Bright, compact and biocompatible quantum dot/rod -bioconjugates for Förster resonance energy transfer based ratiometric biosensing and cellular imaging

Cancer, a generic group of diseases, can affect distant sites of the human body to cause sever health consequences. According to the World Health Organization, 9.8 million people died from cancer in 2015 worldwide, about 1600 people per day. More seriously, the number of new cases is expected to increase 70% by 2030 to cause 12 million deaths globally. Early detection, accurate diagnosis and effective treatment are crucial in increasing cancer survival rates and reducing patients’ suffering. In particular, precise cancer positioning that can guide surgery, chemotherapy and radiotherapy has important clinical significance in successful treatment. The nanotechnology-based diagnosis (e.g. QD/QR-bioconjugate probes) and/or treatment of different cancers have received great attention, which is growing to be a promising field in medical research. Over the past 20 years, not only have QD based probes been widely used in developing immunoassays, cellular labeling, cellular imaging, tissue imaging and in vivo imaging, but also being extended to researches such as the drug target and drug delivery system. And this thesis is composed of two parts: Part I An ultra-efficient ligand-exchange protocol (UCEP) to render commercial hydrophobic QDs completely water-soluble using >50-fold less of the air-stable lipoic acid (LA) based functional ligands with a rapid in situ reduction by tris(2-carboxylethyl phosphine, TECP) has been developed. The resulting water-soluble QDs are compact (Dh 90% of original fluorescence), resisting nonspecific adsorption and displaying good stabilities in biological buffers even with high salt contents (e.g. 2 M NaCl), making them well-suited for cell imaging and ratiometric biosensing. A DHLA-zwitterion capped QD prepared by the UCEP is readily biofunctionalized with hexa-histidine (His8)-tagged small antibody mimetic proteins (also known as Affimers), allowing for rapid, ratiometric detection of its target protein down to 5 pM via the QD-sensitized Förster resonance energy transfer (FRET) readout signal. Moreover, compact biotin functionalized QDs are prepared by a facile, one-step cap-exchange process for ratiometric quantitation detection of 5 pM protein such as NeutrAvidin as well as for fluorescence imaging of target model cancer cells. Part II A stable, water-soluble rod-shaped fluorescence semiconductor nanocrystal (CdSe/CdS core/shell quantum rod, QR) was made by an efficient cap exchange protocol as described in Part I. However, in most cases the fluorescence of the cap-exchanged QR was almost quenched, hindering their biomedical applications. Herein I have solved this problem by discovering a simple method that allows for efficient recovery of the QR quantum yield, making them suitable for biological applications. The resulting water-soluble QRs are compact (Dh 67% of original fluorescence), resisting nonspecific adsorption and displaying good stabilities in biological buffers, making them well-suited for ratiometric biosensing. After tris(2-carboxylethyl phosphine, (TECP) reduction, a dihydrolipoic acid-zwitterion ligand (DHLA-ZW) capped QR was self-assembled with (His8)-tagged anti-yeast SUMO non-antibody binding proteins (nABPs), allowing for ratiometric detection of its target protein down to 5 pM by the QR-sensitized Förster resonance energy transfer (FRET) signal. Furthermore, compact biotin functionalized QRs are prepared by a facile, one-step cap-exchange process for ratiometric quantitation of labelled neutravidin down to 5 pM. Such sensitivity is among the very best for QR-FRET based biosensors

Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the saturation number for singular cubic surfaces

We investigate the distribution of rational points on singular cubic surfaces, whose coordinates have few prime factors. The key tools used are universal torsors, the circle method and results on linear equations in primes.Comment: 19 pages. It has been accepted for publication in SCIENCE CHINA Mathematic

FRNET: Flattened Residual Network for Infant MRI Skull Stripping

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.Comment: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI