Mechanistic insights into the novel glucose-sensitive behavior of P(NIPAM-co-2-AAPBA)

A glucose-sensitive polymer, poly(N-isopropylacrylamide-co-2-acrylamidophenylboronic acid) (P(NIPAM-co-2-AAPBA)), was synthesized by reversible addition fragmentation chain transfer (RAFT) copolymerization. Addition of glucose results in reduced solubility and hence increased turbidity, rather than the normal increase in solubility (decreased turbidity) observed for other PBA-based glucose-sensitive polymers. The novel glucose-sensitive behavior is explained by a new mechanism, in which glucose acts as an additive and depresses the lower critical solution temperature (LCST) of the polymer, instead of increasing solubility by increasing the degree of ionization of the PBA groups. Experimental and theoretic analysis for the influence of glucose on the thermal behavior of P(NIPAM-co-2-AAPBA) reveals that glucose depresses the LCST of P(NIPAM-co-2- AAPBA) copolymers in a two-stage manner, a fast decrease at low glucose concentrations followed by a slow decrease at high glucose concentrations. For low glucose concentrations, the binding of glucose with PBA groups on the polymer chain increases the number of glucose molecules proximal to the polymer which influences the thermal behavior of the polymer, causing a rapid decrease in LCST. Importantly, the transition occurs at a glucose concentration equal to the reciprocal of the binding constant between PBA and glucose, thus providing a novel method to determine the binding constant. Other saccharides, including mannose, galactose and fructose, also depress the LCST of P(NIPAM-co-2-AAPBA) copolymer in the same way

SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery

Abstract With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying "from-to" change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method

Next-Generation Sequencing of Cerebrospinal Fluid for the Diagnosis of Neurocysticercosis

Background: Neurocysticercosis (NCC) is the most common helminthic infection of the central nervous system (CNS). The diagnosis of NCC is sometimes challenging due to its heterogenous clinical manifestations and the variable sensitivity and specificity of neuroimaging and serological tests.Methods: Next-generation sequencing (NGS) of cerebrospinal fluid (CSF) was used to detect pathogens in patients with clinically suspected CNS infections. A series of patients diagnosed with NCC is reviewed here.Results: Using NGS of CSF, four patients were diagnosed with NCC. The reads corresponding to Taenia solium ranged from 478 to 117,362, with genomic coverage of 0.0564–11.15%. Reads corresponding to T. solium were not found in non-template controls and far exceeded those of the background microorganisms in patients with NCC, facilitating the interpretation of the NGS results.Conclusions: This case series demonstrates that NGS of CSF is promising in the diagnosis of NCC in difficult to diagnose cases. Larger studies are needed in the future

The first structure of HIV-1 gp120 with CD4 and CCR5 receptors

Abstract Shaik et al. recently published online the cryo-electron microscopy structure of HIV-1 gp120 in complex with CD4 and CCR5 receptors. This is the first structure of the ternary HIV-1 gp120/CD4/CCR5 complex. This breakthrough of Env structure provides insights into HIV-1 fusion mechanism, CCR5 function, co-receptor switch, and, most importantly, the development of co-receptor-targeted therapeutic inhibitor and HIV-1 vaccine. It also shed lights on the immunogenicity of gp120 by revealing the stably exposed conserved gp41-interactive region of gp120 in the complex

Correlation between discharging property and coatings microstructure during plasma electrolytic oxidation

The voltage-current properties during plasma electrolytic discharge were determined by measuring the current density andcell voltage as functions of processing time and then by mathematical transformation. Correlation between discharge I-V propertyand the coatings microstructure on aluminum alloy during plasma electrolfic oxidation was determined by comparing thevoltage-current properties at different process stages with SEM results of the corresponding coatings. The results show that theuniform passive film corresponds to a I-V property with one critical voltage, and a compound of porous layer and shred ceramicparticles corresponds to a I-Vproperty with two critical voltages. The growth regularity of PEO cermet coatings was also studied

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods

Materials Genome Technology and Its Development Trend

The materials genome initiative (MGI) was launched to reform the traditional modes to develop novel materials, which were expected to shorten the development cycle with simultaneous decreasing of costs. In this paper, the connotations of MGI was discussed in detail, especially from the viewpoint of requirements to aero materials. The development of MGI requires techniques aimed to high throughput computation and experiment, together with large-scale data mining based on material database. To the direction of MGI technique, we emphasize the following four aspects: material information science and database technique, integrated computation, processing simulation of materials and computational simulation of service