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The growing demands of sustainable, efficient, and welfare-conscious pig husbandry have necessitated the adoption of advanced technologies. Among these, RGB imaging and machine vision technology may offer a promising solution for early disease detection and proactive disease management in advanced pig husbandry practices. This review explores innovative applications for monitoring disease symptoms by assessing features that directly or indirectly indicate disease risk, as well as for tracking body weight and overall health. Machine vision and image processing algorithms enable for the real-time detection of subtle changes in pig appearance and behavior that may signify potential health issues. Key indicators include skin lesions, inflammation, ocular and nasal discharge, and deviations in posture and gait, each of which can be detected non-invasively using RGB cameras. Moreover, when integrated with thermal imaging, RGB systems can detect fever, a reliable indicator of infection, while behavioral monitoring systems can track abnormal posture, reduced activity, and altered feeding and drinking habits, which are often precursors to illness. The technology also facilitates the analysis of respiratory symptoms, such as coughing or sneezing (enabling early identification of respiratory diseases, one of the most significant challenges in pig farming), and the assessment of fecal consistency and color (providing valuable insights into digestive health). Early detection of disease or poor health supports proactive interventions, reducing mortality and improving treatment outcomes. Beyond direct symptom monitoring, RGB imaging and machine vision can indirectly assess disease risk by monitoring body weight, feeding behavior, and environmental factors such as overcrowding and temperature. However, further research is needed to refine the accuracy and robustness of algorithms in diverse farming environments. Ultimately, integrating RGB-based machine vision into existing farm management systems could provide continuous, automated surveillance, generating real-time alerts and actionable insights; these can support data-driven disease prevention strategies, reducing the need for mass medication and the development of antimicrobial resistance.

The rice small protein OsS1Fa1, a homolog of spinach S1Fa, plays a significant role in drought tolerance, attributed to its transmembrane domain. In this study, we aim to further elucidate the potential roles of OsS1Fa1 in cold and biotic stresses as an inner nuclear membrane protein. Fluorescence analysis confirmed the localization of OsS1Fa1 to the inner nuclear membrane. Utilizing the bimolecular fluorescence complementation (BiFC) and bacterial infiltration assays with OsS1Fa1 and the inner nuclear membrane protein OsSUN1 (Rice Sad1 and UNC84 (SUN) domain containing 1 (SUN1)), we observed fluorescence detection within the inner nuclear membrane, indicating a direct interaction and colocalization between OsS1Fa1 and OsSUN1. Expression analysis revealed that overexpression of OsS1Fa1 induced the expression of various genes associated with cold and defense responses, including COLD-REGULATED 15A (COR15A), PATHOGENESIS-RELATED PROTEIN 1 (PR1), and PLANT DEFENSIN 1.2 (PDF1.2). Our findings collectively indicate that OsS1Fa1 plays crucial roles in both abiotic and biotic stress tolerance as an inner nuclear membrane protein.

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system.

The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r2) of 0.98, a confidence interval (CI) of −0.14 to −0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m3 and 14.09 ± 2.10 m3, respectively, with an MAE of 0.57 m3, an RMSE of 0.61 m3, an r2 value of 0.97, and a CI of −0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r2 values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and −0.18 for tree spacing and 0.01, −0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards.

Stress by unfavorable environmental conditions, including temperature, light intensity, and photoperiod, significantly impact early-stage growth in crops, such as cucumber seedlings, often resulting in yield reduction and quality degradation. Advanced machine learning (ML) models combined with image-based analysis offer promising solutions for precise, non-invasive stress monitoring. This study aims to classify environmental stress symptom levels in cucumber seedlings using ML models by extracting critical color, texture, and morphological features from RGB images. In a controlled plant factory setup, two-week-old cucumber seedlings were subjected to varied environmental conditions across five chambers with differing temperatures (15, 20, 25, and 30 °C), light intensities (50, 250, and 450 µmol m−2 s−1), and day-night cycles (8/16, 10/14, and 16/8 h). A cost-effective RGB camera, integrated with a microcontroller, captured images from the top of the seedlings over a two-week period, from which sequential forward floating selection (SFFS) and correlation matrices were used to streamline feature extraction. Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. KNN achieved the highest accuracy at 0.94 (94%), followed closely by SVM and RF, both at 93%, while NB reached 88%. Findings suggested that color and texture features were critical indicators of stress, and that the KNN model, with optimized hyperparameters, provided a reliable classification for stress symptom monitoring for seedlings under controlled environments. This study highlights the potential of ML-driven stress symptom detection models for controlled seedling production, enabling real-time decision-making to optimize crop health and productivity.

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R2 up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity.

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture.

