Cancer Drug Screening Scale-up: Combining Biomimetic Microfluidic Platforms and Deep Learning Image Analysis

The development of cancer drugs is usually costly and time-consuming, mainly due to growing complexity in screening large number of candidate compounds and high failure rates in translation from preclinical trials to clinical approval. Despite the great efforts, the preclinical screening platforms combing good clinical relevance and high throughput for large-scale drug testing is still lacking. In addition, accumulating evidence suggests that cancer drug response can be altered by tumor microenvironment (TME), which includes not only cancer cells but also physical, and biochemical cues in niches. To improve the current cancer drug screening assays, it is important to mimic local TME to achieve better physiological relevance. In the first part of this dissertation, three TME-mimicking microfluidic platforms were introduced for three different in-vitro TME-mimicking tumor sphere models: spheres in matrix, self-aggregated spheres, and single-cell clonal spheres. First, a 3D gel-island chip investigated the heterogeneity of single-cell drug responses in biomimetic extracellular matrix (ECM). With 1,500 isolated single cell chambers containing ECM, it was demonstrated that ECM support was favorable for some population of cancer cells to maintain stemness and develop drug resistance. This result suggested the importance of drug screening at single-cell resolution in TME-mimicking platforms. Secondly, a drug combination screening chip enabling high-throughput and scalable combinatorial drug screening was demonstrated for the aggregated sphere model. Instead of screening a single drug on each of the tumors, this chip allows the screening of all pairwise drug combinations from eight different cancer drugs, in total 172 different treatment conditions, and 1,032 tested samples in a single microfluidic chip. The presented design approach was easily scalable to incorporate arbitrary number of drugs for large-scale drug screening. Finally, single-cell Hi-Sphere chip enabled high-throughput clonal sphere culture and selective retrieval. Combining fluorescent dye on-situ staining techniques, we identified rare cancer stem-like cell population and confirms its location at the leading edge of spheres. Advance in experimental throughput generates massive data, which demands the corresponding automatic analysis and intelligent interpretation capabilities. The second part of this dissertation focuses on the applications of computer vision and machine learning algorithms to automated biomedical data processing. Image analysis with convolutional neural network was applied for drug efficacy evaluation in a fast and label-free manner. The estimated drug efficacy is highly correlated with the experimental ground truth (R-value > 0.93), while the predicted half-maximal inhibitory concentration is within 8% error range. In addition, metastatic fast-moving cells could be identified after extracting morphological features from the microscope images and applying deep learning algorithm for image analysis, achieving over 99% accuracy for cell movement direction prediction and 91% for speed prediction. In summary, this dissertation presents high-throughput TME-mimicking microfluidics and deep learning image analysis for large-scale drug screening solutions.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163039/1/zhangzx_1.pd

A Fast and Map-Free Model for Trajectory Prediction in Traffics

To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the efficiency is crucial for various real applications, this paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. The core idea of our model is encoding single-agent's spatial-temporal information in the first stage and exploring multi-agents' spatial-temporal interactions in the second stage. By comprehensively utilizing attention mechanism, LSTM, graph convolution network and temporal transformer in the two stages, our model is able to learn rich dynamic and interaction information of all agents. Our model achieves the highest performance when comparing with existing map-free methods and also exceeds most map-based state-of-the-art methods on the Argoverse dataset. In addition, our model also exhibits a faster inference speed than the baseline methods.Comment: 7 pages, 3 figure

Picroside-I attenuated isoproterenol-induced heart damage via modification of cardio-morphology, infarct size and inflammatory cascade

Purpose: To study the effect of picroside-I (PIC-I) on isoproterenol (ISO)-induced heart damage in rats through determination of infarct size, antioxidant enzymes, cardiac/inflammatory and apoptotic markers, as well as cardio-morphology.Methods: A total of 32 rats were divided equally into 4 groups. Rats in normal control group were treated with saline only, while myocardial infarction (MI) rat model was prepared by intraperitoneal (i.p.) injection of ISO at a concentration of 100 mg/kg. Rats pretreated with PIC-Iat dose 10 mg/kg (i.p) for 28 days and administered with isoproterenol. Another group of rats was administered only with PIC-I (10 mg/kg) for 28 days.Results: After 28 days of pretreatment with PIC-I, there were significant increases in arterial blood pressure and cardiac antioxidants, as well as marked decreases in infarct size, cardiac markers, inflammatory markers and apoptotic markers in rats with ISO-induced heart damage, when compared with rats given ISO alone. Rats administered PIC-I showed better histology, with reduced necrosis and prominent cardiac fibers.Conclusion: PIC-1 pre-treatment for 28 days significantly reversed elevations in infarct size, cardiac/inflammatory and apoptotic markers, and also improved antioxidant status and cardiacmorphology in rats with ISO-induced heart damage. Keywords: Picroside-I, Isoproterenol, Infarct size, Inflammation, Apoptosi

Recent Progress on Mechanical Condition Monitoring and Fault Diagnosis

AbstractMechanical equipments are widely used in various industrial applications. Generally working in severe conditions, mechanical equipments are subjected to progressive deterioration of their state. The mechanical failures account for more than 60% of breakdowns of the system. Therefore, the identification of impending mechanical fault is crucial to prevent the system from malfunction. This paper discusses the most recent progress in the mechanical condition monitoring and fault diagnosis. Excellent work is introduced from the aspects of the fault mechanism research, signal processing and feature extraction, fault reasoning research and equipment development. An overview of some of the existing methods for signal processing and feature extraction is presented. The advantages and disadvantages of these techniques are discussed. The review result suggests that the intelligent information fusion based mechanical fault diagnosis expert system with self-learning and self-updating abilities is the future research trend for the condition monitoring fault diagnosis of mechanical equipments

Digital financial inclusion and the urban–rural income gap in China: empirical research based on the Theil index

This study examined the effect of digital financial inclusion in reducing the urban–rural income inequality in China. Based on citylevel panel data, the results showed that digital financial inclusion narrowed the urban–rural income gap significantly by boosting economic growth. The results were robust when the core explained variables were replaced. Heterogeneity analysis showed that digital financial inclusion indicates regional differences in narrowing the urban–rural income gap. This study puts forward corresponding countermeasures for the development of digital financial inclusion and adds to the research on this very topical subjec

Deep Safe Multi-Task Learning

In recent years, Multi-Task Learning (MTL) has attracted much attention due to its good performance in many applications. However, many existing MTL models cannot guarantee that their performance is no worse than their single-task counterparts on each task. Though some works have empirically observed this phenomenon, little work aims to handle the resulting problem. In this paper, we formally define this phenomenon as negative sharing and define safe multi-task learning where no negative sharing occurs. To achieve safe multi-task learning, we propose a Deep Safe Multi-Task Learning (DSMTL) model with two learning strategies: individual learning and joint learning. We theoretically study the safeness of both learning strategies in the DSMTL model to show that the proposed methods can achieve some versions of safe multi-task learning. Moreover, to improve the scalability of the DSMTL model, we propose an extension, which automatically learns a compact architecture and empirically achieves safe multi-task learning. Extensive experiments on benchmark datasets verify the safeness of the proposed methods