Modulation Of Balance Research Articles

Enhanced performance of AlGaN-based deep-UV LED by incorporating carrier injection balanced modulation layer synergistically with polarization-regulating structures

A comparable concentration of carriers injected and transported into the active region, that is, balanced hole and electron injection, significantly affects the optoelectronic performance of AlGaN-based deep ultraviolet light-emitting diodes (DUV LEDs). In this study, we introduce a novel structure characterized by a carrier injection balanced modulation layer, incorporating a polarization-regulating gradient p-AlGaN in a DUV LED. We conducted a systematic examination of its impact on the carrier injection and transport processes. Theoretical simulations demonstrate the mitigation of abrupt variations in Al content at the interface between electron blocking layer/p-AlGaN and p-AlGaN/p-GaN within the valence bands. Consequently, holes are more likely to be injected into the active region rather than accumulating at these interfaces. Meanwhile, due to the reduced barrier height at the top of the valence band, the holes were efficiently transported into the quantum well and confined with comparable and balanced concentrations of electrons by suppressing overflow, thereby promoting the radiative recombination rate. Compared with the conventional DUV LED, the hole concentration and radiative recombination rate of the designed structure in the final quantum well are significantly increased to 179.8% and 232.3%, respectively. The spontaneous emission intensity achieves nearly twice at the same current injection density. Moreover, the efficiency droop is significantly suppressed when operated at a gradually increasing current density. This study presents a promising approach that can serve as a reference for achieving high-efficiency AlGaN-based DUV LEDs.

Low-dose IL-2 Treatment Rescues Cognitive Deficits by Repairing the Imbalance Between Treg and Th17 Cells at the Middle Alzheimer's Disease Stage.

Multiple studies highlight the role of effector and regulatory CD4+T cells in the pathophysiology of Alzheimer's disease, and foster low-dose IL-2 treatment which induces regulatory CD4+T (Treg) cells expansion and activation as a promising strategy for its treatment. However, studies demonstrating discrepant Treg functions in AD have been reported. In addition, a compromised immune system associated with aging may substantially impact on these processes. Here, we report that there is an altered balance of activity between Treg cells and IL-17-producing helper T (Th17) cells in periphery and brain of APP/PS1 mice along the disease progression. A dramatic loss of the healthy balance of activity between Treg and Th17 cells was found at the middle disease stage. While peripheral low-dose recombinant human IL-2 administration could selectively modulate the abundance of Treg cells and repair the imbalance between Treg and Th17 subsets at the middle disease stage. We further show that modulation of peripheral immune balance through low-dose IL-2 treatment reduces the neuro-inflammation and increases numbers of plaque-associated microglia, accompanied by marked reduction of Aβ plaque deposition and slower cognitive declines in APP/PS1 mice at the middle disease stage. Our study highlights the therapeutic potential of repurposed IL-2 for innovative immunotherapy based on modulation of the homeostasis of CD4+T cell subsets in Alzheimer's disease at the middle disease stage.

Sample-balanced and IoU-guided anchor-free visual tracking

Siamese network-based visual tracking algorithms have achieved excellent performance in recent years, but challenges such as fast target motion, shape and scale variations have made the tracking extremely difficult. The regression of anchor-free tracking has low computational complexity, strong real-time performance, and is suitable for visual tracking. Based on the anchor-free siamese tracking framework, this paper firstly introduces balance factors and modulation coefficients into the cross-entropy loss function to solve the classification inaccuracy caused by the imbalance between positive and negative samples as well as the imbalance between hard and easy samples during the training process, so that the model focuses more on the positive samples and the hard samples that make the major contribution to the training. Secondly, the intersection over union (IoU) loss function of the regression branch is improved, not only focusing on the IoU between the predicted box and the ground truth box, but also considering the aspect ratios of the two boxes and the minimum bounding box area that accommodate the two, which guides the generation of more accurate regression offsets. The overall loss of classification and regression is iteratively minimized and improves the accuracy and robustness of visual tracking. Experiments on four public datasets, OTB2015, VOT2016, UAV123 and GOT-10k, show that the proposed algorithm achieves the state-of-the-art performance.

