Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the development of single-HSI-SR lags far behind that of RGB natural images. In recent years, research on RGB SR has shown that models pre-trained on large-scale benchmark datasets can greatly improve performance on unseen data, which may stand as a remedy for HSI. But how can we transfer the pre-trained RGB model to HSI, to overcome the data-scarcity bottleneck? Because of the significant difference in the channels between the pre-trained RGB model and the HSI, the model cannot focus on the correlation along the spectral dimension, thus limiting its ability to utilize on HSI. Inspired by the HSI spatial-spectral decoupling, we propose a new framework that first fine-tunes the pre-trained model with the spatial components (known as eigenimages), and then infers on unseen HSI using an iterative spectral regularization (ISR) to maintain the spectral correlation. The advantages of our method lie in: 1) we effectively inject the spatial texture processing capabilities of the pre-trained RGB model into HSI while keeping spectral fidelity, 2) learning in the spectral-decorrelated domain can improve the generalizability to spectral-agnostic data, and 3) our inference in the eigenimage domain naturally exploits the spectral low-rank property of HSI, thereby reducing the complexity. This work bridges the gap between pre-trained RGB models and HSI via eigenimages, addressing the issue of limited HSI training data, hence the name EigenSR. Extensive experiments show that EigenSR outperforms the state-of-the-art (SOTA) methods in both spatial and spectral metrics.

Drug combination is a promising therapeutic strategy for complex diseases. However, only a small fraction of potential drug combinations exhibit true synergistic effects, making the prediction of drug synergy a critical yet challenging task. In this study, we propose BridgeSyn, a novel bridge fusion framework for drug synergy prediction. BridgeSyn leverages the knowledge from pretrained biological language models to enrich both drug compound and cell line representations. We introduce a bridging fusion mechanism that employs a set of shared latent tokens derived from global features, serving as a semantic interface to effectively fuse the representations of drug pairs and cell lines. By combining biological prior knowledge with this fusion strategy, BridgeSyn can capture complex biological interactions and achieve superior prediction results. Extensive experiments on two public datasets demonstrate that BridgeSyn consistently outperforms existing computation methods.

This article examines the issue of periodic model predictive control (PMPC) output feedback-based for a class of uncertain norm-bounded systems over digital communication network. A novel adaptive event-triggered stochastic communication protocol (AETSCP) scheduling is devised to alleviate the communication burden of the shared network. Meanwhile, the security issue of hybrid attacks, involving Denial of Service (DoS) attacks and deception attacks on the system, is considered. The quadratic boundedness (QB) technique is utilized to represent the closed-loop stability of the related networked control systems (NCSs). In addition, the periodic model predictive controller, which allows system states to temporarily leave the ellipsoidal set but return to the original ellipsoidal set at finite time steps, is synthesized based on the dynamic output feedback. Subsequently, sufficient conditions are provided to ensure that the augmented states gradually converge and the estimation error bound becomes tighter. It is demonstrated that the proposed algorithm enlarges the attraction region and yields better control performance. Finally, the validity of our proposed algorithm is confirmed by a simulation example.

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

This paper investigates the decay aggregation efficient output feedback model predictive control (DAEOFMPC) problem for networked interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy systems with the adaptive event-triggered (AET) mechanism and deception attack. First of all, this paper employs the AET mechanism to save more communication resources in the networked control system (NCS), considers the impact of deception attacks on the control performance of the extended state system, and introduces a Bernoulli random variable to represent the occurrence of deception attack events. Followed by two sufficient conditions to deal with the gain matrix of the state observer and the estimation error. Then, the decay aggregation strategy is used, and after offline designing an ellipse as large as possible in the direction of the short axis of the projection ellipse of the original elliptic invariant set so that the two ellipses intersect each other, the additional perturbation variables are online optimized, and convex combinations are performed, which reduces the online computational burden and enlarges the initial feasible region. Finally, according to Lyapunov stability theory, the stability of the augmented closed-loop system under deception attacks is guaranteed and satisfies input constraints. By using the CSTR simulation example, the feasibility and validity of the proposed method in this paper are verified.

The expanding utilization of unmanned aerial vehicle (UAV) remote sensing (RS) technology has significantly advanced crop monitoring and detection. Despite its widespread application, the use of UAVs for examining rice grain starch accumulation (GSA) remains in its infancy. The preflowering nutritional organs’ nonstructural carbohydrate transport and the postflowering plant’s photosynthesis products are the primary sources of GSA. This study constructs a dynamic change curve based on the spectral index (SI) red edge re-normalized different vegetation index (RERDVI) before rice flowering. It introduces a novel indicator, the preflowering biomass accumulation dynamics (PBAD), identified through the dynamic curve’s distinct shape characteristics. Results show that PBAD has a good correlation with the aboveground biomass (AGB) at different preflowering stages. After flowering, a nutrient distribution composite index (NDCI) is developed by combining SIs and color indices (CIs), providing a precise monitoring tool for the nitrogen harvest index (NHI), which is important in GSA. By comprehensively considering preflowering nonstructural carbohydrate accumulation (AGB), postflowering photosynthetic capacity (NHI), canopy temperature depression (CTD) sensitive to GSA, and meteorological factors (sunshine duration (SSD) and precipitation), a GSA estimation model based on multisource RS data fusion was constructed using a multiple linear regression (MLR), random forest regression (RFR), and extreme gradient boosting (XGBoost). This approach significantly improved the accuracy of GSA estimation, with the XGBoost model achieving a validation$R^{2}$of 0.76 and a root mean square error (RMSE) of 0.11 kg/m2 on a multiecological dataset, notably reducing the underestimation observed in traditional linear models.

