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Summary: Phosphatidylcholine (PC) plays an important role in maintaining membrane integrity and functionality. In this study, two key genes (Mrpct and Mrpem) putatively involved in the cytidine diphosphate (CDP)‐choline and phosphatidylethanolamine N‐methyltransferase (PEMT) pathways for PC biosynthesis were characterized in the insect pathogenic fungus Metarhizium robertsii. The results indicated that disruption of Mrpct did not lead to any reduction of total PC content but impaired fungal virulence and increased cellular accumulation of triacylglycerol. Deletion of Mrpem reduced PC content and impaired fungal conidiation and infection structure differentiation but did not result in virulence defects. Lipidomic analysis revealed that deletion of Mrpct and Mrpem resulted in dissimilar effects on increase and decrease of PC moieties and other phospholipid species accumulations. Interestingly, we found that these two genes played opposite roles in activation of cell autophagy when the fungi were grown in a nutrient‐rich medium. The connection between PC metabolism and autophagy was confirmed because PC content was drastically reduced in Mratg8Δ and that the addition of PC could rescue null mutant sporulation defect. The results of this study facilitate the understanding of PC metabolism on fungal physiology. [ABSTRACT FROM AUTHOR]

The final product of the glycerol phosphate (GP) pathway is triacylglycerol (TAG) that regulates the homeostasis of energy, fatty acids and phospholipids in cells. The enzymes involved in this pathway have been characterized in many model organisms; however, their contributions to fungal infection are largely unclear. In this study, we performed serial deletion of genes in the GP pathway in the insect pathogenic fungus Metarhizium robertsii. The results indicated that a lysophosphatidate acyltransferase mrLPAAT1 was required for fungal growth, cell differentiation, maintenance of cell polarity and virulence. Lipidomic analysis indicated that deletion of mrLPAAT1 resulted in significant increases in TAG, fatty acids and phosphatidylcholine (PC) but decreased phosphatidic acid (PA), phosphatidylethanolamine (PE) and other species of phospholipids when compared to the wild type. Disruption of the isozymatic gene mrLPAAT2, however, resulted in a reduction in PC but not PA in the mutant cells. There were no changes in development and virulence in ΔmrLPAAT2. Phospholipid feeding assays verified that a PE supplement could rescue the cell differentiation defect in ΔmrLPAAT1. The results of this study reveal that cellular phospholipid homeostasis mediated by the GP pathway regulates fungal growth, cell polarity, differentiation and virulence. [ABSTRACT FROM AUTHOR]

• A comprehensive benchmark of deep learning models was built for weed identification. • A dataset was created with 5187 images for 15 weed classes collected in cotton fields. • Weighted loss function was effective for improving accuracies for minority weed classes. • Deep learning-based cosine similarity was useful for the interpretation of classifications. • Both software programs and the dataset for weed identification are publicly accessible. Precision weed management offers a promising solution for sustainable cropping systems through the use of chemical-reduced/non-chemical robotic weeding techniques, which apply suitable control tactics to individual weeds or small clusters. Therefore, accurate identification of weed species plays a crucial role in such systems to enable precise, individualized weed treatment. Despite recent progress, the development of a robust weed identification and localization system in the presence of unstructured field environments remains a serious challenge, requiring supervised modeling using large volumes of annotated data. This paper makes a first comprehensive evaluation of deep transfer learning (DTL) for identifying common weed species specific to cotton (Gossypium hirsutum L.) production systems in southern United States (U.S.). A new dataset for weed identification was created, consisting of 5187 color images of 15 weed classes collected under natural light conditions and at varied weed growth stages, in cotton fields (primarily in Mississippi and North Carolina) during the 2020 and 2021 growth seasons. We evaluated 35 state-of-the-art deep learning models through transfer learning with repeated holdout validations and established an extensive benchmark for the considered weed identification task. DTL achieved high classification accuracy of F1 scores exceeding 95%, requiring reasonably short training time (less than 2.5 h) across models. ResNeXt101 achieved the best overall F1-score of 98.93 ± 0.34%, whereas 10 out of the 35 models achieved F1 scores near or above 98.0%. However, the performance on minority weed classes with few training samples was less satisfactory for models trained with a conventional, unweighted cross entropy loss function. To address this issue, a weighted cross entropy loss function was adopted, which achieved substantially improved accuracies for minority weed classes (e.g., the F1-scores for Xception and MnasNet on the Spurred Anoda weed increased from 48% to 90% and 50% to 82%, respectively). Furthermore, a deep learning-based cosine similarity metric was employed to analyze the similarity among weed classes, assisting in the interpretation of classifications. Both the codes (https://github.com/Derekabc/CottonWeeds) for model benchmarking and the weed dataset (https://www.kaggle.com/yuzhenlu/cottonweedid15) of this study are made publicly available, which expect to be a valuable resource for future research on weed identification and beyond. [ABSTRACT FROM AUTHOR]

