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The feeding ecology of two populations of the sea anemone Bunodosoma zamponii Gomes, Schama & Solé-Cava 2012 living under different anthropogenic pressures in the coast of Mar del Plata (Buenos Aires, Argentina) was studied. One population was located in Las Delicias (LD; an impacted site adjacent to a wastewater treatment plant) and another in Punta Cantera (PC; the reference site). Both populations of sea anemones were monitored for 2 years and their trophic ecology was analyzed. The mytilid Brachidontes rodriguezii (d'Orbigny, 1842) was the main item found throughout all seasons. Prey diversity was highest in PC during autumn and lowest during winter for both sites. Prey abundance was lower in LD during autumn and summer. Additionally, prey items were larger in PC during autumn, winter, and summer. Item capture probability in autumn was significantly lower in LD. Altogether, the results indicate that the diet of Bunodosoma zamponii differed significantly between sites and such differences varied throughout the year. Most of the differences occurred during autumn and winter, which correspond with a greater concentration of organochlorine pesticides, chlorpyrifos, and polychlorinated biphenyls in anemones from LD registered in previous studies. Overall, this study provides significant knowledge on the feeding ecology of Bunodosoma zamponii that complements the existing information and will provide meaningful insight into the current status of this anemone's populations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased transmissibility, virulence and immune escape abilities have heavily altered the COVID-19 pandemic’s course. Deciphering local and global transmission patterns of those variants is thus key in building a profound understanding of the virus’ spread around the globe. In the present study, we investigate SARS-CoV-2 variant epidemiology in Côte d’Ivoire, Western sub-Saharan Africa. We therefore generated 234 full SARS-CoV-2 genomes stemming from Central and Northern Côte d’Ivoire. Covering the first and second pandemic wave the country had been facing, we identified 20 viral lineages and showed that in Côte d’Ivoire the second pandemic wave in 2021 was driven by the spread of the Alpha (B.1.1.7) and Eta (B.1.525) variant. Our analyses are consistent with a limited number of international introductions of Alpha and Eta into Côte d’Ivoire, and those introduction events mostly stemmed from within the West African subregion. This suggests that subregional travel to Côte d’Ivoire had more impact on local pandemic waves than direct intercontinental travel.

Background High-level BK polyomavirus (BKPyV) replication in allogeneic hematopoietic cell transplantation (HCT) predicts failing immune control and BKPyV-associated hemorrhagic cystitis. Methods To identify molecular markers of BKPyV replication and disease, we scrutinized BKPyV DNA-loads in longitudinal urine and plasma pairs from 20 HCT patients using quantitative nucleic acid testing (QNAT), DNase-I treatment prior to QNAT, next-generation sequencing (NGS), and tested cell-mediated immunity. Results We found that larger QNAT amplicons led to under-quantification and false-negatives results (P < .001). DNase-I reduced urine and plasma BKPyV-loads by >90% (P < .001), indicating non-encapsidated BKPyV genomes. DNase-resistant urine BKPyV-loads remained infectious in cell culture. BKPyV genome fragmentation of ≤250 bp impaired NGS coverage of genetic variation using 1000-bp and 5000-bp amplicons. Conversely, 250-bp amplicons captured viral minority variants. We identified genotype-specific and genotype-independent changes in capsid Vp1 or T-antigen predicted to escape from antibody neutralization or cytotoxic CD8 T-cells, respectively. Genotype-specific changes in immunodominant 9mers were associated with reduced or absent CD8 T-cell responses. Thus, failure to control BKPyV replication in HCT Patients may involve insufficient genotype-specific cytotoxic CD8 T-cell responses, potentially predictable by low neutralizing antibodies as well as genotype-independent immune escape. Conclusions Our results provide new insights for patient evaluation and for designing immune protection through neutralizing antibodies, adoptive T-cell therapy, or vaccines.

The presence of long-lived oscillations in the expectation values of local observables after quantum quenches has recently attracted considerable attention in relation to weak ergodicity breaking. Here we focus on an alternative mechanism that gives rise to such oscillations in a class of systems that support kinematically protected gapped excitations at zero temperature. An open question in this context is whether such oscillations will ultimately decay. We resolve this issue by considering spin models that can be mapped to systems of weakly interacting fermions, which in turn are amenable to an analysis by standard methods based on the BBGKY hierarchy. We find that there is a parametrically large time scale after which the oscillations start to decay., 10+2 pages, 13 Figures

