Carbon footprints: Uncovering multilevel spatiotemporal changes of ship emissions during 2019-2021 in the U.S.

Quantifying and understanding changes in carbon emissions is essential for the U.S. shipping industry to reduce carbon emissions, especially after its return to the Paris Agreement. We estimated carbon emissions from 48,321 ships in the U.S. Exclusive Economic Zone (EEZ) using the power-based method based on 3.6 billion Automatic Identification System (AIS) reports. We explored the characterization of carbon emissions from the national, regional, and port levels during 2019-2021 by allocating emissions on a 1 km*1 km grid through an activity-weighted method. The results show: (1) Due to the COVID-19 pandemic, emissions within the EEZ show a temporal trend of decreased and then rebound, specifically from 32.628 Tg in 2019 to 30.741 Tg in 2020 and then bounced to 31.786 Tg in 2021. The spatial differences in emissions show significant heterogeneity; (2) There are significant differences in emissions by vessel type, flag, and operational mode for the four regions of the U.S. (Great Lakes, Gulf Coast, Pacific Coast, and Atlantic Coast). Thus, emissions in these regions show different variability patterns over three years. Notably, "port congestion" led to record high emissions on the Pacific Coast; (3) Containerized cargo contributes the most to port core area emissions, so most ports with higher throughputs have higher emissions, with Long Beach and Los Angeles having the highest. Emissions from coastal ports are high and volatile, while inland ports are low and stable. This study provides the U.S. with a high spatiotemporal resolution inventory of carbon emissions from ships, and the findings are expected to provide some reference for controlling ship emissions.

Supervised breast cancer prediction using integrated dimensionality reduction convolutional neural network.

OBJECTIVES: Breast cancer is a major health problem with high mortality rates. Early detection of breast cancer will promote treatment. A technology that determines whether a tumor is benign desirable. This article introduces a new method in which deep learning is used to classify breast cancer. METHODS: A new computer-aided detection (CAD) system is presented to classify benign and malignant masses in breast tumor cell samples. In the CAD system, (1) for the pathological data of unbalanced tumors, the training results are biased towards the side with the larger number of samples. This paper uses a Conditional Deep Convolution Generative Adversarial Network (CDCGAN) method to generate small samples by orientation data set to solve the imbalance problem of collected data. (2) For the high-dimensional data redundancy problem, this paper proposes an integrated dimension reduction convolutional neural network (IDRCNN) model, which solves the high-dimensional data dimension reduction problem of breast cancer and extracts effective features. The subsequent classifier found that by using the IDRCNN model proposed in this paper, the accuracy of the model was improved. RESULTS: Experimental results show that IDRCNN combined with the model of CDCGAN model has superior classification performance than existing methods, as revealed by sensitivity, area under the curve (AUC), ROC curve and accuracy, recall, sensitivity, specificity, precision,PPV,NPV and f-values analysis. CONCLUSION: This paper proposes a Conditional Deep Convolution Generative Adversarial Network (CDCGAN) which can solve the imbalance problem of manually collected data by directionally generating small sample data sets. And an integrated dimension reduction convolutional neural network (IDRCNN) model, which solves the high-dimensional data dimension reduction problem of breast cancer and extracts effective features.

Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients.

OBJECTIVE: This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neuroma. METHODS: A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included. Clinical data and raw features from four MRI sequences (T1-weighted, T2-weighted, T1-weighted contrast enhancement, and T2-weighted-Flair images) were analyzed. Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features. Nomogram, machine learning, and convolutional neural network (CNN) models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate model performance. A total of 1050 radiomic parameters were extracted, from which 13 radiomic and 3 clinical features were selected. RESULTS: The CNN model performed best among all prediction models in the test set with an area under the curve (AUC) of 0.89 (95% CI, 0.84-0.91). CONCLUSION: CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma. As such, CNN modeling may serve as a potential decision-making tool for neurosurgery.

A hybrid neural network approach for classifying diabetic retinopathy subtypes.