BACKGROUND Fast and non-destructive determination of rapeseed protein content carries significant implications in rapeseed production. This study presented the first attempt of using Fourier transform mid-infrared photoacoustic spectroscopy ( FTIR-PAS) to quantify protein content of rapeseed. The full-spectrum model was first built using partial least squares ( PLS). Interval selection methods including interval partial least squares ( iPLS), synergy interval partial least squares ( siPLS), backward elimination interval partial least squares ( biPLS) and dynamic backward elimination interval partial least squares (dyn- biPLS) were then employed to select the relevant band or band combination for PLS modeling. RESULTS The full-spectrum PLS model achieved an ratio of prediction to deviation ( RPD) of 2.047. In comparison, all interval selection methods produced better results than full-spectrum modeling. siPLS achieved the best predictive accuracy with an RPD of 3.215 when the spectrum was sectioned into 25 intervals, and two intervals (1198-1335 and 1614-1753 cm−1) were selected. iPLS excelled biPLS and dyn- biPLS, and dyn- biPLS performed slightly better than biPLS. CONCLUSION FTIR-PAS was verified as a promising analytical tool to quantify rapeseed protein content. Interval selection could extract the relevant individual band or synergy band associated with the sample constituent of interest, and then improve the prediction accuracy of the full-spectrum model. © 2013 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

• A contrast-optimization approach was proposed for plant segmentation of color images. • Contrast-enhanced images were compared with index images using five image datasets. • The proposed method consistently enhanced image contrast and segmentation accuracy. • None of nine common color indices were robust enough to varying image conditions. Plant segmentation is a crucial task in computer vision applications for identification/classification and quantification of plant phenotypic features. Robust segmentation of plants is challenged by a variety of factors such as unstructured background, variable illumination, biological variations, and weak plant-background contrast. Existing color indices that are empirically developed in specific applications may not adapt robustly to varying imaging conditions. This study proposes a new method for robust, automatic segmentation of plants from background in color (red-green-blue, RGB) images. This method consists of unconstrained optimization of a linear combination of RGB component images to enhance the contrast between plant and background regions, followed by automatic thresholding of the contrast-enhanced images (CEI s). The validity of this method was demonstrated using five plant image datasets acquired under different field or indoor conditions, with a total of 329 color images as well as ground-truth plant masks. The CEI s along with 10 common index images were evaluated in terms of image contrast and plant segmentation accuracy. The CEI s, based on the maximized foreground-background separability, achieved consistent, substantial improvements in image contrast over the index images, with an average segmentation accuracy of F1 = 95%, which is 4% better than the best accuracy obtained by the indices. The index images were found sensitive to imaging conditions and none of them performed robustly across the datasets. The proposed method is straightforward, easy to implement and can be potentially extended to nonlinear forms of color component combinations or other color spaces and generally useful in plant image analysis for precision agriculture and plant phenotyping. [ABSTRACT FROM AUTHOR]

• An innovative robotic cotton harvester was upgraded from earlier developments and tested in a typical cotton field. • The perception system included a stereovision camera and YOLOv-4 tiny. • The robot detected 78% of the visible bolls, localized 70% of the detected bolls, and picked 83% of the localized bolls. • The robot picked 55 % of the reachable bolls with a cycle time of 8.8 s. • Further development in perception and picking systems can improve the robot's picking rate and decrease its cycle time. Conventional cotton harvesters are efficient but heavy causing soil compaction. They normally perform one harvesting pass, but since cotton bolls mature over two months, the early opened bolls must wait for later ones to be harvested, exposing their fiber to weather and degrading fiber quality. A swarm of small, lightweight robotic cotton harvesters can address these issues. This study presents field tests and evaluations of an innovative robotic cotton harvester prototype. A stereovision camera in conjunction with the YOLOv4-tiny algorithm was used for cotton boll detection and localization. The picking system included a 3-DOF (degree of freedom) linear robotic arm, a three-finger end-effector, and an agile control algorithm. The performance rates of detection, localization, and picking systems were 78.1 %, 70.0 %, and 83.1 %, respectively, with an average cycle time of 8.8 s. Collecting cotton bolls orientation data proved that they tend to stay their faces upward causing difficulty in picking the rear part of the bolls in 40.5 % of cases. Controlling the illumination, developing more robust detection and localization systems, increasing the arm's DOF, enhancing the end-effector's operating speed, and its adaptability to different boll orientations can improve the robot's performance in terms of the picking ratio of the seed cotton and speed. The dataset, including field images, annotations of cotton bolls, and the best training weights, is publicly available at: https://github.com/hussein-pasha/Robotic-Cotton-Harvester. A video demonstration of the harvester being tested in the field is available at: https://youtu.be/IztKk3E7zSc. [ABSTRACT FROM AUTHOR]

