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The plaque reduction neutralization test (PRNT) is a preferred method for the detection of functional, SARS-CoV-2 specific neutralizing antibodies from serum samples. Alternatively, surrogate enzyme-linked immunosorbent assays (ELISAs) using ACE2 as the target structure for the detection of neutralization-competent antibodies have been developed. They are capable of high throughput, have a short turnaround time, and can be performed under standard laboratory safety conditions. However, there are very limited data on their clinical performance and how they compare to the PRNT. We evaluated three surrogate immunoassays (GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit (GenScript Biotech, Piscataway Township, NJ, USA), the TECO® SARS-CoV-2 Neutralization Antibody Assay (TECOmedical AG, Sissach, Switzerland), and the Leinco COVID-19 ImmunoRank™ Neutralization MICRO-ELISA (Leinco Technologies, Fenton, MO, USA)) and one automated quantitative SARS-CoV-2 Spike protein-based IgG antibody assay (Abbott GmbH, Wiesbaden, Germany) by testing 78 clinical samples, including several follow-up samples of six BNT162b2 (BioNTech/Pfizer, Mainz, Germany/New York, NY, USA) vaccinated individuals. Using the PRNT as a reference method, the overall sensitivity of the examined assays ranged from 93.8 to 100% and specificity ranged from 73.9 to 91.3%. Weighted kappa demonstrated a substantial to almost perfect agreement. The findings of our study allow these assays to be considered when a PRNT is not available. However, the latter still should be the preferred choice. For optimal clinical performance, the cut-off value of the TECO assay should be individually adapted.
Oncogenic transformation of lung epithelial cells is a multi-step process, frequently starting with the inactivation of tumor suppressors and subsequent activating mutations in proto-oncogenes, such as members of the PI3K or MAPK family. Cells undergoing transformation have to adjust to changes, such as metabolic requirements. This is achieved, in part, by modulating the protein abundance of transcription factors, which manifest these adjustments. Here, we report that the deubiquitylase USP28 enables oncogenic reprogramming by regulating the protein abundance of proto-oncogenes, such as c-JUN, c-MYC, NOTCH and ΔNP63, at early stages of malignant transformation. USP28 is increased in cancer compared to normal cells due to a feed-forward loop, driven by increased amounts of oncogenic transcription factors, such as c-MYC and c-JUN. Irrespective of oncogenic driver, interference with USP28 abundance or activity suppresses growth and survival of transformed lung cells. Furthermore, inhibition of USP28 via a small molecule inhibitor reset the proteome of transformed cells towards a ‘pre-malignant’ state, and its inhibition cooperated with clinically established compounds used to target EGFRL858R, BRAFV600E or PI3KH1047R driven tumor cells. Targeting USP28 protein abundance already at an early stage via inhibition of its activity therefore is a feasible strategy for the treatment of early stage lung tumours and the observed synergism with current standard of care inhibitors holds the potential for improved targeting of established tumors.
Using walls to navigate the room: egocentric representations of borders for spatial navigation
(2021)
Spatial navigation forms one of the core components of an animal’s behavioural repertoire. Good navigational skills boost survival by allowing one to avoid predators, to search successfully for food in an unpredictable world, and to be able to find a mating partner. As a consequence, the brain has dedicated many of its resources to the processing of spatial information. Decades of seminal work has revealed how the brain is able to form detailed representations of one’s current position, and use an internal cognitive map of the environment to traverse the local space. However, what is much less understood is how neural computations of position depend on distance information of salient external locations such as landmarks, and how these distal places are encoded in the brain.
The work in this thesis explores the role of one brain region in particular, the retrosplenial cortex (RSC), as a key area to implement distance computations in relation to distal landmarks. Previous research has shown that damage to the RSC results in losses of spatial memory and navigation ability, but its exact role in spatial cognition remains unclear. Initial electrophysiological recordings of single cells in the RSC during free exploration behaviour of the animal resulted in the discovery of a new population of neurons that robustly encode distance information towards nearby walls throughout the environment. Activity of these border cells was characterized by high firing rates near all boundaries of the arena that were available to the animal, and sensory manipulation experiments revealed that this activity persisted in the absence of direct visual or somatosensory detection of the wall.