Real-time monitoring and management of lettuce plants, especially during the seedling growth stage, is important in order to protect them from various environmental stresses and to prevent yield loss. Various and similar symptoms may occur as a result of different unfavorable environmental stresses. Visual inspection alone may result in an incorrect and late diagnosis, obstructing subsequent corrective action for the damaged seedlings in an early stage. The purposes of this study were to detect and segment lettuce seedlings from the background of seedling-growing trays using an improved mask R-CNN and to estimate seedling size based on the output of the proposed technique. Lettuce seedling images were taken from a plant factory using an automatic image acquisition tool. The seedlings in the images were 1 to 3 weeks old. An annotation of 1,000 lettuce seedling image datasets was prepared using an online annotation tool. An improved mask R-CNN was implemented using ResNet-101 with CB-Net, which provided enhanced image feature extraction. Transfer learning was used to train the model network with a smaller dataset and reduce the processing time. Bounding boxes and annotations were set as inputs for the training model. Training and test datasets were prepared for the model evaluation. The sizes of the seedlings were determined by applying a binary mask to the output-masked seedling images. The total number of pixels was used to determine the seedling size. The training loss of the selected method was less than 0.20 %. The identification of lettuce seedlings from 150 randomly chosen test photos revealed that the best-fit image had a performance F1 score of 93 %, corresponding to 92 % precision and 95 % recall. A strong linear relationship (R2>0.99) between manual and model-estimated leaf area confirmed the proposed model accuracy throughout the lettuce growth stages. The improved mask R-CNN could detect lettuce seedlings in the tray image background and also extract the leaf area of identified lettuce seedlings.

Environmental factors such as temperature, humidity, light, and CO2 influence plant growth, and unfavorable environmental conditions cause stress in plants, producing symptoms in their early growth stages. The increasing importance of optimizing crop management strategies has led to a rising demand for the precise evaluation of stress symptoms during early plant growth. Advanced technologies are transforming plant health monitoring through enabling image-based stress analysis. Machine learning (ML) models can effectively identify the important features and morphological changes connected with various stress conditions through the use of large datasets acquired from high-resolution plant images. Therefore, the objective of this study was to develop a method for classifying the early-stage stress symptoms of pepper seedlings and enabling their identification and quantification using image processing and a support vector machine (SVM). Two-week-old pepper seedlings were grown under different temperatures (20, 25, and 30 °C), light intensity levels (50, 250, and 450 µmol m−2s−1), and day–night hours (8/16, 10/14, and 16/8) in five controlled plant growth chambers. Images of the seedling canopies were captured daily using a low-cost red, green, and blue (RGB) camera over a two-week period. Eighteen color features, nine texture features using the gray-level co-occurrence matrix (GLCM), and one morphological feature were extracted from each image. A two-way ANOVA and multiple mean comparison (Duncan) analysis were used to determine the statistical significance of the treatment effects. To reduce feature overlap, sequential feature selection (SFS) was applied, and a support vector machine (SVM) was used for stress classification. The SFS method was used to identify the optimal features for the classification model, leading to substantial increases in stress classification accuracy. The SVM model, using these selected features, achieved a classification accuracy of 82% without the SFS and 86% with the SFS. To address overfitting, 5- and 10-fold cross-validation were used, resulting in MAEs of 0.138 and 0.163 for the polynomial kernel, respectively. The SVM model, evaluated with the ROC curve and confusion matrix, achieved a classification accuracy of 85%. This classification approach enables real-time stress monitoring, allowing growers to optimize environmental conditions and enhance seedling growth. Future directions include integrating this system into automated cultivation environments to enable continuous, efficient stress monitoring and response, further improving crop management and productivity.

ABSTRACTBacterial enhancer-binding proteins (bEBPs) acquire a transcriptionally active state via phosphorylation. However, transcriptional activation by the dephosphorylated form of bEBP has been observed in DctD, which belongs to Group I bEBP. The formation of a complex between dephosphorylated DctD (d-DctD) and dephosphorylated IIAGlc (d-IIAGlc) is a prerequisite for the transcriptional activity of d-DctD. In the present study, characteristics of the transcriptionally active complex composed of d-IIAGlc and phosphorylation-deficient DctD (DctDD57Q) of Vibrio vulnificus were investigated in its multimeric conformation and DNA-binding ability. DctDD57Q formed a homodimer that could not bind to the DNA. In contrast, when DctDD57Q formed a complex with d-IIAGlc in a 1:1 molar ratio, it produced two conformations: dimer and dodecamer of the complex. Only the dodecameric complex exhibited ATP-hydrolyzing activity and DNA-binding affinity. For successful DNA-binding and transcriptional activation by the dodecameric d-IIAGlc/DctDD57Q complex, extended upstream activator sequences were required, which encompass the nucleotide sequences homologous to the known DctD-binding site and additional nucleotides downstream. This is the first report to demonstrate the molecular characteristics of a dephosphorylated bEBP complexed with another protein to form a transcriptionally active dodecameric complex, which has an affinity for a specific DNA-binding sequence.IMPORTANCEResponse regulators belonging to the bacterial two-component regulatory system activate the transcription initiation of their regulons when they are phosphorylated by cognate sensor kinases and oligomerized to the appropriate multimeric states. Recently, it has been shown that a dephosphorylated response regulator, DctD, could activate transcription in a phosphorylation-independent manner in Vibrio vulnificus. The dephosphorylated DctD activated transcription as efficiently as phosphorylated DctD when it formed a complex with dephosphorylated form of IIAGlc, a component of the glucose-phosphotransferase system. Functional mimicry of this complex with the typical form of transcriptionally active phosphorylated DctD led us to study the molecular characteristics of this heterodimeric complex. Through systematic analyses, it was surprisingly determined that a multimer constituted with 12 complexes gained the ability to hydrolyze ATP and recognize specific upstream activator sequences containing a typical inverted-repeat sequence flanked by distinct nucleotides.

Spiking neural networks (SNNs) have emerged as a novel approach for reducing computational costs by mimicking the biologically plausible operations of neurons and synapses. In this article, large‐scale analog SNNs are investigated and optimized at the hardware‐level by using SNNSim, the novel simulator for SNNs that employ analog synaptic devices and integrate‐and‐fire (I&F) neuron circuits. SNNSim is a reconfigurable simulator that accurately and very quickly models the behavior of the user‐defined device characteristics and returns key metrics such as area, accuracy, latency, and power consumption as output. Notably, SNNSim exhibits exceptional efficiency, as it can process the entire 10 000 Modified National Institute of Standards and Technology (MNIST) test dataset in a few seconds, whereas SPICE simulations require hours to simulate a single MNIST test data. Using SNNSim, the conversion of artificial neural networks (ANNs) to SNNs is simulated and the performance of the large‐scale analog SNNs is optimized. The results enable the design of accurate, high‐speed, and low‐power operation of large‐scale SNNs. SNNSim code is now available at https://github.com/SMDLGITHUB/SNNSim.

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm−1, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm−1. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.

Mammalian cardiomyocytes have limited regenerative ability. Cardiac disease, such as congenital heart disease and myocardial infarction, causes an initial loss of cardiomyocytes through regulated cell death (RCD). Understanding the mechanisms that govern RCD in the injured myocardium is crucial for developing therapeutics to promote heart regeneration. We previously reported that ferroptosis, a non-apoptotic and iron-dependent form of RCD, is the main contributor to cardiomyocyte death in the injured heart. To investigate the mechanisms underlying the preference for ferroptosis in cardiomyocytes, we examined the effects of anti-ferroptotic reagents in infarcted mouse hearts. The results revealed that the anti-ferroptotic reagent did not improve neonatal heart regeneration, and further compromised the cardiac function of juvenile hearts. On the other hand, ferroptotic cardiomyocytes played a supportive role during wound healing by releasing pro-angiogenic factors. The inhibition of ferroptosis in the regenerating mouse heart altered the immune and angiogenic responses. Our study provides insights into the preference for ferroptosis over other types of RCD in stressed cardiomyocytes, and guidance for designing anti-cell-death therapies for treating heart disease.

Herein, dual‐gate field‐effect transistors (DG FETs) fabricated on Si substrate and a corresponding NOR‐type array designed for low‐power on‐chip trainable hardware neural networks (HNNs) are presented. The fabricated DG FET exhibits notable endurance characteristics, with the subthreshold swing remaining consistently within a 2.45% range of change and ΔV th per cycle maintaining stability at 4.5% over repetitive program and erase operations, up to 104 cycles. Furthermore, a multilevel characteristic is achieved through low‐power program/erase operations based on Fowler–Nordheim (FN) tunneling, which exhibit 0.09 and 0.99 fJ per spike, respectively. These characteristics provide the HNN stability, along with high performance and power efficiency. The NOR‐type array in this work demonstrates selective update and bidirectional vector‐by‐matrix multiplication capabilities. This enables on‐chip training based on a gradient descent algorithm, without the need for an additional array for backpropagation. Subsequently, a simulation of the Modified National Institute of Standards and Technology classification is conducted to evaluate the accuracy and training power consumption of the proposed device in comparison to other two‐terminal memristor devices. The results show that the DG FET array achieves superior accuracy while maintaining over 180.4 times higher energy efficiency, demonstrating the potential of the DG FET as a promising candidate for low‐power HNN applications.

