Plant Phenotyping on Mobile Devices

Plants phenotyping is a fast and non-destructive method to obtain the physiological features of plants, compared with the expensive and time costing chemical analysis with plant sampling. Through plant phenotyping, scientists and farmers can tell plant health status more accurately compared to visual inspection, thus avoid the waste in time and resources and even to predict the productivity. However, the size and price of current plant phenotyping equipment restrict them from being widely applied at a farmer’s household level. Everyday field operation is barely achieved because of the availability of easy-to-carry and cost-effective equipment such as hyper-spectrum cameras, infrared cameras and thermal cameras. A plant phenotyping tool on mobile devices will make plant phenotyping technology more accessible to ordinary farmers and researchers. This application incorporates the use of physical optics, plant science models, and image processing ability of smartphones. With our special optical design, multispectral instead of RGB (red, green and blue) images can be obtained from the smartphones with fairly low cost. Through quick image processing on the smartphones, the APP will provide accurate plant physiological features predictions such as water, chlorophyll, and nitrogen. The sophisticated prediction models are applied which are provided by the Purdue’s plant phenotyping team. Once widely adopted, the information collected by the smartphones with the developed APP will be sent back to Purdue’s plant health big-data database. The feedback will not only allow us to improve our models, but also provide farmers and agricultural researchers easy access to real-time crop plant health data

The Next-Gen Crop Nutrient Stress Identification with High-Precision Sensing Technology in Digital Agriculture

Crop yields are facing significant losses from nutrient deficiencies. Over-fertilizing also has negative economic and environmental impacts. It is challenging to optimize fertilizing without an accurate diagnosis. Recently, plant phenotyping has demonstrated outstanding capabilities in estimating crop traits. As one of the leading technologies, LeafSpec, provides high-quality crop image data for improving phenotyping quality. In this study, novel algorithms are developed for LeafSpec to identify crop nutrient deficiencies more accurately. Combined with UAV system, this technology will bring growers a robust solution for fertilizing diagnosis and scientific crop management

Neural Super-Resolution for Real-time Rendering with Radiance Demodulation

Rendering high-resolution images in real-time applications (e.g., video games, virtual reality) is time-consuming, thus super-resolution technology becomes more and more crucial in real-time rendering. However, it is still challenging to preserve sharp texture details, keep the temporal stability and avoid the ghosting artifacts in the real-time rendering super-resolution. To this end, we introduce radiance demodulation into real-time rendering super-resolution, separating the rendered image or radiance into a lighting component and a material component, due to the fact that the light component tends to be smoother than the rendered image and the high-resolution material component with detailed textures can be easily obtained. Therefore, we perform the super-resolution only on the lighting component and re-modulate with the high-resolution material component to obtain the final super-resolution image. In this way, the texture details can be preserved much better. Then, we propose a reliable warping module by explicitly pointing out the unreliable occluded regions with a motion mask to remove the ghosting artifacts. We further enhance the temporal stability by designing a frame-recurrent neural network to aggregate the previous and current frames, which better captures the spatial-temporal correlation between reconstructed frames. As a result, our method is able to produce temporally stable results in real-time rendering with high-quality details, even in the highly challenging 4 $\times$ 4 super-resolution scenarios

FedDRL: Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning

Federated learning facilitates collaborative data analysis among multiple participants while preserving user privacy. However, conventional federated learning approaches, typically employing weighted average techniques for model fusion, confront two significant challenges: 1. The inclusion of malicious models in the fusion process can drastically undermine the accuracy of the aggregated global model. 2. Due to the heterogeneity problem of devices and data, the number of client samples does not determine the weight value of the model. To solve those challenges, we propose a trustworthy model fusion method based on reinforcement learning (FedDRL), which includes two stages. In the first stage, we propose a reliable client selection mechanism to exclude malicious models from the fusion process. In the second stage, we propose an adaptive model fusion method that dynamically assigns weights based on model quality to aggregate the best global models. Finally, we validate our approach against five distinct model fusion scenarios, demonstrating that our algorithm significantly enhanced reliability without compromising accuracy

Concurrent sintilimab with sequential chemoradiotherapy for unresectable, stage III non-small cell lung cancer: a retrospective study

