Design and Implementation of Position-Encoded Microfluidic Microsphere-Trap Arrays

Microarray devices are useful for detecting and analyzing biological targets, such as DNAs, mRNAs, proteins, etc. Applications of microarrays range from fundamental research to clinical diagnostics and drug discovery. In this dissertation, we consider a microsphere array device with predetermined positions of the microspheres. The microspheres are conjugate on their surfaces with molecular probes to capture the targets, and the targets are identified by the microspheres\u27 positions. We implement the microsphere arrays by employing microfluidic technology and a hydrodynamic trapping mechanism. We call our device microfluidic microsphere-trap arrays. To fully realize the potential of the device in biomedical applications, we utilize statistical performance analysis, mathematical optimization, and finite element fluid dynamics simulations to optimize device design, fabrication, and implementation. Our device is promising as a cost-effective and point-of-care lab-on-a-chip system. We first analyze the statistical performance of position-encoded microsphere arrays in imaging biological targets at different signal-to-noise ratio (SNR) levels. We compute the Ziv-Zakai bound (ZZB) on the errors in estimating the unknown parameters, including the target concentrations. Through numerical examples, we find the SNR level below which the ZZB provides a more accurate prediction of the error than the posterior Cramer-Rao bound (PCRB) does. We further apply the ZZB to select the optimal design parameters, such as the distance between the microspheres, and to investigate the effects of the experimental variables such as the microscope point-spread function. We implement the arrays by using microfluidic technology and hydrodynamic trapping. We design a novel geometric structure for the device, and develop a comprehensive and robust framework to optimize its geometric parameters that maximize the microsphere arrays\u27 packing density. We also simultaneously optimize multiple criteria, such as high microsphere trapping efficiency and low fluidic and imaging errors. Microsphere-trapping experiments performed using the optimized device and an un-optimized device demonstrate easy control of the microspheres\u27 transportation and manipulation in the optimized device. They also show that the optimized device greatly outperforms the un-optimized one. We extend our optimization framework to build a device that enables simultaneous, efficient, and accurate screening of multiple targets in a single microfluidic channel, by immobilizing different-sized microspheres at different regions. Different biomolecules captured on the surfaces of the different-sized microspheres can thus be detected simultaneously by the microspheres\u27 positions. We employ finite element fluid dynamics simulations to investigate hydrodynamic trapping of microspheres, and to study the effects of the geometric parameters and critical fluid velocity. The accuracy of the time-dependent simulations is validated by experimental results. The simulations guide the device design and experimental operation. The guidelines on the simulation set-up and the openly available model will help researchers apply the simulation to similar microfluidic systems that may accommodate a variety of structured particles

