Starch-Based Rehealable and Degradable Bioplastic Enabled by Dynamic Imine Chemistry

The increasingly serious environmental pollution caused by petroleum-based nondegradable plastics has evoked intense research interest in the development of sustainable and degradable bioplastics. Starch is one of the most promising biopolymers for the preparation of bioplastic. However, it is still a great challenge to develop starch bioplastics with high strength, low water sensitivity, and excellent water resistance. Herein, we reported a facile chemical modification method for the synthesis of a novel starch bioplastic. In this process, an easily available starch derivate, dialdehyde starch (DAS), is cross-linked using diamine based on dynamic imine chemistry to prepare DAS-based polyimine (DAS-PI). This DAS-PI exhibits excellent thermal malleability, and it can be easily thermoformed into novel starch bioplastic without using any plasticizer. The resulting starch bioplastic shows high mechanical strength (40.6 MPa), high thermal stability, and excellent water/chemical resistance, as well as heat-induced self-healing ability. Moreover, it can be easily chemically degraded and recycled. This work provides a novel method of producing high-performance starch bioplastics without using plasticizers

Nanoparticle Loading Induced Morphological Transitions and Size Fractionation of Coassemblies from PS‑<i>b</i>‑PAA with Quantum Dots

Inorganic nanoparticles play a very important role in the fabrication and regulation of desirable hybrid structures with block copolymers. In this study, polystyrene-b-poly­(acrylic acid) (PS48-b-PAA67) and oleic acid-capped CdSe/CdS core/shell quantum dots (QDs) are coassembled in tetrahydrofuran (THF) through gradual water addition. QDs are incorporated into the hydrophilic PAA blocks because of the strong coordination between PAA blocks and the surface of QDs. Increasing the weight fraction of QDs (ω = 0–0.44) leads to morphological transitions from hybrid spherical micelles to large compound micelles (LCMs) and then to bowl-shaped structures. The coassembly process is monitored using transmission electron microscopy (TEM). Formation mechanism of different morphologies is further proposed in which the PAA blocks bridging QDs manipulates the polymer chain mobility and the resulting morphology. Furthermore, the size and size distribution of assemblies serving as drug carriers will influence the circulation time, organ distribution and cell entry pathway of assemblies. Therefore, it is important to prepare or isolate assemblies with monodisperse or narrow size distribution for biomedical applications. Here, the centrifugation and membrane filtration techniques are applied to fractionate polydisperse coassemblies, and the results indicate that both techniques provide effective size fractionation

In utero and postnatal exposure to environmental tobacco smoke, blood pressure, and hypertension in children: the Seven Northeastern Cities study

To evaluate the association of environmental tobacco smoke (ETS) exposure with hypertension and blood pressure (BP) in children, a sample of 9,354 children, aged 5–17 years, was studied from seven northeastern cities of China in 2012–2013. The results showesd that significant associations were observed for hypertension with ETS exposure in utero [odds ratio (OR) 1.36, 95% confidence interval (CI) 1.18–1.57], with current major ETS exposure from fathers (1.38, 1.21–1.57) or anyone (1.26, 1.12–1.42), and with intensity of ETS exposure greater than 1 cigarette per day (ORs ranged from 1.20 to 1.35). For SBP, significant associations were only observed in children with major ETS exposure from father and with cigarettes smoking >10/day. When stratified by sex, more significant associations were found in girls than in boys. In conclusion, prenatal and postnatal ETS exposure was significantly associated with increased odds of hypertension in children, especially in girls.</p

Time window selection.

The three subplots represent the precision values for different time windows based on 21 start frames (x axis) and 12 window lengths (7 frames to 29 frames) for phases 1, 2, and 3 (from top to bottom) respectively, and the black bash line in each subplot indicates a precision value of 0.55.</p

iPS progenitor cells vs. MEFs and feature correlation.

(a) shows the examples of iPS progenitor cell images (blue circles) and normal MEFs images (yellow boxes) taken from phase 1, 2 and 3 of field 2 (Left, middle and right). Nucleus and cytoplasm of the enlarged progenitor cells and normal MEFs are colored in light blue and green respectively. (b) shows the Pearson coefficients between remaining types of features in three phases after the first step of feature selection. Note in this figure ellipsoid-prolate is denoted as E-prolate, intensity-StdDev as I-stdDev, intensity-min as I-Min, intensity-max as I-Max, nucleus-cytoplasm volume ratio as Ratio, ellipsoid-oblate as E-oblate.</p

Feature ranking and selection.

This figure shows how the precision values change with the deleted feature in a recursive fashion. Least important features are removed earlier.</p

Model validation.

In all sub-figures, X axis indicates the start frame of the best time windows and the corresponding window length (13 frames) is indicated in the inlet. (a) 5-fold cross-validation precisions over 10 runs. (b) the standard deviation of the average precision of the neighborhood time windows in Fig 6D. (c) the standard deviation of the average precision of the distant windows in Fig 6E. (d) the average precision of seven neighborhood time windows calculated over 10 holdout validation runs. (e) the average precision over 10 independent tests for six best time windows on their corresponding distant windows.</p

Model comparison for different missing frame number and imputation methods.

(a) shows the average precision over six time periods (TP1 to TP6) for each missing frame number and imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray). (b) shows the standard deviation, as a function of missing frame number, of imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray).</p

Flow chart of the machine learning based approach for iPS progenitor cell identification.

In time-lapse imaging, we record the reprogramming process periodically among 54 fields after 48h of viral infection. For retrospective tracking, the figure only shows the reprogramming lineage images of the first frame of all eight phases. Only datasets from phase 1, 2 and 3 are used for model training and testing.</p