In order to cope with the exponentially increasing number of patients infected with SARS‐CoV‐2, European countries made enormous efforts to reorganize medical assistance and several diseases, ...including stroke, were particularly impacted. We report the experience of stroke neurologists from three European countries (Italy, France and Germany) that faced the pandemic at diverse time points and with different approaches, depending on their resources and healthcare system organization. Pre‐hospital and in‐hospital acute stroke pathways were reorganized to prioritize COVID‐19 management and, in severely affected regions of Italy and France, stroke care was centralized to a limited number of centers, whereas the remaining stroke units were dedicated to patients with COVID‐19. Access to acute stroke diagnostics and time‐dependent therapies was limited or delayed because of reduced capacities of emergency services due to the burden of patients with COVID‐19. A marked reduction in the number of patients presenting with transient ischaemic attack and stroke was noted in the emergency departments of all three countries. Although we only have preliminary data, these conditions may have affected stroke outcome. These indirect effects of the COVID‐19 pandemic could negate the efforts of stroke neurologists over the last few years to improve outcome and reduce mortality of stroke patients. Although the SARS‐CoV‐2 infection rate is slowing down in Europe, the effects of ending lockdown in the next months are unpredictable. It is important for the European and world stroke community to share what has been learned so far to be plan strategies to ensure stroke care in the future and upcoming challenging times.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Recent studies have revealed that immune repertoires contain a substantial fraction of public clones, which may be defined as Ab or TCR clonal sequences shared across individuals. It has remained ...unclear whether public clones possess predictable sequence features that differentiate them from private clones, which are believed to be generated largely stochastically. This knowledge gap represents a lack of insight into the shaping of immune repertoire diversity. Leveraging a machine learning approach capable of capturing the high-dimensional compositional information of each clonal sequence (defined by CDR3), we detected predictive public clone and private clone-specific immunogenomic differences concentrated in CDR3's N1-D-N2 region, which allowed the prediction of public and private status with 80% accuracy in humans and mice. Our results unexpectedly demonstrate that public, as well as private, clones possess predictable high-dimensional immunogenomic features. Our support vector machine model could be trained effectively on large published datasets (3 million clonal sequences) and was sufficiently robust for public clone prediction across individuals and studies prepared with different library preparation and high-throughput sequencing protocols. In summary, we have uncovered the existence of high-dimensional immunogenomic rules that shape immune repertoire diversity in a predictable fashion. Our approach may pave the way for the construction of a comprehensive atlas of public mouse and human immune repertoires with potential applications in rational vaccine design and immunotherapeutics.
The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of ...SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.
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•Millions of combinatorial SARS-Cov-2-RBD variants screened by yeast surface display•Machine learning models accurately predict ACE2 binding and antibody escape•Identification of combinatorial mutations that drive escape to multiple antibodies•Assessment of antibody robustness to billions of prospective RBD variants
A machine-learning-guided, protein engineering method enables the prediction of how SARS-CoV-2 RBD combinatorial mutations will impact therapeutic antibody escape and ACE2 affinity. This method facilitates the identification of multisite mutations that are major drivers of antibody escape and the evaluation of neutralizing antibody efficacy on heavily mutated viral variants.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and ...(neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 104 motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.
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•Prediction of antibody-antigen binding is a central question in immunology•A motif vocabulary of paratope-epitope interactions governs antibody specificity•Proof of principle that antibody-antigen binding is predictable•Implications for de novo antibody and (neo-)epitope design
Prediction of antibody-antigen binding is a central question in immunology and of high relevance for predictive antibody and vaccine design. Akbar et al. prove the predictability of antibody-antigen binding by discovering a universal, compact, and immunity-specific motif vocabulary of paratope-epitope interactions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The effects of entrepreneurship education Graevenitz, Georg von; Harhoff, Dietmar; Weber, Richard
Journal of economic behavior & organization,
10/2010, Volume:
76, Issue:
1
Journal Article
Peer reviewed
Open access
Entrepreneurship education ranks high on policy agendas in Europe and the US, but little research is available to assess its impact. To help close this gap we investigate whether entrepreneurship ...education affects intentions to be entrepreneurial uniformly or whether it leads to greater sorting of students. The latter can reduce the average intention to be entrepreneurial and yet be socially beneficial. This paper provides a model of learning in which entrepreneurship education generates signals to students. Drawing on the signals, students evaluate their aptitude for entrepreneurial tasks. The model is tested using data from a compulsory entrepreneurship course. Using ex-ante and ex-post-survey responses from students, we find that intentions to found decline somewhat although the course has significant positive effects on students’ self-assessed entrepreneurial skills. The empirical analysis supports the hypothesis that students receive informative signals and learn about their entrepreneurial aptitude. We outline implications for educators and public policy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Improvements