Markov-chain Monte Carlo (MCMC), the field of stochastic algorithms built on the concept of sampling, has countless applications in science and technology. The overwhelming majority of MCMC algorithms are time-reversible and satisfy the detailed-balance condition, just like physical systems in thermal equilibrium. The underlying Markov chains typically display diffusive dynamics, which leads to a slow exploration of sample space. Significant speed-ups can be achieved by non-reversible MCMC algorithms exhibiting non-equilibrium dynamics, whose steady states exactly reproduce the target equilibrium states of reversible Markov chains. Such algorithms have had successes in applications but are generally difficult to analyze, resulting in a scarcity of exact results. Here, we introduce the "lifted" TASEP (totally asymmetric simple exclusion process) as a paradigm for lifted non-reversible Markov chains. Our model can be viewed as a second-generation lifting of the reversible Metropolis algorithm on a one-dimensional lattice and is exactly solvable by an unusual kind of coordinate Bethe ansatz. We establish the integrability of the model and present strong evidence that the lifting leads to faster relaxation than in the KPZ universality class., 11 pages, 8 figures

Flies are implicated in carrying and mechanically transmitting many primate pathogens. We investigated how fly associations vary across six monkey species (Cercopithecus ascanius, Cercopithecus mitis, Colobus guereza, Lophocebus albigena, Papio anubis, and Piliocolobus tephrosceles) and whether monkey group size impacts fly densities. Fly densities were generally higher inside groups than outside them, and considering data from these primate species together revealed that larger groups harbored more flies. Within species, this pattern was strongest for colobine monkeys, and we speculate this might be due to their smaller home ranges, suggesting that movement patterns may influence fly–primate associations. Fly associations increase with group sizes and may thus represent a cost to sociality.

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input. However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Competition with dynamic input. This includes the collection of a new large-scale dataset from the primary visual cortex of five mice, containing responses from over 38,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input. We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond., arXiv admin note: substantial text overlap with arXiv:2206.08666

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method to study tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a new attention readout for a convolutional data-driven core for neurons in macaque V4 that outperforms the state-of-the-art task-driven ResNet model in predicting neuronal responses. However, as the predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI’s model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across architectures than the state-of-the-art GA while preserving the within-architectures activation and requiring 4.7x less compute time. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting natural images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus EGG provides a general and flexible framework to study coding properties of the visual system in the context of natural images.1

Deciphering the brain’s structure-function relationship is key to understanding the neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, is a classic example of primate neocortex structure-function organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex is debated. A key hurdle in identifying columns is the difficulty of characterizing complex nonlinear neuronal tuning, especially with high-dimensional sensory inputs. Here, we asked whether area V4, a mid-level area of the macaque visual system, is organized into columns. We combined large-scale linear probe recordings with deep learning methods to systematically characterize the tuning of >1,200 V4 neurons usingin silicosynthesis of most exciting images (MEIs), followed byin vivoverification. We found that the MEIs of single V4 neurons exhibited complex features like textures, shapes, or even high-level attributes such as eye-like structures. Neurons recorded on the same silicon probe, inserted orthogonal to the cortical surface, were selective to similar spatial features, as expected from a columnar organization. We quantified this finding using human psychophysics and by measuring MEI similarity in a non-linear embedding space, learned with a contrastive loss. Moreover, the selectivity of the neuronal population was clustered, suggesting that V4 neurons form distinct functional groups of shared feature selectivity, reminiscent of cell types. These functional groups closely mirrored the feature maps of units in artificial vision systems, hinting at shared encoding principles between biological and artificial vision. Our findings provide evidence that columns and functional cell types may constitute universal organizing principles of the primate neocortex, simplifying the cortex’s complexity into simpler circuit motifs which perform canonical computations.

Understanding the brain’s perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain’s nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models—capable of predicting large-scale neuronal activity in response to arbitrary sensory input—can be powerful tools for characterizing neuronal representations by enabling high-throughputin silicoexperiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models—trained on vast quantities of data— have demonstrated remarkable capabilities and generalization, we developed a “foundation model” of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ∼ 1mm3volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended “inception loops” — a paradigm that iterates between large-scale recordings, neural predictive models, andin silicoexperiments followed byin vivoverification — to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs’ efficacyin vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texturelike patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.

A key role of sensory processing is integrating information across space. Neuronal responses in the visual system are influenced by both local features in the receptive field center and contextual information from the surround. While center-surround interactions have been extensively studied using simple stimuli like gratings, investigating these interactions with more complex, ecologically-relevant stimuli is challenging due to the high dimensionality of the stimulus space. We used large-scale neuronal recordings in mouse primary visual cortex to train convolutional neural network (CNN) models that accurately predicted center-surround interactions for natural stimuli. These models enabled us to synthesize surround stimuli that strongly suppressed or enhanced neuronal responses to the optimal center stimulus, as confirmed byin vivoexperiments. In contrast to the common notion that congruent center and surround stimuli are suppressive, we found that excitatory surrounds appeared to complete spatial patterns in the center, while inhibitory surrounds disrupted them. We quantified this effect by demonstrating that CNN-optimized excitatory surround images have strong similarity in neuronal response space with surround images generated by extrapolating the statistical properties of the center, and with patches of natural scenes, which are known to exhibit high spatial correlations. Our findings cannot be explained by theories like redundancy reduction or predictive coding previously linked to contextual modulation in visual cortex. Instead, we demonstrated that a hierarchical probabilistic model incorporating Bayesian inference, and modulating neuronal responses based on prior knowledge of natural scene statistics, can explain our empirical results. We replicated these center-surround effects in the multi-area functional connectomics MICrONS dataset using natural movies as visual stimuli, which opens the way towards understanding circuit level mechanism, such as the contributions of lateral and feedback recurrent connections. Our data-driven modeling approach provides a new understanding of the role of contextual interactions in sensory processing and can be adapted across brain areas, sensory modalities, and species.