Highlights An automated system connects skewering operations for unattended kebab production. Separating and aligning meat cubes, vegetable slices, and shrimp (C-shape). Multi-machine mode for producing meat and vegetables, and shrimp and vegetable kebabs. Barbecue kebab skewers typically consist of various meat choices, including chicken, lamb, pork, or seafood, and vegetables such as onion and pepper. Traditionally, manual kebab-making is a time-consuming and labor-intensive process, which is considered to be replaced by semi-auto and full-auto skewering machines. Currently, semi-auto skewering machines still require two main manual preparation processes: align the small meat and vegetable pieces into lines for skewering operations; superpose the large pieces of meat and vegetables, then do skewering and cutting operations. Fully automated skewering machines also have two different strategies. The first one is to use rotating scrappers and cups with a respective depth to ensure only one item can be loaded into each cup. All the cups are arranged into sequential cup rows and cup lines for skewering operations. However, the cups are frequently left empty because the rotating scrapper design often blocks any items falling into cups. Thus, some processing facilities still need extra labor to manually fill these empty cups. Another solution is the pick-and-place method by using robot arms and real-time image processing equipment, which cannot process irregular food pieces, such as shrimps (C-shape). In this paper, a novel fully automated handling and feeding system has been developed to prepare and connect the skewering operations to establish unattended kebab production lines. The performances of singulation, handling, and feeding operations have been investigated with different parameter settings and food items. Besides, the presented machine system is the first realization of unattended kebab production lines to process shrimps (C-shape), and the first design of using horizontal skewering operation to produce meat and vegetable kebabs (cube+ square piece) and shrimp and vegetable kebabs (C-shape+ square piece). This study will be beneficial for developing more effective next-generation skewering technologies and better value-added meat and seafood products. [ABSTRACT FROM AUTHOR]

• There is a need for public image datasets to enable precision agriculture tasks. • The 34 public image datasets collected under field conditions are surveyed. • The practices on facilitating public image dataset creation are discussed. Computer vision technologies have attracted significant interest in precision agriculture in recent years. At the core of robotics and artificial intelligence, computer vision enables various tasks from planting to harvesting in the crop production cycle to be performed automatically and efficiently. However, the scarcity of public image datasets remains a crucial bottleneck for fast prototyping and evaluation of computer vision and machine learning algorithms for the targeted tasks. Since 2015, a number of image datasets have been established and made publicly available to alleviate this bottleneck. Despite this progress, a dedicated survey on these datasets is still lacking. To fill this gap, this paper makes the first comprehensive but not exhaustive review of the public image datasets collected under field conditions for facilitating precision agriculture, which include 15 datasets on weed control, 10 datasets on fruit detection, and 9 datasets on miscellaneous applications. We survey the main characteristics and applications of these datasets, and discuss the key considerations for creating high-quality public image datasets. This survey paper will be valuable for the research community on the selection of suitable image datasets for algorithm development and identification of where creation of new image datasets is needed to support precision agriculture. [ABSTRACT FROM AUTHOR]

• Three filamentous cyanobacteria strains were selected based on viability in digestate. • The pollutants remove ability from biogas slurry of pig farm was evaluated. • The biochemical composition of algal biomass was examined. • The biodiesel and theoretical biomethane potential of algal biomass were evaluated. • The strain J is the best strain for biogas slurry treat and bioenergy production. The study evaluates 36 filamentous heterocystous cyanobacteria for the treatment of biogas slurry from pig farm and the accumulation of biomass for bioenergy production. The results showed that only the strains B, J, and L were able to adapt to a 10% biogas slurry. The removal rates of ammonia nitrogen, total nitrogen, and total phosphorus for strains J and L were 92.46%–97.97%, 73.79%–79.90%, and 97.14%–98.46%, respectively, higher than that of strain B. Strain J had the highest biomass productivity and lipid productivity. Based on the biodiesel prediction results, it was concluded that strains J and L are more suitable for biodiesel production. The estimation of theoretical methane potential suggests that the algal biomass of strain J also have the desirable possibility of biogas generation. In summary, algal strain J (Nostoc sp.) offers great potential for biogas slurry treatment and for the production of bioenergy. [ABSTRACT FROM AUTHOR]

Insects, which lack the adaptive immune system, have developed sophisticated innate immune system consisting of humoral and cellular immune responses to defend against invading microorganisms. Non-self recognition of microbes is the front line of the innate immune system. Repertoires of pattern recognition receptors (PRRs) recognize the conserved pathogen-associated molecular patterns (PAMPs) present in microbes, such as lipopolysaccharide (LPS), peptidoglycan (PGN), lipoteichoic acid (LTA) and β-1, 3-glucans, and induce innate immune responses. In this review, we summarize current knowledge of the structure, classification and roles of PRRs in innate immunity of the model organism Drosophila melanogaster , focusing mainly on the peptidoglycan recognition proteins (PGRPs), Gram-negative bacteria-binding proteins (GNBPs), scavenger receptors (SRs), thioester-containing proteins (TEPs), and lectins. • Summarize the structure, classification and roles of Drosophila PRRs in innate immunity. • The sensor PGRPs recognize different types of PGN to activate the immune signaling pathways. • The regulatory PGRPs modulate the binding of PGN to sensor PGRPs. • Scavenger receptors, thioester-containing proteins and lectins activate cellular immune responses. [ABSTRACT FROM AUTHOR]

