TALE-based organellar genome editing and gene expression in plants.

KEY MESSAGE: TALE-based editors provide an alternative way to engineer the organellar genomes in plants. We update and discuss the most recent developments of TALE-based organellar genome editing in plants. Gene editing tools have been widely used to modify the nuclear genomes of plants for various basic research and biotechnological applications. The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 editing platform is the most commonly used technique because of its ease of use, fast speed, and low cost; however, it encounters difficulty when being delivered to plant organelles for gene editing. In contrast, protein-based editing technologies, such as transcription activator-like effector (TALE)-based tools, could be easily delivered, expressed, and targeted to organelles in plants via Agrobacteria-mediated nuclear transformation. Therefore, TALE-based editors provide an alternative way to engineer the organellar genomes in plants since the conventional chloroplast transformation method encounters technical challenges and is limited to certain species, and the direct transformation of mitochondria in higher plants is not yet possible. In this review, we update and discuss the most recent developments of TALE-based organellar genome editing in plants.

Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation.

The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times. In this study, we introduce a new lightweight segmentation model, the mobile anti-aliasing attention u-net model (MAAU), which features both encoder and decoder paths. The encoder incorporates an anti-aliasing layer and convolutional blocks to reduce the spatial resolution of input images while avoiding shift equivariance. The decoder uses an attention block and decoder module to capture prominent features in each channel. To address data-related problems, we implemented data augmentation methods such as flip, rotation, shear, translate, and color distortions, which enhanced segmentation efficiency in the international Skin Image Collaboration (ISIC) 2018 and PH2 datasets. Our experimental results demonstrated that our approach had fewer parameters, only 4.2 million, while it outperformed various state-of-the-art segmentation methods.

Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model.

Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM "ScreenNet". It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.

A Transcriptomic Analysis of Tobacco Leaf with the Functional Loss of the Plastid rpoB Operon Caused by TALEN-Mediated Double-Strand Breakage.

At least two sets of RNA polymerase (RNAP), nucleus (NEP)- and plastid (PEP)-encoded polymerases, recognizing distinct promoters exist in the plastids of land plants. Most plastid genes are regulated by multiple promoters with different strengths in their response to developmental stages and environmental cues. Recently, we applied chloroplast-targeted transcription activator-like effector nuclease (cpTALEN) technology to site-specifically cause double-strand DNA breaks in the rpoB gene of tobacco, which encodes the ß-subunit of PEP. The repair of damaged chloroplast DNA (cpDNA) through microhomology-mediated recombination caused the functional loss of the rpoB operon and resulted in the heterotrophic growth of an albino plant. We conducted a genome-wide analysis of the steady state of gene expression in the leaf tissue of PEP-deficient tobacco by RNA-Seq and compared it with that of wild-type plants. The expression of NEP genes was up-regulated in PEP-deficient tobacco; in particular, the level of RpoT3 transcripts encoding the specifically plastid-targeted NEP was significantly increased. Alongside most housekeeping genes, NEP also plays an important role in the regulation of gene expression involved in photosynthesis. In contrast, alongside the photosynthesis-related genes, PEP also plays an important role in the regulation of gene expression involved in some housekeeping functions. Furthermore, the mitochondrial DNA copy number and the level of most mitochondrial protein-coding transcripts were slightly increased in PEP-deficient tobacco. The disruption of PEP function not only affected plastid gene expression, but also nuclear and mitochondrial gene expression. This study demonstrated the intercompartmental retrograde signaling in the regulation of gene expression.

Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution.

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.

Repairing TALEN-mediated double-strand break by microhomology-mediated recombination in tobacco plastids generates abundant subgenomic DNA.

Transcription activator-like effector nuclease (TALEN) technology has been widely used to edit nuclear genomes in plants but rarely for editing organellar genomes. In addition, ciprofloxacin, commonly used to cause the double-strand break of organellar DNA for studying the repair mechanism in plants, confers no organellar selectivity and site-specificity. To demonstrate the feasibility of TALEN-mediated chloroplast DNA editing and to use it for studying the repair mechanism in plastids, we developed a TALEN-mediated editing technology fused with chloroplast transit peptide (cpTALEN) to site-specifically edit the rpoB gene via Agrobacteria-mediated transformation of tobacco leaf. Transgenic plants showed various degrees of chlorotic phenotype. Repairing damaged plastid DNA resulted in point mutation, large deletion and small inversion surrounding the rpoB gene by homologous recombination and/or microhomology-mediated recombination. In an albino line, microhomology-mediated recombination via a pair of 12-bp direct repeats between rpoC2 and ycf2 genes generated the chimeric ycf2-rpoC2 subgenome, with the level about 3- to 5-fold higher for subgenomic DNA than ycf2. Additionally, the expression of chimeric ycf2-rpoC2 transcripts versus ycf2 mRNA agreed well with the level of corresponding DNA. The ycf2-rpoC2 subgenomic DNA might independently and preferentially replicate in plastids.

