Controlled surface initiated polymerization of N-isopropylacrylamide from polycaprolactone substrates for regulating cell attachment and detachment

Poly(ε-caprolactone) (PCL) substrates were modified with thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) brushes to direct and control cellular attachment and detachment. Prior to brush growth, the surface of PCL was activated by a diamine to allow for initiator coupling. Infrared spectra taken before and after cell culturing demonstrated the covalently attached nature of the PNIPAM brushes. PCL is a biocompatible polymer and to prove that the modifications described above did not change this characteristic property, a cell attachment/detachment study was carried out. The modified substrates showed a lower cell attachment when compared to PCL alone and to PCL films modified with the initiator. The possibility to detach the cells in the form of a sheet was proved using PNIPAM-modified PCL films by lowering the temperature to 25 °C. No relevant detachment was shown by the unmodified or by the initiator modified surfaces. This confirmed that the detachment was temperature dependent and not connected to other factors such as polymer swelling. These functionalized polymeric films can find applications as smart cell culture systems in regenerative medicine applications

Impacts of Large-Scale Circulation on Convection: A 2-D Cloud Resolving Model Study

Studies of impacts of large-scale circulation on convection, and the roles of convection in heat and water balances over tropical region are fundamentally important for understanding global climate changes. Heat and water budgets over warm pool (SST=29.5 C) and cold pool (SST=26 C) were analyzed based on simulations of the two-dimensional cloud resolving model. Here the sensitivity of heat and water budgets to different sizes of warm and cold pools is examined

Untethered Swarm Robots with Independent Crawling and Rolling Motions

We have demonstrated mm-scale untethered robots using a single axis magnetic field control to have swarm operations and independent crawling and rolling motions. There are three distinctive accomplishments as compared with the state-of-art: 1) working prototypes in the mm-scale with a moving speed of ~50 body size/s under the crawling mode operation; (2) an ultra-high moving speed of ~277 body size/s under the rolling mode operation; and 3) demonstrations of several complex swarming behaviors including chain-shape formation, crawling, and separation As such, the proposed operation schemes provides a new class of strategies to control mm-scale swarm robots for potential environmental and medical applications, such as minimally invasive surgeries

Static detection of control-flow-related vulnerabilities using graph embedding

© 2019 IEEE. Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges

A high-resolution inland surface water body dataset for the tundra and boreal forests of North America

Inland surface waters are abundant in the tundra and boreal forests of North America, essential to environments and human societies but vulnerable to climate changes. These high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper, we present a water body dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5 million water bodies were identified, with approximately 6 million (∼90 %) of them smaller than 0.1 km2. The dataset provides area and morphological attributes for every water body. During this study, we developed an automated approach for detecting surface water extent and identifying water bodies in the 10 m resolution Sentinel-2 multispectral satellite data to enhance the capability of delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America. The dataset provided a more complete representation of the region than existing regional datasets for North America, e.g., Permafrost Region Pond and Lake (PeRL). The total accuracy of the detected water extent by the WBD-NAHL dataset was 96.36 % through comparison to interpreted data for locations randomly sampled across the region. Compared to the 30 m or coarser-resolution water datasets, e.g., JRC GSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database (GLWD), the WBD-NAHL provided an improved ability on delineating water bodies and reported higher accuracies in the size, number, and perimeter attributes of water body by comparing to PeRL and interpreted regional dataset. This dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (TPDC; http://data.tpdc.ac.cn, last access: 6 June 2022): https://doi.org/10.11888/Hydro.tpdc.271021 (Feng and Sui, 2020).</p

Pore network modeling of catalyst deactivation by coking, from single site to particle, during propane dehydrogenation

A versatile pore network model is used to study deactivation by coking in a single catalyst particle. This approach allows to gain detailed insights into the progression of deactivation from active site, to pore, and to particle – providing valuable information for catalyst design. The model is applied to investigate deactivation by coking during propane dehydrogenation in a Pt‐Sn/Al2O3 catalyst particle. We find that the deactivation process can be separated into two stages when there exist severe diffusion limitation and pore blockage, and the toxicity of coke formed in the later stage is much stronger than of coke formed in the early stage. The reaction temperature and composition change the coking rate and apparent reaction rate, informed by the kinetics, but, remarkably, they do not change the capacity for a catalyst particle to accommodate coke. On the other hand, the pore network structure significantly affects the capacity to contain coke

The native cistrome and sequence motif families of the maize ear

Elucidating the transcriptional regulatory networks that underlie growth and development requires robust ways to define the complete set of transcription factor (TF) binding sites. Although TF-binding sites are known to be generally located within accessible chromatin regions (ACRs), pinpointing these DNA regulatory elements globally remains challenging. Current approaches primarily identify binding sites for a single TF (e.g. ChIP-seq), or globally detect ACRs but lack the resolution to consistently define TF-binding sites (e.g. DNAse-seq, ATAC-seq). To address this challenge, we developed MNase-defined cistrome-Occupancy Analysis (MOA-seq), a high-resolution (< 30 bp), high-throughput, and genome-wide strategy to globally identify putative TF-binding sites within ACRs. We used MOA-seq on developing maize ears as a proof of concept, able to define a cistrome of 145,000 MOA footprints (MFs). While a substantial majority (76%) of the known ATAC-seq ACRs intersected with the MFs, only a minority of MFs overlapped with the ATAC peaks, indicating that the majority of MFs were novel and not detected by ATAC-seq. MFs were associated with promoters and significantly enriched for TF-binding and long-range chromatin interaction sites, including for the well-characterized FASCIATED EAR4, KNOTTED1, and TEOSINTE BRANCHED1. Importantly, the MOA-seq strategy improved the spatial resolution of TF-binding prediction and allowed us to identify 215 motif families collectively distributed over more than 100,000 non-overlapping, putatively-occupied binding sites across the genome. Our study presents a simple, efficient, and high-resolution approach to identify putative TF footprints and binding motifs genome-wide, to ultimately define a native cistrome atlas

Electrospun ZnO Nanowires as Gas Sensors for Ethanol Detection

ZnO nanowires were produced using an electrospinning method and used in gas sensors for the detection of ethanol at 220 °C. This electrospinning technique allows the direct placement of ZnO nanowires during their synthesis to bridge the sensor electrodes. An excellent sensitivity of nearly 90% was obtained at a low ethanol concentration of 10 ppm, and the rest obtained at higher ethanol concentrations, up to 600 ppm, all equal to or greater than 90%