It quickly became apparent that border cell activity was not only modulated by the distance to walls, but was contingent on the direction the animal was facing relative to the boundary. Approximately 40% of neurons displayed significant selectivity to the direction of walls, mostly in the hemifield contra-lateral to the recorded hemisphere, such that a neuron in left RSC is active whenever a wall occupies proximal space on the right side of the animal. Using a cue-rotation paradigm, experiments initially showed that this egocentric direction information was invariant to the physical rotation of the arena. Yet this rotation elicited a corresponding shift in the preferred direction of local head-direction cells, as well as a rotation in the firing fields of spatially-tuned cells in RSC. As a consequence, position and direction encoding in RSC must be bound together, rotating in unison during the environmental manipulations, as information about allocentric boundary locations is integrated with head-direction signals to form egocentric border representations.
It is known that the RSC forms many anatomical connections with other parts of the brain that encode spatial information, like the hippocampus and para-hippocampal areas. The next step was to establish the circuit mechanisms in place for RSC neurons to generate their activity in respect to the distance and direction of walls. A series of inactivation experiments revealed how RSC activity is inter-dependent with one of its communication partners, the medial entorhinal cortex (MEC). Together they form a wider functional network that encodes precise spatial information of borders, with information flowing from the MEC to RSC but not vice versa. While the conjunction between distance and heading direction relative to the outer walls was the main driver of neural activity in RSC, border cells displayed further behavioural correlates related to movement trajectories. Spiking activity in either hemisphere tended to precede turning behaviour on a short time-scale in a way that border cells in the right RSC anticipated right-way turns ~300 ms into the future.
The interpretation of these results is that the RSC’s primary role in spatial cognition is not necessarily on the early sensory processing stage as suggested by previous studies. Instead, it is involved in computations related to the generation of motion plans, using spatial information that is processed in other brain areas to plan and execute future actions. One potential function of the RSC’s role in this process could be to act correctly in relation to the nearby perimeter, such that border cells in one hemisphere are involved in the encoding of walls in the contralateral hemifield, after which the animal makes an ipsilateral turn to avoid collision. Together this supports the idea that the MEC→RSC pathway links the encoding of space and position in the hippocampal system with the brain’s motor action systems, allowing animals to use walls as prominent landmarks to navigate the room.
The aim of this work was to establish a new way of predicting novel dual active compounds by combining classical fingerprint representation with state-of-the-art machine learning algorithms. Advantages and disadvantages of the applied 2D- and 3D-fingerprints were investigated. Further, the impact of various machine learning algorithms was analyzed. The new method developed in this work was used to predict compounds, which inhibit two different targets (LTA4H and sEH) involved in the same disease pattern (inflammation). The development of multitarget drugs has become more important in recent years. Many widespread diseases like metabolic syndrome, or cancer are of a multifactorial nature, which makes them hard to be treated effectively with a single drug. The new in silico method presented in this work can help to accelerate the design and development of multitarget drugs, saving time and efforts.
The nowadays readily available access to a large number of 3D-structures of biological targets and published activity data of millions of synthesized compounds enabled this study and was used as a starting point for this work. Four different data sets were compiled (crystalized ligands from the PDB, active and inactive compounds from ChEMBL23, newly designed compounds using a combinatorial library). Those data sets were collected and processed using an automated KNIME workflow. This automation has the advantage of allowing easy change and update of compound sources and adapted processing ways.
In a next step, the compounds from the compiled data sets were represented using a variety of well-established 2D- and 3D-fingerprints (PLIF, AtomPair, Morgan, FeatMorgan, MACCS). All those fingerprints share the same underlying bit string scheme but vary in the way they describe the molecular structure. Especially the difference between 2D- and 3D-fingerprints was investigated. 2D-fingerprints are solely based on ligand information. 3D-fingerprints, on the other hand, are based on X-ray structure information of protein-ligand complexes. One major difference between 2D- and 3D-fingerprints usage is the need for a 3D-conformation (pose) of the compound in the targets of interest when using 3D-fingerprints. This additional step is time-consuming and brings further uncertainties to the method.
Based on the calculated fingerprints state-of-the-art machine learning algorithms (SVC, RF, XGB and ADA) were used to predict novel dual active compounds. The models were evaluated by 10-fold cross validation and accuracy as the primary measure of model performance was maximized. Second, individual parameters of the four machine learning algorithms were optimized in a grid search to achieve maximal accuracy using the optimized partitioning scheme. Overall accuracies, regardless of fingerprint and machine learning algorithm, are slightly better for LTA4H than for sEH.