in the function, quality of life, and longevity of patients with Duchenne muscular dystrophy (DMD) have been achieved through a multidisciplinary approach to management across a range of ...health-care specialties. In part 3 of this update of the DMD care considerations, we focus on primary care, emergency management, psychosocial care, and transitions of care across the lifespan. Many primary care and emergency medicine clinicians are inexperienced at managing the complications of DMD. We provide a guide to the acute and chronic medical conditions that these first-line providers are likely to encounter. With prolonged survival, individuals with DMD face a unique set of challenges related to psychosocial issues and transitions of care. We discuss assessments and interventions that are designed to improve mental health and independence, functionality, and quality of life in critical domains of living, including health care, education, employment, interpersonal relationships, and intimacy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Particle water and pH are predicted using meteorological observations (relative humidity (RH), temperature (T)), gas/particle composition, and thermodynamic modeling (ISORROPIA-II). A comprehensive ...uncertainty analysis is included, and the model is validated. We investigate mass concentrations of particle water and related particle pH for ambient fine-mode aerosols sampled in a relatively remote Alabama forest during the Southern Oxidant and Aerosol Study (SOAS) in summer and at various sites in the southeastern US during different seasons, as part of the Southeastern Center for Air Pollution and Epidemiology (SCAPE) study. Particle water and pH are closely linked; pH is a measure of the particle H+ aqueous concentration and depends on both the presence of ions and amount of particle liquid water. Levels of particle water, in turn, are determined through water uptake by both the ionic species and organic compounds. Thermodynamic calculations based on measured ion concentrations can predict both pH and liquid water but may be biased since contributions of organic species to liquid water are not considered. In this study, contributions of both the inorganic and organic fractions to aerosol liquid water were considered, and predictions were in good agreement with measured liquid water based on differences in ambient and dry light scattering coefficients (prediction vs. measurement: slope = 0.91, intercept = 0.5 μg m−3, R2 = 0.75). ISORROPIA-II predictions were confirmed by good agreement between predicted and measured ammonia concentrations (slope = 1.07, intercept = −0.12 μg m−3, R2 = 0.76). Based on this study, organic species on average contributed 35% to the total water, with a substantially higher contribution (50%) at night. However, not including contributions of organic water had a minor effect on pH (changes pH by 0.15 to 0.23 units), suggesting that predicted pH without consideration of organic water could be sufficient for the purposes of aqueous secondary organic aerosol (SOA) chemistry. The mean pH predicted in the Alabama forest (SOAS) was 0.94 ± 0.59 (median 0.93). pH diurnal trends followed liquid water and were driven mainly by variability in RH; during SOAS nighttime pH was near 1.5, while daytime pH was near 0.5. pH ranged from 0.5 to 2 in summer and 1 to 3 in the winter at other sites. The systematically low pH levels in the southeast may have important ramifications, such as significantly influencing acid-catalyzed reactions, gas–aerosol partitioning, and mobilization of redox metals and minerals. Particle ion balances or molar ratios, often used to infer pH, do not consider the dissociation state of individual ions or particle liquid water levels and do not correlate with particle pH.
In sorption heat storage, one of the sources of discrepancy between theoretical material based energy storage potential and resulting system performance is the choice of process type. In this paper, ...in order to understand this performance deviation, a sorption heat storage process categorisation is proposed. This is followed by a review of reported sorption systems categorised according to the proposed process classification. An analysis of the reported systems is then undertaken, focusing on the ratio of resulting temperature gain in sorption (ad- or absorption), compared to required temperature lift in desorption. This measure is termed temperature effectiveness and enables a form of system performance evaluation in the broad landscape of sorption thermal energy storage demonstrators. It is argued that other performance parameters such as volumetric energy storage density and volumetric charge and discharge power density are not adequate for comparison due to the highly varying testing conditions applied. From the system evaluation, it is seen that best temperature effectiveness is generally found in a closed, transported process with the ability of single sorbent pass and true counter flow heat exchange.
•The basic sorption thermal energy storage processes are, open fixed, open transported, closed fixed and closed transported.•Temperature effectiveness is a universal means for sorption heat storage system performance comparison.•Closed transported sorption thermal energy storage systems show the best performance in respect to temperature effectiveness.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the first method capable of rendering photo‐realistic and ...temporally coherent results at megapixel resolution. To this end, we introduce a progressively trained multi‐way comb network and a light‐ and contrast‐preserving blending method. We also show that while progressive training enables generation of high‐resolution images, extending the architecture and training data beyond two people allows us to achieve higher fidelity in generated expressions. When compositing the generated expression onto the target face, we show how to adapt the blending strategy to preserve contrast and low‐frequency lighting. Finally, we incorporate a refinement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high‐resolution videos. We conduct an extensive ablation study to show the influence of our design choices on the quality of the swap and compare our work with popular state‐of‐the‐art methods.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
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