incidence rates of presenting complaints and diagnoses were calculated and expressed as, number of events per 1,000 dog years at risk. The presenting complaints with the highest incidence were vomiting (14.21 events per 1,000 dog years at risk in 2016, 15.80 events per 1,000 dog years at risk in 2017) and pruritus (8.79 events per 1,000 dog years at risk in 2016, 10.30 events per 1,000 dog years at risk in 2017). Presenting complaints affecting the gastrointestinal system were the most common (19.20 events per 1,000 dog years at risk in 2016, 20.77 events per 1,000 dog years at risk in 2017). The diagnoses with the highest incidence were otitis externa (34.12 events per 1,000 dog years at risk in 2016, 34.82 events per 1,000 dog years at risk in 2017) and dermatitis (28.05 events per 1,000 dog years at risk in 2016, 29.99 events per 1,000 dog years at risk in 2017). Diagnoses affecting the integument were the most common (216.56 events per 1,000 dog years at risk in 2016, 219.06 events per 1,000 dog years at risk in 2017). The results from this study can aid in the design of relevant veterinary curricula and may be helpful in prioritising research on common clinical conditions.

Estimation of Protein-Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

We here report on nonequilibrium targeted molecular dynamics simulations as a tool for the estimation of protein-ligand unbinding kinetics. Correlating simulations with experimental data from SPR kinetics measurements and X-ray crystallography on two small molecule compound libraries bound to the N-terminal domain of the chaperone Hsp90, we show that the mean nonequilibrium work computed in an ensemble of trajectories of enforced ligand unbinding is a promising predictor for ligand unbinding rates. We furthermore investigate the molecular basis determining unbinding rates within the compound libraries. We propose ligand conformational changes and protein-ligand nonbonded interactions to impact on unbinding rates. Ligands may remain longer at the protein if they exhibit strong electrostatic and/or van der Waals interactions with the target. In the case of ligands with a rigid chemical scaffold that exhibit longer residence times, transient electrostatic interactions with the protein appear to facilitate unbinding. Our results imply that understanding the unbinding pathway and the protein-ligand interactions along this path is crucial for the prediction of small molecule ligands with defined unbinding kinetics.