Challenges of scale down model for disposable bioreactors: Case studies on growth & product quality impacts

Despite wide-spread use of disposable bioreactors, there is a lack of well-established scale-down model for larger scale SUBs. Here we report a case of NS0 cell culture process transfer from 2000L stainless steel bioreactor (SST) to 2000L disposable bioreactor (SUB). Initial attempts in trying to grow the NS0 cells in the small scale 2D bags yielded non-satisfactory results, as growth was impacted by bag material type as well as by suppliers of the same bag material type. However, 3D bags of 50L and above proved to be supportive of the NS0 cell line growth. Even for cell lines that do not have growth issues in SUBs, surprising product quality difference between SUBs and traditional bench top glass bioreactors are still being observed, thus making the bench top glass bioreactors non-ideal as scale down models. We report two cases where glycan profiles of the expressed antibody products show such dramatic differences. In one case, extensive testing of glass bioreactors from various suppliers led to a particular type being able to mimic the glycan profiles from the SUB, whereas in the other case, alternative scale down model had to be identified and the process had to be modified to maintain the glycan profiles when scaling up to the 200L SUB

Three-dimensional reconstruction of medical images based on 3D slicer

The development of imaging has always been the top priority of modern medical advancement. There are also many methods for image processing in brain. 3D Slicer is an open source medical software that can reconstruct and visualize various medical image data in three dimensions. Three-dimensional reconstruction of blood vessels, hematomas, and nerve fiber tissue in brain can better assist doctors in planning operation and surgical implementation

Challenges of scale down model for disposable bioreactors: Case studies on growth & product quality impacts

Despite wide-spread use of disposable bioreactors, there is a lack of well-established scale-down model for larger scale SUBs. Here we report a case of NS0 cell culture process transfer from 2000L stainless steel bioreactor (SST) to 2000L disposable bioreactor (SUB). Initial attempts in trying to grow the NS0 cells in the small scale 2D bags yielded non-satisfactory results, as growth was impacted by bag material type as well as by suppliers of the same bag material type. However, 3D bags of 50L and above proved to be supportive of the NS0 cell line growth. Even for cell lines that do not have growth issues in SUBs, surprising product quality difference between SUBs and traditional bench top glass bioreactors are still being observed, thus making the bench top glass bioreactors non-ideal as scale down models. We report two cases where glycan profiles of the expressed antibody products show such dramatic differences. In one case, extensive testing of glass bioreactors from various suppliers led to a particular type being able to mimic the glycan profiles from the SUB, whereas in the other case, alternative scale down model had to be identified and the process had to be modified to maintain the glycan profiles when scaling up to the 200L SUB

The finite element analysis of the in plane and out of plane harmonic responses of piezoresponse force microscopy cantilever

The harmonic response under the in plane and out of plane driving force separately and model analysis of the widely used SCM-PIT probe were carried out in the consideration of the typical piezoresponse force microscopy working condition by finite element method. It is shown that there are symmetric modes of the resonance at 68, 408, 1139, 2244 kHz, and antisymmetric modes at 646, 1020, and 3077 kHz in the first seven eigenmodes. The symmetric modes of the harmonic response are verified by the frequency sweep method from the piezoresponse phase signals experimentally. It is also revealed that different driving frequencies should be used in the resonance-enhanced PFM imaging in the consideration of the domain structures. The driving frequency of 68, 408, 1139, 2244 kHz should be preferred in the resonance-enhanced PFM imaging of the out of plane domains, while the driving frequency of 646, 1020 and 3077 kHz should be used for the imaging of in plane domains in order of achieved the best resonance-enhanced effect

Rapid Maxillary Anterior Teeth Retraction En Masse by Bone Compression: A Canine Model

The present study sought to establish an animal model to study the feasibility and safety of rapid retraction of maxillary anterior teeth en masse aided by alveolar surgery in order to reduce orthodontic treatment time.Extraction of the maxillary canine and alveolar surgery were performed on twelve adult beagle dogs. After that, the custom-made tooth-borne distraction devices were placed on beagles' teeth. Nine of the dogs were applied compression at 0.5 mm/d for 12 days continuously. The other three received no force as the control group. The animals were killed in 1, 14, and 28 days after the end of the application of compression.The tissue responses were assessed by craniometric measurement as well as histological examination. Gross alterations were evident in the experimental group, characterized by anterior teeth crossbite. The average total movements of incisors within 12 days were 4.63±0.10 mm and the average anchorage losses were 1.25±0.12 mm. Considerable root resorption extending into the dentine could be observed 1 and 14 days after the compression. But after consolidation of 28 days, there were regenerated cementum on the dentine. There was no apparent change in the control group. No obvious tooth loosening, gingival necrosis, pulp degeneration, or other adverse complications appeared in any of the dogs.This is the first experimental study for testing the technique of rapid anterior teeth retraction en masse aided by modified alveolar surgery. Despite a preliminary animal model study, the current findings pave the way for the potential clinical application that can accelerate orthodontic tooth movement without many adverse complications.It may become a novel method to shorten the clinical orthodontic treatment time in the future

Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction

Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN

Fourth-Order Compact Difference Schemes for the Riemann-Liouville and Riesz Derivatives

We propose two new compact difference schemes for numerical approximation of the Riemann-Liouville and Riesz derivatives, respectively. It is shown that these formulas have fourth-order convergence order by means of the Fourier transform method. Finally, some numerical examples are implemented to testify the efficiency of the numerical schemes and confirm the convergence orders

Spore Powder of Ganoderma lucidum Improves Cancer-Related Fatigue in Breast Cancer Patients Undergoing Endocrine Therapy: A Pilot Clinical Trial

The fatigue prevalence in breast cancer survivors is high during the endocrine treatment. However, there are few evidence-based interventions to manage this symptom. The aim of this study was to investigate the effectiveness of spore powder of Ganoderma lucidum for cancer-related fatigue in breast cancer patients undergoing endocrine therapy. Spore powder of Ganoderma lucidum is a kind of Basidiomycete which is a widely used traditional medicine in China. 48 breast cancer patients with cancer-related fatigue undergoing endocrine therapy were randomized into the experimental or control group. FACT-F, HADS, and EORTC QLQ-C30 questionnaires data were collected at baseline and 4 weeks after treatment. The concentrations of TNF-α, IL-6, and liver-kidney functions were measured before and after intervention. The experimental group showed statistically significant improvements in the domains of physical well-being and fatigue subscale after intervention. These patients also reported less anxiety and depression and better quality of life. Immune markers of CRF were significantly lower and no serious adverse effects occurred during the study. This pilot study suggests that spore powder of Ganoderma lucidum may have beneficial effects on cancer-related fatigue and quality of life in breast cancer patients undergoing endocrine therapy without any significant adverse effect

The Expressive Power of Graph Neural Networks: A Survey

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement