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TRIANNI mice carry an entire set of human immunoglobulin V region gene segments and are a powerful tool to rapidly isolate human monoclonal antibodies. After immunizing these mice with DNA encoding the spike protein of SARS-CoV-2 and boosting with spike protein, we identified 29 hybridoma antibodies that reacted with the SARS-CoV-2 spike protein. Nine antibodies neutralize SARS-CoV-2 infection at IC50 values in the subnanomolar range. ELISA-binding studies and DNA sequence analyses revealed one cluster of three clonally related neutralizing antibodies that target the receptor-binding domain and compete with the cellular receptor hACE2. A second cluster of six clonally related neutralizing antibodies bind to the N-terminal domain of the spike protein without competing with the binding of hACE2 or cluster 1 antibodies. SARS-CoV-2 mutants selected for resistance to an antibody from one cluster are still neutralized by an antibody from the other cluster. Antibodies from both clusters markedly reduced viral spread in mice transgenic for human ACE2 and protected the animals from SARS-CoV-2-induced weight loss. The two clusters of potent noncompeting SARS-CoV-2 neutralizing antibodies provide potential candidates for therapy and prophylaxis of COVID-19. The study further supports transgenic animals with a human immunoglobulin gene repertoire as a powerful platform in pandemic preparedness initiatives.
In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.
Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.
The proton-removal mechanism of the 12CB reaction induced on a carbon target via elementary nucleon-nucleon scattering is investigated in exclusive triple-coincidence measurements. The observed two-nucleon angular correlations are found to be consistent with quasi-free scattering of a projectile-like proton off a target-like nucleon. Exclusive cross sections for one-step pp and pn interactions are determined as [formula] and [formula], respectively. The extracted quasi-free component amounts up to 58(4)% of the total proton-removal cross section. The results are compared to total proton-removal cross sections obtained from the experiment and eikonal reaction theory.
NeuLAND (New Large-Area Neutron Detector) is the next-generation neutron detector for the R3B (Reactions with Relativistic Radioactive Beams) experiment at FAIR (Facility for Antiproton and Ion Research). NeuLAND detects neutrons with energies from 100 to 1000 MeV, featuring a high detection efficiency, a high spatial and time resolution, and a large multi-neutron reconstruction efficiency. This is achieved by a highly granular design of organic scintillators: 3000 individual submodules with a size of 5 × 5 × 250 cm3 are arranged in 30 double planes with 100 submodules each, providing an active area of 250 × 250 cm2 and a total depth of 3 m. The spatial resolution due to the granularity together with a time resolution of 150 ps ensures high-resolution capabilities. In conjunction with calorimetric properties, a multi-neutron reconstruction efficiency of 50% to 70% for four-neutron events will be achieved, depending on both the emission scenario and the boundary conditions allowed for the reconstruction method. We present in this paper the final design of the detector as well as results from test measurements and simulations on which this design is based.
Adaptive, synchronous, and mobile online education: developing the ASYMPTOTE learning environment
(2022)
The COVID-19-induced distance education was perceived as highly challenging by teachers and students. A cross-national comparison of five European countries identified several challenges occurred during the distance learning period. On this basis, the article aims to develop a theoretical framework and design requirements for distance and online learning tools. As one example for online learning in mathematics education, the ASYMPTOTE system is introduced. It will be freely available by May 2022. ASYMPTOTE is aimed at the adaptive and synchronous delivery of online education by taking a mobile learning approach. Its core is the so-called digital classroom, which not only allows students to interact with each other or with the teacher but also enables teachers to monitor their students’ work progress in real time. With respect to the theoretical framework, this article analyses to what extent the ASYMPTOTE system meets the requirements of online learning. Overall, the digital classroom can be seen as a promising tool for teachers to carry out appropriate formative assessment and—partly—to maintain personal and content-related interaction at a distance. Moreover, we highlight the availability of this tool. Due to its mobile learning approach, almost all students will be able to participate in lessons conducted with ASYMPTOTE.
