Fungal Secretomics Through iTRAQ-Based Analysis.

Our understanding of how fungi respond and adapt to external environments can be increased by the comprehensive data sets of fungal-secreted proteins. Fungi produce a variety of secreted proteins, and environmental conditions can easily influence the fungal secretome. However, the low abundance of secreted proteins and their post-translational modifications make protein extraction more challenging. Hence, the enrichment of secreted proteins is a crucial procedure for secretome analysis. This chapter illustrates a protocol for iTRAQ-based quantitative secretome analysis describing the example of fungi exposed to different environmental conditions. The fungal-secreted proteins can be extracted by combining ultrafiltration and TCA-acetone precipitation. Subsequently, the secreted proteins can be identified and quantified by the iTRAQ-based quantitative proteomics approach.

Maleic acid and malonic acid reduced the pathogenicity of Sclerotinia sclerotiorum by inhibiting mycelial growth, sclerotia formation and virulence factors.

Sclerotinia sclerotiorum is a necrotrophic plant pathogenic fungus with broad distribution and host range. Bioactive compounds derived from plant extracts have been proven to be effective in controlling S. sclerotiorum. In this study, the mycelial growth of S. sclerotiorum was effectively inhibited by maleic acid, malonic acid, and their combination at a concentration of 2 mg/mL, with respective inhibition rates of 32.5%, 9.98%, and 67.6%. The treatment of detached leaves with the two acids resulted in a decrease in lesion diameters. Interestingly, maleic acid and malonic acid decreased the number of sclerotia while simultaneously increasing their weight. The two acids also disrupted the cell structure of sclerotia, leading to sheet-like electron-thin regions. On a molecular level, maleic acid reduced oxalic acid secretion, upregulated the expression of Ss-Odc2 and downregulated CWDE10, Ss-Bi1 and Ss-Ggt1. Differently, malonic acid downregulated CWDE2 and Ss-Odc1. These findings verified that maleic acid and malonic acid could effectively inhibit S. sclerotiorum, providing promising evidence for the development of an environmentally friendly biocontrol agent.

IMA peptides function in iron homeostasis and cadmium resistance.

Iron (Fe), an essential micronutrient, participates in photosynthesis, respiration, and many other enzymatic reactions. Cadmium (Cd), by contrast, is a toxic element to virtually all living organisms. Both Fe deficiency and Cd toxicity severally impair crop growth and productivity, finally leading to human health issues. Understanding how plants control the uptake and homeostasis of Fe and combat Cd toxicity thus is mandatory to develop Fe-enriched but Cd-cleaned germplasms for human beings. Recent studies in Arabidopsis and rice have revealed that IRON MAN (IMA) peptides stand out as a key regulator to respond to Fe deficiency by competitively interacting with a ubiquitin E3 ligase, thus inhibiting the degradation of IVc subgroup bHLH transcription factors (TFs), mediated by 26 S proteasome. Elevated expression of IMA confers tolerance to Cd stress in both Arabidopsis and wheat by activating the iron deficiency response. Here, we discuss recent breakthroughs that IMA peptides function in the Fe-deficiency response to attain Fe homeostasis and combat Cd toxicity as a potential candidate for phytoremediation.

Modulation of ZnO Nanostructure for Efficient Photocatalytic Performance.

Structure has been considered to play an important role in photocatalytic performance of the semiconductors, but the intrinsic factors were rarely revealed. Herein, ZnO nanomaterials in the structures of thin film, nanowire array and nanosheet array were synthesized, and their structural characteristics, optical properties, photocurrent response and photocatalytic efficiency were compared with each other for illustrating the issue. The photoluminescence intensity decreased in the order of nanosheets, thin film and nanowires for improved lifetime of the photoexcited charges. The absorption of the nanosheets and nanowires improved obviously in the visible range with a redshift of the absorption edge than that of the thin film. The nanowires possessed the highest response current of 82.65 µA at a response time of 2.0 ms in a sensitivity of 87.93 at the light frequency of 1 Hz, and gained the largest catalytic efficiency of 2.45 µg/cm2 h for the methylene blue degradation in UV light. Nevertheless, the improvement of catalytic efficiency of the nanosheets (up to 42.4%) was much larger than that of nanowires (5.7%) and thin film (2.6%) for the Au coating. The analysis revealed that the photocatalytic efficiency of the ZnO nanomaterials was modulated by the structure as it contained different surface area, roughness, defect and doping states, vacancies, polar and non-polar crystalline faces, which would provide structural design of semiconductor nanomaterials for the photoelectric and photocatalytic applications.