Text-Enhanced Scene Image Super-Resolution via Stroke Mask and Orthogonal Attention

Low-resolution text images are very commonplace in real life and their information is hard to be extracted by using existing text recognition methods only. Although this problem can be solved by introducing super-resolution (SR) techniques, most existing SR methods fail to process stroke regions and background regions of input text images distinctively. In this paper, we propose a text-specific super-resolution network named Text Enhanced Attention Network (TEAN) to solve this problem. First of all, we compensate for disadvantages of traditional thresholding mask operation proposed in Text Super-Resolution Network (TSRN) by utilizing deep-learning based semantic segmentation method to get correct masks as prior semantic information and propose a Text-Segmented-Contextual-Attention (TSCA) branch on the basis of them. Besides, we design an Orthogonal Contextual Attention Module (OCAM) working with TSCA to implicitly enhance stroke regions of LR images. Secondly, to effectively fuse shallow features and deep features of SR model, we propose a convolutional structure named Weight Balanced Fusion Module (WBFM) to improve traditional feature fusion methods of SR network. Finally, extensive experiments on TextZoom dataset demonstrate that the proposed network can improve the recognition accuracy of text images on existing text recognition models. Using TEAN to process low-resolution text images improves the recognition accuracy by 25.4% on CRNN, by 17.4% on ASTER, by 17.3% on MORAN, by 20.7% on NRTR, by 17.3% on SAR and by 15.9% on MASTER compared with directly recognizing them, which attains competitive performances against state-of-the-art methods. Furthermore, cross-dataset experiments on IC15_2077 demonstrate that TEAN is helpful for scene text recognition task, especially for low-resolution images even with the cross-domain issue.

Glia instruct axon regeneration via a ternary modulation of neuronal calcium channels in Drosophila

A neuron’s regenerative capacity is governed by its intrinsic and extrinsic environment. Both peripheral and central neurons exhibit cell-type-dependent axon regeneration, but the underlying mechanism is unclear. Glia provide a milieu essential for regeneration. However, the routes of glia-neuron signaling remain underexplored. Here, we show that regeneration specificity is determined by the axotomy-induced Ca2+ transients only in the fly regenerative neurons, which is mediated by L-type calcium channels, constituting the core intrinsic machinery. Peripheral glia regulate axon regeneration via a three-layered and balanced modulation. Glia-derived tumor necrosis factor acts through its neuronal receptor to maintain calcium channel expression after injury. Glia sustain calcium channel opening by enhancing membrane hyperpolarization via the inwardly-rectifying potassium channel (Irk1). Glia also release adenosine which signals through neuronal adenosine receptor (AdoR) to activate HCN channels (Ih) and dampen Ca2+ transients. Together, we identify a multifaceted glia-neuron coupling which can be hijacked to promote neural repair.

Multispectral Balanced Automatic Fault Diagnosis for Rolling Bearings under Variable Speed Conditions

As a key component of machine, most rolling bearings operate under variable speed conditions. Therefore, it is critical to complete automatic fault diagnosis for rolling bearings under variable speed conditions. Although there have been many research studies on fault diagnosis in recent years, the following two problems still exist in fault diagnosis of variable speed bearing: (1) due to the large range of energy distribution for signals under variable speed conditions, the existed signal processing methods lead to the loss of fault information; (2) when directional filtering is carried out according to four different types of faults, the difference in amplitudes of the obtained spectrums is large. This means that the filtering result with the maximum amplitude will be determined as the fault type by mistake. In order to integrate the information scattered across different frequency spectrums and use reasonable filtering to complete automatic diagnosis, Multispectral Balanced Automatic Fault Diagnosis is proposed for rolling bearings under variable speed conditions. On the one hand, signals are preprocessed by the Multispectral Lossless Preprocessing Module, which can eliminate the influence of variable rotating speeds and avoid the loss of fault information. On the other hand, the Balanced Envelope Demodulation Module is designed to realize automatic fault diagnosis by Protrugram and Balancing Envelope Spectrum. The effectiveness of the proposed method is verified by simulated signals and experimental data. Results indicate that the method can complete automatic fault diagnosis of rolling bearings under variable speed conditions with an accuracy of 76%, which outperforms state-of-the-art methods.

Joint domain symmetry and predictive balance for cross-dataset EEG emotion recognition

Background:Cross-dataset EEG emotion recognition is an extremely challenging task, since data distributions of EEG from different datasets are greatly different, which makes the universal models yield unsatisfactory results. Although there are many methods have been proposed to reduce cross-dataset distribution discrepancies, they still neglected the following two problems. (1) Label space inconsistency: emotional label spaces of subjects from different datasets are different; (2) Uncertainty propagation: the uncertainty of misclassified emotion samples will propagate between datasets. New Method:To solve these problems, we propose a novel method called domain symmetry and predictive balance (DSPB). For the problem of label space inconsistency, a domain symmetry module is designed to make label spaces of source and target domain to be the same, which randomly selects samples from the source domain and put into the target domain. For the problem of uncertainty propagation, a predictive balance module is proposed to reduce the prediction score of incorrect samples and then effectively reduce distribution differences between EEG from different datasets. Results:Experimental results show that our method achieve 61.48% average accuracies on the three cross-dataset tasks. Moreover, we find that gamma is the most relevant to emotion recognition among the five frequency bands, and the prefrontal and temporal brain regions are the channels carrying the most emotional information among the 62 brain channels. Comparison with existing methods:Compared with the partial domain adaptation method (SPDA) and the unsupervised domain adaptation (MS-MDA), our method improves average accuracies by 15.60% and 23.11%, respectively. Conclusion:Besides, data distributions of EEG from different datasets but with the same emotional labels have been well aligned, which demonstrates the effectiveness of DSPB.

Weight-guided feature fusion and non-local balance model for aluminum surface defect detection

Aluminum surface defect detection plays a crucial role in the manufacturing industry. Due to the complexity of aluminum surface defects, the existing defect detection methods have false and missed detection problems. To address the characteristics of aluminum surface defects and the problems of existing methods, we propose a weight-guided feature fusion and non-local balance model to improve the detection effect. Firstly, we design the feature extraction network cross-stage partial ConvNeXt, which achieves adequate feature extraction while reducing the model’s size. In addition, we propose a weight-guided feature fusion and non-local balanced feature pyramid (WBFPN). Specifically, we design a weight-guided feature fusion module to replace the simple feature fusion method so that the WBFPN can suppress interference information when fusing feature maps at different scales. The non-local balancing module captures the long-range dependencies of image features and effectively balances small target defects’ detail and semantic information. Finally, the confidence loss was redefined to effectively solve the problem of poor detection effect caused by the imbalance of positive and negative samples. Experimental results show that the average accuracy of the proposed model reaches 91.9%, and the detection speed is high, which meets the requirement of real-time defect detection.

Evidence for the Beneficial Effects of Brazilian Native Fruits and Their By-Products on Human Intestinal Microbiota and Repercussions on Non-Communicable Chronic Diseases-A Review.

Non-communicable chronic diseases (NCDs) are the most widespread cause of mortality worldwide. Intestinal microbiota balance can be altered by changes in the abundance and/or diversity of intestinal microbiota, indicating a role of intestinal microbiota in NCD development. This review discusses the findings of in vitro studies, pre-clinical studies and clinical trials on the effects of Brazilian native fruits, their by-products, as well as their bioactive compounds on human intestinal microbiota and NCD. The major bioactive compounds in Brazilian native fruits and their by-products, and the impacts of their administration on outcomes linked to intestinal microbiota modulation are discussed. Mechanisms of intestinal microbiota affecting NCD could be linked to the modulation of absorption and energy balance, immune and endocrine systems, and inflammatory response. Brazilian native fruits, such as acerola, açaí, baru, buriti, guava, jabuticaba, juçara, and passion fruit, have several bioactive compounds, soluble and insoluble fibers, and a variety of phenolic compounds, which are capable of changing these key mechanisms. Brazilian native fruits and their by-products can help to promote positive intestinal and systemic health benefits by driving alterations in the composition of the human intestinal microbiota, and increasing the production of distinct short-chain fatty acids and phenolic metabolites, thereby enhancing intestinal integrity and homeostasis. Evidence from available literature shows that the modulatory impacts of Brazilian native fruits and their by-products on the composition and metabolic activity of the intestinal microbiota could improve several clinical repercussions associated with NCD, reinforcing the influence of intestinal microbiota in extra-intestinal outcomes.

Simulating fate and transport of non-steroidal anti-inflammatory drugs using Root Zone Water Quality Model 2.

The fate and transport of non-steroidal anti-inflammatory drugs (NSAIDs) in soil are determined by various processes, and the complexity of the system lends itself to the use of computer simulation models to help understand it. This study demonstrated the first attempt to use empirical data from lab incubation and field studies to parameterize and test a process-based agricultural systems model, Root Zone Water Quality Model 2 (RZWQM2), to simulate the fate and transport of naproxen (NPX), ibuprofen (IBF), and ketoprofen (KTF) in field-based lysimeters amended with alkaline-treated biosolids (ATBs). The model calibrated for the soil-water balance module and contaminant transport module was used to predict water seepage through the soil profile in 2017 and 2018 within a 15% error of the field measured data, with model performance statistics such as Nash-Sutcliffe model efficiency (NSE) and R2 all greater than 0.70. The overall predicted percent recovery of initial spiked NSAIDs in both soil and water samples, after further calibration of the contaminant transport module, was within the same order of magnitude as the measured data. The model underestimated the percent recovery of initial spiked NSAIDs at the 30- to 55-cm soil depth for all treatments on day 3. The calibrated soil subsurface aerobic half-lives of NPX and IBF were found to be considerably lower than their laboratory-measured half-lives obtained from the incubation study. The overall performance of RZWQM2 in simulating the soil hydrology and behavior of NSAIDs in soil profiles receiving various rates of ATB amendments was satisfactory.

Optimal Reconstruction of Single-Pixel Images through Feature Feedback Mechanism and Attention

The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs differently in simulated and real-world experiments. We propose an end-to-end neural network capable of reconstructing 2D images from experimentally obtained 1D signals optimally. In order to improve the image quality of real-time single-pixel imaging, we built a feedback module in the hidden layer of the recurrent neural network to implement feature feedback. The feedback module fuses high-level features of undersampled images with low-level features through dense jump connections and multi-scale balanced attention modules to gradually optimize the feature extraction process and reconstruct high-quality images. In addition, we introduce a learning strategy that combines mean loss with frequency domain loss to improve the network’s ability to reconstruct complex undersampled images. In this paper, the factors that lead to the degradation of single-pixel imaging are analyzed, and a network degradation model suitable for physical imaging systems is designed. The experiment results indicate that the reconstructed images utilizing the proposed method have better quality metrics and visual effects than the excellent methods in the field of single-pixel imaging.

Prognostic and therapeutic implications of iron-related cell death pathways in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a blood cancer that is diverse in terms of its molecular abnormalities and clinical outcomes. Iron homeostasis and cell death pathways play crucial roles in cancer pathogenesis, including AML. The objective of this study was to examine the clinical significance of genes involved in iron-related cell death and apoptotic pathways in AML, with the intention of providing insights that could have prognostic implications and facilitate the development of targeted therapeutic interventions. Gene expression profiles, clinical information, and molecular alterations were integrated from multiple datasets, including TCGA-LAML and GSE71014. Our analysis identified specific molecular subtypes of acute myeloid leukemia (AML) displaying varying outcomes, patterns of immune cell infiltration, and profiles of drug sensitivity for targeted therapies based on the expression of genes involved in iron-related apoptotic and cell death pathways. We further developed a risk model based on four genes, which demonstrated promising prognostic value in both the training and validation cohorts, indicating the potential of this model for clinical decision-making and risk stratification in AML. Subsequently, Western blot analysis showed that the expression levels of C-Myc and CyclinD1 were significantly reduced after CD4 expression levels were knocked down. The findings underscore the potential of iron-related cell death pathways as prognostic biomarkers and therapeutic targets in AML, paving the way for further research aimed at understanding the molecular mechanisms underlying the correlation between iron balance, apoptosis regulation, and immune modulation in the bone marrow microenvironment.