Objective: Diabetic retinopathy is a prevalent complication among diabetic patients that, if not predicted and treated promptly, can lead to blindness. This paper proposes a method for accurately and swiftly predicting the degree of diabetic retinopathy using a hybrid neural network model. Timely prediction of diabetic retinopathy is crucial in preventing blindness associated with this condition. Methods: This study aims to enhance the prediction accuracy of diabetic retinopathy by utilizing the hybrid neural network model EfficientNet and Swin Transformer. The specific methodology includes: (1) combining local and global features to accurately capture lesion characteristics by leveraging the strengths of both Swin Transformer and EfficientNet models; (2) improving prediction accuracy through a comprehensive analysis of the model's training details and applying data augmentation techniques such as Gaussian blur to enhance the hybrid model's performance; (3) validating the effectiveness and utility of the proposed hybrid model for diabetic retinopathy detection through extensive experimental evaluations and comparisons with other deep learning models. Results: The hybrid model was trained and tested on the large-scale real-world diabetic retinopathy detection dataset APTOS 2019 Blindness Detection. The experimental results show that the hybrid model in this paper achieves the best results in all metrics, including sensitivity of 0.95, specificity of 0.98, accuracy of 0.97, and AUC of 0.97. The performance of the model is significantly improved compared to the mainstream methods currently employed. In addition, the model provides interpretable neural network details through class activation maps, which enables the visualization of diabetic retinopathy. This feature helps physicians to make more accurate diagnosis and treatment decisions. The model proposed in this paper shows higher accuracy in detecting and diagnosing diabetic retinopathy, which is crucial for the treatment and rehabilitation of diabetic patients. Conclusion: The hybrid neural network model based on EfficientNet and Swin Transformer significantly contributes to the prediction of diabetic retinopathy. By combining local and global features, the model achieves improved prediction accuracy. The validity and utility of the model are verified through experimental evaluations. This research provides robust support for the early diagnosis and treatment of diabetic patients.

The purple acid phosphatase GmPAP17 predominantly enhances phosphorus use efficiency in soybean.

Purple acid phosphatase (PAP) is an important plant acid phosphatase, which can secrete to the rhizosphere to decompose organophosphorus, promote phosphorus use efficiency, plant growth and development. However, little is known about the functions of intracellular PAP in plants, especially for soybean. Our previous study integrating QTL mapping and transcriptome analysis identified an promising low phosphorus (LP)-induced gene GmPAP17. Here, we determined that GmPAP17 was mainly expressed in roots and had a strong response to LP stress. Furthermore, and the relative expression in the root of LP tolerant genotypes NN94-156 was significantly greater than that of LP sensitive genotype Bogao after LP stress treatment. The overexpression of GmPAP17 significantly enhanced both acid phosphatase activity and growth performance of hairy roots under LP stress condition, it was vice versa for RNAi interference of GmPAP17, indicating that GmPAP17 plays an important role in P use efficiency. Moreover, yeast two-hybrid and bimolecular fluorescence complementation analysis showed that GmRAP2.2 was involved in the regulation network of GmPAP17. Taken together, our results suggest that GmPAP17 is a novel plant PAP that functions in the adaptation of soybean to LP stress, possibly through its involvement in P recycling in plants.

GmWRKY46, a WRKY transcription factor, negatively regulates phosphorus tolerance primarily through modifying root morphology in soybean.

Phosphorus (P) deficiency affects soybean growth and development, resulting in significant reduction of yields. However, the regulatory mechanism of P deficiency tolerance in soybean is still largely unclear. WRKY transcription factors are a family of regulators involved in a variety of abiotic stresses in plants while rarely reported in P deficiency. Here, we demonstrated that a soybean GmWRKY46 gene, belonging to group III of WRKY TF family, was involved in the regulation of P deficiency tolerance in soybean. The expression of GmWRKY46 in low P sensitive soybean varieties was significantly higher than that in tolerant soybean varieties. It was primarily expressed in roots and strongly induced by P deprivation. GmWRKY46 was localized in the nucleus. Compared with the control expressing the empty vector, overexpression of GmWRKY46 in soybean hairy roots exhibited more sensitive phenotypes to low P stress, while the RNA interfered GmWRKY46 significantly enhanced P deficiency tolerance by increasing the proliferation, elongation and P absorption efficiency of hairy roots. Expression patterns of a number of P-responsive genes (GmPht1;1, GmPht1;4, GmPTF1, GmACP1, GmPAP21 and GmExpansin-A7) were altered in both overexpression and gene silenced plants. The results provided a novel insight into how soybean responds to low P stress and new gene that may be used to improve soybean low P tolerance through gene editing approach.

Novel target sites for soybean yield enhancement by photosynthesis.

