DNA SEPARATION AT A STRETCH AND MULTISTAGE MAGNETIC SEPARATION OF MICROSPHERES

This thesis consists of two parts. The first part focuses on development of a novel DNA separation technology by tethering DNA strands to a solid surface and then stretching the DNA with an electric field. The anchor is such designed that the critical force to detach a DNA is independent of its size. Because the stretching force is proportional to the DNA net charge, a gradual increase of the electric field leads to size-based removal of the DNA from the surface and thus DNA separation. This strategy may provide a convenient, low-cost, and high-speed alternative to existing methods for DNA separation, because sieving matrices are not required, separated DNA can be readily recovered, and in principle, there is no upper limit on the length of DNA that can be separated. Using this method, we have demonstrated (i) efficient separation of lambda double-stranded DNA (dsDNA) (48,502 bp) from human genomic dsDNA (>100 kbp) in a dc electric field applied between two parallel plates, (ii) separation of short single-stranded DNA (ssDNA) with less than 100 nucleotides (nt) at 10-nt resolution by tethering and stretching DNA in microfluidic channels filled with a low conductivity buffer, and (iii) separation of short ssDNA by taking the advantage of the strong yet evolving non-uniform electric field near the charged Au surface in contact with an electrolyte. The second part of my thesis focuses on development of a multistage separation technology to circumvent the challenge caused by non-specific interactions in current single-stage magnetic separation techniques. The key idea is to allow the magnetic particles (MNPs) to reversibly capture and release the targets by manipulating the hydrophobic interaction between the MNPs and the targets. This will be enabled by attaching temperature-responsive polymers to both the MNPs and the targets. Through temperature cycling, which triggers the reversible hydrophilic-to-hydrophobic phase transition of the polymers, the targets can be reversibly captured and released by the MNPs (due to hydrophobic interaction) at a higher efficiency than the non-targets which may also be captured and released by the MNPs due to non-specific interactions. The difference in the capture-and-release efficiencies of targets versus non-targets in a single cycle will be amplified by multiple separation stages, following a similar concept to the distillation process. As a proof-of-concept demonstration, we have demonstrated efficient separation of poly(N-isopropylacrylamide) (PNIPAM, a temperature responsive polymer)-functionalized polystyrene (PS) microspheres from bare PS microspheres by using PNIPAM-functionalized MNPs. The overall enrichment factor is observed to significantly increase with the number of separation stages, and reaches as high as 1.87 E+5 after 5 stages

Phase Coupled Firing of Prefrontal Parvalbumin Interneuron With High Frequency Oscillations

The prefrontal cortex (PFC) plays a central role in executive functions and inhibitory control over many cognitive behaviors. Dynamic changes in local field potentials (LFPs), such as gamma oscillation, have been hypothesized to be important for attentive behaviors and modulated by local interneurons such as parvalbumin (PV) cells. However, the precise relationships between the firing patterns of PV interneurons and temporal dynamics of PFC activities remains elusive. In this study, by combining in vivo electrophysiological recordings with optogenetics, we investigated the activities of prefrontal PV interneurons and categorized them into three subtypes based on their distinct firing rates under different behavioral states. Interestingly, all the three subtypes of interneurons showed strong phase-locked firing to cortical high frequency oscillations (HFOs), but not to theta or gamma oscillations, despite of behavior states. Moreover, we showed that sustained optogenetic stimulation (over a period of 10 s) of PV interneurons can consequently modulate the activities of local pyramidal neurons. Interestingly, such optogenetic manipulations only showed moderate effects on LFPs in the PFC. We conclude that prefrontal PV interneurons are consist of several subclasses of cells with distinct state-dependent modulation of firing rates, selectively coupled to HFOs

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol

MULTI-VESSELS COLLISION AVOIDANCE STRATEGY FOR AUTONOMOUS SURFACE VEHICLES BASED ON GENETIC ALGORITHM IN CONGESTED PORT ENVIRONMENT

An improved genetic collision avoidance algorithm is proposed in this study to address the problem that Autonomous Surface Vehicles (ASV) need to comply with the collision avoidance rules at sea in congested sea areas. Firstly, a collision risk index model for ASV safe encounters is established taking into account the international rules for collision avoidance. The ASV collision risk index and the distance of safe encounters are taken as boundary values of the correlation membership function of the collision risk index model to calculate the optimal heading of ASV in real-time. Secondly, the genetic coding, fitness function, and basic parameters of the genetic algorithm are designed to construct the collision avoidance decision system. Finally, the simulation of collision avoidance between ASV and several obstacle vessels is performed, including the simulation of three collision avoidance states head-on situation, crossing situation, and overtaking situation. The results show that the proposed intelligent genetic algorithm considering the rules of collision avoidance at sea can effectively avoid multiple other vessels in different situations

SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Unsupervised Learning

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers in the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from ∼33.3k PMC papers in the CORD19 dataset, released on June 13, 2020. The source code is released at https://gitee.com/irlab/SmartCiteCon

Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval

Different from Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to learn a more accurate image representation that has adaptive attention to the reference image for various manipulation descriptions. In this paper, we propose a novel context-dependent mapping network, named Context-I2W, for adaptively converting description-relevant Image information into a pseudo-word token composed of the description for accurate ZS-CIR. Specifically, an Intent View Selector first dynamically learns a rotation rule to map the identical image to a task-specific manipulation view. Then a Visual Target Extractor further captures local information covering the main targets in ZS-CIR tasks under the guidance of multiple learnable queries. The two complementary modules work together to map an image to a context-dependent pseudo-word token without extra supervision. Our model shows strong generalization ability on four ZS-CIR tasks, including domain conversion, object composition, object manipulation, and attribute manipulation. It obtains consistent and significant performance boosts ranging from 1.88% to 3.60% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/context_i2w