BackgroundConcurrent programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) inhibitors with sequential chemoradiotherapy (SCRT) have been reported in only a limited number of studies involving patients with unresectable stage III non-small-cell lung cancer (NSCLC). A retrospective study was conducted to systematically analyze the efficacy and safety of the emerging therapy among Chinese patients.Materials and methodsWe included patients with unresectable, stage III NSCLC who received concurrent sintilimab with chemotherapy or chemotherapy alone for 3-6 cycles, followed by radical radiotherapy at the First Hospital of Jilin University from Dec 15, 2019, to Jul 15, 2022. The primary end point was the objective response rate (ORR). The secondary end points included progression-free survival (PFS), overall survival (OS), 12-month and 18-month PFS rates, the duration of response (DoR), and safety.ResultsThe retrospective study involved 77 patients, of which 49 receiving concurrent sintilimab with SCRT were assigned to cohort A, and 28 receiving SCRT alone were assigned to cohort B. The ORR was significantly higher in cohort A (79.6%, 95% CI 65.7–89.8) than in cohort B (35.7%, 95% CI 18.6–55.9) (p<0.001). Median PFS was significantly longer in cohort A than in cohort B (NR [95% CI 21.4–NR] vs. 16.0 months [13.0–22.5]; HR 0.375, 95% CI 0.192–0.735; p=0.003). The PFS rates at 12 and 18 months were 84.8% (95% CI 75.0–95.9) and 71.3% (95% CI 58.7–86.7) in cohort A and 75.0% (95% CI 60.6–92.9) and 38.3% (95% CI 23.7–61.7) in cohort B, respectively. Grade 3 or 4 adverse events (AEs) were reported in 19 patients (38.8%) and seven patients (25.0%) in two cohorts, respectively. Grade 3 or 4 pneumonitis or immune-mediated pneumonitis, radiation pneumonitis, and pneumonia occurred in five (10.2%), four (8.2%), and two (4.1%) cohort A patients, and zero, two (7.1%), and two (7.1%) cohort B patients, respectively. Only cohort A reported AE leading to death in one (2.0%) patient (immune-mediated pneumonitis).ConclusionConcurrent sintilimab with SCRT resulted in a significantly better ORR and longer PFS than SCRT alone, with manageable safety profiles in Chinese patients with unresectable stage III NSCLC

Cellular immunotherapy as maintenance therapy prolongs the survival of the patients with small cell lung cancer in extensive stage

AbstractBackgroundSmall cell lung cancer (SCLC) is the most devastating type of human lung cancer. Patients usually present with disseminated disease to many organs (extensive stage). This study was to investigate the efficacy and safety of cellular immunotherapy (CIT) with autologous natural killer (NK), γδT, and cytokine-induced killer (CIK) cells as maintenance therapy for extensive-stage SCLC (ES-SCLC) patients.MethodsA pilot prospective cohort study was conducted with ES-SCLC patients who had responded to initial chemotherapy. Patients received either CIT as maintenance therapy (CIT group), or no treatment (control group). Progression-free survival (PFS), overall survival (OS), and adverse effects were compared.ResultsForty-nine patients were recruited in this study, with 19 patients in the CIT group and 30 patients in the control group. The patient characteristics of the 2 groups were comparable except for age, as patients in the CIT group were older than those in the control group (P < 0.05). PFS in the CIT group was superior to the control group (5 vs. 3.1 months, P = 0.020; HR, 0.489, 95% CI, 0.264–0.909, P = 0.024). OS of the CIT group was also longer than that of the control group (13.3 vs. 8.2 months, P = 0.044; HR, 0.528, 95% CI, 0.280–0.996, P = 0.048, respectively). No significant adverse reactions occurred in patients undergoing CIT.ConclusionsCIT maintenance therapy in ES-SCLC prolonged survival with only minimal side effects. Integrating CIT into the current treatment may be a novel strategy for ES-SCLC patients, although further multi-center randomized trials are needed

Increased KIF15 Expression Predicts a Poor Prognosis in Patients with Lung Adenocarcinoma

Background/Aims: Lung cancer is the leading cause of cancer-related deaths worldwide. The outcome of patients with non-small cell lung cancer remains poor; the 5-year survival rate for stage IV non-small cell lung cancer is only 1.0%. KIF15 is a tetrameric kinesin spindle motor that has been investigated for its regulation of mitosis. While the roles of kinesin motor proteins in the regulation of mitosis and their potentials as therapeutic targets in pancreatic cancer have been described previously, the role of KIF15 in lung cancer development remains unknown. Methods: Paired lung carcinoma specimens and matched adjacent normal tissues were used for protein analysis. Clinical data were obtained from medical records. We first examined KIF15 messenger RNA expression in The Cancer Genome Atlas database, and then determined KIF15 protein levels using immunohistochemistry and western blotting. Differences between the groups were analyzed using repeated measures analysis of variance. Overall survival was analyzed using the Kaplan–Meier method. Cell-cycle and proliferation assays were conducted using A549, NCI-H1299, and NCI-H226 cells. Results: KIF15 was significantly upregulated at both the messenger RNA and protein levels in human lung tumor tissues. In patients with lung adenocarcinoma, KIF15 expression was positively associated with disease stages; high KIF15 expression predicted a poor prognosis. KIF15 knockdown using short hairpin RNA in two human lung adenocarcinoma cell lines induced G1/S phase cell cycle arrest and inhibited cell growth, but there was no effect in human lung squamous cell carcinoma. Conclusion: Our findings show that KIF15 is involved in lung cancer carcinogenesis. KIF15 could therefore serve as a specific prognostic marker for patients with lung adenocarcinoma