ABSTRACT The KEOPS (kinase, putative endopeptidase, and other proteins of small size) complex has critical functions in eukaryotes; however, its role in fungal pathogens remains elusive. Herein, we comprehensively analyzed the pathobiological functions of the fungal KEOPS complex in Cryptococcus neoformans (Cn), which causes fatal meningoencephalitis in humans. We identified four CnKEOPS components: Pcc1, Kae1, Bud32, and Cgi121. Deletion of PCC1, KAE1, or BUD32 caused severe defects in vegetative growth, cell cycle control, sexual development, general stress responses, and virulence factor production, whereas deletion of CGI121 led to similar but less severe defects. This suggests that Pcc1, Kae1, and Bud32 are the core KEOPS components, and Cgi121 may play auxiliary roles. Nevertheless, all KEOPS components were essential for C. neoformans pathogenicity. Although the CnKEOPS complex appeared to have a conserved linear arrangement of Pcc1-Kae1-Bud32-Cgi121, as supported by physical interaction between Pcc1-Kae1 and Kae1-Bud32, CnBud32 was found to have a unique extended loop region that was critical for the KEOPS functions. Interestingly, CnBud32 exhibited both kinase activity-dependent and -independent functions. Supporting its pleiotropic roles, the CnKEOPS complex not only played conserved roles in t6A modification of ANN codon-recognizing tRNAs but also acted as a major transcriptional regulator, thus controlling hundreds of genes involved in various cellular processes, particularly ergosterol biosynthesis. In conclusion, the KEOPS complex plays both evolutionarily conserved and divergent roles in controlling the pathobiological features of C. neoformans and could be an anticryptococcal drug target. IMPORTANCE The cellular function and structural configuration of the KEOPS complex have been elucidated in some eukaryotes and archaea but have never been fully characterized in fungal pathogens. Here, we comprehensively analyzed the pathobiological roles of the KEOPS complex in the globally prevalent fungal meningitis-causing pathogen C. neoformans. The CnKEOPS complex, composed of a linear arrangement of Pcc1-Kae1-Bud32-Cgi121, not only played evolutionarily conserved roles in growth, sexual development, stress responses, and tRNA modification but also had unique roles in controlling virulence factor production and pathogenicity. Notably, a unique extended loop structure in CnBud32 is critical for the KEOPS complex in C. neoformans. Supporting its pleiotropic roles, transcriptome analysis revealed that the CnKEOPS complex governs several hundreds of genes involved in carbon and amino acid metabolism, pheromone response, and ergosterol biosynthesis. Therefore, this study provides novel insights into the fungal KEOPS complex that could be exploited as a potential antifungal drug target.

Abstract Background A foodborne pathogen, Vibrio vulnificus, encounters normal microflora inhabiting the gut environments prior to causing fatal septicemia or gastroenteritis and should overcome the barriers derived from the gut commensals for successful infection. Its interactions with gut commensals during the infection process, however, have not yet been understood. In the present study, the effect of V. vulnificus on the community structures of gut microbiota in mice was examined. Results Analyses of microbiota in the fecal samples of mice that died due to V. vulnificus infection revealed the decreased abundance of bacteria belonged to Bacteroidetes, notably, the species Bacteroides vulgatus. In vitro coculturing of the two bacterial species resulted in the decreased survival of B. vulgatus. The antagonistic effect of V. vulnificus against B. vulgatus was found to be mediated by cyclo-Phe-Pro (cFP), one of the major compounds secreted by V. vulnificus. cFP-treated B. vulgatus showed collapsed cellular morphology with an undulated cell surface, enlarged periplasmic space, and lysed membranes, suggesting the occurrence of membrane disruption. The degree of membrane disruption caused by cFP was dependent upon the cellular levels of ObgE in B. vulgatus. Recombinant ObgE exhibited a high affinity to cFP at a 1:1 ratio. When mice were orally injected with cFP, their feces contained significantly reduced B. vulgatus levels, and their susceptibility to V. vulnificus infection was considerably increased. Conclusions This study demonstrates that V. vulnificus-derived cFP modulates the abundance of the predominant species among gut commensals, which made V. vulnificus increase its pathogenicity in the hosts. Video abstract

Abstract Biofilm formation of Vibrio vulnificus is initiated by adherence of flagellated cells to surfaces, and then flagellum-driven motility is not necessary during biofilm maturation. Once matured biofilms are constructed, cells become flagellated and swim to disperse from biofilms. As a consequence, timely regulations of the flagellar components’ expression are crucial to complete a biofilm life-cycle. In this study, we demonstrated that flagellins’ production is regulated in a biofilm stage-specific manner, via activities of a protease DegQ and a chaperone FlaJ. Among four flagellin subunits for V. vulnificus filament, FlaC had the highest affinities to hook-associated proteins, and is critical for maturating flagellum, showed the least susceptibility to DegQ due to the presence of methionine residues in its DegQ-sensitive domains, ND1 and CD0. Therefore, differential regulation by DegQ and FlaJ controls the cytoplasmic stability of flagellins, which further determines the motility-dependent, stage-specific development of biofilms.

