Synthesis and Characterization of Biodegradable Electrospun Polyurethanes For Biomedical Applications

Two types of polyesters [(Poly(ε-caprolactone) and Poly(δ-valerolactone)] were synthesized by ring-opening polymerization with diethylene glycol as a initiator and stannous octate as a catalyst. The corresponding polyurethanes were synthesized by two-step polymerization with 1,4-diisocyanate butane (BDI) and reacted with 1,4-diamino butane (Putrescine) chain extender. The polymers were characterized by Proton Nuclear Magnetic Resonance (1H NMR), Fourier Transform Infrared Spectroscopy (FT-IR), Raman Spectroscopy, Size-exclusive Chromatography (SEC), Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). The resulting polyurethanes were fabricated into nanofibers using an electrospinning technique. The morphological, structural characterizations and thermal properties of the bulk polyurethanes and electrospun polyurethanes nanofibers were analyzed by Scanning Electron Microscopy (SEM), FT-IR, Raman, TGA and DSC. The degradability of electrospun polyurethane nanofibers were investigated in phosphate buffer solution (pH=7.2) at 37°C at a period of 5 days

Cerato-ulmin hydrophobin-coated air bubbles and oil droplets: Stability, shapes, and interfacial behavior

Hydrophobins are small amphipathic proteins: one side shuns water, the other seeks it. Unlike common amphipathic molecules (e.g., sodium dodecyl sulfate or SDS used in soap), hydrophobins are nearly rigid, thanks to an elaborate disulfide crosslinking network that stabilizes their compact, globular structure. Hydrophobins have been called nature’s Janus particles, and nature makes them by the ton in mushrooms and other forms of fungi. The present dissertation research concerns a particular hydrophobin cerato-ulmin (CU). This thesis is directed toward (1) the exploration of the CU’s ability to encapsulate whether gaseous or liquid and the stability of the resulting bubbles and droplets, (2) the understanding of the intermediate structures coated with CU, such as cylindrical and toroidal shapes, (3) the understanding of the interfacial behavior of CU alone and the complex interfacial interaction at air-water and oil-water interfaces, and (4) the exploration of delivering hydrophobic molecules by CU. Firstly, CU is found to stabilize cylindrical microbubbles upon simple agitation of its dilute suspension or sausage-like oil droplets in the presence of nonpolar solvents. No emulsifier or other polymer is required to trap either air or oil, suggesting that CU provides both emulsification and strength. Air or oil can be trapped directly without a fluid carrier. The bubbles or droplets are numerous and remain in suspension long enough for facile study. Secondly, manipulation of pressure in a prescribed sequence introduces shape transitions of the bubbles from cylinders to spheres and ultimately torus. Bending elastic energy and curvature model are used to explain toroidal shapes and their stability. The solid-like CU films are stiff enough to retain the unusual shapes. Thirdly, CU molecules are prone to adsorb to the air-water and oil-water interfaces and the adsorption is irreversible. The interfacial moduli are often about ten times stronger than membranes formed by traditional surfactant molecules, although CU films can be disrupted by SDS above its critical micelle concentration. Finally, the diffusive behavior of small debris ejected from CU air bubbles and hydrophobic particles inside CU oil droplets was characterized by differential dynamic microscopy. The particles freely follow Brownian diffusion soon after encapsulation, but lose their diffusive motion as the solvent evaporates.Ph.D

Responses of seasonal indicators to extreme droughts in southwest China

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region

Polymorphisms of the BCL2 gene associated with susceptibility to tuberculosis

Although tuberculosis (TB) is a serious public health concern, we still don’t understand why only 10% of people infected will develop the disease. Apoptosis plays a role in the interaction of Mycobacterium tuberculosis (Mtb) with the human host and it may be modified by subtle alterations in the B-cell lymphoma 2 (BCL2) gene, an anti-apoptotic regulatory element. Therefore, we investigated whether there is an association between BCL2 polymorphisms and susceptibility to TB by analyzing 130 TB cases, 108 subjects with latent TB infection (LTBI), and 163 healthy controls (HC). Logistic regression was used to calculate odds ratios (ORs) and 95% confidential intervals (95% CIs) for possible associations between single nucleotide polymorphisms (SNPs) in BCL2 and the risk of tuberculosis. We found that the G allele of rs80030866 (OR=0.62, 95%CI:0.42-0.91, P=0.015), and also the G allele of rs9955190 (OR=0.58, 95%CI:0.38-0.88, P=0.011) were less frequent in the TB group compared with the LTBI group. In addition, individuals with rs2551402 CC genotype were more likely to have LTBI than those with AA genotype (OR=2.166, 95%CI:1.046-4.484, P=0.037). Our study suggests that BCL2 gene polymorphisms may be correlated with susceptibility to both TB and LTBI

RankDNN: Learning to Rank for Few-shot Learning

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankMLP can be built on top of any state-of-the-art feature extractors, and our entire deep neural network is called the ranking deep neural network, or RankDNN. Meanwhile, RankDNN can be flexibly fused with other post-processing methods. During the meta test, RankDNN ranks support images according to their similarity with the query samples, and each query sample is assigned the class label of its nearest neighbor. Experiments demonstrate that RankDNN can effectively improve the performance of its baselines based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning benchmarks, including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments on the cross-domain challenge demonstrate the superior transferability of RankDNN.The code is available at: https://github.com/guoqianyu-alberta/RankDNN.Comment: 12 pages, 4 figures. Accepted to AAAI2023. The code is available at: https://github.com/guoqianyu-alberta/RankDN