Background: Metabarcoding of invertebrate derived DNA (iDNA) is increasingly used to describe vertebrate diversity in terrestrial ecosystems. Fly iDNA has also shown potential as a tool for detecting pathogens. Combining these approaches make fly iDNA a promising tool for understanding the ecology and distribution of novel pathogens or emerging infectious diseases. Aims and Methods: We used fly iDNA to explore the geographical distribution of Bacillus cereus biovar anthracis (Bcbva) along a gradient from the forest within Taï National Park (TNP), Côte d'Ivoire, out to surrounding villages. We tested fly pools (N=100 pools of 5 flies) collected in the forest (N=25 pools), along the forest edge (N=50 pools), and near surrounding villages (N=25 pools) for Bcbva. Using the same iDNA, we sought to reconstruct fly and mammal communities with metabarcoding, with the aim of investigating potential links with Bcbva detection. Results: We detected Bcbva in 5 / 100 fly pools and positivity varied significantly across the habitat types (forest = 4 / 25, edge = 1 / 50, village = 0 / 25). It was possible to culture Bcbva from all positive fly pools, confirming their positivity, while sequencing of their whole genomes revealed a considerable portion of known genomic diversity for this pathogen. iDNA generated data about the mammal and fly communities in these habitats, revealing the highest mammal diversity in the forest and considerable changes in fly community composition along the gradient. Bcbva host range estimates from fly iDNA were largely identical to the results of long term carcass monitoring efforts in the region. Conclusion: We show that fly iDNA can generate data on the geographic distribution and host range of a pathogen at kilometer scales, as well as reveal the pathogen’s phylogenetic diversity. Our results highlight the power of fly iDNA for mammal biomonitoring and pathogen surveillance. Data: The raw fastq files from the metabarcoding experiments are uploaded, as well as a excel file, with a description of the samples and associated metadata., The research project was funded by the Deutsche Forschungsgemeinschaft (DFG), Grant ID- LE 1813/17-1

Imaging of iron-based nanoparticles (NPs) remains challenging because of the presence of endogenous iron in tissues that is difficult to distinguish from exogenous iron originating from the NPs. Here, an analytical cascade for characterizing the biodistribution of biomedically relevant iron-based NPs from the organ scale to the cellular and subcellular scales is introduced. The biodistribution on an organ level is assessed by elemental analysis and quantification of magnetic iron by electron paramagnetic resonance, which allowed differentiation of exogenous and endogenous iron. Complementary to these bulk analysis techniques, correlative whole-slide optical and electron microscopy provided spatially resolved insight into the biodistribution of endo- and exogenous iron accumulation in macrophages, with single-cell and single-particle resolution, revealing coaccumulation of iron NPs with endogenous iron in splenic macrophages. Subsequent transmission electron microscopy revealed two types of morphologically distinct iron-containing structures (exogenous nanoparticles and endogenous ferritin) within membrane-bound vesicles in the cytoplasm, hinting at an attempt of splenic macrophages to extract and recycle iron from exogenous nanoparticles. Overall, this strategy enables the distinction of endo- and exogenous iron across scales (from cm to nm, based on the analysis of thousands of cells) and illustrates distribution on organ, cell, and organelle levels., Small Methods, 7 (2), ISSN:2366-9608

In recent years the selective inhibition of FKBP51 has emerged as a possible treatment for chronic pain, obesity-induced diabetes, or depression. All currently known advanced FKBP51-selective inhibitors, including the widely used SAFit2, contain a cyclohexyl residue as a key motif for enabling selectivity over the closest homolog and anti-target FKBP52. During a structure-based SAR exploration we surprisingly discovered thiophenes as highly efficient cyclohexyl replacement moieties that retain the strong selectivity of SAFit-type inhibitors for FKBP51 over FKBP52. Cocrystal structures revealed that the thiophene-containing moieties enable selectivity by stabilizing a flipped-out conformation of Phe67 of FKBP51. Our best compound 19b potently binds to FKBP51 biochemically as well as in mammalian cells, desensitize TRPV1 in primary sensory neurons, and has an acceptable PK profile in mice, suggesting its use as novel tool compound for studying FKBP51 in animal models of neuropathic pain.

