Structural Basis and Catalytic Mechanism for the Dual Functional Endo-β-N-Acetylglucosaminidase A

Endo-β-N-acetylglucosaminidases (ENGases) are dual specificity enzymes with an ability to catalyze hydrolysis and transglycosylation reactions. Recently, these enzymes have become the focus of intense research because of their potential for synthesis of glycopeptides. We have determined the 3D structures of an ENGase from Arthrobacter protophormiae (Endo-A) in 3 forms, one in native form, one in complex with Man3GlcNAc-thiazoline and another in complex with GlcNAc-Asn. The carbohydrate moiety sits above the TIM-barrel in a cleft region surrounded by aromatic residues. The conserved essential catalytic residues – E173, N171 and Y205 are within hydrogen bonding distance of the substrate. W216 and W244 regulate access to the active site during transglycosylation by serving as “gate-keepers”. Interestingly, Y299F mutation resulted in a 3 fold increase in the transglycosylation activity. The structure provides insights into the catalytic mechanism of GH85 family of glycoside hydrolases at molecular level and could assist rational engineering of ENGases

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

LGAG-Net: Lesion-Guided Adaptive Graph Network for Bone Abnormality Detection From Musculoskeletal Radiograph

Musculoskeletal abnormality is routinely presented in tissues and organs of the human locomotor system across the life course, and it is essential to detect musculoskeletal abnormality in X-rays. However, it is difficult to diagnose musculoskeletal abnormality from Radiographs due to the following issues: 1) There are other interfering organ tissues in the complicated backgrounds; 2)The MURA dataset contains seven different musculoskeletal radiographs, which makes general convolution neural networks unable to model the weird relationship between them. To address such problems, a Lesion-Guided Adaptive Graph Network (LGAG-Net) is proposed for bone abnormality detection from musculoskeletal radiograph, where the Lesion-guided Recurrent Feature Sampling (LRFS) module is first designed to localize the corresponding musculoskeletal abnormality regions, and then the Adaptive Graphsage Attention (AGA) module is developed to perform bone abnormality detection on the located musculoskeletal abnormality regions. Experiments on MURA dataset show that the proposed LGAG-Net can achieve an accuracy of 87.81% and Cohen Kappa statistic of 0.868, which outperforms the state-of-the-art methods, assisting the radiologists to rapidly estimate the physical development in patients

Efficient Decolorization of Azo Dye Orange II in a UV-Fe3+-PMS-Oxalate System

International audienceThe decolorization of azo dye Orange II using a UVA-Fe3+-PMS-oxalate system was studied. A series of experiments was performed to investigate the effects of several variables, including the pH, PMS dosage, Fe3+ concentration, oxalate concentration, and coexisting anions. The results revealed that a lower pH facilitated the decolorization, and relatively high decolorization efficiency (97.5%) could be achieved within 5 min at pH 3.0. The electron paramagnetic resonance (ESR) and radical quenching experiments revealed that SO4•− played a crucial role in the decolorization of Orange II (85.8%), •OH was of secondary importance (9%), and 1O2 made a small contribution to the decolorization (5.2%). Furthermore, the formation of •OH in the experimental system strongly depended on HO2•/O2•−. These reactive oxidants were able to directly attack the azo bond of the luminescent group in Orange II and initiate the decolorization process. The efficient UVA-Fe3+-PMS-oxalate system showed great application potential in the treatment of wastewater contaminated by azo dyes

Joint Machine Selection and Buffer Allocation in Large Split and Merge Manufacturing Systems

This study focuses on the simultaneous optimization of machines and buffers in split and merge production systems. The objective was to minimize the total investment cost under a minimum throughput rate and maximum cycle time constraints. It is challenging to solve this type of stochastic resource allocation problem due to the phenomenon of the combinatorial explosion search space and the inability to obtain closed-form expressions for the optimization model. In this paper, a decomposition-coordination method (DCM) is proposed to optimize the machine types used, the number of machines, and the capacities of buffers of general feed-forward topology systems efficiently and accurately. Instead of directly targeting large-scale systems, the DCM decomposes the original system into several small decoupled systems with added coordination variables and then separately optimizes each decomposed system. An optimal or near-optimal solution is obtained after several iterations of the decoupled system optimization and coordination variable updating. Moreover, we develop a simulated annealing algorithm and non-dominated sorting genetic algorithm-II as benchmark algorithms and provide a parameter calibration analysis of the two metaheuristics. Finally, comprehensive numerical experiments are performed to demonstrate the performances of the DCM, and a multifactorial experimental analysis is conducted to determine the influence of the split and merge system parameters on the performances of the DCM. The results confirmed that the scale of the system, complexity of topology, cycle time constraint, traffic intensity, price ratio, and their interactions significantly influenced the total cost obtained from the DCM, whereas the scale of the system, traffic intensity, and price ratio significantly affected the computation time

Research on Multi-Objective Scheduling Algorithm of Job Shop Considering Limited Storage and Transportation Capacity

As with the continuous improvement of the workshop automation rate and the importance in energy consumption, more and more enterprises not only need to make scheduling decision on production equipment, but also need to consider whether the scheduling of transportation equipment supports scheduling decisions on workshop production. At the same time, because both workshop production scheduling decision and transportation scheduling decision are NP-hard problems, it is necessary to design an efficient algorithm to improve productivity of the workshop. In order to solve this problem, firstly, based on the analysis of the problem structure, production environment and optimization objectives, a “manufacturing-transportation” multi-objective joint scheduling optimization mathematical model is established. By converting the energy consumption into the total transportation time objective of the transportation equipment, both total transportation time and makespan are taken as the optimization objectives. Secondly, based on the design idea of memetic algorithm (MA), non-dominated sorting genetic algorithm-II(NSGA-II) is employed as the basis framework of our new developed algorithm. An effective discrete encoding scheme of MO-MA, a new initialization method for initial population and a neighborhood search mechanism based on critical path are incorporated into our new proposed algorithm. Then the parameter design of the algorithm is completed through variance analysis. Finally, the proposed algorithm is compared and analyzed with other algorithms in the dimension of hypervolume and Set Coverage (SC), and advantages of the algorithm in solving this problem are verified

Effects of extreme hydrostatic pressure on the molecular structure and properties of the elastomeric material for soft robots

The successful exploration of the Mariana Trench, Earth's deepest trench, by soft robots inspired by deep-sea organisms, showcasing the potential of soft robots for extreme deep-sea exploration. However, deep-sea extreme pressure significantly alters the structure and properties of robot materials, affecting their detectability. In this study, to ensure soft robots maintain excellent performance even in such extreme environments, meticulous attention is devoted to these pressure-induced changes before designing them. The results demonstrate that applying pressure (416.67 MPa) can induce the glass transition in silicone rubber (SR) even at room temperature. Unlike the traditional realization mechanism (cooling material to its glass transition temperature (Tg)), the realization mechanism through pressurizing is reported that adjusting the Tg of target material to approach a specific temperature. Furthermore, the different transition mechanisms under the two realizations are also revealed. Based the dynamic analysis of SR under extremely low temperature and high pressure, the glass transition pressure (Pg) is proposed, and the glass transition strategy by jointly regulating temperature and pressure to construct equivalent extreme pressure environment is also investigated. This study, taking SR as the case study, holds significant importance for the future development of extreme pressure-resistant soft robots for extreme environment exploration