The Cyanobacterial “Nutraceutical” Phycocyanobilin Inhibits Cysteine Protease Legumain

The blue biliprotein phycocyanin, produced by photo-autotrophic cyanobacteria including spirulina (Arthrospira) and marketed as a natural food supplement or “nutraceutical,” is reported to have anti-inflammatory, antioxidant, immunomodulatory, and anticancer activity. These diverse biological activities have been specifically attributed to the phycocyanin chromophore, phycocyanobilin (PCB). However, the mechanism of action of PCB and the molecular targets responsible for the beneficial properties of PCB are not well understood. We have developed a procedure to rapidly cleave the PCB pigment from phycocyanin by ethanolysis and then characterized it as an electrophilic natural product that interacts covalently with thiol nucleophiles but lacks any appreciable cytotoxicity or antibacterial activity against common pathogens and gut microbes. We then designed alkyne-bearing PCB probes for use in chemical proteomics target deconvolution studies. Target identification and validation revealed the cysteine protease legumain (also known as asparaginyl endopeptidase, AEP) to be a target of PCB. Inhibition of this target may account for PCB's diverse reported biological activities.     

Longitudinal deep truck: Deep longitudinal model with application to sim2real deep reinforcement learning for heavy-duty truck control in the field

To develop cooperative adaptive cruise control (CACC), the choice of control approach often influences and can limit the choice of model structure, and vice versa. For heavy-duty trucks, practical application of CACC in the field is heavily influenced by the accuracy of the used model. Deep learning and deep reinforcement learning (deep-RL) have recently been used to demonstrate improved modeling and control performance for vehicles such as cars and quadrotors compared to state-of-the-art. The literature on the application of deep learning and deep-RL for heavy-duty trucks in the field, which are significantly more complex than cars, is still sparse, however. In this article, we develop a two-layer gray-box deep learning model to capture longitudinal dynamics of heavy-duty trucks while abstracting their complexity and present an approach to properly break the nested feedback loops in the model for training. We compare this model with three other alternative models and show that it achieves $~10x$ better general performance compared to a standard artificial neural network and results in $~4x$ and $~40x$ slower steady-state acceleration and speed error growth rates, respectively. We then present an architecture to utilize these deep learning models within the deep-RL framework and use it to develop baseline CACC controllers that can be zero-shot transferred to the field. To carry out the work, we present a setup of differently configured trucks along with their interface architecture and stochastic driving cycle generators for data collection. Numerical validation of the approach demonstrated stationary and bounded modeling error, and demonstrated transfer of CACC controllers with consistent overshoot bounds and a stable approximately-zero steady-state error. Validation from field experiments demonstrated similarly consistent results. Compared to a state-of-the-art benchmark, the deep-RL controller achieved lower speed and time-gap error variance but higher time-gap error offset.     

Use of Profilometry-Based Indentation Plastometry to Study the Effects of Pipe Wall Flattening on Tensile Stress–Strain Curves of Steels

Herein, the flattening and subsequent tensile testing (in the hoop direction) of steel pipes used for transmission of oil and gas are concerned. A particular focus is on the use of a novel indentation plastometry test (PIP), applied to the outer free surface of an as-received pipe. This allows a stress–strain curve to be obtained from a relatively small volume (a disk of diameter about 1 mm and thickness around 100–200 μm). Whole section and reduced section tensile testing, of as-received and flattened samples are carried out. Four different pipes are studied. While there are some variations between them, there is a general trend for near-surface regions of the pipe to be a little harder than the interior, and for flattened pipes to be a little harder than unflattened ones, although these are not dramatic or well-defined effects. PIP testing also confirms that these pipes exhibit little or no anisotropy. It is in general concluded that PIP-derived stress–strain curves for testing of the outside of a pipe are likely to be quite close to those obtained by tensile testing of the whole section in the hoop direction, after flattening.     

Profilometry-Based Indentation Plastometry Testing for Characterization of Case-Hardened Steels

An attraction of the profilometry-based indentation plastometry (PIP) procedure is that, while it involves interrogation of volumes sufficiently large to ensure that bulk properties are obtained, it still allows stress–strain curves to be inferred for relatively small regions, such that local properties can be mapped where they are changing over short distances. It is employed here to obtain these characteristics as a function of depth in samples that have been case hardened by the diffusional penetration of carbon, to a depth of just over a mm. This has been done for a grade of steel that is commonly treated in this way. The thickness of the layer characterized by the PIP test is around 200 μm. In addition, curvature measurements on strip samples, after incremental removal of thin layers, have been used to evaluate the (compressive) residual stresses in near-surface regions. These range up to around 200 MPa. Such stresses have only a small effect on the PIP measurements. The carburization raises the peak yield stress from the base level of around 1000 MPa to about 1400 MPa, followed by considerable work hardening. The reliability of these PIP-derived stress–strain relationships has been confirmed by comparing experimental outcomes of Vickers hardness tests with FEM predictions based on their use.     

Indentation Plastometry for Study of Anisotropy and Inhomogeneity in Maraging Steel Produced by Laser Powder Bed Fusion

This work concerns the use of profilometry-based indentation plastometry (PIP) to obtain mechanical property information for maraging steel samples produced via an additive manufacturing route (laser powder bed fusion). Bars are produced in both “horizontal” (all material close to the build plate) and “vertical” (progressively increasing distance from the build plate) configurations. Samples are mechanically tested in both as-built and age-hardened conditions. Stress–strain curves from uniaxial testing (tensile and compressive) are compared with those from PIP testing. Tensile test data suggest significant anisotropy, with the horizontal direction harder than the vertical direction. However, systematic compressive tests, allowing curves to be obtained for both build and transverse directions in various locations, indicate that there is no anisotropy anywhere in these materials. This is consistent with electron backscattered diffraction results, indicating that there is no significant texture in these materials. It is also consistent with the outcomes of PIP testing, which can detect anisotropy with high sensitivity. Furthermore, both PIP testing and compression testing results indicate that the changing growth conditions at different distances from the build plate can lead to strength variations. It seems likely that what has previously been interpreted as anisotropy in the tensile response is in fact due to inhomogeneity of this type.     

Indentation Plastometry of Particulate Metal Matrix Composites,Highlighting Effects of Microstructural Scale

Herein, it is concerned with the use of profilometry-based indentation plastometry (PIP) to obtain mechanical property information for particulate metal matrix composites (MMCs). This type of test, together with conventional uniaxial testing, has been applied to four different MMCs (produced with various particulate contents and processing conditions). It is shown that reliable stress–strain curves can be obtained using PIP, although the possibility of premature (prenecking) fracture should be noted. Close attention is paid to scale effects. As a consequence of variations in local spatial distributions of particulate, the “representative volume” of these materials can be relatively large. This can lead to a certain amount of scatter in PIP profiles and it is advisable to carry out a number of repeat PIP tests in order to obtain macroscopic properties. Nevertheless, it is shown that PIP testing can reliably detect the relatively minor (macroscopic) anisotropy exhibited by forged materials of this type.