Flies form high-density associations with human settlements and groups of nonhuman primates andare implicated in transmitting pathogens. We investigate the movement of nonhuman primate-associated flies across landscapes surrounding Kibale National Park, Uganda, using a mark–recapture experiment. Flies were marked in nine nonhuman primate groups at the forest edge (x = 929 flies per group), and we then attempted to recapture them in more anthropized areas (50 m, 200 m and 500 m from where marked; 2–21 days after marking). Flies marked in nonhuman primate groups were recaptured in human areas (19/28,615 recaptured). Metabarcoding of the flies in nonhuman primate groups revealed the DNA of multiple eukaryotic primate parasites. Taken together, these results demonstrate the potential of flies to serve as vectors between nonhuman primates, livestock and humans at this biodiverse interface. Uploaded here, are the fastq files containing metabarcoding reads generated as part of this experiment. Each pair of reads contains a mix of metabarcoding data for the18S rRNA of eukaryotic parasites andcytochrome oxidase C subunit 1, which were generated from pool of fly legs (see methods for details). Poor quality of the R2 read precluded its inclusion in the analysis, but they are uploaded here for completeness. The sample IDs in the fastq file names correspond to the sample metadata contained in the file Table S1.&nbsp

Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a few shortcomings: it has recently been shown that this method is unsuitable to transfer simple inductive biases like shift equivariance, struggles to transfer out of domain generalization, and optimization time is magnitudes longer compared to default non-KD model training. To improve these aspects of KD, we propose Hard Augmentations for Robust Distillation (HARD), a generally applicable data augmentation framework, that generates synthetic data points for which the teacher and the student disagree. We show in a simple toy example that our augmentation framework solves the problem of transferring simple equivariances with KD. We then apply our framework in real-world tasks for a variety of augmentation models, ranging from simple spatial transformations to unconstrained image manipulations with a pretrained variational autoencoder. We find that our learned augmentations significantly improve KD performance on in-domain and out-of-domain evaluation. Moreover, our method outperforms even state-of-the-art data augmentations and since the augmented training inputs can be visualized, they offer a qualitative insight into the properties that are transferred from the teacher to the student. Thus HARD represents a generally applicable, dynamically optimized data augmentation technique tailored to improve the generalization and convergence speed of models trained with KD.

Background T-cell receptor excision circle (TREC)-based newborn screening (NBS) for severe combined immunodeficiencies (SCID) was introduced in Germany in August 2019. Methods Children with abnormal TREC-NBS were referred to a newly established network of Combined Immunodeficiency (CID) Clinics and Centers. The Working Group for Pediatric Immunology (API) and German Society for Newborn Screening (DGNS) performed 6-monthly surveys to assess the TREC-NBS process after 2.5 years. Results Among 1.9 million screened newborns, 88 patients with congenital T-cell lymphocytopenia were identified (25 SCID, 17 leaky SCID/Omenn Syndrome (OS)/idiopathic T-cell lymphocytopenia and 46 syndromic disorders). A genetic diagnosis was established in 88%. Twenty-six patients underwent hematopoietic stem cell transplantation (HSCT), 23/26 within 4 months of life. Of these, 25/26 (96%) were alive at last follow-up. Two patients presented with in-utero onset OS and died shortly after birth. Five patients with syndromic disorders underwent thymus transplantation. Eight syndromic patients deceased, all from non-immunological complications. TREC-NBS missed one patient, who later presented clinically, and one tracking failure occurred after an inconclusive screening result. Conclusion TREC-NBS was successfully incorporated into German NBS and represents the largest prospective European TREC-NBS cohort at this point. The incidence of SCID/leaky SCID/OS in Germany is approximately 1:54.000. The overall incidence of severe congenital T-cell lymphocytopenia is 1:21.000. The newly founded API-CID network facilitates tracking and treatment of identified patients. Short-term HSCT outcome was excellent, but NBS and transplant registries will remain essential to evaluate the long-term outcome and to compare results across the rising numbers of TREC-NBS programs across Europe.

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

Las enzimas digestivas lipasa pancreática y α-glucosidasa, son importantes en el metabolismo de lípidos y carbohidratos, convirtiéndose en blancos terapéuticos atractivos para el tratamiento de la obesidad y patologías asociadas.

Distinct SARS-CoV-2 lineages, discovered through various genomic surveillance initiatives, have emerged during the pandemic following unprecedented reductions in worldwide human mobility. We here describe a SARS-CoV-2 lineage - designated B.1.620 - discovered in Lithuania and carrying many mutations and deletions in the spike protein shared with widespread variants of concern (VOCs), including E484K, S477N and deletions HV69Δ, Y144Δ, and LLA241/243Δ. As well as documenting the suite of mutations this lineage carries, we also describe its potential to be resistant to neutralising antibodies, accompanying travel histories for a subset of European cases, evidence of local B.1.620 transmission in Europe with a focus on Lithuania, and significance of its prevalence in Central Africa owing to recent genome sequencing efforts there. We make a case for its likely Central African origin using advanced phylogeographic inference methodologies incorporating recorded travel histories of infected travellers., Here, the authors describe the emergence and spread of a new potential SARS-CoV-2 variant of interest, B.1.620. They show that this lineage, first identified in Lithuania, has established local transmission in Europe on multiple occasions and likely emerged in Central Africa.

