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Anti-amyloid drug design Amyloid diseases such as Alzheimer's are associated with the transformation of normally soluble proteins into amyloid fibrils — elongated, unbranched protein aggregates. Inhibitors of pathological amyloid fibril formation may be useful as therapeutics if they are sufficiently specific. Sievers et al . use computer-aided, structure-based design, with known atomic structures of segments of amyloid fibres as templates, to evolve a D-amino-acid peptide that delays amyloid fibril formation. Many globular and natively disordered proteins can convert into amyloid fibrils. These fibrils are associated with numerous pathologies 1 as well as with normal cellular functions 2 , 3 , and frequently form during protein denaturation 4 , 5 . Inhibitors of pathological amyloid fibril formation could be useful in the development of therapeutics, provided that the inhibitors were specific enough to avoid interfering with normal processes. Here we show that computer-aided, structure-based design can yield highly specific peptide inhibitors of amyloid formation. Using known atomic structures of segments of amyloid fibrils as templates, we have designed and characterized an all- d -amino-acid inhibitor of the fibril formation of the tau protein associated with Alzheimer’s disease, and a non-natural l -amino-acid inhibitor of an amyloid fibril that enhances sexual transmission of human immunodeficiency virus. Our results indicate that peptides from structure-based designs can disrupt the fibril formation of full-length proteins, including those, such as tau protein, that lack fully ordered native structures. Because the inhibiting peptides have been designed on structures of dual-β-sheet ‘steric zippers’, the successful inhibition of amyloid fibril formation strengthens the hypothesis that amyloid spines contain steric zippers.

During the Cretaceous, there were two factors that had important influences on the East Asian climate, the East Asian coastal mountains and Earth's orbital cycling. An important question is how the coastal mountains modulated the variability of East Asian climate over orbital timescales. Here, we perform simulations with the coastal mountains of 0, 2, and 4 km high and three orbital configurations to answer the question. Our results show that a mountain range at the East Asian coast can amplify the impacts of orbital forcing on East Asian climate. Specifically, precipitation over the Songliao Basin in Northeastern China has significant changes as the coastal mountain range is about 4 km high. Combining our simulation results with orbitally‐controlled sedimentary deposits from the Songliao Basin, we conclude that the altitude of the coastal mountain range was very likely higher than 2 km in the Late Cretaceous. Plain Language Summary Tectonic events and solar insolation are the two important factors impacting variations of the climate system in the geological past. Regional climate responses to variations in the radiation from the sun over 104–105 years were often magnified or dampened by tectonic events. Cretaceous sedimentary records in East Asia suggest that East Asian climate was influenced by the solar insolation. Geological evidence showed that a mountain range existed along the East Asian coast then. Would this mountain range modulate impacts of solar insolation on East Asian climate? Our modeling results show that the influence of solar insolation on East Asian climate can be amplified by the coastal mountain range, depending on the mountain elevation. When the coastal mountain range is ∼2 km high, the amplification effects become significant. When its altitude reaches ∼4 km, the response of East Asian climate to solar insolation is considerably strengthened, and such a condition is supported by the rhythm induced by the climate variation due to solar insolation archived in the Cretaceous strata in the Songliao Basin. Thus, we speculate that the East Asian coastal mountains might have reached an altitude more than 2 km in the Late Cretaceous. Key Points East Asian climate was sensitive to orbital forcing in the Late Cretaceous East Asian coastal mountains amplified orbital forcing on East Asian climate variability East Asian coastal mountains were likely higher than 2 km in the Late Cretaceous

Fibrils and oligomers are the aggregated protein agents of neuronal dysfunction in ALS diseases. Whereas we now know much about fibril architecture, atomic structures of disease-related oligomers have eluded determination. Here, we determine the corkscrew-like structure of a cytotoxic segment of superoxide dismutase 1 (SOD1) in its oligomeric state. Mutations that prevent formation of this structure eliminate cytotoxicity of the segment in isolation as well as cytotoxicity of the ALS-linked mutants of SOD1 in primary motor neurons and in a Danio rerio (zebrafish) model of ALS. Cytotoxicity assays suggest that toxicity is a property of soluble oligomers, and not large insoluble aggregates. Our work adds to evidence that the toxic oligomeric entities in protein aggregation diseases contain antiparallel, out-of-register β-sheet structures and identifies a target for structure-based therapeutics in ALS.

To identify the polar parts in Rhubarb that cause hepatotoxicity and explore the underlying mechanisms. The rat model of liver cancer was established by gavage of diethylnitrosamine (DEN; 0.002 g/rat) for 14 weeks. Starting from the 11th week, Rhubarb granule (4 g/kg), aqueous, ethyl acetate and n-butanol extract of Rhubarb or Rhein equivalent to a dose of 4 g/kg Rhubarb granule were administered intragastrically for 4 consecutive weeks. Liver tissues from rats treated with DEN and Rhubarb granules were used for non-targeted metabolomics analysis. The correlation between pyruvate kinase isozyme type M2 (PKM2) expression level and the progress and prognosis of hepatocellular carcinoma (HCC) was evaluated through bioinformatics analysis based on TCGA database. Liver tissues and blood samples from rats treated with DEN and aqueous, ethyl acetate and n-butanol extract of Rhubarb were used for the screening of hepatotoxic polar parts of Rhubarb. The liver injuries were evaluated by the changes in pathology, liver function, and the expression levels of proliferating cell nuclear antigen (PCNA) and transforming growth factor beta1 (TGF-β1). The mechanism studies focus on PKM2 expression, and the metabolic reprogramming via detecting the activities of lactate dehydrogenase A (LDHA) and isocitrate dehydrogenase (ICDH). Furthermore, molecular docking analysis was performed to validate the target interaction between Rhein and PKM2, and the hepatotoxicity of Rhein was evaluated by testing liver function in the DEN-induced liver cancer model. The non-targeted metabolomics analysis revealed that Rhubarb promoted aerobic glycolysis in the rat model of DEN-induced liver cancer. And bioinformatics analysis revealed that high PKM2 expression was closely related to the progression and poor prognosis of HCC. In vivo studies indicated that the aqueous extract of Rhubarb, but not ethyl acetate and n-butanol extract, promoted the liver injuries induced by DEN. The mechanism study showed that the aqueous extract of Rhubarb increased the expression of PKM2 and promoted aerobic glycolysis. Moreover, Rhein had a strong binding affinity for PKM2 and aggravated liver injury in the DEN-induced liver cancer model. Aqueous extract of Rhubarb promoted hepatotoxicity via facilitating PKM2-mediated aerobic glycolysis in the rat model of DEN-induced liver cancer.