Predicting sympathovagal balance using parameters of breathing patterns in abdominal breathing

Introduction. Abdominal breathing is utilized as a non-pharmacological treatment method for various stress-related conditions and autonomic dysfunctions. The objective of the study was to determine the predictors in the modulation of sympathovagal balance, as indicated by the ratio of low frequency to high frequency power of heart rate variability, by utilizing the respiratory pattern parameters recorded during the abdominal breathing model. Material and methods. The study involved a group of 101 healthy subjects, where the breathing pattern was recorded using a respiratory induction plethysmograph. Heart activity was estimated through electrocardiography, followed by heart rate variability analysis during both resting and abdominal breathing. Eight parameters of the breathing pattern were recorded in the subjects during resting breathing and abdominal breathing, presumed to be predictors of the ratio of low frequency to high frequency power of heart rate variability. Separate predictive models were created for this ratio for both the resting and abdominal breathing types. Results. The multilinear regression analysis revealed that the primary predictor with the highest predictive power for determining the balance between sympathetic and parasympathetic cardiac influence, as indicated by the low frequency spectral power to high frequency spectral power ratio, in individuals practicing abdominal breathing is Tidal Volume (unstandardized coefficient = 5.007). This was followed by the duration of expiration (coefficient = -3.831) and respiratory minute-volume (coefficient = 4.415), both of which were recorded during resting breathing. In the abdominal breathing model, the most effective predictors were found to be time-related parameters, specifically the frequency of breathing during abdominal breathing (coefficient = -5.953), the duration of the inspiratory phase (coefficient = -4.037), and the duration of the expiration phase (coefficient = -4.194). Conclusions. Abdominal breathing has the potential to normalize sympathovagal balance by adjusting the duration of inspiration or expiration. Further studies should be conducted to investigate the practical application of breathing pattern parameters in restoring the low frequency to high frequency (LF/HF) ratio, particularly in disorders characterized by elevated sympathovagal balance.

FocusDet: towards high-quality digital printing fabric defect detection

Deep-learning models have been effectively applied to the fabric defect detection field, in which dilemmas still exist for further improving product quality. For the self-built digital printing fabric defect detection dataset, the dilemmas can be expressed in aspects. First, the existing detection models are more inclined to learn many shot categories (head classes) and directly ignore low shot categories (tail classes); Second, the sampled positive and negative anchors in each mini-batch are not equally important, therefore they should be unequally attended to according to their importance. To solve these problems, in this article, a high-quality model for digital printing fabric defect detection was proposed, termed FocusDet. Specially, we construct the model based on the Faster-RCNN framework with two well-designed modules: the balanced group softmax module and the importance-based sample reweighting module, which improve the detection accuracy. Experimental results demonstrate that our proposed model reaches state-of-the-art accuracy on COCO metrics compared with other advanced detection models in the digital printing fabric defect detection dataset.

Divergent Pharmacology and Biased Signaling of the Four Melanocortin-4 Receptor Isoforms in Rainbow Trout (Oncorhynchus mykiss).