Immunomagnetic separation in a novel cavity-added serpentine microchannel structure for the selective isolation of lung adenocarcinoma cells.

The manipulation and separation of circulating tumor cells (CTCs) in continuous fluidic flows play an essential role in various biomedical applications, particularly the early diagnosis and treatment of diseases. Recent advances in magnetic bead development have provided promising solutions to the challenges encountered in CTC manipulation and isolation. In this study, we proposed a biomicrofluidic platform for specifically isolating human lung carcinoma A549 cells in microfluidic channels. The principle of separation was based on the effect of the magnetic field on aptamer-conjugated magnetic beads, also known as immunomagnetic beads, in a serpentine microchannel with added cavities (SMAC). The magnetic cell separation performance of the proposed structure was modeled and simulated by using COMSOL Multiphysics. The experimental procedures for aptamer molecular conjugation on 1.36 µm-diameter magnetic beads and magnetic bead immobilization on A549 cells were also reported. The lung carcinoma cell-bead complexes were then experimentally separated by an external magnetic field. Separation performance was also confirmed by optical microscopic observations and fluorescence analysis, which showed the high selectivity and efficiency of the proposed system in the isolation and capture of A549 cells in our proposed SMAC. At the flow rate of 5 µL/s, the capture rate of human lung carcinoma cells exceeded 70% in less than 15 min, whereas that of the nontarget cells was approximately 4%. The proposed platform demonstrated its potential for high selectivity, portability, and facile operation, which are suitable considerations for developing point-of-care applications for various biological and clinical purposes.

Mask Attention-SRGAN for Mobile Sensing Networks.

Biometrics has been shown to be an effective solution for the identity recognition problem, and iris recognition, as well as face recognition, are accurate biometric modalities, among others. The higher resolution inside the crucial region reveals details of the physiological characteristics which provides discriminative information to achieve extremely high recognition rate. Due to the growing needs for the IoT device in various applications, the image sensor is gradually integrated in the IoT device to decrease the cost, and low-cost image sensors may be preferable than high-cost ones. However, low-cost image sensors may not satisfy the minimum requirement of the resolution, which definitely leads to the decrease of the recognition accuracy. Therefore, how to maintain high accuracy for biometric systems without using expensive high-cost image sensors in mobile sensing networks becomes an interesting and important issue. In this paper, we proposed MA-SRGAN, a single image super-resolution (SISR) algorithm, based on the mask-attention mechanism used in Generative Adversarial Network (GAN). We modified the latest state-of-the-art (nESRGAN+) in the GAN-based SR model by adding an extra part of a discriminator with an additional loss term to force the GAN to pay more attention within the region of interest (ROI). The experiments were performed on the CASIA-Thousand-v4 dataset and the Celeb Attribute dataset. The experimental results show that the proposed method successfully learns the details of features inside the crucial region by enhancing the recognition accuracies after image super-resolution (SR).

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks.

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

Blockchain-Based Reversible Data Hiding for Securing Medical Images.

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation.

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net.

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called "Interleaved Residual U-Net" (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only.

Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor cost, researchers endeavor to establish a generalized BP estimation model using only PPG signals. In this paper, we propose a deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation. The effectiveness and accuracy of our proposed model was evaluated by the root mean square error (RMSE), mean absolute error (MAE), the Association for the Advancement of Medical Instrumentation (AAMI) standard and the British Hypertension Society (BHS) standard. Experimental results showed that the RMSEs in systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 4.643 mmHg and 3.307 mmHg, respectively, across 9000 subjects, with 80.63% of absolute errors among estimated SBP records lower than 5 mmHg and 90.19% of absolute errors among estimated DBP records lower than 5 mmHg. We demonstrated that our proposed model has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works.

Overexpression of a multifunctional ß-glucosidase gene from thermophilic archaeon Sulfolobus solfataricus in transgenic tobacco could facilitate glucose release and its use as a reporter.