Reactive polymersomes represent a versatile artificial cargo carrier system that can facilitate an immediate release in response to a specific stimulus. The herein presented oxidation‐sensitive polymersomes feature a time‐delayed release mechanism in an oxidative environment, which can be precisely adjusted by either tuning the membrane thickness or partial pre‐oxidation. These polymeric vesicles are conveniently prepared by PISA allowing the straightforward and effective in situ encapsulation of cargo molecules, as shown for dyes and enzymes. Kinetic studies revealed a critical degree of oxidation causing the destabilization of the membrane, while no release of the cargo is observed beforehand. The encapsulation of glucose oxidase directly transforms these polymersomes into glucose‐sensitive vesicles, as small molecules including sugars can passively penetrate their membrane. Considering the ease of preparation, these polymersomes represent a versatile platform for the confinement and burst release of cargo molecules after a precisely adjustable time span in the presence of specific triggers, such as H2O2 or glucose., Polymersomes made by PISA based on thioether‐functionalized polymers as membrane‐forming block show a spontaneous membrane destabilization once a critical degree of oxidation is reached. Fine‐tuning of the degradation time can be performed either by adjusting the membrane thickness or by partial pre‐oxidation and can be further exploited to tailor the burst release of encapsulated dyes or enzymes in the presence of specific triggers.

Millions of patients every year undergo gastrointestinal surgery. While often lifesaving, sutured and stapled reconnections leak in around 10% of cases. Currently, surgeons rely on the monitoring of surrogate markers and clinical symptoms, which often lack sensitivity and specificity, hence only offering late-stage detection of fully developed leaks. Here, we present a holistic solution in the form of a modular, intelligent suture support sealant patch capable of containing and detecting leaks early. The pH and/or enzyme-responsive triggerable sensing elements can be read out by point-of-need ultrasound imaging. We demonstrate reliable detection of the breaching of sutures, in as little as 3 hours in intestinal leak scenarios and 15 minutes in gastric leak conditions. This technology paves the way for next-generation suture support materials that seal and offer disambiguation in cases of anastomotic leaks based on point-of-need monitoring, without reliance on complex electronics or bulky (bio)electronic implantables., Nature Communications, 13 (1), ISSN:2041-1723

Background: Most nations worldwide have aging populations. With age, sensory, cognitive and motor abilities decline and the risk for neurodegenerative disorders increases. These multiple impairments influence the quality of life and increase the need for care, thus putting a high burden on society, the economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, in particular ones that are potentially modifiable by each subject through choice of lifestyle. However, large-scale studies that investigate the influence of multiple multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse. Methods: We propose a Machine Learning model that simultaneously predicts multiple cognitive and motor outcome measurements on a personalized level recorded from one learned composite score. This personalized composite score is derived by the model from a large set of multi-modal components from the TREND cohort including genetic, biofluid, clinical, demographic and lifestyle factors. Results: We found that a model based on a single composite score was able to predict cognitive and motor abilities almost as well as a flexible regression model specifically trained for each single clinical score. In contrast to the flexible regression model, our composite score-based model is able to identify factors that globally influence cognitive and motoric abilities as measured by multiple clinical scores. The model identified several risk and protective factors for healthy aging and recovered physical exercise as a major, modifiable, protective factor. Conclusion: We conclude that our low parametric modeling approach successfully recovered known risk and protective factors of healthy aging on a personalized level while providing an interpretable composite score. The modeling approach is generalizable and could also be applied to other cohorts to investigate the complex interplay of risk and protective factors along with effect sizes from different dimensions such as lifestyle, medical, genetic and biochemical data.

The detrital zircon grains from two catchments in the East Eifel Volcanic Field consist of crystals sourced from proximal deposits of Quaternary trachytic–phonolitic volcanic centres, Hocheifel Paleogene trachytic rocks and the Paleozoic basement. The outcrop patterns indicate the derivation of a significant detrital zircon population from deposits of the c. 13 ka Lower and Upper Laacher See Tephra. Intercalated Middle Laacher See Tephra, consisting of valley-filling ignimbrites, by contrast, is a minor zircon source despite thick ignimbrite deposits in one of the catchments. This reflects zircon undersaturation in the hotter and less evolved phonolite tapped from deeper parts of the magma reservoir as the eruption unfolded, as well as fewer antecrysts recycled from the plutonic carapace. A previously unrecognized zircon age population of c. 63 ka, mostly derived from Lower Laacher See Tephra, marks the onset of the presence of evolved magma at the top of the reservoir. Correlative Hf and O isotopic analysis reveals significant crustal interactions during phonolite differentiation in a shallow reservoir, with the assimilation of cogenetic, but hydrothermally modified, plutonic margins. Raman spectra indicate that magmatically heated crustal zircon xenocrysts are absent. Detrital zircon can thus provide more integrated insights into the evolution of Quaternary magma systems than the punctuated sampling of volcanic or cogenetic plutonic rocks. Supplementary material: Data tables presenting U–Th, U–Pb, O–Hf and Raman geochronology results are available at https://doi.org/10.6084/m9.figshare.c.6123823

