Enhancing chromatographic separations of recombinant proteins from canola extracts by genetic design and characterization of protein binding regions

Canola was studied as an alternative source to traditional microbial systems as a recombinant protein production host. Various chromatographic methods were used in this work to systematically characterize the native canola protein elution profiles, and different genetically engineered proteins were selected to explore the opportunities presented for effective protein recovery;Native canola protein was eluted into two major peaks in linear salt gradient elution on cation-exchange chromatography. T4 lysozyme and its mutants, by both point mutation and fusion, were used as model proteins to investigate the charge effect on protein elution. It was found that the single mutant T4 lysozyme was eluted in the low canola background valley between the two major canola protein peaks under the experiment conditions, and the purity of collected lysozyme was more than 90%;Canola protein elution profile presented three possible target sites for selective recombinant protein purification on a strong anion exchanger. beta-Glucuronidase (GUS) and fusions were used as the model proteins. Wild-type GUS was found to be eluted roughly at the first target site with the least eluent salt concentration, and GUS fusions with more than 10 aspartates in the tail were moved to the second site (higher salt concentration) with lower canola protein background. The third site with the highest salt elution was out of reach. The enrichment factor of fusion GUS at the second site was three to four times higher than that of wild-type GUS;Stoichiometric displacement model was used to characterize the series of GUS proteins (wild-type GUS and its fusions) under isocratic elution. It was found that the fusion with 15 aspartates did not follow the trend of change in protein specific parameters, Z and I, as the number of aspartates was less than 10. The Z and I values were used in an equation and lumped dispersion model to predict the protein elution under various gradient slopes. Both methods could reasonably well predict the protein retention and the influence of gradient change on the protein elution, and the simulation could also pick up the shape of the protein peaks;Immobilized metal affinity chromatography (IMAC) was also used to explore the possibility of purifying proteins with poly-histidine fusions. GUS-(his) 6, as our model protein, could be purified to almost homogeneous purity on Co2+ columns with iminodiacetate (IDA) and nitrilotriacetate (NTA) as the immobilization ligand. The recognition of metal ions on protein surface histidine distribution was found following the order Cu2+ \u3e Ni2+ \u3e Zn2+ \u3e Co2+. The binding mechanism was proposed to describe the interaction between ploy-his tagged protein with immobilized metal ions when IDA and NTA were used as the chelating ligands

Cross Contrastive Feature Perturbation for Domain Generalization

Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. Yet, these approaches can hardly deal with the restriction that the samples synthesized from various domains can cause semantic distortion. In this paper, we propose an online one-stage Cross Contrasting Feature Perturbation (CCFP) framework to simulate domain shift by generating perturbed features in the latent space while regularizing the model prediction against domain shift. Different from the previous fixed synthesizing strategy, we design modules with learnable feature perturbations and semantic consistency constraints. In contrast to prior work, our method does not use any generative-based models or domain labels. We conduct extensive experiments on a standard DomainBed benchmark with a strict evaluation protocol for a fair comparison. Comprehensive experiments show that our method outperforms the previous state-of-the-art, and quantitative analyses illustrate that our approach can alleviate the domain shift problem in out-of-distribution (OOD) scenarios

The Impact of Adding Online-to-Offline Service Platform Channels on Firms' Offline and Total Sales and Profits

Online-to-offline service platform (O2OSP) channels offer innovative means for customers to order local, daily services online (via apps) and have them delivered almost instantly offline. By comparing the business models underlying O2OSP, traditional online and offline, and platform based e-commerce channels, this article aims to identify the short- and long-term impacts of adding an O2OSP channel on firms' offline and total sales and profits. The analysis focuses primarily on a recent set of daily data gathered from a Chinese fast-food restaurant chain with 35 physical stores that also participates in four food delivery O2OSP channels. The panel data regressions with fixed effects reveal that adding O2OSP channels hurts offline and total profits in the short run but improves offline and total sales and profits in the long run. Specifically, offline and total sales increase by 23.28% and 33.94%, respectively. Thus, the O2OSP channel can serve as a complement to, rather than a substitute for, the offline channel. These results challenge previous research on the sales effects of adding (pure) online or offline channels and highlight the attractiveness of O2OSP channels for improving sales and profits. However, negative interaction effects among different O2OSP channels also signal that adding more O2OSP channels does not necessarily lead to profitable growth. (C) 2019 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved

Voltage Balancing Sorting Algorithm with Reduced Switching Frequency for Modular Multilevel Converters

PhD ThesisOver the last decade, Modular Multilevel Converters (MMCs) have been developed for medium- to high-voltage applications. They exhibit distinct features such as modularity, scalability, high degrees of redundancy and high-quality output voltage with the superior harmonic performance that reduces the requirement for filters. These features are unique to MMCs, thereby giving them a competitive advantage as an industrial solution over other voltage source multilevel converters. However, there are challenges associated with such converters when numerous submodules (SMs) are considered. The issues involved include voltage-balancing of the distributed SM, circulating current suppression, reliability, and increased complexity in the circuit configuration. The focus of this research is the voltage balancing of SMs. The most common and effective method of voltage-balancing is based on the well-known sorting algorithm, which results in higher switching frequency compared to other methods. This leads to substantially higher switching losses and hence lower efficiency, particularly when there are high numbers of SMs. Furthermore, the increased execution and calculation time leads to high computational complexity when the number of SM is high. This thesis proposes three new voltage balancing schemes to reduce the unnecessary switching events which are typically generated by the conventional sorting algorithm (CSA) and to reduce computational complexity: 1. The Index Selection Algorithm (ISA) is based on a constraint band of permissible voltage ripples and existing gate signals to offer three index options. This technique selects the optimum choice based on the number of SMs contained in the band. 2. The Hybrid Heap Sorting Algorithm (HSA) replaces the CSA with the heap sorting II algorithm. With this technique, the computational complexity is significantly decreased. 3. The Priority-based Sorting Algorithm (PSA) clusters the SMs of converter into different priority groups according to a pre-defined voltage ripple range along with the gate signal information of the previous sampling period. It helps to reduce the switching frequency by only selecting the necessary priority groups to be involved in the sorting stage. Another benefit of this scheme is its flexibility and great dynamic response to different pre-defined range. All the proposed algorithms produce fewer switching events and incur a lower computational cost, resulting in higher efficiency without detriment to the quality of the output waveform. The proposed voltage balancing schemes are tested using 4- and 22- level MMC models which were built using MATLAB/Simulink to investigate their performance. The converter performance is also validated for a small-scale 4-level MMC that was designed, built, and tested in the laboratory. The validation shows that the proposed algorithms clearly reduce the number of switching events. In addition, the algorithm can be easily incorporated without requiring hardware modifications

HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes

The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings. However, the previous 3DGS-based methods have limitations in urban scenes due to reliance on initial Structure-from-Motion(SfM) points and difficulties in rendering distant, sky and low-texture areas. To overcome these challenges, we propose a hybrid optimization method named HO-Gaussian, which combines a grid-based volume with the 3DGS pipeline. HO-Gaussian eliminates the dependency on SfM point initialization, allowing for rendering of urban scenes, and incorporates the Point Densitification to enhance rendering quality in problematic regions during training. Furthermore, we introduce Gaussian Direction Encoding as an alternative for spherical harmonics in the rendering pipeline, which enables view-dependent color representation. To account for multi-camera systems, we introduce neural warping to enhance object consistency across different cameras. Experimental results on widely used autonomous driving datasets demonstrate that HO-Gaussian achieves photo-realistic rendering in real-time on multi-camera urban datasets

Understanding Multimodal Deep Neural Networks: A Concept Selection View

The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic. Due to the complex structure and the massive pre-training data, it is often regarded as a black-box model that is too difficult to understand and interpret. Concept-based models map the black-box visual representations extracted by deep neural networks onto a set of human-understandable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. However, these methods involve the datasets labeled with fine-grained attributes by expert knowledge, which incur high costs and introduce excessive human prior knowledge and bias. In this paper, we observe the long-tail distribution of concepts, based on which we propose a two-stage Concept Selection Model (CSM) to mine core concepts without introducing any human priors. The concept greedy rough selection algorithm is applied to extract head concepts, and then the concept mask fine selection method performs the extraction of core concepts. Experiments show that our approach achieves comparable performance to end-to-end black-box models, and human evaluation demonstrates that the concepts discovered by our method are interpretable and comprehensible for humans

Understanding Multimodal Deep Neural Networks: A Concept Selection View

The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic. Due to the complex structure and the massive pre-training data, it is often regarded as a black-box model that is too difficult to understand and interpret. Concept-based models map the black-box visual representations extracted by deep neural networks onto a set of human-understandable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. However, these methods involve the datasets labeled with fine-grained attributes by expert knowledge, which incur high costs and introduce excessive human prior knowledge and bias. In this paper, we observe the long-tail distribution of concepts, based on which we propose a two-stage Concept Selection Model (CSM) to mine core concepts without introducing any human priors. The concept greedy rough selection algorithm is applied to extract head concepts, and then the concept mask fine selection method performs the extraction of core concepts. Experiments show that our approach achieves comparable performance to end-to-end black-box models, and human evaluation demonstrates that the concepts discovered by our method are interpretable and comprehensible for humans

DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes

Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand. Existing methods for such scenes usually involve with spatial warping, geometric supervision from zero-shot normal or depth estimation, or scene division strategies, where the synthesized views are often blurry or fail to meet the requirement of efficient rendering. To address the above challenges, this paper presents a novel framework that learns a density space from the scenes to guide the construction of a point-based renderer, dubbed as DGNR (Density-Guided Neural Rendering). In DGNR, geometric priors are no longer needed, which can be intrinsically learned from the density space through volumetric rendering. Specifically, we make use of a differentiable renderer to synthesize images from the neural density features obtained from the learned density space. A density-based fusion module and geometric regularization are proposed to optimize the density space. By conducting experiments on a widely used autonomous driving dataset, we have validated the effectiveness of DGNR in synthesizing photorealistic driving scenes and achieving real-time capable rendering

NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering

Recent advances in neural implicit fields enables rapidly reconstructing 3D geometry from multi-view images. Beyond that, recovering physical properties such as material and illumination is essential for enabling more applications. This paper presents a new method that effectively learns relightable neural surface using pre-intergrated rendering, which simultaneously learns geometry, material and illumination within the neural implicit field. The key insight of our work is that these properties are closely related to each other, and optimizing them in a collaborative manner would lead to consistent improvements. Specifically, we propose NeuS-PIR, a method that factorizes the radiance field into a spatially varying material field and a differentiable environment cubemap, and jointly learns it with geometry represented by neural surface. Our experiments demonstrate that the proposed method outperforms the state-of-the-art method in both synthetic and real datasets