The ß-glucosidase, which hydrolyzes the ß(1-4) glucosidic linkage of disaccharides, oligosaccharides and glucose-substituted molecules, has been used in many biotechnological applications. The current commercial source of ß-glucosidase is mainly microbial fermentation. Plants have been developed as bioreactors to produce various kinds of proteins including ß-glucosidase because of the potential low cost. Sulfolobus solfataricus is a thermoacidophilic archaeon that can grow optimally at high temperature, around 80 °C, and pH 2-4. We overexpressed the ß-glucosidase gene from S. solfataricus in transgenic tobacco via Agrobacteria-mediated transformation. Three transgenic tobacco lines with ß-glucosidase gene expression driven by the rbcS promoter were obtained, and the recombinant proteins were accumulated in chloroplasts, endoplasmic reticulum and vacuoles up to 1%, 0.6% and 0.3% of total soluble protein, respectively. By stacking the transgenes via crossing distinct transgenic events, the level of ß-glucosidase in plants could further increase. The plant-expressed ß-glucosidase had optimal activity at 80 °C and pH 5-6. In addition, the plant-expressed ß-glucosidase showed high thermostability; on heat pre-treatment at 80 °C for 2 h, approximately 70% residual activity remained. Furthermore, wind-dried leaf tissues of transgenic plants showed good stability in short-term storage at room temperature, with ß-glucosidase activity of about 80% still remaining after 1 week of storage as compared with fresh leaf. Furthermore, we demonstrated the possibility of using the archaebacterial ß-glucosidase gene as a reporter in plants based on alternative ß-galactosidase activity.

A Maze Matrix-Based Secret Image Sharing Scheme with Cheater Detection.

Secret image sharing is a technique for sharing a secret message in such a fashion that stego image shadows are generated and distributed to individual participants. Without the complete set of shadows shared among all participants, the secret could not be deciphered. This technique may serve as a crucial means for protecting private data in massive Internet of things applications. This can be realized by distributing the stego image shadows to different devices on the Internet so that only the ones who are authorized to access these devices can extract the secret message. In this paper, we proposed a secret image sharing scheme based on a novel maze matrix. A pair of image shadows were produced by hiding secret data into two distinct cover images under the guidance of the maze matrix. A two-layered cheat detection mechanism was devised based on the special characteristics of the proposed maze matrix. In addition to the conventional joint cheating detection, the proposed scheme was able to identify the tampered shadow presented by a cheater without the information from other shadows. Furthermore, in order to improve time efficiency, we derived a pair of Lagrange polynomials to compute the exact pixel values of the shadow images instead of resorting to time-consuming and computationally expensive conventional searching strategies. Experimental results demonstrated the effectiveness and efficiency of the proposed secret sharing scheme and cheat detection mechanism.

An Efficient Data-Hiding Scheme Based on Multidimensional Mini-SuDoKu.

The massive Internet of Things (IoT) connecting various types of intelligent sensors for goods tracking in logistics, environmental monitoring and smart grid management is a crucial future ICT. High-end security and low power consumption are major requirements in scaling up the IoT. In this research, we propose an efficient data-hiding scheme to deal with the security problems and power saving issues of multimedia communication among IoT devises. Data hiding is the practice of hiding secret data into cover images in order to conceal and prevent secret data from being intercepted by malicious attackers. One of the established research streams of data-hiding methods is based on reference matrices (RM). In this study, we propose an efficient data-hiding scheme based on multidimensional mini-SuDoKu RM. The proposed RM possesses high complexity and can effectively improve the security of data hiding. In addition, this study also defines a range locator function which can significantly improve the embedding efficiency of multidimensional RM. Experimental results show that our data-hiding scheme can not only obtain better image quality, but also achieve higher embedding capacity than other related schemes.

Algebraic secret sharing using privacy homomorphisms for IoT-based healthcare systems.

Healthcare industry is one of the promising fields adopting the Internet of Things (IoT) solutions. In this paper, we study secret sharing mechanisms towards resolving privacy and security issues in IoT-based healthcare applications. In particular, we show how multiple sources are possible to share their data amongst a group of participants without revealing their own data to one another as well as the dealer. Only an authorised subset of participants is able to reconstruct the data. A collusion of fewer participants has no better chance of guessing the private data than a non-participant who has no shares at all. To realise this system, we introduce a novel research upon secret sharing in the encrypted domain. In modern healthcare industry, a patient's health Article often contains data acquired from various sensor nodes. In order to protect information privacy, the data from sensor nodes is encrypted at once and shared among a number of cloud servers of medical institutions via a gateway device. The complete health Article will be retrieved for diagnosis only if the number of presented shares meets the access policy. The retrieval procedure does not involve decryption and therefore the scheme is favourable in some time-sensitive circumstances such as a surgical emergency. We analyse the pros and cons of several possible solutions and develop practical secret sharing schemes for IoT- based healthcare systems.

Whole plastid transcriptomes reveal abundant RNA editing sites and differential editing status in Phalaenopsis aphrodite subsp. formosana.