Nano-sized metal organic frameworks (nanoMOFs) have gained increasing importance in biomedicine due to their tunable properties. In addition to their use as carriers in drug delivery, nanoMOFs containing hafnium have been successfully employed as radio-enhancers augmenting damage caused by X-ray irradiation in tumor tissue. While results are encouraging, there is little mechanistic understanding available, and the biological fate of these radio-enhancer nanoparticles remains largely unexplored. Here, we synthesized a selection of group IV metal-based (Hf, Ti, Ti/Zr) nanoMOFs and investigated their cell compatibility and radio-enhancement performance in direct comparison to the corresponding metal oxides. We report surprising radio-enhancement performance of Ti-containing nanoMOFs reaching dose modifying ratios of 3.84 in human sarcoma cells and no relevant dose modification in healthy human fibroblasts. These Ti-based nanoMOFs even outperformed previously reported Hf-based nanoMOFs as well as equimolar group IV metal oxides in direct benchmarking experiments. While group IV nanoMOFs were well-tolerated by cells in the absence of irradiation, the nanoMOFs partially dissolved in lysosomal buffer conditions showing distinctly different chemical stability compared to widely researched group IV oxides (TiO2, ZrO2, and HfO2). Taken together, this study illustrates the promising potential of Ti-based nanoMOFs for radio-enhancement and provides insight into the intracellular fate and stability of group IV nanoMOFs., Biomaterials Science, 10 (22), ISSN:2047-4830, ISSN:2047-4849

Human respiratory pathogens have repeatedly caused lethal outbreaks in wild great apes across Africa, leading to population declines. Nonetheless, our knowledge of potential genomic changes associated with pathogen introduction and spread at the human-great ape interface remains sparse. Here, we made use of target enrichment coupled with next generation sequencing to non-invasively investigate five outbreaks of human-introduced respiratory disease in wild chimpanzees living in Taï National Park, Ivory Coast. By retrieving 34 complete viral genomes and three distinct constellations of pneumococcal virulence factors, we provide genomic insights into these spillover events and describe a framework for non-invasive genomic surveillance in wildlife.

Authors of the March WHO report into how COVID-19 emerged warn that further delay makes crucial inquiry biologically difficult. Authors of the March WHO report into how COVID-19 emerged warn that further delay makes crucial inquiry biologically difficult.

Responsive polymers, which become protonated at decreasing pH, are considered a milestone in the development of synthetic cell entry vectors. Exact correlations between their properties and their ability to escape the endosome, however, often remain elusive due to hydrophobic interactions or limitations in the design of water-soluble materials with suitable basicity. Here, we present a series of well-defined, hydrophilic polypiperazines, where systematic variation of the amino moiety facilitates an unprecedented fine-tuning of the basicity or pKa value within the physiologically relevant range (pH 6-7.4). Coincubation of HEK 293T cells with various probes, including small fluorophores or functioning proteins, revealed a rapid increase of endosomal release for polymers with pKa values above 6.5 or 7 in serum-free or serum-containing media, respectively. Similarly, cytotoxic effects became severe at increased pKa values (>7). Although the window for effective transport appears narrow, the discovered correlations offer a principal guideline for the design of effective polymers for endosomal escape.

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. While many morphological surveys focus on branching patterns of individual cells, fewer have been devoted to sub-cellular structure of cells. Electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. Here we present the first systematic survey of the somatic region of nearly 100,000 cortical cells, using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to recapitulate many known aspects of cortical organization, while also revealing novel relationships. Parameters of cell size, nuclear infolding and somatic synaptic innervation co-vary with distinct patterns across depth and between types. Further, we describe how these subcellular features can be used to create highly accurate predictions of cell-types across large scale EM datasets. More generally, our results suggest that the shifts in cellular physiology and molecular programming seen across cell types accompany profound differences in the fine-scale structure of cells.