Proteomic and metabolomic analyses uncover integrative mechanisms in Sesuvium portulacastrum tolerance to salt stress

IntroductionSalt stress is a major constraint affecting crop productivity worldwide. Investigation of halophytes could provide valuable information for improving economically important crops to tolerate salt stress and for more effectively using halophytes to remediate saline environments. Sesuvium portulacastrum L. is a halophyte species widely distributed in tropical and subtropical coastal regions and can absorb a large amount of sodium (Na). This study was to analyze S. portulacastrum responses to salt stress at morphological, physiological, proteomic, and metabolomic levels and pursue a better understanding of mechanisms behind its salt tolerance. MethodsThe initial experiment evaluated morphological responses of S. portulacastrum to different concentrations of NaCl in a hydroponic system, and subsequent experiments compared physiological, proteomic, and metabolomic changes in S. portulacastrum after being exposed to 0.4 M NaCl for 24 h as immediate salt stress (IS) to 14 days as adaptive salt stress (AS). Through these analyses, a working model to illustrate the integrative responses of S. portulacastrum to salt stress was proposed.ResultsPlants grown in 0.4 M NaCl were morphologically comparable to those grown in the control treatment. Physiological changes varied in control, IS, and AS plants based on the measured parameters. Proteomic analysis identified a total of 47 and 248 differentially expressed proteins (DEPs) in leaves and roots, respectively. KEGG analysis showed that DEPs, especially those occurring in roots, were largely related to metabolic pathways. Root metabolomic analysis showed that 292 differentially expressed metabolites (DEMs) occurred in IS plants and 371 in AS plants. Among them, 20.63% of upregulated DEMs were related to phenolic acid metabolism. DiscussionBased on the integrative analysis of proteomics and metabolomics, signal transduction and phenolic acid metabolism appeared to be crucial for S. portulacastrum to tolerate salt stress. Specifically, Ca2+, ABA, and JA signalings coordinately regulated salt tolerance in S. portulacastrum. The stress initially activated phenylpropanoid biosynthesis pathway through Ca2+ signal transduction and increased the content of metabolites, such as coniferin. Meanwhile, the stress inhibited MAPK signaling pathway through ABA and JA signal transduction, which promoted Na sequestration into the vacuole to maintain ROS homeostasis and enhanced S. portulacastrum tolerance to salt stress

Lipid engineering combined with systematic metabolic engineering of <i>Saccharomyces cerevisiae</i> for high-yield production of lycopene

Saccharomyces cerevisiae is an efficient host for natural-compound production and preferentially employed in academic studies and bioindustries. However, S. cerevisiae exhibits limited production capacity for lipophilic natural products, especially compounds that accumulate intracellularly, such as polyketides and carotenoids, with some engineered compounds displaying cytotoxicity. In this study, we used a nature-inspired strategy to establish an effective platform to improve lipid oil–triacylglycerol (TAG) metabolism and enable increased lycopene accumulation. Through systematic traditional engineering methods, we achieved relatively high-level production at 56.2 mg lycopene/g cell dry weight (cdw). To focus on TAG metabolism in order to increase lycopene accumulation, we overexpressed key genes associated with fatty acid synthesis and TAG production, followed by modulation of TAG fatty acyl composition by overexpressing a fatty acid desaturase (OLE1) and deletion of Seipin (FLD1), which regulates lipid-droplet size. Results showed that the engineered strain produced 70.5 mg lycopene/g cdw, a 25% increase relative to the original high-yield strain, with lycopene production reaching 2.37 g/L and 73.3 mg/g cdw in fed-batch fermentation and representing the highest lycopene yield in S. cerevisiae reported to date. These findings offer an effective strategy for extended systematic metabolic engineering through lipid engineering