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Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

Immunoglobulin G (IgG) antibodies are a critical component of the adaptive immune system, binding to and neutralizing pathogens and other foreign substances. Recent advances in molecular antibody biology and structural protein engineering enabled the modification of IgG antibodies to enhance their therapeutic potential. This review summarizes recent progress in both natural and engineered structural modifications of IgG antibodies, including allotypic variation, glycosylation, Fc engineering, and Fc gamma receptor binding optimization. We discuss the functional consequences of these modifications to highlight their potential for therapeutical applications.

Land cover (LC) maps are used extensively for nature conservation and landscape planning, but low spatial resolution and coarse LC schemas typically limit their applicability to large, broadly defined habitats. In order to target smaller and more-specific habitats, LC maps must be developed at high resolution and fine class detail using automated methods that can efficiently scale to large areas of interest. In this work, we present a Machine Learning approach that addresses this challenge. First, we developed a multi-stage semantic segmentation approach that uses Convolutional Neural Networks (CNNs) to classify LC across the Peak District National Park (PDNP, 1439 km2) in the UK using a detailed, hierarchical LC schema. High-level classes were predicted with 95% accuracy and were subsequently used as masks to predict low-level classes with 72% to 92% accuracy. Next, we used these predictions to analyse the degree and distribution of fragmentation of one specific habitat—wet grassland and rush pasture—at the landscape scale in the PDNP. We found that fragmentation varied across areas designated as primary habitat, highlighting the importance of high-resolution LC maps provided by CNN-powered analysis for nature conservation.

Immune-mediated platelet refractoriness (PR) remains a significant problem in the setting of platelet transfusion and is predominantly caused by the presence of alloantibodies directed against class I human leukocyte antigens (HLA). Opsonization of donor platelets with these alloantibodies can result in rapid clearance after transfusion via multiple mechanisms, including antibody dependent cellular phagocytosis (ADCP). Interestingly, not all alloimmunized patients develop PR to unmatched platelet transfusions, suggesting variation in HLA-specific IgG responses between patients. Previously, we observed that the glycosylation profile of anti-HLA antibodies was highly variable between PR patients, especially with respect to Fc galactosylation, sialylation and fucosylation. In the current study, we investigated the effect of different Fc glycosylation patterns, with known effects on complement deposition and FcγR binding, on phagocytosis of opsonized platelets by monocyte-derived human macrophages. We found that the phagocytosis of antibody- and complement-opsonized platelets, by monocyte derived M1 macrophages, was unaffected by these qualitative IgG-glycan differences.

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet, it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here, we recorded the activity from ∼40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ∼200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

Introduction Although recently published evidence favours transradial access (TRA) when using large-bore guiding catheters for percutaneous coronary intervention (PCI) of complex coronary lesions, the femoral artery will still be used in a considerate proportion of patients undergoing complex PCI, especially in PCI of chronic total occlusions (CTO). Ultrasound-guided puncture of the femoral artery may reduce clinically relevant access site complications, but robust evidence is lacking up to date.Methods and analysis A total of 542 patients undergoing complex PCI, defined as PCI of CTO, complex bifurcation, heavy calcified lesion or left main, in which the 7-F or 8-F transfemoral access is required, will be randomised to ultrasound-guided puncture or fluoroscopy-guided puncture. The primary outcome is the incidence of the composite end-point of clinically relevant access site related bleeding and/or vascular complications requiring intervention. Access site complications and major adverse cardiovascular events up to 1 month will also be compared between both groups.Ethics and dissemination Ethical approval for the study was granted by the local Ethics Committee (‘Medisch Ethische Toetsing Commissie Isala Zwolle’) for all Dutch sites, ‘Comité Medische Ethiek Ziekenhuis Oost-Limburg’ for Hospital Oost-Limburg, ‘Comité d’éthique CHU-Charleroi—ISPPC’ for Centre Hospilatier Universitaire de Charleroi and ‘Ethik Kommission de Ärztekammer Nordrhein’ for Elisabeth-Krankenhaus). The trial outcomes will be published in peer-reviewed journals of the concerned literature. The ultrasound guided transfemoral access in complex large bore PCI trial has been administered in the ClinicalTrials.gov database, reference number: NCT03846752.Registration details ClinicalTrials.gov identifier: NCT03846752.

Abstract The clumped isotope composition (Δ47, the anomaly of the mass 47 isotopologue relative to the abundance expected from a random isotope distribution) of CO2 has been suggested as an additional tracer for gross CO2 fluxes. However, the effect of photosynthetic gas exchange on Δ47 has not been directly determined and two indirect/conceptual studies reported contradicting results. In this study, we quantify the effect of photosynthetic gas exchange on Δ47 of CO2 using leaf cuvette experiments with one C4 and two C3 plants. The experimental results are supported by calculations with a leaf cuvette model. Our results demonstrate the important roles of the Δ47 value of CO2 entering the leaf, kinetic fractionation as CO2 diffuses into, and out of the leaf and CO2–H2O isotope exchange with leaf water. We experimentally confirm the previously suggested dependence of Δ47 of CO2 in the air surrounding a leaf on the stomatal conductance and back-diffusion flux. Gas exchange can enrich or deplete the Δ47 of CO2 depending on the Δ47 of CO2 entering the leaf and the fraction of CO2 exchanged with leaf water and diffused back to the atmosphere, but under typical ambient conditions, it will lead to a decrease in Δ47.