• Chlorophyll fluorescence imaging was used for detection of chilling injury in cucumbers. • Automatic vignetting correction was proposed for enhancing fluorescence images. • Structured illumination was effective for enhancing the quality of fluorescence images. Chlorophyll fluorescence imaging (CFI) is useful for detecting physiological disorders or defects for green-skinned horticultural products, because defective and normal plant tissues would have different responses to ultraviolet (UV) or short-wavelength visible excitation. This study was intended to evaluate the effectiveness of a new CFI approach by using structured illumination coupled with a proposed automated method for vignetting correction of chlorophyll fluorescence images, for enhanced detection of chilling injury in cucumbers. A CFI system with UV-blue light as an excitation source under structured illumination was assembled. Spectral images over the spectral region of 660–800 nm in 5 nm increments were first acquired from chilling-treated cucumbers under uniform UV-blue illumination to determine appropriate wavebands for implementation of CFI under structured illumination. Further experiment was conducted on a larger group of chilling treated cucumbers to acquire chlorophyll fluorescence images under structured illumination for two wavebands centered at 675 nm and 750 nm. An automatic method for vignetting correction of fluorescence images was proposed by using a modified bi-dimensional empirical mode decomposition (BEMD) technique. Results showed that the chlorophyll fluorescence spectra of cucumbers were characterized by two emission peaks around the regions of 685–690 nm and 740–745 nm respectively. The proposed BEMD method was effective for vignetting correction of fluorescence images, which eliminates the need of using a physical fluorescence target for image correction. Moreover, compared to uniform illumination, structured illumination was found to provide significantly better fluorescence images in terms of the image sharpness and contrast between the normal and chilling-injury tissues, which were inductive to enhancing the detection of chilling injury in cucumbers. [ABSTRACT FROM AUTHOR]

Simple Summary: Spermatogenesis, which is regulated by many different genes, is a conserved process across species to produce mature sperm for animal reproduction. Fox transcription factors can bind to DNA sequences in the promoters to regulate gene expression. FoxD subfamily members are mainly involved in metabolism and early organ development. In Drosophila melanogaster, FoxD subfamily member Fd59a may regulate the development of the nervous system and control the egg-laying behavior of females. However, the functions of insect FoxD members are still largely unknown. In this study, we investigated the role of Fd59a in the spermatogenesis of Drosophila. We found that mutations in Fd59a caused swelling of the apical region in the testis, resulting in fewer mature sperm in the seminal vesicle and significantly lower fertility of Fd59a mutant males compared to the control flies. We also found that the homeostasis of the testis stem cell niche in Fd59a mutant and RNAi flies was disrupted, causing increased apoptosis of sperm bundles. RNA sequencing and qRT-PCR results suggested that Fd59a can regulate the expression of genes related to reproductive process and cell death. Our collective results indicated that Fd59a plays a key role in Drosophila spermatogenesis, which will help to understand the role of FoxD members in insect spermatogenesis. Spermatogenesis is critical for insect reproduction and is regulated by many different genes. In this study, we found that Forkhead transcription factor Fd59a functions as a key factor in the spermatogenesis of Drosophila melanogaster. Fd59a contains a conversed Forkhead domain, and it is clustered to the FoxD subfamily with other FoxD members from some insect and vertebrate species. Mutations in Fd59a caused swelling in the apical region of the testis. More importantly, fewer mature sperm were present in the seminal vesicle of Fd59a mutant flies compared to the control flies, and the fertility of Fd59a2/2 mutant males was significantly lower than that of the control flies. Immunofluorescence staining showed that the homeostasis of the testis stem cell niche in Fd59a2/2 mutant and Fd59a RNAi flies was disrupted and the apoptosis of sperm bundles was increased. Furthermore, results from RNA sequencing and qRT-PCR suggested that Fd59a can regulate the expression of genes related to reproductive process and cell death. Taken together, our results indicated that Fd59a plays a key role in the spermatogenesis of Drosophila. [ABSTRACT FROM AUTHOR]

• VNIR-HSI (400–1000 nm) was used to determine the DMC, SSC, and Firmness of sweetpotato. • Important feature wavebands were selected using GA and CARS. • SHAP value was used to explain the importance of the selected features. • The spatial distribution of the quality attributes was visualized using prediction maps. The quality evaluation of sweetpotatoes is of utmost importance during postharvest handling as it significantly impacts consumer satisfaction, nutritional value, and market competitiveness. This study presents an innovative approach that integrates explainable artificial intelligence (AI) with hyperspectral imaging to enhance the assessment of three important quality attributes in sweetpotatoes, i.e., dry matter content, soluble solid content, and firmness. Sweetpotato samples of three different varieties, including "Bayou Belle", "Murasaki", and "Orleans", were imaged using a portable visible near-infrared hyperspectral imaging (VNIR-HSI) camera, with a 400–1000 nm spectral range. The extracted spectral data were used to select key wavelengths, develop multivariate regression models, and utilize SHapley Additive exPlanations (SHAP) values to ascertain model effectiveness and interpretability. The regression models (dry matter: R2 p = 0.92, RMSEP = 1.50 % and RPD = 5.58; soluble solid content: R2 p = 0.66, RMSEP = 0.85obrix, and RPD = 1.72; firmness: R2 p = 0.85; RMSEP = 1.66 N and RPD = 2.63) developed with key wavelengths were used to generate prediction maps to visualize the spatial distribution of response attributes, facilitating an improved evaluation of sweetpotato quality. The study demonstrated that the combination of HSI, variable selection, and explainable AI has the potential to enhance the quality assessment of sweetpotatoes, ensuring supplies of higher quality products to consumers. [ABSTRACT FROM AUTHOR]

Spermatogenesis is critical for insect reproduction and the process is regulated by multiple genes. Glycosyltransferases have been shown to participate in the development of Drosophila melanogaster; however, their role in spermatogenesis is still unclear. In this study, we found that α1,4‐galactosyltransferase 1 (α4GT1) was expressed at a significantly higher level in the testis than in the ovary of Drosophila. Importantly, the hatching rate was significantly decreased when α4GT1 RNA interference (RNAi) males were crossed with w1118 females, with only a few mature sperm being present in the seminal vesicle of α4GT1 RNAi flies. Immunofluorescence staining further revealed that the individualization complex (IC) in the testes from α4GT1 RNAi flies was scattered and did not move synchronically, compared with the clustered IC observed in the control flies. Terminal deoxyribonucleotide transferase (TdT)‐mediated dUTP nick end labeling (TUNEL) assay showed that apoptosis signals in the sperm bundles of α4GT1 RNAi flies were significantly increased. Moreover, the expression of several individualization‐related genes, such as Shrub, Obp44a and Hanabi, was significantly decreased, whereas the expression of several apoptosis‐related genes, including Dronc and Drice, was significantly increased in the testes of α4GT1 RNAi flies. Together, these results suggest that α4GT1 may play dual roles in Drosophila spermatogenesis by regulating the sperm individualization process and maintaining the survival of sperm bundles. [ABSTRACT FROM AUTHOR]

Spermatogenesis is a critical part of reproduction in insects; however, its molecular mechanism is still largely unknown. In this study, we identified a testis‐specific gene CG3526 in Drosophila melanogaster. Bioinformatics analysis showed that CG3526 contains a zinc binding domain and 2 C2H2 type zinc fingers, and it is clustered to the vertebrate really interesting new gene (RING) family E3 ubiquitin‐protein ligases. When CG3526 was knocked down by RNA interference (RNAi), the testis became much smaller in size, and the apical tip exhibited a sharp and thin end instead of the blunt and round shape in the control testis. More importantly, compared to the control flies, only a few mature sperm were present in the seminal vesicle of C587‐Gal4 > CG3526 RNAi flies. Immunofluorescence staining of the testis from CG3526 RNAi flies showed that the homeostasis of testis stem cell niche was disrupted, cell distribution in the apical tip was scattered, and the process of spermatogenesis was not completed. Furthermore, we found that the phenotype of CG3526 RNAi flies' testis was similar to that of testis of Stat92E RNAi flies, the expression level of CG3526 was significantly downregulated in the Stat92EF06346 mutant flies, and the promoter activity of CG3526 was upregulated by STAT92E. Taken together, our results indicated that CG3526 is a downstream effector gene in the JAK‐STAT signaling pathway that plays a key role in the spermatogenesis of Drosophila. [ABSTRACT FROM AUTHOR]

• A robotic cotton harvester with a three-finger end-effector was developed and tested. • A closed-loop control algorithm with shorter and fewer substeps performed best. • Manipulating the end-effector wisely led to faster and cleaner picking. • A faster arm and end-effector, and better perception system can enhance performance. • An AI-driven control algorithm can operate the whole system efficiently. Cotton is conventionally harvested with large and costly harvesters once at the end of the growing season. Fiber in the early-opened bolls is left on the plants till the end of the season to be harvested, resulting in its exposure to weather and thus degraded quality. Furthermore, heavy cotton harvesters can compact the soil and adversely affect soil health and crop yield. These issues can potentially be addressed by using small robotic cotton harvesters that can harvest multiple times during the season, picking seed cotton soon after the bolls open. Robotic cotton harvesters are not currently available. This article presents the system integration and performance evaluation of a new robotic cotton harvester prototype consisting of a 3-DOF (degrees of freedom) linear robotic arm with a custom three-finger end-effector, integrated with a deep-learning based perception module to enable fast and automatic harvesting of cotton. The perception system utilized a stereovision camera and an onboard computer with a trained YOLOv4-tiny algorithm for detecting and locating cotton bolls within the plant. Three different control algorithms were examined to control manipulation of the arm and run the end-effector throughout the harvesting process. Performance of the harvester was evaluated in terms of picking ratio – picked seed cotton over available seed cotton in the image – and cycle time through laboratory tests using real cotton plants. Results showed that a closed-loop control algorithm using continuous feedback from cotton boll images during picking was the most efficient, successfully harvesting 72% of the seed cotton with an average cycle time of 8.8 s. [ABSTRACT FROM AUTHOR]

Fourier transform mid infrared (FT-MIR) spectroscopy combined with modeling techniques has been studied as a useful tool for multivariate chemical analysis in agricultural research. A drawback of this method is the sample preparation requirement, in which samples must be dried and fine ground for accurate model calibrations. For research involving large sample sets, this may dramatically increase the time and cost of analysis. This study investigates the effect of fine grinding on model performance using leaf tissue from a variety of crop species. Dried leaf samples (N = 300) from various environmental conditions were obtained with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques. Scanning was repeated after fine grinding for 2, 5, and 10 min. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations. All analytes except for boron, iron, and zinc were well-modeled (average R2 > 0.7), with higher R2 values on ATR spectra. The 5 min level of fine grinding was found to be most optimal considering overall model performance and sample preparation time. [ABSTRACT FROM AUTHOR]

• A 12-class image dataset consisting of 9370 bounding boxes was created for common weeds in cotton. • An extensive benchmark of 18 YOLO object detection models was established for weed detection. • The effect of data augmentation on weed detection was assessed. • The dataset and software programs for model benchmarking are publicly accessible. Weeds are among the major threats to cotton production. Overreliance on herbicides for weed control has accelerated the evolution of herbicide-resistance in weeds and caused increasing concerns about environments, food safety and human health. Machine vision systems for automated/robotic weeding have received growing interest towards the realization of integrated, sustainable weed management. However, in the presence of unstructured field environments and significant biological variability of weeds, it remains a serious challenge to develop reliable weed identification and detection systems. A promising solution to address this challenge are the development of arge-scale, annotated image datasets of weeds specific to cropping systems and data-driven AI (artificial intelligence) models for weed detection. Among various deep learning architectures, a diversity of YOLO (You Only Look Once) detectors is well-suited for real-time application and has enjoyed great popularity for generic object detection. This study presents a new dataset (CottoWeedDet12) of weeds important to cotton production in the southern United States (U.S.); it consists of 5648 images of 12 weed classes with a total of 9370 bounding box annotations, collected under natural light conditions and at varied weed growth stages in cotton fields. A novel, comprehensive benchmark of 25 state-of-the-art YOLO object detectors of seven versions including YOLOv3, YOLOv4, Scaled-YOLOv4, YOLOR and YOLOv5, YOLOv6 and YOLOv7, has been established for weed detection on the dataset. Evaluated through the Monte-Caro cross validation with 5 replications, the detection accuracy in terms of mAP@0.5 ranged from 88.14 % by YOLOv3-tiny to 95.22 % by YOLOv4, and the accuracy in terms of mAP@[0.5:0.95] ranged from 68.18 % by YOLOv3-tiny to 89.72 % by Scaled-YOLOv4. All the YOLO models especially YOLOv5n and YOLOv5s have shown great potential for real-time weed detection, and data augmentation could increase weed detection accuracy. Both the weed detection dataset 2 2 https://doi.org/10.5281/zenodo.7535814 and software program codes for model benchmarking in this study are publicly available 3 3 https://github.com/DongChen06/DCW , which will be to be valuable resources for promoting future research on big data and AI-empowered weed detection and control for cotton and potentially other crops. [ABSTRACT FROM AUTHOR]

• Present the first study on applying diffusion models through transfer learning to generate high-quality images for weed recognition based on a multi-class weed dataset, CottonWeedID15. • A post-processing approach based on realism score is developed and employed to automatically remove low-quality samples after the training to improve the quality of the generated samples. • Four deep learning models are evaluated on the expanding dataset with synthetic images through transfer learning, showing significant performance improvement on the weed classification accuracy. • Conduct comprehensive experiments, and the results show that the proposed approach consistently outperforms several state-of-the-art generative adversarial networks in terms of sample quality and diversity. The codes of this study are open-sourced at: https://github.com/DongChen06/DMWeeds. Weed management plays an important role in crop yield and quality protection. Conventional weed control methods largely rely on intensive, blanket herbicide application, which incurs significant management costs and poses hazards to the environment and human health. Machine vision-based automated weeding has gained increasing attention for sustainable weed management through weed recognition and site-specific treatments. However, it remains a challenging task to reliably recognize weeds in variable field conditions, in part due to the difficulty curating large-scale, expert-labeled weed image datasets for supervised training of weed recognition algorithms. Data augmentation methods, including traditional geometric/color transformations and more advanced generative adversarial networks (GANs) can supplement data collection and labeling efforts by algorithmically expanding the scale of datasets. Recently, diffusion models have emerged in the field of image synthesis, providing a new means for augmenting image datasets to power machine vision systems. This study presents a novel investigation of the efficacy of diffusion models for generating weed images to enhance weed identification. Experiments on two public multi-class large weed datasets showed that diffusion models yielded the best trade-off between sample fidelity and diversity and obtained the highest Fréchet Inception Distance, compared to GANs (BigGAN, StyleGAN2, StyleGAN3). For instance, on a ten-class weed dataset (CottonWeedID10), the inclusion of synthetic weed images led to improvements by 1.17% (97.30% to 98.47), 1.21% (97.92% to 99.13%), and 2.30% (96.06% to 98.27%) in accuracy, precision, and recall, respectively, in weed classification by four deep learning models (i.e., VGG16, Inception-v3, Inception-v3, and ResNet50). Models trained using only 10% of real images with the remainder being synthetic data resulted in testing accuracy exceeding 94%. [ABSTRACT FROM AUTHOR]

Abstract An important innate immune response in Drosophila melanogaster is the production of antimicrobial peptides (AMPs). Expression of AMP genes is mediated by the Toll and immune deficiency (IMD) pathways via NF-κB transcription factors Dorsal, DIF and Relish. Dorsal and DIF act downstream of the Toll pathway, whereas Relish acts in the IMD pathway. Dorsal and DIF are held inactive in the cytoplasm by the IκB protein Cactus, while Relish contains an IκB-like inhibitory domain at the C-terminus. NF-κB factors normally form homodimers and heterodimers to regulate gene expression, but formation of heterodimers between Relish and DIF or Dorsal and the specificity and activity of the three NF-κB homodimers and heterodimers are not well understood. In this study, we compared the activity of Rel homology domains (RHDs) of Dorsal, DIF and Relish in activation of Drosophila AMP gene promoters, demonstrated that Relish-RHD (Rel-RHD) interacted with both Dorsal-RHD and DIF-RHD, Relish-N interacted with DIF and Dorsal, and overexpression of individual RHD and co-expression of any two RHDs activated the activity of AMP gene promoters to various levels, suggesting formation of homodimers and heterodimers among Dorsal, DIF and Relish. Rel-RHD homodimers were stronger activators than heterodimers of Rel-RHD with either DIF-RHD or Dorsal-RHD, while DIF-RHD-Dorsal-RHD heterodimers were stronger activators than either DIF-RHD or Dorsal-RHD homodimers in activation of AMP gene promoters. We also identified the nucleotides at the 6th and 8th positions of the 3' half-sites of the κB motifs that are important for the specificity and activity of NF-κB transcription factors. Highlights • Relish-N interacted with DIF and Dorsal, suggesting formation of NF-κB homodimers and heterodimers. • Homodimers and heterodimers of DIF, Dorsal and Relish RHDs activated antimicrobial peptide gene promoters. • Relish-RHD homodimers were stronger activators than heterodimers of Relish-RHD with DIF-RHD or Dorsal-RHD. • DIF-RHD-Dorsal-RHD heterodimers were stronger activators than DIF-RHD or Dorsal-RHD homodimers. • The nucleotides at the 6th and 8th positions of NF-κB motifs are important for specificity and activity of NF-κB factors. [ABSTRACT FROM AUTHOR]

In this study, novel Zn-Sn-xBi high-temperature solders were designed to join sintered NdFeB permanent magnets and steel. The effect of Bi addition on the corrosion resistance and mechanical properties of the soldered joints was systematically investigated. The results indicated that introducing Bi with specific content obviously decreased the undercooling of the solder. Reducing the undercooling by promoting the solidification process and the formation of Bi precipitates as nucleation sites could refine the microstructure. The improvement of corrosion resistance was attributed to the finer microstructure and the Bi precipitates serving as anodic barriers. Moreover, the sound soldered joints were obtained with finer β -Sn dendrites and a more uniform distribution of Bi precipitates when appropriate Bi was added. The shear strength of the soldered joint with 5 wt% Bi addition was 36.6% higher than the shear strength of the ZS soldered joint. • Sintered NdFeB permanent magnets and steel were connected successfully using Zn-Sn-xBi solders. • The formation of Bi precipitates was attributed to Bi addition in Zn-Sn high-temperature solders. β • Enhanced corrosion resistance resulted from refined microstructure and anodic barriers. • Enhanced shear strength resulted from refined microstructure and precipitation strengthening. [ABSTRACT FROM AUTHOR]