Band alignment of ZnO-based nanorod arrays for enhanced visible light photocatalytic performance.

A ternary semiconductor ZnO/MoS2/Ag2S nanorod array in an intimate core-shell structure was synthesized on glass substrates. The physicochemical properties and photocatalytical performance of the specimen were characterized and compared with single ZnO and binary ZnO/Ag2S and ZnO/MoS2 nanorod arrays. It is found that the coating layers depressed the band edge emission of the ZnO core, improved light absorption in the visible range, reduced charge transfer resistance, and increased photocatalytic activity. The ternary heterojunction nanorod array possessed full solar absorption with an efficiency of 52.88% for the degradation of methylene blue under visible light in 30 min. The efficiency was higher than other arrays and was 7.6 times that of the ZnO array. Theory analysis revealed that the coating layer brought different band alignment in the heterojunctions for efficient charge separation and conduction, which was beneficial for the photocatalytic performance.

Physicochemical, structural and nutritional properties of steamed bread fortified with red beetroot powder and their changes during breadmaking process.

Beetroot (Beta vulgaris) is a source of diverse nutrients such as dietary fibers and betalains. Chinese steamed bread (CSB) has gained popularity in recent years. Red beetroot powder (RBP) was added (up to 70%) in wheat flour to make nutritionally fortified CSB. RBP addition greatly decreased specific volume (1.39 to 0.53 mL/g) and staling rate (4.14 to 2.59%), while increasing hardness (2882 to 15056 g) and chewiness (1923 to 3174 g) since RBP affected gluten secondary structure and weakened dough strength. More importantly, CSB containing RBP exhibited improved in vitro antioxidant potential and reduced estimated glycemic index (70.8 to 60.7). The betalains were largely degraded during steaming due to the isomerization of betanin to isobetanin. Sensory analysis showed that wheat flour could be substituted by RBP up to 10% without compromising the eating quality of the CSB. The findings indicated the feasibility of formulating beetroot-fortified foods with enhanced nutritional quality.

Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis.

Positron emission tomography (PET) is a typical nuclear imaging technique, which can provide crucial functional information for early brain disease diagnosis. Generally, clinically acceptable PET images are obtained by injecting a standard-dose radioactive tracer into human body, while on the other hand the cumulative radiation exposure inevitably raises concerns about potential health risks. However, reducing the tracer dose will increase the noise and artifacts of the reconstructed PET image. For the purpose of acquiring high-quality PET images while reducing radiation exposure, in this paper, we innovatively present an adaptive rectification based generative adversarial network with spectrum constraint, named AR-GAN, which uses low-dose PET (LPET) image to synthesize standard-dose PET (SPET) image of high-quality. Specifically, considering the existing differences between the synthesized SPET image by traditional GAN and the real SPET image, an adaptive rectification network (AR-Net) is devised to estimate the residual between the preliminarily predicted image and the real SPET image, based on the hypothesis that a more realistic rectified image can be obtained by incorporating both the residual and the preliminarily predicted PET image. Moreover, to address the issue of high-frequency distortions in the output image, we employ a spectral regularization term in the training optimization objective to constrain the consistency of the synthesized image and the real image in the frequency domain, which further preserves the high-frequency detailed information and improves synthesis performance. Validations on both the phantom dataset and the clinical dataset show that the proposed AR-GAN can estimate SPET images from LPET images effectively and outperform other state-of-the-art image synthesis approaches.

Microbes: a potential tool for selenium biofortification.

Selenium (Se) is a component of many enzymes and indispensable for human health due to its characteristics of reducing oxidative stress and enhancing immunity. Human beings take Se mainly from Se-containing crops. Taking measures to biofortify crops with Se may lead to improved public health. Se accumulation in plants mainly depends on the content and bioavailability of Se in soil. Beneficial microbes may change the chemical form and bioavailability of Se. This review highlights the potential role of microbes in promoting Se uptake and accumulation in crops and the related mechanisms. The potential approaches of microbial enhancement of Se biofortification can be summarized in the following four aspects: (1) microbes alter soil properties and impact the redox chemistry of Se to improve the bioavailability of Se in soil; (2) beneficial microbes regulate root morphology and stimulate the development of plants through the release of certain secretions, facilitating Se uptake in plants; (3) microbes upregulate the expression of certain genes and proteins that are related to Se metabolism in plants; and (4) the inoculation of microbes give rise to the generation of certain metabolites in plants contributing to Se absorption. Considering the ecological safety and economic feasibility, microbial enhancement is a potential tool for Se biofortification. For further study, the recombination and establishment of synthesis microbes is of potential benefit in Se-enrichment agriculture.