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the Sensorium benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (Sensorium) or stimulus plus behavior (Sensorium+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond., NeurIPS 2022 Competition Track

Nanoparticle-based radioenhancement is a promising strategy for extending the therapeutic ratio of radiotherapy. While (pre)clinical results are encouraging, sound mechanistic understanding of nanoparticle radioenhancement, especially the effects of nanomaterial selection and irradiation conditions, has yet to be achieved. Here, we investigate the radioenhancement mechanisms of selected metal oxide nanomaterials (including SiO2, TiO2, WO3 and HfO2), TiN and Au nanoparticles for radiotherapy utilizing photons (150 kVp and 6 MV) and 100 MeV protons. While Au nanoparticles show outstanding radioenhancement properties in kV irradiation settings, where the photoelectric effect is dominant, these properties are attenuated to baseline levels for clinically more relevant irradiation with MV photons and protons. In contrast, HfO2 nanoparticles retain some of their radioenhancement properties in MV photon and proton therapies. Interestingly, TiO2 nanoparticles, which have a comparatively low effective atomic number, show significant radioenhancement efficacies in all three irradiation settings, which can be attributed to the strong radiocatalytic activity of TiO2, leading to the formation of hydroxyl radicals, and nuclear interactions with protons. Taken together, our data enable the extraction of general design criteria for nanoparticle radioenhancers for different treatment modalities, paving the way to performance-optimized nanotherapeutics for precision radiotherapy., Nature Communications, 13 (1), ISSN:2041-1723

Background Spinocerebellar Ataxia type 2 is a slowly progressive adult onset ataxia with a broad clinical presentation. Case presentation We describe a man with Spinocerebellar Ataxia type 2 with chronic, severe, and recurrent rhabdomyolysis, as part of the cerebellar ataxia genetic spectrum. Initially rhabdomyolysis was refractory to multiple medications, but entirely resolved and remained in chronic remission with pregabalin. Conclusions This is the first report of Spinocerebellar Ataxia type 2 associated with chronic, severe, recurrent rhabdomyolysis as part of its genetic phenotype responsive to pregabalin.

Polymerization-induced self-assembly (PISA) is an emerging methodology for the in situ preparation of complex polymeric nanostructures in aqueous solution. However, the scope of monomers allowing morphology transitions remains limited, which is related to the low solubility of many monomers in water. Morphology transitions have therefore been restricted to more hydrophilic monomers necessitating rather long hydrophobic blocks to induce aggregation. Even longer ones are required to induce the changes of the morphology deviating from a spherical shape – a fact that limits the accessible hydrophobic domain sizes or morphologies. Here, we demonstrate that N-acryloyl thiomorpholine (NAT) represents a unique monomer which is fully miscible with water, but results in hydrophobic polymers at degrees of polymerization (DP) below 10, while morphology transitions can occur at DPs of 25 and even less. Synthesizing over 70 block copolymers in total we identified key parameters, such as hydrophilic block length, temperature, ratio of co-solvent, and concentration, influencing the self-assembly process. While the high glass transition temperature (Tg) of PNAT may cause frozen and kinetically trapped micellar cores, suitable synthesis conditions enable access to all common morphologies including spheres, worms, and vesicles as well as intermediate phases. Applying this technique, various nanostructures are reproducibly formed in situ in aqueous dispersions rendering the presented PISA system a highly versatile, new route to functional block copolymer nanostructures for various applications.

Responses to natural stimuli in area V4 – a mid-level area of the visual ventral stream – are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional role of V4 in object classification. However, we currently do not know if and to what extent V4 plays a role in solvingothercomputational objectives. Here, we investigated normative accounts of V4 (and V1 for comparison) by predicting macaque single-neuron responses to natural images from the representations extracted by 23 CNNs trained on different computer vision tasks including semantic, geometric, 2D, and 3D types of tasks. We found that V4 was best predicted by semantic classification features and exhibited high task selectivity, while the choice of task was less consequential to V1 performance. Consistent with traditional characterizations of V4 function that show its high-dimensional tuning to various 2D and 3D stimulus directions, we found that diverse non-semantic tasks explained aspects of V4 function beyond those captured by individual semantic tasks. Nevertheless, jointly considering the features of a pair of semantic classification tasks was sufficient to yield one of our top V4 models, solidifying V4’s main functional role in semantic processing and suggesting that V4’s affinity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.

We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture the stimulus-conditioned variability including noise correlations. This allows us to train the model end-to-end without the need for sophisticated probabilistic approximations associated with many latent state models for stimulus-conditioned fluctuations. We train the model on the responses of thousands of neurons from multiple areas of the mouse visual cortex to natural images. We show that our model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability. Furthermore, it successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation, and other factors that vary systematically with brain area or retinotopic location. Overall, our model accurately accounts for two critical sources of neural variability while avoiding several complexities associated with many existing latent state models. It thus provides a useful tool for uncovering the interplay between different factors that contribute to variability in neural activity.