The Black Rice Debate discusses the agency of enslaved Africans in rice cultivation in the Americas from the seventeenth century onward. With regard to Suriname, it was considered highly unlikely that rice grew there before 1730 and that Africans at that time knew how to grow rice. This contradicts Maroon oral history, which dates circa 1690 as the time that their first ancestors escaped into the interior forests with rice. Based on Dutch archival records, we show that rice was present in Suriname before the Dutch conquered the colony in 1668. Shortly afterward, it became a staple food as a means to mitigate the ongoing food scarcity. Enslaved Africans played a crucial role in the establishment of designated plantations to grow subsistence crops. Archival records also indicate that conditions were present as early as 1686 for enslaved Africans to escape slavery, bringing rice with them. Our research highlights the importance of connecting archival data to geographical, botanical, and oral history research to reveal misrepresented historical actors, such as enslaved Africans. [ABSTRACT FROM AUTHOR]

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions. Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context. Author summary: Neuronal activity is highly dynamic—our brain never responds to the same situation in exactly the same way. How do we extract information from such dynamic signals? The classical answer is: averaging neuronal activity across repetitions of the same stimulus to detect its consistent aspects. This logic is widespread—it is hard to find a neuroscience study that does not contain averages. But how well do averages represent the computations that happen in the brain moment by moment? We developed a simple test that probes two assumptions implicit in averaging: Reliability: Neuronal responses repeat consistently enough across stimulus repetitions that the average remains recognizable. Behavioural relevance: Neuronal responses that are more similar to the average, are more likely to evoke correct behaviour. We apply this test to two example data sets featuring population recordings in mice performing perceptual tasks. We show that both assumptions were largely fulfilled in the first data set, but not in the second; suggesting that the relevance of averaging varies across contexts, e.g. due to experimental control levels and neuronal diversity. Most importantly, we encourage neuroscientists to use our test to gauge whether averages reflect informative aspects of neuronal activity in their data. [ABSTRACT FROM AUTHOR]

In this work, we use a boundary layer resolved model to improve a Lagrangian point particle model to simulate the combustion of single iron particles. By resolving the full boundary layer, mass and heat transfer are accurately modeled, including Stefan flow. Therefore, the model is suitable to improve point particle models. This work focuses on the first stage of iron combustion, which lasts up to the maximum temperature. Temperature- and composition-dependent properties are used and phase transitions from solid to liquid and liquid to gas are taken into account. The Nusselt and Sherwood correlations are investigated in conditions typical for iron particle combustion. It is found that the $$1/2$$ 1 / 2 -film temperature is the best film rule to use to model heat- and mass transfer for iron particle combustion. The boundary layer resolved model is used to validate the point particle models. Then, the model is systematically elaborated by including a temperature-dependent particle density, slip velocity and Stefan flow. The individual and combined effect of these phenomena on the burn duration are investigated. Including all these effects decreases the time to maximum temperature by around $$25\% $$ 25 %. Furthermore, it is shown that if one neglects physical phenomena like slip and Stefan flow, but uses the $$1/3$$ 1 / 3 -film rule instead of the $$1/2$$ 1 / 2 -film rule, errors cancel and still reasonable agreement is obtained with experiments. [ABSTRACT FROM AUTHOR]

Immune-mediated platelet refractoriness (PR) remains a significant problem in the setting of platelet transfusion and is predominantly caused by the presence of alloantibodies directed against class I human leukocyte antigens (HLA). Opsonization of donor platelets with these alloantibodies can result in rapid clearance after transfusion via multiple mechanisms, including antibody dependent cellular phagocytosis (ADCP). Interestingly, not all alloimmunized patients develop PR to unmatched platelet transfusions, suggesting variation in HLA-specific IgG responses between patients. Previously, we observed that the glycosylation profile of anti-HLA antibodies was highly variable between PR patients, especially with respect to Fc galactosylation, sialylation and fucosylation. In the current study, we investigated the effect of different Fc glycosylation patterns, with known effects on complement deposition and FcyR binding, on phagocytosis of opsonized platelets by monocyte-derived human macrophages. We found that the phagocytosis of antibody- and complement-opsonized platelets, by monocyte derived M1 macrophages, was unaffected by these qualitative IgG-glycan differences. [ABSTRACT FROM AUTHOR]