Photo-Induced Depolymerisation: Recent Advances and Future Challenges

Facing the growing environmental issues provoked by the use of nondegradable polymers in many fields (for example, packing, building, and clothing), tremendous efforts have been made to explore photodegradable materials to alleviate the increase in plastic pollution. Photodegradable materials would exploit significant advantages presented by the use of light, such as abundance, safety and the ability to easily tune intensity and wavelength. In particular, photo-induced depolymerisation has received increasing attention, which could enable polymers to degrade to their original monomers or small molecules under certain photoirradiation conditions. Most importantly, the obtained molecules or monomers via photo-induced depolymerisation could be conveniently recycled or further transformed to other high-value-added products, which is of great benefit for environmental protection. This Review summarizes recent advances in the growing field of photo-induced depolymerisation and also considers future challenges that must be addressed. It aims to encourage new researchers to enter this flourishing area and presents a brief guide to the field

Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis

Small $p$-values are often required to be accurately estimated in large scale genomic studies for the adjustment of multiple hypothesis tests and the ranking of genomic features based on their statistical significance. For those complicated test statistics whose cumulative distribution functions are analytically intractable, existing methods usually do not work well with small $p$-values due to lack of accuracy or computational restrictions. We propose a general approach for accurately and efficiently calculating small $p$-values for a broad range of complicated test statistics based on the principle of the cross-entropy method and Markov chain Monte Carlo sampling techniques. We evaluate the performance of the proposed algorithm through simulations and demonstrate its application to three real examples in genomic studies. The results show that our approach can accurately evaluate small to extremely small $p$-values (e.g. $10^{-6}$ to $10^{-100}$). The proposed algorithm is helpful to the improvement of existing test procedures and the development of new test procedures in genomic studies.Comment: 34 pages, 1 figure, 4 table

Engineering a ratiometric fluorescent sensor membrane containing carbon dots for efficient fluoride detection and removal

Fluoride anion pollution is one of the main problems that needs to be addressed in contaminated water. Herein, we have developed a novel sensing platform using a pyrene boronic acid and carbon dots (CDs) for the selective detection and removal of fluoride (F−) ion at environmentally relevant levels. The probe consists of pyrene-boronic acid (PyB) moieties immobilized on to the surface of water-soluble CDs. The pyrene-boronic acid-based CDs (CDs-PyB) result in a sensor whose response is linear for F− concentrations over a range from 0 to 200 µM (R2 = 0.996) with a detection limit of 5.9 × 10−5 M and display high selectivity for F− over other anions. In addition, an amino-modified cellulose membrane containing CDs-PyB has been prepared for practical sensing and removal of F−. The cellulose membrane-based sensor shows great potential for the detection of F− with a high sensitivity, and excellent F− adsorption and removal efficiency of 90.2%. Moreover, an MTT assay for the membrane demonstrates high cell proliferation ca 400% after 5 days culture, indicating excellent cytocompatibility. Our approach offers a promising direction for the construction of other sensors by simply swapping the current probe with suitable replacements for a variety of relevant applications using biocompatible and abundant naturally based materials.</p

Protease‐Activatable Hybrid Nanoprobe for Tumor Imaging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108698/1/adfm201400419.pd

Recombinant TAT–gelonin fusion toxin: Synthesis and characterization of heparin/protamine‐regulated cell transduction

Protein toxins, such as gelonin, are highly desirable anti‐cancer drug candidates due to their unparalleled potency and repetitive reaction mechanism in inhibiting protein translation. However, for its potential application in cancer therapy, there remains the cell membrane barrier that allows permeation of only small molecules, which must be overcome. To address this challenge, we conjugated gelonin with a protein transduction domain (PTD), the TAT peptide, via genetic recombination. The chimeric TAT–gelonin fusion protein (TAT‐Gel) retained equipotent N ‐glycosidase activity yet displayed greater cell uptake than unmodified recombinant gelonin (rGel), thereby yielding a significantly augmented cytotoxic activity. Remarkably, TAT‐Gel displayed up to 177‐fold lower IC 50 (avg. 54.3 n M ) than rGel (avg. IC 50 : 3640 n M ) in tested cell lines. This enhanced cytotoxicity, however, also raised potential toxicity concerns due to the non‐selectivity of PTD in its mediated cell transduction. To solve this problem, we investigated the plausibility of regulating the cell transduction of TAT‐Gel via a reversible masking using heparin and protamine. Here, we demonstrated, both in vitro and in vivo , that the cell transduction of TAT‐Gel can be completely curbed with heparin and yet this heparin block can be efficiently reversed by the addition of protamine. This reversible tight regulation of the cell transduction of TAT‐Gel by heparin and protamine sheds light of possible application of TAT‐Gel in achieving a highly effective yet safe drug therapy for the treatment of tumors. © 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 103A: 409–419, 2015.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109572/1/jbma35188.pd

Correlation model between mesostructure and gradation of asphalt mixture based on statistical method

Asphalt mixture has complex gradation and mesostructure. Accurate prediction of the relationship between gradation and mesostructure is of great significance for the establishment of mesostructure numerical simulation model and image-based gradation detection. In this paper, featurization, stepwise regression, econometric hypothesis test are utilized for establishing the predicting models. Firstly, asphalt mixtures with 64 kinds of gradation are scanned by Computed Tomography (CT) to obtain the mesostructure images; Then a series of mesostructure parameters of voids and aggregates are put forward. On this basis, the relationship model between gradation and mesostructure is established and verified by featurization and statistical modeling method. The results show that for predicting the passing percentage of the 4.75 mm sieve and the mean value of average distance between aggregate centroids for 9.5–4.75 mm aggregates, the prediction error of passing percentage is acceptable. It illustrates that the relationship model between gradation and mesostructure established by statistical method is effective, and it is significance for material design and testing under the condition of big data in the future