The melanocortin-4 receptor (MC4R) is essential for the modulation of energy balance and reproduction in both fish and mammals. Rainbow trout (Oncorhynchus mykiss) has been extensively studied in various fields and provides a unique opportunity to investigate divergent physiological roles of paralogues. Herein we identified four trout mc4r (mc4ra1, mc4ra2, mc4rb1, and mc4rb2) genes. Four trout Mc4rs (omMc4rs) were homologous to those of teleost and mammalian MC4Rs. Multiple sequence alignments, a phylogenetic tree, chromosomal synteny analyses, and pharmacological studies showed that trout mc4r genes may have undergone different evolutionary processes. All four trout Mc4rs bound to two peptide agonists and elevated intracellular cAMP levels dose-dependently. High basal cAMP levels were observed at two omMc4rs, which were decreased by Agouti-related peptide. Only omMc4rb2 was constitutively active in the ERK1/2 signaling pathway. Ipsen 5i, ML00253764, and MCL0020 were biased allosteric modulators of omMc4rb1 with selective activation upon ERK1/2 signaling. ML00253764 behaved as an allosteric agonist in Gs-cAMP signaling of omMc4rb2. This study will lay the foundation for future physiological studies of various mc4r paralogs and reveal the evolution of MC4R in vertebrates.

A Hybrid Submodule Three-Phase Multiplexing Arm Modular Multilevel Converter With Wide Operation Range and DC-Fault Blocking Capability

Three-phase multiplexing arm modular multilevel converter (TPMA-MMC) possesses lower cost, smaller volume, and higher efficiency compared with MMC. However, the limited operation range limits its popularity. Hence, in this article, it redefines arm submodule (SM) configuration type, where the conventional arm is configured with full-bridge submodules (FBSMs), and the multiplexing arm is formed by half-bridge submodules (HBSMs). Meanwhile, a new energy balance principle and modulation scheme are proposed to expand TPMA-MMC’s operation range. Furthermore, the DC-fault blocking schemes could be implemented to realize DC-fault tolerance reliably without the multiplexing arm blocking. Compared with hybrid MMC(H-MMC), it could reduce by 33.33% in submodules a least, which leads lower footprint and operation loss. Due to reregulating the energy flowing path of multiplexing arm, and rearranging the chain-link voltage of multiplexing and conventional arm, it could reduce the required SM capacitance and further decrease 65% energy storage requirement, resulting in improving the power density. Finally, the topology control scheme and DC-fault blocking scheme are verified by simulation and scale-down prototype experimental results.

Road Traffic Sign Detection Method Based on RTS R-CNN Instance Segmentation Network.

With the rapid development of the autonomous driving industry, there is increasing research on related perception tasks. However, research on road surface traffic sign detection tasks is still limited. There are two main challenges to this task. First, when the target object's pixel ratio is small, the detection accuracy often decreases. Second, the existing publicly available road surface traffic sign datasets have limited image data. To address these issues, this paper proposes a new instance segmentation network, RTS R-CNN, for road surface traffic sign detection tasks based on Mask R-CNN. The network can accurately perceive road surface traffic signs and provide important information for the autonomous driving decision-making system. Specifically, CSPDarkNet53_ECA is proposed in the feature extraction stage to enhance the performance of deep convolutional networks by increasing inter-channel interactions. Second, to improve the network's detection accuracy for small target objects, GR-PAFPN is proposed in the feature fusion part, which uses a residual feature enhancement module (RFA) and atrous spatial pyramid pooling (ASPP) to optimize PAFPN and introduces a balanced feature pyramid module (BFP) to handle the imbalanced feature information at different resolutions. Finally, data augmentation is used to generate more data and prevent overfitting in specific scenarios. The proposed method has been tested on the open-source dataset Ceymo, achieving a Macro F1-score of 87.56%, which is 2.3% higher than the baseline method, while the inference speed reaches 23.5 FPS.

Membrane estrogen receptor-α contributes to female protection against high-fat diet-induced metabolic disorders.

