Practical Deep Dispersed Watermarking with Synchronization and Fusion

Deep learning based blind watermarking works have gradually emerged and achieved impressive performance. However, previous deep watermarking studies mainly focus on fixed low-resolution images while paying less attention to arbitrary resolution images, especially widespread high-resolution images nowadays. Moreover, most works usually demonstrate robustness against typical non-geometric attacks (\textit{e.g.}, JPEG compression) but ignore common geometric attacks (\textit{e.g.}, Rotate) and more challenging combined attacks. To overcome the above limitations, we propose a practical deep \textbf{D}ispersed \textbf{W}atermarking with \textbf{S}ynchronization and \textbf{F}usion, called \textbf{\proposed}. Specifically, given an arbitrary-resolution cover image, we adopt a dispersed embedding scheme which sparsely and randomly selects several fixed small-size cover blocks to embed a consistent watermark message by a well-trained encoder. In the extraction stage, we first design a watermark synchronization module to locate and rectify the encoded blocks in the noised watermarked image. We then utilize a decoder to obtain messages embedded in these blocks, and propose a message fusion strategy based on similarity to make full use of the consistency among messages, thus determining a reliable message. Extensive experiments conducted on different datasets convincingly demonstrate the effectiveness of our proposed {\proposed}. Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5.28\% and 5.93\% against single and combined attacks, respectively, and show less file size increment and better visual quality. Our code is available at https://github.com/bytedance/DWSF.Comment: Accpeted by ACM MM 202

Two-photon Bio-imaging with a Mode-locked Semiconductor Laser

Abstract: We demonstrated two-photon imaging of biological tissues by employing a mode-locked semiconductor laser. Kilowatt-peak-power second-harmonic pulses were obtained from amplified 1.55-µm optical pulses, and were used for two-photon excitation. ©2006 Optical Society of America OCIS codes: (140.5960) Semiconductor lasers; (170.3880) Medical and biological imaging. 1

Two-photon fluorescence imaging of intracellular hydrogen peroxide with chemoselective fluorescent probes

Abstract. We present the application of two-photon fluorescence (TPF) imaging to monitor intracellular hydrogen peroxide (H2O2) production in brain cells. For selective imaging of H2O2 over other reactive oxygen species, we employed small-molecule fluorescent probes that utilize a chemoselective boronate deprotection mechanism. Peroxyfluor-6 acetoxymethyl ester detects global cellular H2O2 and mitochondria peroxy yellow 1 detects mitochondrial H2O2. Two-photon absorption cross sections for these H2O2 probes are measured with a mode-locked Ti:sapphire laser in the wavelength range of 720 to 1040 nm. TPF imaging is demonstrated in the HT22 cell line to monitor both cytoplasmic H2O2 and localized H2O2 production in mitochondria. Endogenous cytoplasmic H2O2 production is detected with TPF imaging in rat astrocytes modified with d-amino acid oxidase. The TPF H2O2 imaging demonstrated that these chemoselective probes are powerful tools for the detection of intracellular H2O2

B-RAF kinase drives developmental axon growth and promotes axon regeneration in the injured mature CNS

Activation of intrinsic growth programs that promote developmental axon growth may also facilitate axon regeneration in injured adult neurons. Here, we demonstrate that conditional activation of B-RAF kinase alone in mouse embryonic neurons is sufficient to drive the growth of long-range peripheral sensory axon projections in vivo in the absence of upstream neurotrophin signaling. We further show that activated B-RAF signaling enables robust regenerative growth of sensory axons into the spinal cord after a dorsal root crush as well as substantial axon regrowth in the crush-lesioned optic nerve. Finally, the combination of B-RAF gain-of-function and PTEN loss-of-function promotes optic nerve axon extension beyond what would be predicted for a simple additive effect. We conclude that cell-intrinsic RAF signaling is a crucial pathway promoting developmental and regenerative axon growth in the peripheral and central nervous systems

Schiff base nanoarchitectonics for supramolecular assembly of dipeptide as drug carriers

Development of peptide-based supramolecular materials with hierarchical morphology and tunable guest loading displays broad potential as drug carrier in view of biocompatibility and biodegradability. Herein, we report a facile Schiff base nanoarchitectonic for supramolecular assembly of diphenylalanine (FF) metastable gel. The addition of trace glutaraldehyde (GA)/H2O solution induces the Schiff base reaction between GA and FF accompanying by phase transition from gel to solution. FF nanoparticles and hierarchical beaded nanofibers with autofluorescence properties can be constructed by regulating the competitive assembly between FF-H2O and FF-GA oligomer. Moreover, various guest molecules with different hydrophilic and hydrophobic properties can be easily loaded into such assembled particles and its release can be triggered under weak alkaline conditions, which show the potential application of the assembled FF system as drug carriers. (c) 2022 Elsevier Inc. All rights reserved

Schiff base nanoarchitectonics for supramolecular assembly of dipeptide as drug carriers

Development of peptide-based supramolecular materials with hierarchical morphology and tunable guest loading displays broad potential as drug carrier in view of biocompatibility and biodegradability. Herein, we report a facile Schiff base nanoarchitectonic for supramolecular assembly of diphenylalanine (FF) metastable gel. The addition of trace glutaraldehyde (GA)/H2O solution induces the Schiff base reaction between GA and FF accompanying by phase transition from gel to solution. FF nanoparticles and hierarchical beaded nanofibers with autofluorescence properties can be constructed by regulating the competitive assembly between FF-H2O and FF-GA oligomer. Moreover, various guest molecules with different hydrophilic and hydrophobic properties can be easily loaded into such assembled particles and its release can be triggered under weak alkaline conditions, which show the potential application of the assembled FF system as drug carriers. (c) 2022 Elsevier Inc. All rights reserved

Development and Application of Transcriptome-Derived Microsatellites in Actinidia eriantha (Actinidiaceae)

Actinidia eriantha Benth. is a diploid perennial woody vine native to China and is recognized as a valuable species for commercial kiwifruit improvement with high levels of ascorbic acid as well as having been used in traditional Chinese medicine. Due to the lack of genomic resources for the species, microsatellite markers for population genetics studies are scarce. In this study, RNASeq was conducted on fruit tissue of A. eriantha, yielding 5,678,129 reads with a total output of 3.41 Gb. De novo assembly yielded 69,783 non-redundant unigenes (41.3 Mb), of which 21,730 were annotated using protein databases. A total of 8,658 EST-SSR loci were identified in 7,495 unigene sequences, for which primer pairs were successfully designed for 3,842 loci (44.4%). Among these, 183 primer pairs were assayed for PCR amplification, yielding 69 with detectable polymorphism in A. eriantha. Additionally, 61 of the 69 polymorphic loci could be successfully amplified in at least one other Actinidia species. Of these, 14 polymorphic loci (mean N-A = 6.07 +/- 2.30) were randomly selected for assessing levels of genetic diversity and population structure within A. eriantha. Finally, a neighbor-joining tree and Bayesian clustering analysis showed distinct clustering into two groups (K = 2), agreeing with the geographical distributions of these populations. Overall, our results will facilitate further studies of genetic diversity within A. eriantha and will aid in discriminating outlier loci involved in local adaptation

Concrete Mix Design for Completely Recycled Fine Aggregate by Modified Packing Density Method

The undesirable properties of conventional recycled fine aggregate (RFA) often limit its application in the construction industry. To overcome this challenge, a method for preparing completely recycled fine aggregate (CRFA), which crushes all concrete waste only into fine aggregate, was proposed. The obtained CRFA had high apparent density, and its water absorption was lower than that of the conventional RFA. To take advantage of the CRFA, this paper introduced the modified packing density method for the CRFA concrete mix design. The modified packing density method took account of the powder with a particle size of smaller than 75 μm in the CRFA and balanced both the void ratio and the specific surface area of the aggregate system. Concrete (grade C55) was prepared using the CRFA to validate the feasibility of the proposed method. The unit price of the prepared CRFA concrete was around 12.7% lower than that of the natural aggregate concrete. Additionally, the proposed procedure for the concrete mixture design could recycle all concrete waste into the new concrete and replace all the natural fine aggregate in the concrete mixture

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA–A and LMWHA–E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA–A and LMWHA–E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA–A and LMWHA–E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares–discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)–SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA–A and LMWHA–E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules

Performances of Concrete Columns with Modular UHPC Permanent Formworks Under Axial Load

Abstract This research proposed the modular prefabricated permanent formwork system made of ultra-high-performance concrete (UHPC). Two kinds of modular formwork shapes were designed: the flat formwork and the ribbed. The experimental investigation on the axial compression performance of the composite columns that consist of the normal strength concrete (NSC) core and the modular UHPC permanent formwork was demonstrated. Compared with the flat formwork, the ribbed formwork exhibited better bonding with the NSC core. As observed from the test results, the composite column with the ribbed formwork presented a similar axial behavior as the NSC column with a slight improvement in ultimate loads. Therefore, the modular UHPC ribbed permanent formwork could be regarded as the additional cover to the conventional NSC column. In addition, the finite element analysis (FEA) model was also developed to simulate the composite columns numerically. The predicted capacities agreed with the experimental results, which validated the numerical models. The crack pattern estimated by the FEA model revealed that the interaction between the permanent formwork and the inner concrete introduced many tiny cracks to the concrete core. However, as protected by the UHPC permanent formwork, the overall durability of the composite columns can still be enhanced