The journey from tech transfer to BLA submission: Case study of a NS0 cell culture process from 2000L stainless steel bioreactor to 2000L disposable bioreactor

A case study of NS0 cell culture process transfer from 2000L stainless steel bioreactor (SST) to 2000L disposable bioreactor (SUB), and through to process validation and BLA submission is reported for production of an antibody therapeutics in this poster. Initial attempts in growing 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 different suppliers of the same bag material type. However, 3D bags of 50L and above proved to be supportive of the NS0 cell line growth. Process characterization (PC) and process validation (PV) efforts were initiated after successful scale up to the 2000L SUB. Scale down model (3L) was qualified using bench top glass bioreactors, and PC studies identified several critical process parameters (CPPs). Successful process performance qualification (PPQ) campaign followed and BLA was submitted in 2017

A bibliometric study of the research field of experimental philosophy of language

The past eighteen years witnessed the rapid development of experimental philosophy of language. Adopting a bibliometric approach, this study examines the research trends and status quo of this burgeoning field based on a corpus of 237 publications retrieved from PhilPapers. It is observed that experimental philosophy of language has undergone three stages, the initiation stage, the development stage, and the extension stage, across which there is a clear upward trend in the annual number of publications. Michael Devitt, Edouard Machery, John Turri, Nat Hansen, et al., are found to be the most productive philosophers, testifying their leading positions in this field. Journals, instead of books, are the major homes of works in this area. The analysis also yields a list of influential works, including the seminal work “Semantics, Cross-cultural Style” and other significant publications on the semantics of various types of expressions. Relatedly, the major research themes are found to include not only intuitions about the reference of proper names, but also a wide array of philosophically and linguistically interesting issues like the meaning of color adjectives, epistemic modals, and predicates of personal taste, the norms of assertions and the essence of lies, etc. These findings showcase that experimental philosophy of language has broadened the research territory and offered deep insights into central issues of philosophy of language that are beyond the reach of the conventional armchair methodology

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 origin of cross-cultural differences in referential intuitions: Perspective taking in the Gödel case

In this paper, we aim to trace the origin of the systematic cross-cultural variations in referential intuitions by investigating the effects of perspective taking on people’s responses in the Gödel-style probes through two novel experiments. Here is how we will proceed. In section 2, we first briefly introduce the MMNS (2004) study, and then critically review the two relevant studies conducted by Sytsma and colleagues (i.e., Sytsma and Livengood 2011; Sytsma et al. 2015). In section 3, we introduce the literature on cross-cultural variation in perspective taking in cultural psychology, which together with the conjecture of perspectival ambiguity leads to the hypothesis of our current study. In sections 4 and 5, two new experiments on how perspective taking affects people’s responses in hypothetical stories modelled on the Gödel thought experiment will be reported. Based on the empirical findings, in section 6 we argue that the robust cross-cultural variations thus far observed in people’s responses to the Gödel cases are largely attributable to culturally specific perspective-taking strategies, which provides new support for the proposal previously made by Sytsma and Livengood (2011). The implications of the experimental results for the ongoing work of testing the theories of refence of names and for the current metaphilosophical debate on the robustness of philosopher’s intuitions are also drawn in this section. Finally, the major conclusions and contributions of the current study are highlighted in section 7

Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation

Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. However, due to the continuous alternations of the road segments and intersections in a path, the dynamic features are supposed to be coupled and interactive. Therefore, modeling one of them limits further improvement in accuracy of estimating travel time. To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments. In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale network architectures for capturing multi-level spatial-temporal information from the dual graph. Finally, we employ the multi-task learning approach to estimate the travel time of a given whole route, each road segment and intersection simultaneously. We conduct extensive experiments to evaluate our proposed model on three real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines

Cross‐cultural variation and perspectivalism: Alignment of two red herrings?

In this brief reply I respond to criticisms of my book, The referential mechanism of proper names, from Michael Devitt and Nicolo D'Agruma. I focus on the question of whether the perspectivism advocated in the book explains the empirical results there detailed

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

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