Estrogen Receptor α (ERα) is a significant modulator of energy balance and lipid/glucose metabolisms. Beyond the classical nuclear actions of the receptor, rapid activation of intracellular signaling pathways is mediated by a sub-fraction of ERα localized to the plasma membrane, known as Membrane Initiated Steroid Signaling (MISS). However, whether membrane ERα is involved in the protective metabolic actions of endogenous estrogens in conditions of nutritional challenge, and thus contributes to sex differences in the susceptibility to metabolic diseases, remains to be clarified. Male and female C451A-ERα mice, harboring a point mutation which results in the abolition of membrane localization and MISS-related effects of the receptor, and their wild-type littermates (WT-ERα) were maintained on a normal chow diet (NCD) or fed a high-fat diet (HFD). Body weight gain, body composition and glucose tolerance were monitored. Insulin sensitivity and energy balance regulation were further investigated in HFD-fed female mice. C451A-ERα genotype had no influence on body weight gain, adipose tissue accumulation and glucose tolerance in NCD-fed mice of both sexes followed up to 7 months of age, nor male mice fed a HFD for 12 weeks. In contrast, compared to WT-ERα littermates, HFD-fed C451A-ERα female mice exhibited: 1) accelerated fat mass accumulation, liver steatosis and impaired glucose tolerance; 2) whole-body insulin resistance, assessed by hyperinsulinemic-euglycemic clamps, and altered insulin-induced signaling in skeletal muscle and liver; 3) significant decrease in energy expenditure associated with histological and functional abnormalities of brown adipose tissue and a defect in thermogenesis regulation in response to cold exposure. Besides the well-characterized role of ERα nuclear actions, membrane-initiated ERα extra-nuclear signaling contributes to female, but not to male, protection against HFD-induced obesity and associated metabolic disorders in mouse.

Knowledge sharing-based multi-block federated learning for few-shot oil layer identification

Oil Layer Identification (OLI) plays a significant role in petroleum development, but is unavoidably negatively affected by small samples, non-reservoirs interference, and layer classes imbalance. Existing transfer learning methods partially address the problem of insufficient samples in OLI. Nevertheless, they ignore the geological differences between blocks. This paper introduces Multi-Block Federated Learning (MBFL) to train a generalized pre-trained model, which consists of a Mask Attention Network (MAN), Class Balance Module (CBM), and Dynamic Weighted Fusion (DWF). MBFL uses MAN to dynamically avoid non-reservoir interference while learning the relationship between reservoirs and non-reservoirs; MBFL uses CBM to overcome layer classes imbalance and uses DWF to optimize traditional federated learning for addressing geological differences among blocks. Experimental results demonstrate that MBFL achieves an average accuracy of 89.32%, and an average F1 score of 81.74% with a real-world dataset. The results of two public datasets demonstrate the generalization of MBFL.

Light-mediated anthocyanin biosynthesis in rose petals involves a balanced regulatory module comprising transcription factors RhHY5, RhMYB114a, and RhMYB3b.

Roses are significant botanical species with both ornamental and economic value, displaying diverse floral traits, particularly an extensive array of petal colors. The red pigmentation of rose petals is predominantly attributed to anthocyanin accumulation. However, the underlying regulatory mechanism of anthocyanin biosynthesis in roses remains elusive. This study presents a novel light-responsive regulatory module governing anthocyanin biosynthesis in rose petals, which involves the transcription factors RhHY5, RhMYB114a, and RhMYB3b. Under light conditions (1000-1500 μmol m-2 s-1), RhHY5 represses RhMYB3b expression and induces RhMYB114a expression, positively regulating anthocyanin biosynthesis in rose petals. Notably, activation of anthocyanin structural genes probably involves an interaction and synergy between RhHY5 and the MYB114a-bHLH3-WD40 complex. Additionally, RhMYB3b is activated by RhMYB114a to prevent excessive accumulation of anthocyanin. Conversely, under low light conditions (<10 μmol m-2 s-1), the degradation of RhHY5 leads to down-regulation of RhMYB114a and up-regulation of RhMYB3b, which in turn inhibits the expression of both RhMYB114a and anthocyanin structural genes. Additionally, RhMYB3b competes with RhMYB114a for binding to RhbHLH3 and the promoters of anthocyanin-related structural genes. Overall, our study uncovers a complex light-mediated regulatory network that governs anthocyanin biosynthesis in rose petals, providing new insights into the molecular mechanisms underlying petal color formation in rose.