BACKGROUND: RNA editing is a process of post-transcriptional level of gene regulation by nucleotide modification. Previously, the chloroplast DNA of Taiwan endemic moth orchid, P. aphrodite subsp. formosana was determined, and 44 RNA editing sites were identified from 24 plastid protein-coding transcripts of leaf tissue via RT-PCR and then conventional Sanger sequencing. However, the RNA editing status of whole-plastid transcripts in leaf and other distinct tissue types in moth orchids has not been addressed. To sensitively and extensively examine the plastid RNA editing status of moth orchid, RNA-Seq was used to investigate the editing status of whole-plastid transcripts from leaf and floral tissues by mapping the sequence reads to the corresponding cpDNA template. With the threshold of at least 5% C-to-U or U-to-C conversion events observed in sequence reads considered as RNA editing sites. RESULTS: In total, 137 edits with 126 C-to-U and 11 U-to-C conversions, including 93 newly discovered edits, were identified in plastid transcripts, representing an average of 0.09% of the nucleotides examined in moth orchid. Overall, 110 and 106 edits were present in leaf and floral tissues, respectively, with 79 edits in common. As well, 79 edits were involved in protein-coding transcripts, and the 58 nucleotide conversions caused the non-synonymous substitution. At least 32 edits showed significant (≧20%) differential editing between leaf and floral tissues. Finally, RNA editing in trnM is required for the formation of a standard clover-leaf structure. CONCLUSIONS: We identified 137 edits in plastid transcripts of moth orchid, the highest number reported so far in monocots. The consequence of RNA editing in protein-coding transcripts mainly cause the amino acid change and tend to increase the hydrophobicity as well as conservation among plant phylogeny. RNA editing occurred in non-protein-coding transcripts such as tRNA, introns and untranslated regulatory regions could affect the formation and stability of secondary structure, which might play an important role in the regulation of gene expression. Furthermore, some unidentified tissue-specific factors might be required for regulating RNA editing in moth orchid.

The effects of fluorine-contained molecules on improving the polymer solar cell by curing the anomalous S-shaped I-V curve.

In this study, we investigate the effects of fluorinated poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate) buffer layer on the performance of polymer photovoltaic cells. We demonstrate for the first time, the deterioration of the device performance can be effectively mended by modifying the interface between the active layer and buffer layer with heptadecafluoro-1,1,2,2-tetra-hydro-decyl trimethoxysilane (PFDS) and perfluorononane. Device performance shows a substantial enhancement of short-circuit current from 7.90 to 9.39 mA/cm(2) and fill factor from 27% to 53%. The overall device efficiency was improved from 0.98% to 3.12% for PFDS modified device. The mechanism of S-shape curing is also discussed. In addition, the stability of modified devices shows significant improvement than those without modification. The efficiency of the modified devices retains about half (1.88%) of its initial efficiency (4.1%) after 30 d compared to the unmodified ones (0.61%), under air atmosphere.

The blue fluorescent protein from Vibrio vulnificus CKM-1 is a useful reporter for plant research.

BACKGROUND: The mBFP is an improved variant of NADPH-dependent blue fluorescent protein that was originally identified from the non-bioluminescent pathogenic bacteria Vibrio vulnificus CKM-1. To explore the application of mBFP in plants, the mBFP gene expression was driven by one of the three promoters, namely, leaf-specific (RbcS), hypoxia-inducible (Adh) or auxin-inducible (DR5) promoters, in different plant tissues such as leaves, roots and flowers under diverse treatments. In addition, the expressed mBFP protein was targeted to five subcellular compartments such as cytosol, endoplasmic reticulum, apoplast, chloroplast and mitochondria, respectively, in plant cells. RESULTS: When the mBFP was transiently expressed in the tobacco leaves and floral tissues of moth orchid, the cytosol and apoplast exhibited brighter blue fluorescence than other compartments. The recombinant mBFP-mS1C fusion protein exhibited enhanced fluorescence intensity that was correlated with more abundant RNA transcripts (1.8 fold) as compared with a control. In the root tips of horizontally grown transgenic Arabidopsis, mBFP could be induced as a reporter under hypoxia condition. Furthermore, the mBFP was localized to the expected subcellular compartments, except that dual targeting was found when the mBFP was fused with the mitochondria-targeting signal peptide. Additionally, the brightness of mBFP blue fluorescence was correlated with NADPH concentration. CONCLUSION: The NADPH-dependent blue fluorescent protein could serve as a useful reporter in plants under aerobic or hypoxic condition. However, to avoid masking the mitochondrial targeting signal, fusing mBFP as a fusion tag in the C-terminal will be better when the mBFP is applied in mitochondria trafficking study. Furthermore, mBFP might have the potential to be further adopted as a NADPH biosensor in plant cells. Future codon optimization of mBFP for plants could significantly enhance its brightness and expand its potential applications.