Fragment-based screening identifies molecules targeting the substrate-binding ankyrin repeat domains of tankyrase

The PARP enzyme and scaffolding protein tankyrase (TNKS, TNKS2) uses its ankyrin repeat clusters (ARCs) to bind a wide range of proteins and thereby controls diverse cellular functions. A number of these are implicated in cancer-relevant processes, including Wnt/β-catenin signalling, Hippo signalling and telomere maintenance. The ARCs recognise a conserved tankyrase-binding peptide motif (TBM). All currently available tankyrase inhibitors target the catalytic domain and inhibit tankyrase's poly(ADP-ribosyl)ation function. However, there is emerging evidence that catalysis-independent "scaffolding" mechanisms contribute to tankyrase function. Here we report a fragment-based screening programme against tankyrase ARC domains, using a combination of biophysical assays, including differential scanning fluorimetry (DSF) and nuclear magnetic resonance (NMR) spectroscopy. We identify fragment molecules that will serve as starting points for the development of tankyrase substrate binding antagonists. Such compounds will enable probing the scaffolding functions of tankyrase, and may, in the future, provide potential alternative therapeutic approaches to inhibiting tankyrase activity in cancer and other conditions

Dynamic Mechanical Properties of Rolled Thin-Walled Steel Plates (TWSPs) Used for W-Beam Guardrails under Low and Medium Strain Rates

Accurately considering the dynamic mechanical properties of rolled thin-walled steel plates (TWSPs) under low and medium strain rates is the basis of numerical simulations of W-beam guardrails subjected to vehicle impact. Uniaxial tensile tests were conducted on specimens extracted from different locations (flat TWSPs without cold rolling treatment, and the cross-sectional centers and slopes of rolled TWSPs) and under different strain rates (ε˙ = 0.00025, 0.01, and 50 s−1). Based on experimental and numerical results, the cross-sectional center of a rolled TWSP is recommended as the representative sampling location for uniaxial tensile tests. Additional uniaxial tensile tests with wider strain rates of 10, 100, and 200 s−1 were also conducted on specimens at the recommended sampling location (cross-sectional center) of rolled TWSPs. It was found that the Cowper–Symonds model with parameters of C = 40 s−1 and p = 5 recommend by Symonds significantly overestimated the strain rate effects of the rolled TWSP material in the low and medium strain-rate region. The model with calibrated parameters of C = 4814 s−1 and p = 2.9 was recommended for considering the influences of strain rate effects on the dynamic mechanical properties of rolled TWSP at low to medium strain rates

Phytochemical and antifungal studies on Terminalia mollis and Terminalia brachystemma

Phytochemical investigation of the stem bark of Terminalia mollis afforded friedelin (1), catechin with epicatechin (2), gallocatechin with epigallocatechin (3) and 3-O-methylellagic acid 4'-O-alpha-rhamnopyranoside (4). Arjunolic acid with 2 alpha, 3 beta, 23-trihydroxy-urs-12-en-28-oic acid (5), 2 alpha-hydroxyursolic acid (6), gallic acid (7), chebulanin (8) and 2 ''-O-galloylvitexin (9) were isolated from the leaf. Chebulanin (8), betulinic acid (10), ursolic acid (11), catechin (12), isoorientin (13), orientin (14), isovitexin (15) and punicalagin (16) were isolated from Terminalia brachystemma leaf The first full unambiguous NMR assignments for (4) and (8), and revised assignments for (9), are reported. Compound (16) showed good activity against three Candida species

Colorimetric broth microdilution method for the antifungal screening of plant extracts against yeasts

Screening plant extracts for antifungal activity is increasing due to demand for new antifungal agents, but the testing methods present many challenges. Standard broth microdilution methods for antifungal susceptibility testing of available antifungal agents are available now, but these methods are optimised for single compounds instead of crude plant extracts. In this study we evaluated the standard NCCLS method as well as a modification which uses spectrophotometric determination of the end-points with a plate reader. We also evaluated another standard method, the EUCAST method, which is a similar microdilution assay to the NCCLS method, but uses a larger inoculum size and a higher glucose concentration in the medium as well as spectrophotometric end-point determination. The results showed that all three methods had some drawbacks for testing plant extracts and thus we modified the NCCLS broth microdilution method by including a colorimetric indicator—resazurin for end-point determination. This modified method showed good reproducibility and clear-cut end-point, plus the end-point determination needed no instruments. It enabled us to evaluate the activity of a selection of extracts from six Combretaceous plants against three Candida spp. and thus provided pharmacological evidence for some traditional uses of these plants while assisting the identification of the active ingredients

The Prevalence and Associations of Peripheral Retinopathy: Baseline Study of Guangzhou Office Computer Workers

Purpose. To determine the prevalence of peripheral retinopathy and its associated risk factors among a sample of Guangzhou office computer workers. Methods. A cross-sectional study of Guangzhou Chinese computer workstations and operators in different departments and units of the Guangzhou Power Supply Bureau, China, in 2016. Peripheral retinopathy was recorded and analyzed using a scanning laser ophthalmoscope (SLO; Optos, Daytona, United Kingdom) and slit-lamp microscopy combined with a three-mirror contact lens. Results. The 1934 eyes of 967 subjects (513 females and 454 males) were included in this study. In total, 79.1% of the eyes were myopic in workers aged 20–29 years, 72.9% in workers aged 30–39 years, 62.2% in workers aged 40–49 years, and 43.4% in workers aged 50–59 years (p<0.001). Most eyes had optic nerve crescents (81.3%). Various peripheral degenerations were found: 7 eyes (0.4%) had microcystoid degeneration, 40 (2.1%) had peripheral pigmentary degeneration, 87 (4.5%) had lattice degeneration, and 4 (0.2%) had snail-track degeneration. Nineteen (1.0%) eyes had paving-stone degeneration, 11 (0.6%) eyes had a retinal hole or tear, and 16 (0.8%) eyes had chorioretinal degeneration. Multivariate regression confirmed that greater axial length (OR: 1.18 (1.03, 1.35), p=0.012) and more serious spherical equivalent (OR: 0.82 (0.77, 0.88), p<0.001) were significant risk factors for peripheral retinal changes. Conclusion. Peripheral retinal degenerative changes were found in a larger proportion of younger computer workers than older ones. Myopia is occurring in younger and younger people, accompanied by peripheral retinal degeneration

Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10–46 Hz, with a record high power density of 17 mW/cm3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%–100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation

Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10–46 Hz, with a record high power density of 17 mW/cm3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%–100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation

Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10–46 Hz, with a record high power density of 17 mW/cm3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%–100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation