Design of fusion enzymes for biocatalytic applications in aqueous and non-aqueous media

Biocatalytic cascades play a fundamental role in sustainable chemical synthesis. Fusion enzymes are one of the powerful toolboxes to enable the tailored combination of multiple enzymes for efficient cooperative cascades. Especially, this approach offers a substantial potential for the practical application of cofactor-dependent oxidoreductases by forming cofactor self-sufficient cascades. Adequate cofactor recycling while keeping the oxidized/reduced cofactor in a confined microenvironment benefits from the fusion fashion and makes the use of oxidoreductases in harsh non-aqueous media practical. In this mini-review, we have summarized the application of various fusion enzymes in aqueous and non-aqueous media with a focus on the discussion of linker design within oxidoreductases. The design and properties of the reported linkers have been reviewed in detail. Besides, the substrate loadings in these studies have been listed to showcase one of the key limitations (low solubility of hydrophobic substrates) of aqueous biocatalysis when it comes to efficiency and economic feasibility. Therefore, a straightforward strategy of applying non-aqueous media has been briefly discussed while the potential of using the fusion oxidoreductase of interest in organic media was highlighted. Copyright © 2022 Ma, Zhang, Vernet and Kara

Crosslinked Aggregates of Fusion Enzymes in Microaqueous Organic Media

Baeyer-Villiger monooxygenases (BVMOs) are attractive for selectively oxidizing various ketones using oxygen into valuable esters and lactones. However, the application of BVMOs is restrained by cofactor dependency and enzyme instability combined with water-related downsides such as low substrate loading, low oxygen capacity, and water-induced side reactions. Herein, we described a redox-neutral linear cascade with in-situ cofactor regeneration catalyzed by fused alcohol dehydrogenase and cyclohexanone monooxygenase in aqueous and microaqueous organic media. The cascade conditions have been optimized regarding substrate concentrations as well as the amounts of enzymes and cofactors with the Design of Experiments (DoE). The carrier-free immobilization technique, crosslinked enzyme aggregates (CLEAs), was applied to fusion enzymes. The resultant fusion CLEAs were proven to function in microaqueous organic systems, in which the enzyme ratios, water contents (0.5–5 vol. %), and stability have been systematically studied. The fusion CLEAs showed promising operational (up to 5 cycles) and storage stability

Analysis of risk factors for recurrence in infertile endometrial cancer patients after in vitro fertilization treatment

PurposeTo investigate the oncologic outcomes of patients with early-stage endometrioid endometrial cancer (EEC) treated with in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) following fertility-sparing treatment (FST).MethodsA total of 62 patients who underwent IVF/ICSI treatment in a single fertility center between June 2010 and December 2021 after conservative treatment for early-stage EEC were assigned to a recurrence group and a non-recurrence group. Their clinical characteristics and disease outcomes were retrospectively evaluated.ResultsThe 62 women with complete remission (CR) after FST for EEC underwent 103 IVF cycles, resulting in 41 fresh embryo transfers (ETs) and 70 frozen–thawed transfers; 27 (43.55%) achieved clinical pregnancies, and 20 (32.26%) gave birth to a total of 23 live neonates. Additionally, nine patients had live births from natural pregnancies after IVF failure, bringing the cumulative live birth rate to 46.77% (29/62). After a median follow-up period of 53.88 months (range 20.2–127.5 months), 17 patients (27.42%) experienced recurrence within 2.8 to 57.9 months after the first controlled ovarian stimulation (COS). The probability of relapse at 1, 2, and 3 years after the initiation of COS was 14.52% (9/62), 21% (13/62), and 25.81% (16/62), respectively. Factors such as the time to CR, the time to IVF, the frequency of COS, maintenance treatment before IVF, and histology type were not found to significantly affect recurrence (p = 0.079, 0.182, 0.093, 0.267, and 0.41, respectively). Live births (hazard ratio (HR): 0.28, 95% CI: 0.082–0.962, p = 0.043) and the protocol of letrozole plus gonadotropin-releasing hormone (GnRH) antagonist/agonist used during IVF (HR: 0.1, 95% CI: 0.011–0.882, p = 0.038) were identified as independent favorable factors for recurrence.ConclusionsLive birth was associated with decreased recurrence of EEC. Reducing estrogen levels during COS may serve to mitigate the risk of endometrial cancer recurrence

Multiscale reconstruction of porous media based on multiple dictionaries learning

Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize macro-pores and micro-pores in a large-FoV (field of view) high-resolution three-dimensional pore structure model. This paper proposes a multiscale reconstruction algorithm based on multiple dictionaries learning, in which edge patterns and micro-pore patterns from homology high-resolution pore structure are introduced into low-resolution pore structure to build a fine multiscale pore structure model. The qualitative and quantitative comparisons of the experimental results show that the results of multiscale reconstruction are similar to the real high-resolution pore structure in terms of complex pore geometry and pore surface morphology. The geometric, topological and permeability properties of multiscale reconstruction results are almost identical to those of the real high-resolution pore structures. The experiments also demonstrate the proposal algorithm is capable of multiscale reconstruction without regard to the size of the input. This work provides an effective method for fine multiscale modeling of porous media

SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection

In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boosts detection performance. Our strategy involves a data enhancement method named Polar Sampling, which densifies sparse objects and trains an assistant model to generate high-quality features as the supervision. These features are then used to train the LiDAR-Camera fusion model, where the fusion feature is optimized to simulate the generated high-quality features. Furthermore, we propose a simple yet effective deep fusion module, which contiguously gains superior performance compared with previous fusion methods with SupFusion strategy. In such a manner, our proposal shares the following advantages. Firstly, SupFusion introduces auxiliary feature-level supervision which could boost LiDAR-Camera detection performance without introducing extra inference costs. Secondly, the proposed deep fusion could continuously improve the detector's abilities. Our proposed SupFusion and deep fusion module is plug-and-play, we make extensive experiments to demonstrate its effectiveness. Specifically, we gain around 2% 3D mAP improvements on KITTI benchmark based on multiple LiDAR-Camera 3D detectors.Comment: Accepted to ICCV202

RepVGG:Making VGG-style ConvNets Great Again

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG.Comment: CVPR 202

Combustion Catalyst: Nano‐Fe2O3 and Nano‐Thermite Al/ Fe2O3 with Different Shapes

In order to enable the energetic materials to possess a more powerful performance, adding combustion catalysts is a quite effective method. Granular, oval, and polyhedral Fe2O3 particles have been prepared by the hydrothermal method and used to fabricate Al/Fe2O3 thermites. All the Fe2O3 and Al/Fe2O3 thermite samples were characterized using a combination of experimental techniques including scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), X‐ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), transmission electron microscope (TEM), and high‐resolution TEM (HRTEM). The non‐isothermal decomposition kinetics of the composites and nitrocellulose (NC) can be modeled by the Avrami‐Erofeev equation f(α)=3(1–α)[–ln(1–α)]1/3/2 in differential form. Through the thermogravimetric analysis infrared (TG‐IR) analysis of decomposition processes and products, it is speculated that Fe2O3 and Al/Fe2O3 can effectively accelerate the thermal decomposition reaction rate of NC by promoting the O‐NO2 bond cleavage. Adding oxides or thermites can distinctly increase the burning rate, decrease the burning rate pressure exponent, increase the flame temperature, and improve the combustion wave structures of the ammonium perchlorate/hydroxyl‐terminated polybutadiene (AP/HTPB) propellants. Among the three studied, different shapes of Fe2O3, the granular Fe2O3, and its corresponding thermites (Al/Fe2O3(H)) exhibit the highest burning rate due to larger surface area associated with smaller particle size. Moreover, Al/Fe2O3(H) thermites have more effective combustion‐supporting ability for AP/HTPB propellants than Fe2O3 structures and the other two as‐prepared Al/Fe2O3 thermites