Isolation of Circulating Tumor Cells and Clusters from Blood with Application in Drug Screening

Circulating tumor cell (CTC) plays a significant role to lead tumor become life-threatening. The appearance of CTC in the circulating system of tumor patients is deemed as the start of metastasis. To obtain CTC from blood is critical for vast biomedical applications, such as using CTC for DNA sequencing to reveal the gene difference between CTC and tumor cells at original site, creating in vitro tumor models based on CTC, and develop new and effective therapeutic schemes, etc. Based on this, the focus of the dissertation mainly on the research towards CTC. In brief, the dissertation demonstrates several techniques on how to isolate them from patient blood samples, how to use them as seed to form 3D tumor models, and how to use these 3D tumor models for highly efficient anti-tumor drug screening.Firstly, a wavy-herringbone (wavy-HB) structured microfluidic device is developed to effectively and selectively capture and release circulating tumor cells (CTCs) by using immunoaffinity and magnetic force. The device is designed to create passive turbulence and increase the possibility of tumor cells colliding onto the device wall. Under an external magnetic field, magnetic particles (MPs) coated with anti-EpCAM against tumor cell surface protein (EpCAM) are immobilized over the wavy-HB surface to capture tumor cells. After removing the magnetic field, the captured cells with surplus MPs are released from the device and collected, thus cells can be re-cultured for further analysis. On optimized conditions, the capture efficiency of tumor cells can be as high as 92%±2.8%. Capture experiments are also performed on whole blood samples and the capture efficiency is in a high range of 81%-95%, based on different tumor cell concentrations.Next, to isolate CTC clusters (CTCC), which has been shown to have higher invasiveness than CTC, a spiral channeled microfluidic device is introduced. By the centrifugal force created by spiral channels in microfluidic conditions, the device can isolate three types of cells, i.e. white blood cell (WBC), CTC, and CTCC. Due to the size difference among these cells, when flowing into the microfluidic device, the different centrifugal force they experience is different, and this difference enables them to exit from different outlets of the device. At lower flow rate, WBC could be firstly isolated, while CTC and CTCC could be isolated at higher flow rate. This device is able to isolate rare CTC and CTCC from massive WBC, so this device with this method can be potentially used for isolation of CTC and CTCC from patient blood samples.To use the CTC and CTCC obtained from the first two techniques, a facile method for generation of tumor spheroids in large quantity with controllable size and high uniformity is presented. HCT116 cells are used as the model cell line. Individual tumor cells are sparsely seeded onto petri-dishes. After a few days of growth, separated cellular islets are formed and then detached by dispase while maintaining their sheet shape. These detached cell sheets are transferred to dispase-doped media under orbital shaking conditions. Assisted by the shear flow under shaking and inhibition of cell-to-extracellular matrix junctions by dispase, the cell sheets curl up and eventually tumor spheroids are formed. The average size of the spheroids can be controlled by tuning the cell sheet culturing period and spheroid shaking period. The uniformity can be controlled by a set of sieves which were home-made using stainless steel meshes. Since this method is based on simple petri-dish cell culturing and shaking, it is rather facile for forming tumor spheroids with no theoretical quantity limit. This method has been used to form HeLa, A431 and U87 MG tumor spheroids and application of the formed tumor spheroids in drug screening is also demonstrated. The viability, 3D structure, and necrosis of the spheroids are characterized.Finally, to more closely mimicking the microenvironment of in vivo tumor, a bi-layer microfluidic device is presented to facilitate anti-tumor drug screening. The bi-layer microfluidic device consists of two PDMS pieces with channels and the two pieces are separated by a semi-permeable membrane to allow water, oxygen and nutrition supply but prevent cell migration. The two channels on the two PDMS pieces have a long overlapping to ensure a larger exchange area to mimic the blood vessel-tumor model. High concentration of EC is firstly seeded onto the membrane through the apical channel, and after two-day culture to ensure a confluent EC monolayer forming, tumor spheroids laden Matrigel is seeded into the basal channel. After the Matrigel is cured, the device is ready for drug test. Confocal and ImageJ are used to assess the efficacy of different concentration of drugs and combination of drugs therapies. Optical coherence tomography is employed to determine the tumor shrinkage after drug treatment

SSformer: A Lightweight Transformer for Semantic Segmentation

It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of inductive biases of local neighborhoods and possess a high time complexity. Recently, Swin Transformer sets a new record in various vision tasks by using hierarchical architecture and shifted windows while being more efficient. However, as Swin Transformer is specifically designed for image classification, it may achieve suboptimal performance on dense prediction-based segmentation task. Further, simply combing Swin Transformer with existing methods would lead to the boost of model size and parameters for the final segmentation model. In this paper, we rethink the Swin Transformer for semantic segmentation, and design a lightweight yet effective transformer model, called SSformer. In this model, considering the inherent hierarchical design of Swin Transformer, we propose a decoder to aggregate information from different layers, thus obtaining both local and global attentions. Experimental results show the proposed SSformer yields comparable mIoU performance with state-of-the-art models, while maintaining a smaller model size and lower compute

Improved Dynamic Regret of Distributed Online Multiple Frank-Wolfe Convex Optimization

In this paper, we consider a distributed online convex optimization problem over a time-varying multi-agent network. The goal of this network is to minimize a global loss function through local computation and communication with neighbors. To effectively handle the optimization problem with a high-dimensional and structural constraint set, we develop a distributed online multiple Frank-Wolfe algorithm to avoid the expensive computational cost of projection operation. The dynamic regret bounds are established as $\mathcal{O}(T^{1-\gamma}+H_T)$ with the linear oracle number $\mathcal{O} (T^{1+\gamma})$, which depends on the horizon (total iteration number) $T$, the function variation $H_T$, and the tuning parameter $0<\gamma<1$. In particular, when the prior knowledge of $H_T$ and $T$ is available, the bound can be enhanced to $\mathcal{O} (1+H_T)$. Moreover, we illustrate the significant advantages of the multiple iteration technique and reveal a trade-off between dynamic regret bound, computational cost, and communication cost. Finally, the performance of our algorithm is verified and compared through the distributed online ridge regression problems with two constraint sets

Adaptive Random Fourier Features Kernel LMS

We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient descent, this algorithm uses a preset number of random Fourier features to save computation cost. However, as an extra flexibility, it can adapt the inherent kernel bandwidth in the random Fourier features in an online manner. This adaptation mechanism allows to alleviate the problem of selecting the kernel bandwidth beforehand for the benefit of an improved tracking in non-stationary circumstances. Simulation results confirm that the proposed algorithm achieves a performance improvement in terms of convergence rate, error at steady-state and tracking ability over other kernel adaptive filters with preset kernel bandwidth.Comment: 5 pages, 2 figure

UperFormer: A Multi-scale Transformer-based Decoder for Semantic Segmentation

While a large number of recent works on semantic segmentation focus on designing and incorporating a transformer-based encoder, much less attention and vigor have been devoted to transformer-based decoders. For such a task whose hallmark quest is pixel-accurate prediction, we argue that the decoder stage is just as crucial as that of the encoder in achieving superior segmentation performance, by disentangling and refining the high-level cues and working out object boundaries with pixel-level precision. In this paper, we propose a novel transformer-based decoder called UperFormer, which is plug-and-play for hierarchical encoders and attains high quality segmentation results regardless of encoder architecture. UperFormer is equipped with carefully designed multi-head skip attention units and novel upsampling operations. Multi-head skip attention is able to fuse multi-scale features from backbones with those in decoders. The upsampling operation, which incorporates feature from encoder, can be more friendly for object localization. It brings a 0.4% to 3.2% increase compared with traditional upsampling methods. By combining UperFormer with Swin Transformer (Swin-T), a fully transformer-based symmetric network is formed for semantic segmentation tasks. Extensive experiments show that our proposed approach is highly effective and computationally efficient. On Cityscapes dataset, we achieve state-of-the-art performance. On the more challenging ADE20K dataset, our best model yields a single-scale mIoU of 50.18, and a multi-scale mIoU of 51.8, which is on-par with the current state-of-art model, while we drastically cut the number of FLOPs by 53.5%. Our source code and models are publicly available at: https://github.com/shiwt03/UperForme

Revealing the cosmic web dependent halo bias

Halo bias is the one of the key ingredients of the halo models. It was shown at a given redshift to be only dependent, to the first order, on the halo mass. In this study, four types of cosmic web environments: clusters, filaments, sheets and voids are defined within a state of the art high resolution $N$-body simulation. Within those environments, we use both halo-dark matter cross-correlation and halo-halo auto correlation functions to probe the clustering properties of halos. The nature of the halo bias differs strongly among the four different cosmic web environments we describe. With respect to the overall population, halos in clusters have significantly lower biases in the {$10^{11.0}\sim 10^{13.5}h^{-1}\rm M_\odot$} mass range. In other environments however, halos show extremely enhanced biases up to a factor 10 in voids for halos of mass {$\sim 10^{12.0}h^{-1}\rm M_\odot$}. Such a strong cosmic web environment dependence in the halo bias may play an important role in future cosmological and galaxy formation studies. Within this cosmic web framework, the age dependency of halo bias is found to be only significant in clusters and filaments for relatively small halos \la 10^{12.5}\msunh.Comment: 14 pages, 14 figures, ApJ accepte