The Delta robot is an over-actuated parallel robot with highly nonlinear kinematics and dynamics. Designing the control for a Delta robot to carry out various operations is a challenging task. Various advanced control algorithms, such as adaptive control, sliding mode control, and model predictive control, have been investigated for trajectory tracking of the Delta robot. However, these control algorithms require a reliable input–output model of the Delta robot. To address this issue, we have created a control-affine neural network model of the Delta robot with stepper motors. This is a completely data-driven model intended for control design consideration and is not derivable from Newton’s law or Lagrange’s equation. The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking is then designed with the help of the neural network model. Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot. Future work will include experimental validation of the proposed neural network input–output model for control design for the Delta robot. Furthermore, transfer learnings can be conducted to further refine the neural network input–output model and the sliding mode control when new experimental data become available.

The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics.

Hypertension is the primary cause of cardiovascular diseases, and its worldwide prevalence has continued to increase recently. Aortic fibre remodelling is critical in the development of hypertension and is strikingly age-related. However, the underlying microlevel variations remain unknown. This study quantitatively evaluated the hypertension-induced microstructural remodelling of aortic fibres from a cross-age perspective by combining label-free multiphoton microscopy (MPM) imaging with a three-dimensional (3D) grey-level co-occurrence matrix (GLCM) algorithm. First, MPM imaging of aortic collagen and elastin fibres was performed on spontaneously hypertensive rats and Wistar–Kyoto controls across three critical age stages (prehypertension, developing hypertension and stable hypertension) and two aortic segments (abdominal aorta and thoracic aorta). Subsequently, the 3D GLCM texture features that were significantly correlated with hypertension or age-related hypertension were identified. By deciphering these features, we revealed quantitative details of hypertension-induced aortic remodelling, hypertension-accelerated aortic ageing and the heterogeneous response of different aortic segments to hypertension from the perspective of the fibre microstructure. The proposed method and derived findings may shed new light on the mechanism of age-related hypertension and contribute significantly to the research on cardiovascular diseases.

The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO 2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO 2 concentrations.

The North Atlantic Oscillation (NAO) is one of the main modes of climate variability and the dominant mode of large-scale atmospheric variability in the North Atlantic basin and has large impacts on the European climate, yet the behaviour of NAO in the future remains uncertain. Here we assess the NAO response in the past and in a warming climate by looking at a comprehensive set of coupled model simulations performed by the Paleoclimate Model Intercomparison Project (PMIP) and the Coupled Model Intercomparison Project (CMIP) for four experiments: the mid-Holocene (6 ka; midHolocene), the Last Glacial Maximum (21 ka; lgm), the Last Interglacial (127 ka; lig127k) and an idealised greenhouse gas (GHG)-forced experiment from the CMIP DECK with abrupt quadrupled CO.sub.2 (abrupt4xCO2). Although there are various setups across experiments, the midHolocene and lig127k are mainly characterised by altered orbital configurations, inducing variations in the seasonal cycle and redistributing energy across latitudes. The lgm and abrupt4xCO2 are characterised by GHG forcings that induce substantial global temperature change, and the lgm includes large ice sheets that alter the storm-tracks. Our results show that the NAO alters in these latter two experiments, but is not sensitive to the orbital configurations. NAO weakens in response to cooling and strengthens as a result of warming. The associated teleconnections change consistently with the theory and are sensitive to the change in NAO amplitude. The two orbital experiments do not show a clear change in associated temperature and precipitation. The weakened NAO in the lgm is associated with a cooler and drier northern Europe, while the enhanced NAO in the abrupt4xCO2 causes a warmer and wetter northern Europe as compared to the piControl. No clear relationship is found in the ENSO-NAO teleconnection.

The wheel–rail contact forces are responsible for hunting instability of high-speed train and fatigue damage of the wheel and rail. The wheel–rail contact forces are usually nonlinear, motion-dependent, time-varying and destabilizing, and cannot be directly measured when the train is in motion. In this paper, we present a useful tool for engineers to estimate the wheel–rail contact forces from train motion signals. In particular, an extended state observer is adopted to estimate the wheel–rail contact forces of a high-speed train bogie from its motion measurements. We make use of the condition for observability of the extended state observer to investigate the design of motion sensors in terms of the needed number and types. The accurate estimated wheel–rail contact forces together with the corresponding motion measurements are then used to estimate parameters of a wheel–rail contact force model with the help of recursive least squares algorithm. Extensive numerical simulations are presented to demonstrate and validate the proposed method. The research concept and proposed method of this paper are innovative. The results indicate that the extended state observer delivers excellent estimation performance for the bogie with a limited number of motion measurements. The new findings of the paper can lead to the development of new tools for engineers to use to monitor the health of wheel and rail from motion signals of the train.