Recent advances and perspectives in photo-induced enhanced Raman spectroscopy.

Phototreatment is at the leading edge of a research hot topic as a driving force for structural transformation, spectral and electromagnetism improvements, and the functional performance of nanomaterials. Light irradiation can excite surface plasmons in noble metal nanoparticles, create electron-hole pairs, and produce charge transfer in semiconductor substrates, which have led to it being widely used in surface-enhanced Raman spectroscopy (SERS) for life sciences, environmental protection, and biological analysis. Photo-induced enhanced Raman spectroscopy (PIERS) is a new technology developed on the basis of traditional SERS and has proven to be an efficient way to resolve several critical challenges thanks to its incomparable superiority for incontiguous operation, efficient charge separation and enrichment, and a large signal enhancement for a wide range of biomolecules at the trace level. This makes PIERS a powerful technique with very appealing and promising applications in various branches of analytical science. In this review, the enhancement mechanisms of PIERS are analyzed in comparison with SERS. Afterward, the parameters influencing the enhancement of PIERS, including the substrate, light irradiation, and relaxation are discussed in detail. Finally, some perspectives on further developments of PIERS are exemplified. The PIERS technique will continue to evolve and grow with new developments and its successful application in bioanalysis and life sciences.

Deep learning-based multi-modal computing with feature disentanglement for MRI image synthesis.

PURPOSE: Different Magnetic resonance imaging (MRI) modalities of the same anatomical structure are required to present different pathological information from the physical level for diagnostic needs. However, it is often difficult to obtain full-sequence MRI images of patients owing to limitations such as time consumption and high cost. The purpose of this work is to develop an algorithm for target MRI sequences prediction with high accuracy, and provide more information for clinical diagnosis. METHODS: We propose a deep learning-based multi-modal computing model for MRI synthesis with feature disentanglement strategy. To take full advantage of the complementary information provided by different modalities, multi-modal MRI sequences are utilized as input. Notably, the proposed approach decomposes each input modality into modality-invariant space with shared information and modality-specific space with specific information, so that features are extracted separately to effectively process the input data. Subsequently, both of them are fused through the adaptive instance normalization (AdaIN) layer in the decoder. In addition, to address the lack of specific information of the target modality in the test phase, a local adaptive fusion (LAF) module is adopted to generate a modality-like pseudo-target with specific information similar to the ground truth. RESULTS: To evaluate the synthesis performance, we verify our method on the BRATS2015 dataset of 164 subjects. The experimental results demonstrate our approach significantly outperforms the benchmark method and other state-of-the-art medical image synthesis methods in both quantitative and qualitative measures. Compared with the pix2pixGANs method, the PSNR improves from 23.68 to 24.8. Moreover the ablation studies have also verified the effectiveness of important components of the proposed method. CONCLUSION: The proposed method could be effective in prediction of target MRI sequences, and useful for clinical diagnosis and treatment.

MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation.

BACKGROUND: An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task. OBJECTIVES: The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility. METHODS: In this paper, we propose a novel feature selection algorithm for the identification of the margin of NPC image, named as modified random forest recursive feature selection (MRF-RFS). Specifically, to obtain a more discriminative feature subset for segmentation, a modified recursive feature selection method is applied to the original handcrafted feature set. Moreover, we combine the proposed feature selection method with the classical random forest (RF) in the training stage to take full advantage of its intrinsic property (i.e., feature importance measure). RESULTS: To evaluate the segmentation performance, we verify our method on the T1-weighted MRI images of 18 NPC patients. The experimental results demonstrate that the proposed MRF-RFS method outperforms the baseline methods and deep learning methods on the task of segmenting NPC images. CONCLUSION: The proposed method could be effective in NPC diagnosis and useful for guiding radiation therapy.