Summary Cordycepin (COR) and pentostatin (PTN) are adenosine analogs with related bioactivity profiles as both mimic adenosine and can inhibit some of the processes that are adenosine dependent. Both COR and PTN are also natural products and were originally isolated from the fungus Cordyceps militaris and the bacterium Streptomyces antibioticus , respectively. Here, we report that not only is PTN produced by C. militaris but that biosynthesis of COR is coupled with PTN production by a single gene cluster. We also demonstrate that this coupling is an important point of metabolic regulation where PTN safeguards COR from deamination by inhibiting adenosine deaminase (ADA) activity. ADA is not inhibited until COR reaches self-toxic levels, at which point ADA derepression occurs allowing for detoxification of COR to 3′-deoxyinosine. Finally, we show that using our biosynthetic insights, we can engineer C. militaris to produce higher levels of COR and PTN. [ABSTRACT FROM AUTHOR]

Drosophila NF-κB transcription factor Dif has been well known for its function in innate immunity, and recent study also reveals its role in neuronal cells. However, the underlying mechanisms of Dif in the brain remain elusive. In this study, we aim to investigate the function of Dif in Drosophila brain development and how Dif regulates structure and plasticity of the brain to affect aging and behaviors. Based on the analysis of differentially expressed genes, we identified key genes associated with cell division, development and aging in the brain of Dif 1 loss of function mutant. In Dif 1 larvae, we found that the metamorphosis and brain development were delayed, and cell division was decreased. In Dif 1 adults, the number of neuron cells was reduced in the brain, the lifespan and locomotor activity were decreased, protein markers associated with aging-related neurodegenerative diseases in the brain were altered in abundance or activity. Our results indicated that Dif plays a crucial role in brain plasticity and neurogenesis, dysfunction of Dif delays larval brain development and impacts proliferation of neuronal cells, resulting in aging adult brain by regulating expression of key genes in multiple signaling pathways involved in cell division, neurogenesis and aging., Competing Interests: Declaration of competing interest The authors have declared that no competing interests exist., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Many types of viruses infect insects and other arthropods. In contrast, little is known about how arthropods sense viruses, although several innate immune pathways including Toll have antiviral functions. Large DNA viruses in the family Baculoviridae are used to control a number of pest insects. Here, we studied Spodoptera litura and Autographa californica multiple nucleopolyhedrovirus (AcMNPV) to test the hypothesis that one or more myeloid differentiation-like (ML) proteins and Toll family members sense baculoviruses. We identified 11 ML and 12 Toll genes in the S. litura genome. A series of experiments indicated that S. litura ML protein 11 (SlML-11) binds the budded form of AcMNPV and partners with S. litura Toll5 (SlToll5). SlML-11 also bound sphingomyelin (SPM), which is a component of the virion envelope. Disabling SlML-11 and SlToll5 increased susceptibility to infection, while priming larvae with SPM reduced susceptibility as measured by increased survival to the adult stage and clearance of AcMNPV from individuals that emerged as adults. We conclude that SPM is a pathogen-associated molecular pattern molecule while SlML-11 and SlToll5 interact to function as a pattern recognition receptor that senses AcMNPV., Competing Interests: Competing interests statement:The authors declare no competing interest.

Blueberry is an important perennial fruit crop with expanding consumption and production worldwide. Consumer demand for blueberries has grown due to the desirable flavor and numerous health benefits, and fresh market production in the U.S. has risen in turn. U.S. imports have also increased to satisfy year-round consumer demand for fresh blueberries. Pre- and post-harvest fruit diseases such as anthracnose (caused by Colletotrichum spp.) and botrytis fruit rot (caused by Botrytis spp.) have a significant impact on fruit quality and consumer acceptance. These are also among the most difficult diseases to control in the blueberry cropping system. These latent pathogens can cause significant losses both in the field, and especially during transport and marketplace storage. Although both diseases result in rotted fruit, the biology and infection strategies of the causal pathogens are very different, and the management strategies differ. Innovations for management, such as improved molecular detection assays for fungicide resistance, postharvest imaging, breeding resistant cultivars, and biopesticides have been developed for improved fruit quality. Development and integration of new strategies is critical for the long-term success of the blueberry industry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Neugebauer, Mattupalli, Hu, Oliver, VanderWeide, Lu, Sullivan, Stockwell, Oudemans and Miles.)

Macroautophagy/autophagy is a conserved process in eukaryotic cells to degrade and recycle damaged intracellular components. Higher level of autophagy in the brain has been observed, and autophagy dysfunction has an impact on neuronal health, but the molecular mechanism is unclear. In this study, we showed that overexpression of Toll-1 and Toll-7 receptors, as well as active Spätzle proteins in Drosophil a S2 cells enhanced autophagy, and Toll-1/Toll-7 activated autophagy was dependent on Tube-Pelle-PP2A. Interestingly, Toll-1 but not Toll-7 mediated autophagy was dMyd88 dependent. Importantly, we observed that loss of functions in Toll-1 and Toll-7 receptors and PP2A activity in flies decreased autophagy level, resulting in the loss of dopamine (DA) neurons and reduced fly motion. Our results indicated that proper activation of Toll-1 and Toll-7 pathways and PP2A activity in the brain are necessary to sustain autophagy level for DA neuron survival., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)