The human parasite Plasmodium malariae has relatives infecting African apes (Plasmodium rodhaini) and New World monkeys (Plasmodium brasilianum), but its origins remain unknown. Using a novel approach to characterise P. malariae-related sequences in wild and captive African apes, we found that this group comprises three distinct lineages, one of which represents a previously unknown, highly divergent species infecting chimpanzees, bonobos and gorillas across central Africa. A second ape-derived lineage is much more closely related to the third, human-infective lineage P. malariae, but exhibits little evidence of genetic exchange with it, and so likely represents a separate species. Moreover, the levels and nature of genetic polymorphisms in P. malariae indicate that it resulted from the zoonotic transmission of an African ape parasite, reminiscent of the origin of P. falciparum. In contrast, P. brasilianum falls within the radiation of human P. malariae, and thus reflects a recent anthroponosis.

Signal stability is crucial for an accurate diagnosis via magnetic particle imaging (MPI). However, MPI-tracer nanoparticles frequently agglomerate during their in vivo applications leading to particle interactions altering the signal. Here, we investigate the influence of such magnetic coupling phenomena on the MPI signal. We prepared Zn0.4Fe2.6O4 nanoparticles by flame spray synthesis and controlled their inter-particle distance by varying SiO2 coating thickness. The silica shell affected the magnetic properties indicating stronger particle interactions for a smaller inter-particle distance. The SiO2-coated Zn0.4Fe2.6O4 outperformed the bare sample in magnetic particle spectroscopy (MPS) in terms of signal/noise, however, the shell thickness itself only weakly influenced the MPS signal. To investigate the importance of magnetic coupling effects in more detail, we benchmarked the MPS signal of the bare and SiO2-coated Zn-ferrites against commercially available PVP-coated Fe3O4 nanoparticles in water and PBS. PBS is known to destabilize nanoparticle colloids mimicking in vivo-like agglomeration. The bare and coated Zn-ferrites showed excellent signal stability, despite their agglomeration in PBS. We attribute this to their process-intrinsic aggregated morphology formed during their flame-synthesis, which generates an MPS signal only little affected by PBS. On the other hand, the MPS signal of commercial PVP-coated Fe3O4 strongly decreased in PBS compared to water, indicating strongly changed particle interactions. The relevance of this effect was further investigated in a human cell model. For PVP-coated Fe3O4, we detected a strong discrepancy between the particle concentration obtained from the MPS signal and the actual concentration determined via ICP-MS. The same trend was observed during their MPI analysis; while SiO2-coated Zn-ferrites could be precisely located in water and PBS, PVP-coated Fe3O4 could not be detected in PBS at all. This drastically limits the sensitivity and also general applicability of these commercial tracers for MPI and illustrates the advantages of our flame-made Zn-ferrites concerning signal stability and ultimately diagnostic accuracy., Nanoscale, 14 (19), ISSN:2040-3364, ISSN:2040-3372

Background: The majority of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are admitted to the Intensive Care Unit (ICU) for mechanical ventilation. The role of multi-organ failure during ICU admission as driver for outcome remains to be investigated yet.Design and setting: Prospective cohort of mechanically ventilated critically ill with SARS-CoV-2 infection.Participants and methods: 94 participants of the MaastrICCht cohort (21% women) had a median length of stay of 16 days (maximum of 77). After division into survivors (n = 59) and non-survivors (n = 35), we analysed 1555 serial SOFA scores using linear mixed-effects models.Results: Survivors improved one SOFA score point more per 5 days (95% CI: 4-8) than non-survivors. Adjustment for age, sex, and chronic lung, renal and liver disease, body-mass index, diabetes mellitus, cardiovascular risk fac-tors, and Acute Physiology and Chronic Health Evaluation II score did not change this result. This association was stronger for women than men (P-interaction = 0.043).Conclusions: The decrease in SOFA score associated with survival suggests multi-organ failure involvement dur -ing mechanical ventilation in patients with SARS-CoV-2. Surviving women appeared to improve faster than sur-viving men. Serial SOFA scores may unravel an unfavourable trajectory and guide decisions in mechanically ventilated patients with SARS-CoV-2.(c) 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Block copolymer micelles have received increasing attention in the last decades, in particular for their appealing properties in nanomedicine. However, systematic investigations of the interaction between polymeric micelles and immune cells are still rare. Therefore, broader studies comparing the structural effects remain inevitable for a comprehensive understanding of the immune response and for the design of efficient, nonimmunogenic delivery systems. Here, we present novel block copolymer micelles with the same hydrophobic core, based on a copolymer of BA and VDM, and various hydrophilic shells ranging from common PEG derivatives to morpholine-based materials. The influence of these shells on innate immune responses was studied in detail. In addition, we investigated the impact of micelle stability by varying the cross-linking density in the micellar core. Surprisingly, whereas different shells had only a minor impact on immune response, micelles with reduced cross-linking density considerably enhanced the release of cytokines from isolated human monocytes. Moreover, the uptake of non-cross-linked micelles by monocytes was significantly higher as compared to cross-linked materials. Our study emphasizes the importance of the micellar stability on the interaction with the immune system, which is the key for any stealth properties in vivo. Polymers based on morpholines result in a similar low response as the PEG derivative and may represent an interesting alternative to the common PEGylation.