Study of internet usage in the fresh produce supply chain in the UK and China

A thesis submitted for the degree of Master of Philosophy of the University of BedfordshireFresh produce supply chain management faces a high level of complexity and uncertainty and a number of challenges due to fresh produce's perishable, seasonal and fragile characteristics. It is argued that effective implementation of Information and Communication Technologies (leTs) has great potential for improving efficiency and reducing wastage within the fresh produce (fruit and vegetable) supply chain. While' the Internet is used by many small and medium-sized enterprises (SMEs) in the fresh produce industry, the extent to which it is applied and further developed after the initial adoption varies widely. Much research has been carried out to investigate Internet adoption and usage, but very limited effort has been focused on the identification of the current level of technology integration and deve!opment and the factors affecting the level of the development after the adoption, especially in the context of SMEs in the fresh produce supply chain. This research attempts to address this issue by developing a theoretical framework to illustrate the evolutionary process of Internet adoption and diffusion and to identify factors affecting the development of Internet-based supply chains by following the Technological/Organisational/Environmental (TOE) framevork. First, five development levels of post-adoption of Internet technologies in the supply chain were defined, and factors from the technological, organisational and environmentalcontexts were identified according to literatures and exploratory interviews. Second, questionnaire surveys were conducted in the UK and China to investigate the current situation of internet technologies used by SMEs in the fresh produce supply chains in the two countries. Finally, factors the proposed framework were validated and discussed. The empirical findings show that the Internet is no longer a new technology for most fresh produce SMEs in the UK and China. However, a large proportion of SMEs surveyed are still using basic functions of the Internet, and there is little difference between the UK and Chinese SMEs when comparing the use of complex applications in the supply chains. The results also show that most of the factors in the organisational and technological contexts are positively related to the current development levels of the Internet-based supply chain, whereas, in the environmental context, pressures from customers in the UK and mutual trust among partners in China have a significant impact on current development levels. Additionally, in both countries, companies in a better development level of Internet-based supply chain would achieve a higher degree of integration in their supply chain in five years. Overall, the research has made a number of important contributions to knowledge, current debate and practice in an under-researched sector. The five-level post-adoption framework can be adapted to identify ICT development levels and key factors in other sectors. The empirical data collected has added value to and sheds lights on the current applications of the Internet in the supply chain in general, and in the fresh produce SMEs in China and the UK in particular. The key factors identified as impeding the further development of the Internet, such as factors related to the business environments in the UK and China, will help government policy-makers, supply chain facilitators and IT service providers to be more focused in their efforts to improve the situation and to stimulate the further diffusion of emerging Internet technologies. The research has certain limitations due to the time constraints and sample selections. These limitations provide a platform for directing future research

Der sehnsüchtige Androgyne

Die Kulturgeschichte der männlichen Homosexualität und deren filmischen Darstellungen im Queer-Film in China; die Arbeit beinhaltet eine DVD mit Filmszenen

User Multi-Interest Modeling for Behavioral Cognition

Representation modeling based on user behavior sequences is an important direction in user cognition. In this study, we propose a novel framework called Multi-Interest User Representation Model. Specifically, the model consists of two sub-models. The first sub-module is used to encode user behaviors in any period into a super-high dimensional sparse vector. Then, we design a self-supervised network to map vectors in the first module to low-dimensional dense user representations by contrastive learning. With the help of a novel attention module which can learn multi-interests of user, the second sub-module achieves almost lossless dimensionality reduction. Experiments on several benchmark datasets show that our approach works well and outperforms state-of-the-art unsupervised representation methods in different downstream tasks.Comment: during peer revie

Factors influencing drivers' queue-jumping behavior at urban intersections: A covariance-based structural equation modeling analysis

Queue-jumping is widely acknowledged as one of the most vexing driving behaviors and a prevalent traffic violation at urban intersections in China, exerting detrimental effects on both traffic operational efficiency and safety. To investigate the motivational factors underlying drivers' queue-jumping behavior at urban intersections, a questionnaire was designed to collect data based on an extended theory of planned behavior (TPB). A total of 427 valid responses were received through an online self-reported questionnaire survey conducted in China. The Pearson's chi-square test was employed to examine potential demographic disparities in self-reported queue-jumping behavior among drivers at urban intersections. Covariance-based structural equation modeling (CB-SEM) with bootstrapping was utilized to elucidate the impact of various factors on drivers' engagement in queue-jumping behavior. The findings revealed significant gender and age differences regarding drivers' propensity for queue-jumping at urban intersections, with male and young drivers exhibiting higher inclination compared to female and older counterparts, respectively. Furthermore, the extended TPB effectively accounted for both behavioral intention and actual occurrence of queue-jumping among drivers at urban intersections. Behavioral intention (β = 0.391, p = 0.002) and perceived behavior control (β = 0.282, p = 0.002) emerged as influential determinants of queue-jumping. Among all influencing factors shaping drivers' behavioral intention toward engaging queue-jumping at urban intersections, attitude (β = 0.316, p = 0.005) proved to be the most significant factor followed by perceived risk (β = 0.230, p = 0.001), moral norms (β = 0.184, p = 0.002), subjective norms (β = 0.175, p = 0.002), and perceived behavior control (β = 0.122, p = 0.05). These results offer valuable insights for urban road traffic managers seeking effective strategies for public awareness campaigns as well as practical intervention measures aimed at curbing improper driving behavior of queue-jumping at urban intersections

