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Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. Results: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. Conclusions: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS. The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html. [ABSTRACT FROM AUTHOR]

Vortex-induced vibration (VIV) of long-span bridges can be of large amplitude, which can influence serviceability. Therefore, it is important to predict the response of vortex-induced vibration to aid the management of long-span bridges. A novel data-driven model is proposed to predict the time history of the dynamic response of VIV events. Specifically, the proposed model consists of gated recurrent unit (GRU) neural networks and the Newmark-beta method. GRU neural networks can perform accurate sequential prediction, and the Newmark-beta method can complement the physical meaning of the middle output of the proposed model. To aid the accurate prediction of the amplitude of VIV events, the proposed model employs weighted mean square error as the loss function, which can put more emphasis on the amplitude. The proposed model is validated on measured VIV events of a long-span suspension bridge. The weighted mean absolute percentage error and Pearson correlation coefficient of the trained model indicate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R2 increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales. [ABSTRACT FROM AUTHOR]

Peanuts are prone to insect damage, breakage, germination, mildew, and other defects, which makes the quality of peanuts uneven. The difference in peanut pod quality makes the price and economic benefit also have a big difference. The classification of peanut pods according to quality is an important part of improving the product grade and market competitiveness. Real-time, accurate, and non-destructive quality detection of peanut pods can effectively improve the utilization and commercial value of peanuts. The strong subjectivity of manual detection and the low efficiency and low accuracy of mechanical detection have caused considerable wastage. Therefore, the present study proposed a new convolutional neural network for the peanut pod quality detection algorithm (PQDA) based on an improved ResNet. Compared to previous models, this model is more practical with high accuracy, lightweight, and easy nesting. Firstly, the detection and classification effects of ResNet18, AlexNet, and VGG16 are compared, and ResNet18 was determined to be the best backbone feature extraction network for model training. Secondly, three models were designed to optimize and improve the algorithm. The KRSNet module was added to the algorithm to make the model lightweight. The CSPNet module was added to the algorithm to improve the learning efficiency of each feature layer. The Convolutional Block Attention Module (CBAM) was added to the algorithm to improve its ability to capture more feature information about peanut pods. The experimental ablation results show that the precision of the improved model PQDA reaches 98.1%, and the size of parameters is only 32.63 M. Finally, the optimized model was applied to other peanut pod varieties for generalization experiments, and the accuracy reached 89.6% and 90.0%, indicating the effectiveness of the proposed peanut pod quality detection model. Furthermore, the model is suitable for deployment on embedded resource-limited devices, such as mobile terminals, to achieve the real-time and accurate detection of peanut pod quality. [ABSTRACT FROM AUTHOR]

• Spatial–temporal dimension reduction of rs-fMRI is developed for automatic PD diagnosis. • Both region-specific and patient-specific diagnoses can be achieved with nonlinear manifold learning. • Temporal lobe, partial Parietal lobe, partial Occipital lobe, and motor region have high classifiability for PD diagnosis using rs-fMRI and the proposed method. • Multi-region classification can significantly improve the PD diagnosis accuracy compared with the single region classification (from 87.8% to 96.4%). Parkinson's disease (PD) is one of the leading causes of neurological disability, and its prevalence is expected to increase rapidly in the following few decades. PD diagnosis heavily depends on clinical features using the patient's symptoms. Therefore, an accurate, robust, and non-invasive bio-marker is of critical clinical importance for PD. This study proposes to develop a new bio-marker for PD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI). Unlike most existing rs-fMRI data analytics using correlational analysis, a Topological Machine Learning approach is proposed to construct the bio-marker. The default functional network is identified first using rs-fMRI. Next, rs-fMRI's high dimensional spatial–temporal data structure is mapped on a Riemann Manifold using topological dimensional reduction. Following the topological dimensional reduction, machine learning is used for classification and sensitivity analysis. The proposed methodology is applied to three open fMRI databases for demonstration and validation. The PD diagnosis accuracy can reach 96.4 % when the proposed methodology is used. Thus, rs-fMRI and topological machine learning provide a quantifiable and verifiable bio-marker for future PD early detection and treatment evaluation. [ABSTRACT FROM AUTHOR]

Tire states and capacity monitoring is critical for vehicle and wheel stabilization controls in automated driving and active safety systems. Tire capacity, which represents the performance margin of tire forces from its limits, determines the operational range for vehicle control systems and their actuation through steering or torques at each tire to maintain stability while performing trajectory following. This paper presents a generic tire capacity identification framework that can handle different normal loads, road surface friction, and combined-slip driving scenarios, which are challenging for stabilization and tracking control programs in automated driving systems. A novel measuring method for generating force-training data is designed by combining the indoor tire test procedure and tread rubber friction test rig, in order to obtain adequate and high-quality benchmark datasets. The results from large data sets from road experimenting and indoor tire test facilities, including pure- and combined-slip conditions, confirm effectiveness of the developed learning-based tire capacity estimation which utilizes notions from the model description with bounded uncertainty. More importantly, the proposed method can provide reliable tire properties ranging from the linear to the sliding regions. Further validation is performed on a real test car with on-board sensory measurements, and the results confirm accuracy of the proposed method for various free rolling and hard launch/brake scenarios. [ABSTRACT FROM AUTHOR]

Understanding the dynamic behavior of tires and their interactions with roads plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about tire–road interactions through tire-embedded sensors is desirable for developing enhanced vehicle control systems. Thus, the main objectives of this research are: 1) to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles and 2) to develop a lateral force predictor based on a machine learning tool, more specifically, the Gaussian process regression (GPR) technique. It is determined that the proposed intelligent tire system can provide reliable information about the tire–road interactions even in the case of high slip angles. In addition, lateral force models based on GPR can predict forces very well, outperforming other machine learning models and providing levels of uncertainty that can be useful for designing vehicle control strategies. [ABSTRACT FROM AUTHOR]

Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. Key points: •This mini-review updates the literature on almost all published GSMMs since 1999. •Detailed insights into the development of the first- and next-generation GSMMs. •The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized. [ABSTRACT FROM AUTHOR]

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors. Measurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and duration [ABSTRACT FROM AUTHOR]

The concept of intelligent tires has drawn the attention of researchers in the areas of autonomous driving, advanced vehicle control, and artificial intelligence. The focus of this paper is on intelligent tires and the application of machine learning techniques to tire force estimation. We present an intelligent tire system with a tri-axial acceleration sensor, which is installed onto the inner liner of the tire. Neural Network techniques are used for real-time processing of the sensor data. The accelerometer is capable of measuring the acceleration in x,y, and z directions. When the accelerometer enters the tire contact patch, it starts generating signals until it fully leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual forces are measured. Signals generated by the accelerometer and MTS Flat-Trac testing system are used for training three different machine learning techniques with the purpose of online prediction of tire forces. It is shown that the developed intelligent tire in conjunction with machine learning is effective in accurate prediction of tire forces under different driving conditions. The results presented in this work will open a new avenue of research in the area of intelligent tires, vehicle systems, and tire force estimation. [ABSTRACT FROM AUTHOR]

[Display omitted] Recent advances in artificial intelligence, computer science, communication, sensing and actuation technologies have resulted in the development of several novel intelligent systems. At the same time, the emergence of nanogenerators has opened a new research avenue with the overarching goal of developing self-powered sensing systems. The concepts of self-powered sensing, based on nanogenerators and intelligent systems can be fused together to open a new area of interdisciplinary research. In this article, we aim to show how these two emerging technologies have been combined to develop self-powered intelligent sensing systems. We first focus on the main keywords in the area of nanogenerators. Keyword co-occurrence network graphs are generated based on the most used keywords in the area of nanogenerators to select key concepts that are directly connected to the concept of intelligent systems. Thus, a detailed review is provided on different intelligent self-powered sensing systems based on nanogenerators. We also discuss the challenges presented by combining intelligent systems and self-powered sensing. As most of intelligent devices rely on machine learning techniques, a comprehensive section is allocated to this topic to focus on its applications in nanogenerator-based devices. [ABSTRACT FROM AUTHOR]

Tire slip angle is a vital parameter in tire/vehicle dynamics and control. This paper proposes an accurate estimation method by the fusion of intelligent tire technology and machine-learning techniques. The intelligent tire is equipped by MEMS accelerometers attached to its inner liner. First, we describe the intelligent tire system along with the implemented testing apparatus. Second, experimental results under different loading and velocity conditions are provided. Then, we show the procedure of data processing, which will be used for training five different machine learning techniques to estimate tire slip angles. The results show that the machine learning techniques, especially in frequency domain, can accurately estimate tire slip angles up to 10 degrees. More importantly, with the accurate tire slip angle estimation, all other states and parameters can be easily and precisely obtained, which is significant to vehicle advanced control, and thus this study has a high potential to obviously improve the vehicle safety especially in extreme maneuvers. [ABSTRACT FROM AUTHOR]

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning (ML) algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the reference value of slip ratio can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, ML algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which creates new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation. • An intelligent tire system is presented to develop a novel slip ratio estimation method. • Important features are identified by analyzing the correlation between the accelerations and slip ratio. • Different machine learning algorithms are trained to build the slip ratio estimation models. • The proposed method can accurately estimate the slip ratio range up to ± 30% under different working conditions. [ABSTRACT FROM AUTHOR]

In this paper, an iterative learning control method based on the recognition, response, and memory mechanism of immune system (IRRM-ILC) is proposed. According to the immunological recognition, response, and memory processes, several new components are designed and then combined with the conventional ILC. The proposed IRRM-ILC controller can mitigate the disturbances by detecting, identifying, and memorizing them. Thereby, the anti-disturbance ability of the conventional ILC is enhanced. The proposed IRRM-ILC is applied to a temperature control system of wet spinning coagulation process. Simulation results with random and repeated disturbances demonstrate that the IRRM-ILC has a better performance than the conventional ILC in terms of convergence and stability in the presence of disturbances. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models.Methods: EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis. Three radiomics and three clinical models were then constructed for comparison.Results: Patients with EfficientNetV2-based model score ≤ 0.5 had better TTP than those with higher scores (hazard ratio [HR]: 0.32, 95%CI: 0.22-0.46, P < 0.0001; HR: 0.28, 95%CI: 0.20-0.41, P < 0.0001; and HR: 0.55, 95%CI: 0.36-0.88, P = 0.005 in the training, validation, and test datasets, respectively). Patients with model score ≤ 0.5 had better OS (38.8 months vs 20.9 months, HR: 0.58, 95%CI: 0.37-0.90, P = 0.008). Compared with the radiomics (intra-tumoral and peri-tumoral) and three clinical models, the EfficientNetV2-based model showed better survival prognosis for TACE (P < 0.05) in the test dataset.Conclusions: The EfficientNetV2-based model enables assessment of both TTP and OS prognosis of TACE in treatment-naïve, intermediate-stage HCC. Patients with lower scores will benefit from TACE. The model can potentially be used by clinicians to improve decision making regarding TACE treatment choices. [ABSTRACT FROM AUTHOR]