Photosynthesis plays an important role in plant growth and development. Increasing photosynthetic rate is a main objective of improving crop productivity. Chlorophyll fluorescence is an effective method for quickly evaluating photosynthesis. In this study, four representative chlorophyll fluorescence parameters, that is, maximum quantum efficiency of photosystem II, quantum efficiency of PSII, photochemical quenching, and non-photochemical quenching, of 219 diverse soybean accessions were measured across three environments. The underlying genetic architecture was analyzed by genome-wide association study. Forty-eight SNPs were detected to associate with the four traits and explained 10.43-20.41% of the phenotypic variation. Nine candidate genes in the stable QTLs were predicted. Great differences in the expression levels of the candidate genes existed between the high photosynthetic efficiency accessions and low photosynthetic efficiency accessions. In all, we uncover 17 QTLs associated with photosynthesis-related traits and nine genes that may participate in the regulation of photosynthesis, which can provide references for revealing the genetic mechanism of photosynthesis. These QTLs and candidate genes will provide new targets for crop yield improvement through increasing photosynthesis.

Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting soybean genotypes subjected to phosphate starvation.

BACKGROUND: Phosphorus (P) is essential for plant growth and development, and low-phosphorus (LP) stress is a major factor limiting the growth and yield of soybean. Long noncoding RNAs (lncRNAs) have recently been reported to be key regulators in the responses of plants to stress conditions, but the mechanism through which LP stress mediates the biogenesis of lncRNAs in soybean remains unclear. RESULTS: In this study, to explore the response mechanisms of lncRNAs to LP stress, we used the roots of two representative soybean genotypes that present opposite responses to P deficiency, namely, a P-sensitive genotype (Bogao) and a P-tolerant genotype (NN94156), for the construction of RNA sequencing (RNA-seq) libraries. In total, 4,166 novel lncRNAs, including 525 differentially expressed (DE) lncRNAs, were identified from the two genotypes at different P levels. GO and KEGG analyses indicated that numerous DE lncRNAs might be involved in diverse biological processes related to phosphate, such as lipid metabolic processes, catalytic activity, cell membrane formation, signal transduction, and nitrogen fixation. Moreover, lncRNA-mRNA-miRNA and lncRNA-mRNA networks were constructed, and the results identified several promising lncRNAs that might be highly valuable for further analysis of the mechanism underlying the response of soybean to LP stress. CONCLUSIONS: These results revealed that LP stress can significantly alter the genome-wide profiles of lncRNAs, particularly those of the P-sensitive genotype Bogao. Our findings increase the understanding of and provide new insights into the function of lncRNAs in the responses of soybean to P stress.

QTL mapping of the genetic basis of stem diameter in soybean.

MAIN CONCLUSION: QTL mapping of stem diameter was carried out in three RIL populations using a high-density genetic map, and candidate genes related to stem diameter were predicted. Stem diameter is an important agronomic trait affecting soybean lodging and productivity. However, this trait is underexploited, and the underlying genetic mechanism in soybean remains unclear. In this study, three recombinant inbred line (RIL) populations, including 156 F10 lines from Nannong 94-156 × Bogao (N × B), 127 F9 lines from Dongnong 50 × Williams 82 (D × W), and 146 F9 lines from Suinong 14 × Enrei (S × E), were used to identify QTLs for soybean stem diameter across multiple environments. Phenotype analysis revealed that stem diameter exhibited strong positive correlations with plant height and 100-seed weight, two of the most important yield components. A total of 12 QTLs for stem diameter were identified on eight chromosomes across three RIL populations and five environments. The most influential QTL that was stably identified across all the populations and environments, q11, explained 12.58-26.63% of the phenotypic variation. Detection of several environment-specific QTLs, including q14, q16, and q20, suggests that environments may also have important effects in shaping the natural variation in soybean stem diameter. Furthermore, we predicted candidate genes underlying the QTLs and found that several promising candidate genes may be responsible for the variation in stem diameter in soybean. Overall, the markers/genes linked closely or underlying the major QTLs may be used for marker-assisted selection of soybean varieties to enhance lodging resistance and even yield. Our results lay the foundation for the fine mapping of stem development-related genes to reveal the molecular mechanisms.

Genome-wide identification of low phosphorus responsive microRNAs in two soybean genotypes by high-throughput sequencing.