ABSTRACT Exopolysaccharides (EPSs), biofilm-maturing components of Vibrio vulnificus, are abundantly produced when the expression of two major EPS gene clusters is activated by an enhancer-binding transcription factor, DctD2, whose expression and phosphorylation are induced by dicarboxylic acids. Surprisingly, when glucose was supplied to V. vulnificus, similar levels of expression of these clusters occurred, even in the absence of dicarboxylic acids. This glucose-dependent activation was also mediated by DctD2, whose expression was sequentially activated by the transcription regulator NtrC. Most DctD2 in cells grown without dicarboxylic acids was present in a dephosphorylated state, known as the transcriptionally inactive form. However, in the presence of glucose, a dephosphorylated component of the glucose-specific phosphotransferase system, d-IIAGlc, interacted with dephosphorylated DctD2 (d-DctD2). While d-DctD2 did not show any affinity to a DNA fragment containing the DctD-binding sequences, the complex of d-DctD2 and d-IIAGlc exhibited specific and efficient DNA binding, similar to the phosphorylated DctD2. The d-DctD2-mediated activation of the EPS gene clusters’ expression was not fully achieved in cells grown with mannose. Furthermore, the degrees of expression of the clusters under glycerol were less than those under mannose. This was caused by an antagonistic and competitive effect of GlpK, whose expression was increased by glycerol, in forming a complex with d-DctD2 by d-IIAGlc. The data demonstrate a novel regulatory pathway for V. vulnificus EPS biosynthesis and biofilm maturation in the presence of glucose, which is mediated by d-DctD2 through its transition to the transcriptionally active state by interacting with available d-IIAGlc. IMPORTANCE Transcription regulation by bacterial two-component systems is achieved by a response regulator upon its transition to the transcriptionally active form via kinase activity of its cognate sensor under specific conditions. A well-known response regulator, DctD, is converted to its phosphorylated form when DctB senses ambient dicarboxylic acids. Phospho-DctD induces expression of its regulon, including the gene clusters for biosynthesis of exopolysaccharides (EPSs), the essential constituents of biofilm matrix. In the absence of dicarboxylic acids, however, DctD-mediated induction of these EPS gene clusters and biofilm maturation was observed if glucose was supplied. This suggests that dephospho-DctD could play a role in activating the transcription of target genes. A component of glucose-phosphotransferase system, IIAGlc, was present in a dephosphorylated state in the presence of glucose. Dephospho-DctD formed a complex with dephospho-IIAGlc and was converted to a transcriptionally active state. These findings suggest the other response regulators could also have alternative pathways of activation independent of phosphorylation.

Rain removal in videos is a problem that has attracted tremendous interest of researchers within the field of deep learning. Although deep-learning-based rain removal methods outperform large number of conventional vision methods, some technical issues that need to be resolved remain. In this article, we propose a new deep learning method for video rain removal based on recurrent neural network (RNN) architecture. Pseudo groundtruth was generated from real rainy video sequence by temporal filtering for supervised learning. Instead of focusing on various shapes of rain streaks similar to conventional methods, in this article, we focused on the changing behaviors of rain streaks. To accomplish this, images of progressive rain streaks were generated from the real rain videos and are sequentially fed to the network in a decreasing rain order. Multiple images with different amounts of rain streaks were used as RNN inputs to more efficiently identify rain streaks and then remove them. Experimental results demonstrate that our method is suitable for a wide range of rainy images. Moreover, experiments performed on both real-world and synthetic images demonstrate that our proposed method can achieve competitive results in comparison with the benchmarked and conventional approaches for rain streak removal from images.

Abstract LeuO plays the role of a master regulator in the cyclic-L-phenylalanine-L-proline (cFP)-dependent signaling pathway in Vibrio vulnificus. cFP, as shown through isothermal titration calorimetry analysis, binds specifically to the periplasmic domain of ToxR. Binding of cFP triggers a change in the cytoplasmic domain of ToxR, which then activates transcription of leuO encoding a LysR-type regulator. LeuO binds to the region upstream of its own coding sequence, inhibiting its own transcription and maintaining a controlled level of expression. A five-bp deletion in this region abolished expression of LeuO, but a ten-bp deletion did not, suggesting that a DNA bending mechanism is involved in the regulation. Furthermore, binding of RNA polymerase was significantly lower both in the deletion of the ToxR binding site and in the five-bp deletion, but not in the ten-bp deletion, as shown in pull-down assays using an antibody against RNA polymerase subunit α. In summary, multiple factors are involved in control of the expression of LeuO, a master regulator that orchestrates downstream regulators to modulate factors required for survival and pathogenicity of the pathogen.

Abstract Background Seed size has been extensively studied in crop plants, as it determines crop yield. However, the mechanism of seed development remains elusive. In this study, we explored the mechanism of seed development in rice (Oryza sativa L.), and identified proteins affecting seed size. Results Proteomic analysis showed that glyceraldehyde 3-phosphate dehydrogenase, chitinase 14 (CHT14), and phosphoglycerate kinase (PGK) accumulated to high levels in the seeds of the natural japonica rice mutant Oochikara, which carries a loss-of-function mutation in the grain width 2 (GW2) gene; GW2 encodes a RING-type E3 ubiquitin ligase. In vitro pull-down and ubiquitination assays showed that CHT14 and PGK directly interacted with GW2 but were not ubiquitinated by GW2. Immunoblot analysis revealed that protein disulfide isomerase-like 1-1 accumulated to high levels in young developing seeds of the gw2 mutant compared with the wild type. Histochemical β-glucuronidase staining showed strong expression of GW2 in leaf and root tissues but weak expression in leaf sheaths and internodes. In addition, transformation of the green fluorescent protein (GFP) gene under the control of the GW2 promoter in rice revealed GFP expression in the aleurone layer of seeds. Conclusions Collectively, these results suggest that GW2 regulates seed size through direct interactions with proteins involved in carbohydrate metabolism by modulating their activity or stability and controlling disulfide bond formation in various proteins during seed development. Additionally, GW2 participates in vegetative as well as reproductive growth, and protects the seed from pathogen attack.