Leverage Business Analytics and OWA to Recommend Appropriate Projects in Crowdfunding Platform

Nowadays, crowdfunding is becoming more and more popular. Many studies have been published on the crowdfunding platform from different perspectives. However, among all these studies, few are concerned about the recommendation methods, which, in effect, are highly beneficial to crowdfunding websites and the participants. Having considered the situation talked above, this paper works out the several features from the relative projects of user’s current browsing project. Then we give different weights to each feature based on selective attention phenomenon, and adopt the method of OWA operator to calculate the final score of each relative project and accomplish our model by picking out the four projects with different outstanding characteristics. Finally, according to the statistics on China’s famous crowdfunding website, we conducted a group of contrast experiments and eventually testified that our proposed model could, to some extent, help classify and give recommendation effectively. Furthermore, the results of this research can give guidance to the management of crowdfunding websites and they are also very significant advices for the future crowdfunding website development

GraphGPT: Graph Learning with Generative Pre-trained Transformers

We introduce \textit{GraphGPT}, a novel model for Graph learning by self-supervised Generative Pre-training Transformers. Our model transforms each graph or sampled subgraph into a sequence of tokens representing the node, edge and attributes reversibly using the Eulerian path first. Then we feed the tokens into a standard transformer decoder and pre-train it with the next-token-prediction (NTP) task. Lastly, we fine-tune the GraphGPT model with the supervised tasks. This intuitive, yet effective model achieves superior or close results to the state-of-the-art methods for the graph-, edge- and node-level tasks on the large scale molecular dataset PCQM4Mv2, the protein-protein association dataset ogbl-ppa and the ogbn-proteins dataset from the Open Graph Benchmark (OGB). Furthermore, the generative pre-training enables us to train GraphGPT up to 400M+ parameters with consistently increasing performance, which is beyond the capability of GNNs and previous graph transformers. The source code and pre-trained checkpoints will be released soon\footnote{\url{https://github.com/alibaba/graph-gpt}} to pave the way for the graph foundation model research, and also to assist the scientific discovery in pharmaceutical, chemistry, material and bio-informatics domains, etc.Comment: 9 page

Hippocampal CA3 inhibitory neurons receive extensive noncanonical synaptic inputs from CA1 and subicular complex.

Hippocampal CA3 is traditionally conceptualized as a brain region within a unidirectional feedforward trisynaptic pathway that links major hippocampal subregions. Recent genomic and viral tracing studies indicate that the anatomical connectivity of CA3 and the trisynaptic pathway is more complex than initially expected and suggests that there may be cell type-specific input gradients throughout the three-dimensional hippocampal structure. In several recent studies using multiple viral tracing approaches, we describe subdivisions of the subiculum complex and ventral hippocampal CA1 that show significant back projections to CA1 and CA3 excitatory neurons. These novel connections form noncanonical circuits that run in the opposite direction relative to the well-characterized feedforward pathway. Diverse subtypes of GABAergic inhibitory neurons participate within the trisynaptic pathway. In the present study, we have applied monosynaptic retrograde viral tracing to examine noncanonical synaptic inputs from CA1 and subicular complex to the inhibitory neuron in hippocampal CA3. We quantitatively mapped synaptic inputs to CA3 inhibitory neurons to understand how they are connected within and beyond the hippocampus formation. Major brain regions that provide typical inputs to CA3 inhibitory neurons include the medial septum, the dentate gyrus, the entorhinal cortex, and CA3. Noncanonical inputs from ventral CA1 and subicular complex to CA3 inhibitory neurons follow a proximodistal topographic gradient with regard to CA3 subregions. We find novel noncanonical circuit connections between inhibitory CA3 neurons and ventral CA1, subiculum complex, and other brain regions. These results provide a new anatomical connectivity basis to further study the function of CA3 inhibitory neurons