The goal to predict dual active compounds was realized by comparing the set of predicted to be active compounds for LTA4H and sEH. For the 3D-fingerprint PLIF the machine learning algorithm Random Forest was chosen, from which compounds for synthesis and testing were selected. Of 115 predicted to be active compounds, six compounds were cherry picked. Two compounds showed very good/moderate dual inhibitory activity. Of the 2D-fingerprints, the AtomPair fingerprint in combination with the machine learning algorithm Random Forest was chosen from which compounds were selected for synthesis and testing. 116 compounds were predicted to be dual active against LTA4H and sEH. One of those compounds showed good dual inhibitory activity.
In this work it was possible to show advantages and disadvantages of using 2D- and 3D-fingerprints in combination with machine learning algorithms. Both strategies (2D: ligand-based, 3D: structure-based) lead to the prediction of novel dual active compounds with moderate to very good inhibitory activity. The method developed in this work is able to predict dual active compounds with very good inhibitory activity and novel (previously unknown) scaffolds inhibiting the targets LTA4H and sEH. This contribution to in silico drug design is promising and can be used for the prediction of novel dual active compounds. Those compounds can further be optimized regarding binding affinity, solubility and further pharmacological and physicochemical properties.
During dynamic ultrasound assessments, unintended transducer movement over the skin needs to be prevented as it may bias the results. The present study investigated the validity of two methods quantifying transducer motion. An ultrasound transducer was moved on a pre-specified 3 cm distance over the semitendinosus muscle of eleven adults (35.8 ± 9.8 years), stopping briefly at intervals of 0.5 cm. Transducer motion was quantified (1) measuring the 2-D displacement of the shadow produced by reflective tape (RT) attached to the skin and (2) using a marker-based, three-dimensional movement analysis system (MAS). Differences between methods were detected with Wilcoxon tests; associations were checked by means of intraclass correlation coefficients (ICC 3.1) and Bland–Altman plots. Values for RT (r = 0.57, p < 0.001) and MAS (r = 0.19, p = 0.002) were significantly higher than true distances (TD). Strong correlations were found between RT and TD (ICC: 0.98, p < 0.001), MAS and TD (ICC: 0.95, p < 0.001), and MAS and RT (ICC: 0.97, p < 0.001). Bland–Altman plots showed narrow limits of agreement for both RT (−0.49 to 0.13 cm) and MAS (−0.49 to 0.34 cm) versus TD. RT and MAS are valid methods to quantify US transducer movement. In view of its low costs and complexity, RT can particularly be recommended for application in research and clinical practice. View Full-Text
Keywords: ultrasound; reflective tape; transducer movement
Scholars and international organizations engaged in institutional reconstruction converge in recognizing political corruption as a cause or a consequence of conflicts. Anticorruption is thus generally considered a centrepiece of institutional reconstruction programmes. A common approach to anticorruption within this context aims primarily to counter the negative political, social, and economic effects of political corruption, or implement legal anticorruption standards and punitive measures. We offer a normative critical discussion of this approach, particularly when it is initiated and sustained by external entities. We recast the focus from an outward to an inward perspective on institutional action and failure centred on the institutional interactions between officeholders. In so doing, we offer the normative tools to reconceptualize anticorruption in terms of an institutional ethics of ‘office accountability’ that draws on an institution’s internal resources of self-correction as per the officeholders’ interrelated work.
Der Kartograph und Mathematiker Carsten Niebuhr beteiligte sich Mitte des 18. Jahrhunderts an einer beschwerlichen Expedition in den arabischen und vorderasiatischen Raum. Seine Erkenntnisse und Erfahrungen verewigte er anschließend in seinem Buch »Die Arabische Reise«. Studierende eines Seminars der Vorderasiatischen Archäologie haben nun in einer Ausstellung, die ab Ende Oktober im Dithmarscher Dom gezeigt wird, Niebuhrs erstaunlich genaue und differenzierte Beobachtungen nachgezeichnet und mit dem heutigen Forschungsstand verglichen.
Abstract: The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
Author Summary: Human visual perception is a complex cognitive feat known to be mediated by distinct cortical regions of the brain. However, the exact function of these regions remains unknown, and thus it remains unclear how those regions together orchestrate visual perception. Here, we apply an AI-driven brain mapping approach to reveal visual brain function. This approach integrates multiple artificial deep neural networks trained on a diverse set of functions with functional recordings of the whole human brain. Our results reveal a systematic tiling of visual cortex by mapping regions to particular functions of the deep networks. Together this constitutes a comprehensive account of the functions of the distinct cortical regions of the brain that mediate human visual perception.