Small peptides and proteins play critical regulatory roles in plant development and environmental stress responses; however, only a few of these molecules have been identified and characterized to date because of their poor annotation and other experimental challenges. Here, we present that rice (Oryza sativa L.) OsS1Fa1, a small 76-amino acid protein, confers drought stress tolerance in Arabidopsis thaliana. OsS1Fa1 was highly expressed in leaf, culm, and root tissues of rice seedlings during vegetative growth and was significantly induced under drought stress. OsS1Fa1 overexpression in Arabidopsis induced the expression of selected drought-responsive genes and enhanced the survival rate of transgenic lines under drought. The proteasome inhibitor MG132 protected the OsS1Fa1 protein from degradation. Together, our data indicate that the small protein OsS1Fa1 is induced by drought and is post-translationally regulated, and the ectopic expression of OsS1Fa1 protects plants from drought stress.

ABSTRACT The pathogenic bacterium Vibrio vulnificus exhibits the ability to form biofilm, for which initiation is dependent upon swimming motility by virtue of a polar flagellum. The filament of its flagellum is composed of multiple flagellin subunits, FlaA, -B, -C, and -D. In V. vulnificus genomes, however, open reading frames (ORFs) annotated by FlaE and -F are also present. Although neither FlaE nor FlaF is involved in filament formation and cellular motility, they are well expressed and secreted to the extracellular milieu through the secretion apparatus for flagellar assembly. In the extrapolymeric matrix of V. vulnificus biofilm, significant levels of FlaEF were detected. Mutants defective in both flaE and flaF formed significantly decreased biofilms compared to the wild-type biofilm. Thus, the potential role of FlaEF during the biofilm-forming process was investigated by exogenous addition of recombinant FlaEF (rFlaEF) to the biofilm assays. The added rFlaE and rFlaF were predominantly incorporated into the biofilm matrix formed by the wild type. However, biofilms formed by a mutant defective in exopolysaccharide (EPS) biosynthesis were not affected by added FlaEF. These results raised a possibility that FlaEF specifically interact with EPS within the biofilm matrix. In vitro pulldown assays using His-tagged rFlaEF or rFlaC revealed the specific binding of EPS to rFlaEF but not to rFlaC. Taken together, our results demonstrate that V. vulnificus FlaEF, flagellin-homologous proteins (FHPs), are crucial for biofilm formation by directly interacting with the essential determinant for biofilm maturation, EPS. Further analyses performed with other pathogenic Vibrio species demonstrated both the presence of FHPs and their important role in biofilm formation. IMPORTANCE Flagellar filaments of the pathogenic Vibrio species, including V. vulnificus, V. parahaemolyticus, and V. cholerae, are composed of multiple flagellin subunits. In their genomes, however, there are higher numbers of the ORFs encoding flagellin-like proteins than the numbers of flagellin subunits required for filament assembly. Since these flagellin-homologous proteins (FHPs) are well expressed and excreted to environments via a flagellin transport channel, their extracellular role in the pathogenic Vibrio has been enigmatic. Their biological significance, which is not related with flagellar functions, has been revealed to be in maturation of biofilm structures. Among various components of the extracellular polymeric matrix produced in the V. vulnificus biofilms, the exopolysaccharides (EPS) are dominant constituents and crucial in maturation of biofilms. The enhancing role of the V. vulnificus FHPs in biofilm formation requires the presence of EPS, as indicated by highly specific interactions among two FHPs and three EPS.

Cosmetic lateral canthoplasty, in which the size of the eye is increased by extending the palpebral fissure and decreasing the degree of the eye slant, has become a prevalent procedure for East Asians. However, it is not uncommon for there to be complications or unfavorable results after the surgery. With this in mind, the authors have designed a surgical method to reduce complications in cosmetic lateral canthoplasty by preserving the lateral canthal angle. We discuss here the anatomy required for surgery, the surgical methods, and methods for reducing complications during cosmetic lateral canthoplasty.

Undifferentiated embryonal sarcoma of the liver (UESL) is rare primary hepatic sarcoma and is known to occur in pediatric patients. This case is the UESL occurred in a 51-year old male patient. Multilocular cystic lesion was composed of primitive spindle cells without specific differentiation. This rare case would help to review differential diagnosis of primary sarcoma in liver and cystic neoplasm of the liver.