Background: To test the impact of urethral sphincter length (USL) and anatomic variants of prostatic apex (Lee-type classification) in preoperative multiparametric magnet resonance imaging (mpMRI) on mid-term continence in prostate cancer patients treated with radical prostatectomy (RP). Methods: We relied on an institutional tertiary-care database to identify patients who underwent RP between 03/2018 and 12/2019 with preoperative mpMRI and data available on mid-term (>6 months post-surgery) urinary continence, defined as usage 0/1 (-safety) pad/24 h. Univariable and multivariable logistic regression models were fitted to test for predictor status of USL and prostatic apex variants, defined in mpMRI measurements. Results: Of 68 eligible patients, rate of mid-term urinary continence was 81% (n = 55). Median coronal (15.1 vs. 12.5 mm) and sagittal (15.4 vs. 11.1 mm) USL were longer in patients reporting urinary continence in mid-term follow-up (both p < 0.01). No difference was recorded for prostatic apex variants distribution (Lee-type) between continent vs. incontinent patients (p = 0.4). In separate multivariable logistic regression models, coronal (odds ratio (OR): 1.35) and sagittal (OR: 1.67) USL, but not Lee-type, were independent predictors for mid-term continence. Conclusion: USL, but not apex anatomy, in preoperative mpMRI was associated with higher rates of urinary continence at mid-term follow-up.
The proton drip-line nucleus 17Ne is investigated experimentally in order to determine its two-proton halo character. A fully exclusive measurement of the 17Ne(p, 2p)16F∗ →15O+p quasi-free one-proton knockout reaction has been performed at GSI at around 500 MeV/nucleon beam energy. All particles resulting from the scattering process have been detected. The relevant reconstructed quantities are the angles of the two protons scattered in quasi-elastic kinematics, the decay of 16F into 15O (including γ decays from excited states) and a proton, as well as the 15O+p relative-energy spectrum and the 16F momentum distributions. The latter two quantities allow an independent and consistent determination of the fractions of l = 0 and l = 2 motion of the valence protons in 17Ne. With a resulting relatively small l = 0 component of only around 35(3)%, it is concluded that 17Ne exhibits a rather modest halo character only. The quantitative agreement of the two values deduced from the energy spectrum and the momentum distributions supports the theoretical treatment of the calculation of momentum distributions after quasi-free knockout reactions at high energies by taking into account distortions based on the Glauber theory. Moreover, the experimental data allow the separation of valence-proton knockout and knockout from the 15O core. The latter process contributes with 11.8(3.1) mb around 40% to the total proton-knockout cross section of 30.3(2.3) mb, which explains previously reported contradicting conclusions derived from inclusive cross sections.
The evolution of the traditional nuclear magic numbers away from the valley of stability is an active field of research. Experimental efforts focus on providing key spectroscopic information that will shed light into the structure of exotic nuclei and understanding the driving mechanism behind the shell evolution. In this work, we investigate the spin-orbit shell gap towards the neutron dripline. To do so, we employed (p,2p) quasi-free scattering reactions to measure the proton component of the state of 16,18,20C. The experimental findings support the notion of a moderate reduction of the proton spin-orbit splitting, at variance to recent claims for a prevalent magic number towards the neutron dripline.
Nitro fatty acids (NFAs) are endogenously generated lipid mediators deriving from reactions of unsaturated electrophilic fatty acids with reactive nitrogen species. Furthermore, Mediterranean diets can be a source of NFA. These highly electrophilic fatty acids can undergo Michael addition reaction with cysteine residues, leading to post-translational modifications (PTM) of selected regulatory proteins. Such modifications are capable of changing target protein function during cell signaling or in biosynthetic pathways. NFA target proteins include the peroxisome proliferator-activated receptor γ (PPAR-γ), the pro-inflammatory and tumorigenic nuclear factor-κB (NF-κB) signaling pathway, the pro-inflammatory 5-lipoxygenases (5-LO) biosynthesis pathway as well as soluble epoxide hydrolase (sEH), which is essentially involved in the regulation of vascular tone. In several animal models of inflammation and cancer, the therapeutic efficacy of well-tolerated NFA has been demonstrated. This has already led to clinical phase II studies investigating possible therapeutic effects of NFA in subjects with pulmonary arterial hypertension. Albeit Michael acceptors feature a broad spectrum of bioactivity, they have for a rather long time been avoided as drug candidates owing to their presumed unselective reactivity and toxicity. However, targeted covalent modification of regulatory proteins by Michael acceptors became recognized as a promising approach to drug discovery with the recent FDA approvals of the cancer therapeutics, afatanib (2013), ibrutinib (2013), and osimertinib (2015). Furthermore, the Michael acceptor, neratinib, a dual inhibitor of the human epidermal growth factor receptor 2 and epidermal growth factor receptor, was recently approved by the FDA (2017) and by the EMA (2018) for the treatment of breast cancer. Finally, a number of further Michael acceptor drug candidates are currently under clinical investigation for pharmacotherapy of inflammation and cancer. In this review, we focus on the pharmacology of NFA and other Michael acceptor drugs, summarizing their potential as an emerging class of future antiphlogistics and adjuvant in tumor therapeutics.