MicroRNAs (miRNAs) have been reported to be correlated with various stress responses in soybean, but only a few miRNAs have been demonstrated to respond to low phosphorus (LP) stress. To unravel the response mechanisms of miRNAs to low-P stress, the roots of two representative soybean genotypes with different P efficiency, Nannong94-156 (a LP-tolerant genotype) and Bogao (a LP-sensitive genotype), were used for the construction of RNA sequencing (RNA-seq) libraries under low/normal-P treatment by high-throughput sequencing. In total, 603 existing miRNAs and 1699 novel miRNAs belonging to 248 and 1582 families in all samples were identified, respectively. Among these miRNAs, 777 miRNAs were differentially expressed (DE) across different P levels and genotypes. Furthermore, putative targets of DE miRNAs were predicted, and these miRNAs mainly targeted ERF (ethylene responsive factor), auxin response factors (ARF), zinc finger protein, MYB, and NAC domain transcription factors. Gene ontology (GO) analysis showed that targets of DE miRNAs were significantly enriched in binding, metabolic processes, biological regulation, response to stress, and phosphorus metabolic processes. In addition, the expression profiles of chosen P-responsive miRNAs and target genes were validated by quantitative real-time PCR (qRT-PCR). Our study focused on genome-wide miRNA identification in two representative soybean genotypes under low-P stress. Overall, the DE miRNAs across different P levels and genotypes and their putative target genes will provide useful information for further study of miRNAs mediating low-P response and facilitate improvements in soybean breeding.

High-density QTL mapping of leaf-related traits and chlorophyll content in three soybean RIL populations.

BACKGROUND: Leaf size and shape, which affect light capture, and chlorophyll content are important factors affecting photosynthetic efficiency. Genetic variation of these components significantly affects yield potential and seed quality. Identification of the genetic basis for these traits and the relationship between them is of great practical significance for achieving ideal plant architecture and high photosynthetic efficiency for improved yield. RESULTS: Here, we undertook a large-scale linkage mapping study using three mapping populations to determine the genetic interplay between soybean leaf-related traits and chlorophyll content across two environments. Correlation analysis revealed a significant negative correlation between leaf size and shape, while both traits were positively correlated with chlorophyll content. This phenotypic relationship was verified across the three mapping populations as determined by principal component analysis, suggesting that these traits are under the control of complex and interrelated genetic components. The QTLs for leaf-related traits and chlorophyll are partly shared, which further supports the close genetic relationship between the two traits. The largest-effect major loci, q20, was stably identified across all population and environments and harbored the narrow leaflet gene Gm-JAG1 (Ln/ln), which is a key regulator of leaflet shape in soybean. CONCLUSION: Our results uncover several major QTLs (q4-1, q4-2, q11, q13, q18 and q20) and its candidate genes specific or common to leaf-related traits and chlorophyll, and also show a complex epistatic interaction between the two traits. The SNP markers closely linked to these valuable QTLs could be used for molecular design breeding with improved plant architecture, photosynthetic capacity and even yield.

Transcriptome-wide identification of novel circular RNAs in soybean in response to low-phosphorus stress.

Low-phosphorus (LP) stress is a major factor limiting the growth and yield of soybean. Circular RNAs (circRNAs) are novel noncoding RNAs that play a crucial role in plant responses to abiotic stress. However, how LP stress mediates the biogenesis of circRNAs in soybean remains unclear. Here, to explore the response mechanisms of circRNAs to LP stress, the roots of two representative soybean genotypes with different P-use efficiency, Bogao (a LP-sensitive genotype) and Nannong 94156 (a LP-tolerant genotype), were used for the construction of RNA sequencing (RNA-seq) libraries and circRNA identification. In total, 371 novel circRNA candidates, including 120 significantly differentially expressed (DE) circRNAs, were identified across different P levels and genotypes. More DE circRNAs were significantly regulated by LP stress in Bogao than in NN94156, suggesting that the tolerant genotype was less affected by LP stress than the sensitive genotype was; in other words, NN94156 may have a better ability to maintain P homeostasis under LP stress. Moreover, a positive correlation was observed between the expression patterns of P stress-induced circRNAs and their circRNA-host genes. Gene Ontology (GO) enrichment analysis of these circRNA-host genes and microRNA (miRNA)-targeted genes indicated that these DE circRNAs were involved mainly in defense responses, ADP binding, nucleoside binding, organic substance catabolic processes, oxidoreductase activity, and signal transduction. Together, our results revealed that LP stress can significantly alter the genome-wide profiles of circRNAs and indicated that the regulation of circRNAs was both genotype and environment specific in response to LP stress. LP-induced circRNAs might provide a rich resource for LP-responsive circRNA candidates for future studies.