Background: Hepatitis B virus (HBV) plays well-known roles in tumorigenesis of hepatocellular carcinoma (HCC) in infected patients. However, HBV-associated protein status in tumor tissues and the relevance to tumor behavior has not been reported. Our study aimed to examine the expression of HBV-associated proteins in HCC and adjacent nontumorous tissue and their clinicopathologic implication in HCC patients. Methods: HBV surface antigen (HBsAg), HBV core antigen (HBcAg), and HBV X protein (HBx) were assessed in 328 HBV-associated HCCs and in 155 matched nontumorous tissues by immunohistochemistry staining. Results: The positive rates of HBsAg and cytoplasmic HBx staining in tumor tissue were lower than those in nontumorous tissue (7.3% vs. 57.4%, p < .001; 43.4% vs. 81.3%, p < .001). Conversely, nuclear HBx was detected more frequently in tumors than in nontumorous tissue (52.1% vs. 30.3%, p < .001). HCCs expressing HBsAg, HBcAg, or cytoplasmic HBx had smaller size; lower Edmondson-Steiner (ES) nuclear grade, pT stage, and serum alpha-fetoprotein, and less angioinvasion than HCCs not expressing HBV-associated proteins. Exceptionally, nuclear HBx-positive HCCs showed higher ES nuclear grade and more frequent large-vessel invasion than did nuclear HBx-negative HCCs. In survival analysis, only nuclear HBx-positive HCCs had shorter disease-free survival than nuclear HBx-negative HCCs in pT1 and ES nuclear grade 1–2 HCC subgroup (median, 126 months vs. 35 months; p = .015). Conclusions: Our data confirmed that expression of normal HBV-associated proteins generally decreases in tumor cells in comparison to nontumorous hepatocytes, with the exception of nuclear HBx, which suggests that nuclear HBx plays a role in recurrence of well-differentiated and early-stage HCCs.

This paper proposes a new method to get explicit expressions of some quantities associated with performance analysis of the maximum likelihood DOA algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation of the paper is to make a quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive a closed-form expression of the MSE of the DOA estimate based on the Taylor series expansion. Based on the Taylor series expansion and approximation, we get explicit expressions of the MSEs of estimates of azimuth angles of all incident signals. The validity of the derived expressions is shown by comparing the analytic results with the simulation results.

Vibrio parahaemolyticus can cause gastrointestinal illness through consumption of seafood. Despite frequent food-borne outbreaks of V. parahaemolyticus, only 19 strains have subjected to complete whole-genome analysis. In this study, a novel strain of V. parahaemolyticus, designated FORC_022 (Food-borne pathogen Omics Research Center_022), was isolated from soy sauce marinated crabs, and its genome and transcriptome were analyzed to elucidate the pathogenic mechanisms. FORC_022 did not include major virulence factors of thermostable direct hemolysin (tdh) and TDH-related hemolysin (trh). However, FORC_022 showed high cytotoxicity and had several V. parahaemolyticus islands (VPaIs) and other virulence factors, such as various secretion systems (types I, II, III, IV, and VI), in comparative genome analysis with CDC_K4557 (the most similar strain) and RIMD2210633 (genome island marker strain). FORC_022 harbored additional virulence genes, including accessory cholera enterotoxin, zona occludens toxin, and tight adhesion (tad) locus, compared with CDC_K4557. In addition, O3 serotype specific gene and the marker gene of pandemic O3:K6 serotype (toxRS) were detected in FORC_022. The expressions levels of genes involved in adherence and carbohydrate transporter were high, whereas those of genes involved in motility, arginine biosynthesis, and proline metabolism were low after exposure to crabs. Moreover, the virulence factors of the type III secretion system, tad locus, and thermolabile hemolysin were overexpressed. Therefore, the risk of foodborne-illness may be high following consumption of FORC_022 contaminated crab. These results provided molecular information regarding the survival and pathogenesis of V. parahaemolyticus FORC_022 strain in contaminated crab and may have applications in food safety.

ABSTRACT Capsular polysaccharide (CPS) is essential for the dispersal of biofilms formed by the pathogenic bacterium Vibrio vulnificus. CPS production is induced by the quorum-sensing (QS) master regulator SmcR when biofilms mature. However, V. vulnificus biofilms formed under heat shock conditions did not exhibit the dispersion stage. Transcripts of the CPS gene cluster were at basal levels in the heat-exposed cell owing to reduced cellular levels of SmcR. At least two proteases induced by heat shock, ClpPA and Lon, were responsible for determining the instability of SmcR. In vitro and in vivo assays demonstrated that SmcR levels were regulated via proteolysis by these proteases, with preferential proteolysis of monomeric SmcR. Thus, CPS production was not induced by QS when bacteria were heat treated. Further studies performed with other Vibrio species demonstrated that high temperature deactivated the QS circuits by increased proteolysis of their QS master regulators, thus resulting in alterations to the QS-regulated phenotypes, including biofilm formation. IMPORTANCE The term "quorum-sensing mechanism" is used to describe diverse bacterial cell density-dependent activities that are achieved by sensing of the signaling molecules and subsequent signal transduction to the master regulators. These well-known bacterial regulatory systems regulate the expression of diverse virulence factors and the construction of biofilms in pathogenic bacteria. There have been numerous studies designed to control bacterial quorum sensing by using small molecules to antagonize the quorum-sensing regulatory components or to interfere with the signaling molecules. In the present study, we showed that the quorum-sensing regulatory circuits of pathogenic Vibrio species were deactivated by heat shock treatment via highly increased proteolysis of the master transcription factors. Our results showed a new mode of quorum deactivation which can be achieved under conditions of high but nonlethal temperature even if the ambient signaling molecules may reach the levels representing high cell density.