Hyperspectral anomaly detection (HAD) has attracted increasing attention due to its economical and efficient applications. The main challenge lies in the data-starved problem of hyperspectral images (HSIs) and the costliness of manual annotation, making it heavily reliant on the model’s adaptability and robustness to unseen scenes under limited samples. Self-supervised learning offers a solution to this urgency via mining meaningful representations from the data itself. One promising paradigm is leveraging untrained neural networks to reconstruct the background component for revealing anomalous information. Its capability stems from the network architecture and the training process rather than learning from expensive and strongly domain-dependent data, which is naturally applicable to HAD. In this article, to handle the urgent requirement for self-supervised learning in HAD, we propose a multiscale network (termed MSNet) that detects anomalies with enhanced separation training. The network architecture consists of several multiscale convolutional encoder-decoder (CED) layers, considering the spatial characteristics of the anomalies. To suppress the anomalies during background reconstruction, we adopt a new separation training strategy by introducing a soft separator for better practicality on larger datasets. Extensive experiments conducted on five commonly used datasets and the HAD100 dataset, demonstrate the superiority of our method over its counterparts. Our code is available athttps://github.com/enter-i-username/MSNet.

Spectral remote sensing can effectively, rapidly, and nondestructively detect the nitrogen status of crop plants. Estimation of crop leaf nitrogen concentration (LNC, %) using canopy bidirectional reflectance factor (BRF) is an effective method to diagnose nitrogen deficiency in crops. It is challenging to estimate LNC with empirical remote sensing models because the variability of the canopy structure at different growth stages affects the model accuracy. Over the years, the canopy scattering coefficient [CSC, the ratio of BRF to directional area scattering factor (DASF)] has been used for LNC estimation by suppressing the effect of the canopy structure on BRF. However, this method often regards leaves as the main factor and has less consideration for the canopy structure effects on BRF caused by other organs (e.g., panicles). Incorporating the changes with the emergence of rice panicles into the DASF algorithm may generate reliable results in LNC estimation. Herein, we propose the PROSPECT-P model, which is based on the PROSPECT model and combines the panicle spectra to quantify the structural properties of the panicles and realize the simulation of the panicle albedo at different growth stages. Utilizing the spectral invariants theory, a panicle-leaf structure correction factor (DASFLP) was calculated based on canopy BRF, panicle albedo, leaf albedo, and canopy component fraction. CSC after correction for panicle and leaf structure (CSCLP) from 400–2500 nm can be obtained by the ratio of BRF and DASFLP. The CSCLP was further subjected to continuous wavelet analysis (CWA) and achieved an accurate estimation of the LNC using four machine learning (ML) models. The results showed that when combined with eight wavelet features (WFs), CSCLP can accurately invert rice LNC using the random forest algorithm ($ {R} ^{2} =0.81$, RMSE =0.30, RE =14.02%), which was more exact than CSC ($ {R} ^{2} =0.76$, RMSE =0.33, RE =16.13%) that corrected only for leaf structure. Moreover, the results on UAV multispectral also showed that UAV-CSCLP predicted LNC by XGBoost model ($ {R} ^{2} =0.61$, RMSE =0.35, RE =17.27%) more accurately than the traditional method UAV-CSC ($ {R} ^{2} =0.50$, RMSE =0.40, RE =19.52%) on the independent test set. Herein, we propose the accurate inversion of crop growth parameters by remote sensing using PROSPECT-P to correct for panicle and leaf structure effects.

The sparse regression method is known for its ability to unmix hyperspectral data, but it can be computationally expensive and accurately insufficient due to the large scale and high coherence of the spectral library. To address this issue, a new approach called layered sparse regression unmixing (termed LSU) has been proposed in this paper. This method involves breaking down the sparse unmixing process into multilayers, each of which interactively learns a row-sparsity-promoting abundance matrix and fine-tunes active library atoms based on measured activeness. By doing so, LSU outputs both a learned abundance matrix and an optimal library that can best model each mixed pixel in the scene. The proposed LSU can be efficiently solved by the alternating direction method of the multipliers framework. Experimental results obtained from simulated and real hyperspectral images demonstrate the effectiveness of LSU. The demo of the proposed LSU will be publicly available at https://github.com/XiangfeiShen/Layered_Sparse_Regression_Unmixing.

The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.