An inflammatory form of phagocyte death evoked by the Gram-negative bacterium Vibrio (V.) vulnificus (WT) is one of hallmarks to promote their colonization, but the virulence factor and infectious mechanism involved in this process remain largely unknown. Here, we identified extracellular metalloprotease VvpM as a new virulence factor and investigated the molecular mechanism of VvpM which acts during the regulation of the inflammatory form of macrophage death and bacterial colonization. Mutation of the vvpM gene appeared to play major role in the prevention of IL-1β production due to V. vulnificus infection in macrophage. However, the recombinant protein (r) VvpM caused IL-1β production coupled with necrotic cell death, which is highly susceptible to the knockdown of annexin A2 (ANXA2) located in both membrane lipid and non-lipid rafts. In lipid rafts, rVvpM recruited NOX enzymes coupled with ANXA2 to facilitate the production of ROS responsible for the epigenetic and transcriptional regulation of NF-κB in the IL-1β promoter. rVvpM acting on non-lipid rafts increased LC3 puncta formation and autophagic flux, which are required for the mRNA expression of Atg5 involved in the autophagosome formation process. The autophagy activation caused by rVvpM induced NLRP3 inflammasome-dependent caspase-1 activation in the promoting of IL-1β production. In mouse models of V. vulnificus infection, the VvpM mutant failed to elevate the level of pro-inflammatory responses closely related to IL-1β production and prevented bacterial colonization. These findings delineate VvpM efficiently regulates two pathogenic pathways that stimulate NF-κB-dependent IL-1β production and autophagy-mediated NLRP3 inflammasome via distinct spatial targeting by ANXA2.

VvhA produced by Vibrio vulnificus exhibits cytolytic activity to human cells including erythrocytes. Since haemolysis by VvhA may provide iron for bacterial growth and pathogenicity, we investigated the expression of VvhA to elucidate the regulatory roles of Fur, a major transcription factor controlling iron-homeostasis. Fur repressed the transcription of vvhBA operon via binding to the promoter region. However, haemolysin content and haemolytic activity were lowered in cell-free supernatant of fur mutant. This discrepancy between the levels of vvhA transcript and VvhA protein in fur mutant was caused by exoproteolytic activities of the elastase VvpE and another metalloprotease VvpM, which were also regulated by Fur. vvpE gene expression was repressed by Fur via binding to the Fur-box homologous region. Regulation of VvpM expression by Fur did not occur at the level of vvpM transcription. In vitro proteolysis assays showed that both proteases efficiently degraded VvhA. In addition, the extracellular levels of VvhA were higher in culture supernatants of vvpE or vvpM mutants than in the wild type. Thus this study demonstrates that Fur regulates haemolysin production at the transcription level of the vvhBA operon and at the post-translation level by regulating the expressions of two VvhA-degrading exoproteases, VvpE and VvpM. This protocol can be applied to other Vibrio strains with haemolysin activities, such as Vibrio parahaemolyticus (V. parahaemolyticus) or other human pathogen strains with similar heamolysin activities.

End-binding 1 (EB1) proteins are evolutionarily conserved components of microtubule (MT) plus-end tracking protein that regulate MT dynamics. Giardia lamblia, with two nuclei and cytoskeletal structures, requires accurate MT distribution for division. In this study, we show that a single EB1 homolog gene of G. lamblia regulates MT dynamics in mitosis. The haemagglutinin-tagged G. lamblia EB1 (GlEB1) localizes to the nuclear envelopes and median bodies, and is transiently present in mitotic spindles of dividing cells. Knockdown of GlEB1 expression using the morpholinos-based anti-EB1 oligonucleotides, resulted in a significant defect in mitosis of Giardia trophozoites. The MT-binding assays using recombinant GlEB1 (rGlEB1) proteins demonstrated that rGlEB1102-238, but not rGlEB11-184, maintains an MT-binding ability comparable with that of the full length protein, rGlEB11-238. Size exclusion chromatography showed that rGlEB1 is present as a dimer formed by its C-terminal domain and a disulfide bond. In vitro-mutagenesis of GlEB1 indicated that an intermolecular disulfide bond is made between cysteine #13 of the two monomers. Complementation assay using the BIM1 knockout mutant yeast, the yeast homolog of mammalian EB1, indicated that expression of the C13S mutant GlEB1 protein cannot rescue the mitotic defect of the BIM1 mutant yeast. These results suggest that dimerization of GlEB1 via the 13th cysteine residues plays a role during mitosis in Giardia.