Splicing-dependent NMD does not require the EJC in Schizosaccharomyces pombe

Nonsense-mediated mRNA decay (NMD) is a translation-linked process that destroys mRNAs with premature translation termination codons (PTCs). In mammalian cells, NMD is also linked to pre-mRNA splicing, usually PTCs trigger strong NMD only when positioned upstream of at least one intron. The exon junction complex (EJC) is believed to mediate the link between splicing and NMD in these systems. Here, we report that in Schizosaccharomyces pombe splicing also enhances NMD, but against the EJC model prediction, an intron stimulated NMD regardless of whether it is positioned upstream or downstream of the PTC and EJC components are not required. Still the effect of splicing seems to be direct—we have found that the important NMD determinant is the proximity of an intron to the PTC, not just the occurrence of splicing. On the basis of these results, we propose a new model to explain how splicing could affect NMD

9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation

The advancements in vision-based tactile sensors have boosted the aptitude of robots to perform contact-rich manipulation, particularly when precise positioning and contact state of the manipulated objects are crucial for successful execution. In this work, we present 9DTact, a straightforward yet versatile tactile sensor that offers 3D shape reconstruction and 6D force estimation capabilities. Conceptually, 9DTact is designed to be highly compact, robust, and adaptable to various robotic platforms. Moreover, it is low-cost and easy-to-fabricate, requiring minimal assembly skills. Functionally, 9DTact builds upon the optical principles of DTact and is optimized to achieve 3D shape reconstruction with enhanced accuracy and efficiency. Remarkably, we leverage the optical and deformable properties of the translucent gel so that 9DTact can perform 6D force estimation without the participation of auxiliary markers or patterns on the gel surface. More specifically, we collect a dataset consisting of approximately 100,000 image-force pairs from 175 complex objects and train a neural network to regress the 6D force, which can generalize to unseen objects. To promote the development and applications of vision-based tactile sensors, we open-source both the hardware and software of 9DTact, along with a comprehensive video tutorial, all of which are available at https://linchangyi1.github.io/9DTact.Comment: Project Website: https://linchangyi1.github.io/9DTact

Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach

Large numbers of radiographic images are available in knee radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels due to limitations of human annotations. The purpose of our study was to develop an automated labeling approach that improves the image classification model to distinguish normal knee images from those with abnormalities or prior arthroplasty. The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled data, further improving the image classification performance for knee radiographic diagnosis. We developed our approach using 7,382 patients and validated it on a separate set of 637 patients. The final image classification model, trained using both manually labeled and pseudo-labeled data, had the higher weighted average AUC (WAUC: 0.903) value and higher AUC-ROC values among all classes (normal AUC-ROC: 0.894; abnormal AUC-ROC: 0.896, arthroplasty AUC-ROC: 0.990) compared to the baseline model (WAUC=0.857; normal AUC-ROC: 0.842; abnormal AUC-ROC: 0.848, arthroplasty AUC-ROC: 0.987), trained using only manually labeled data. DeLong tests show that the improvement is significant on normal (p-value<0.002) and abnormal (p-value<0.001) images. Our findings demonstrated that the proposed automated labeling approach significantly improves the performance of image classification for radiographic knee diagnosis, allowing for facilitating patient care and curation of large knee datasets.Comment: This is the preprint versio

Secondary frequency control of islanded microgrid considering wind and solar stochastics

As the high penetration of wind and photovoltaic distributed generation (DG) in the microgrid, the stochastic and low inertia emerge, bringing more challenges especially when the microgrid operates in isolated islands. Nevertheless, the reserve power of DGs in deloading control mode can be utilized for frequency regulation and mitigating frequency excursion. This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming(QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reDGce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.Comment: Accepted by Acta energiae solaris sinica [In Chinese

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products

The characteristics analysis and cogging torque optimization of a surface-interior permanent magnet synchronous motor

This paper proposes optimal stator skewed slot analytical method for cogging torque reduction in surface-interior permanent magnet synchronous motor(SIPMSM) and analyzes the characteristics of SIPMSM. The series-parallel equivalent magnetic circuit models(EMCMs) of SIPMSM is built based on the characteristics of magnetic circuits, which is used to design the basic electromagnetic parameters of SIPMSM. Analytical expressions of cogging torque are derived from applying analytical techniques. Stator skewed slot for cogging torque minimum is adopted, and the stator skewed slot pitch is confirmed based on the analytical expressions of the resultant cogging torque. The cogging torque, torque ripple, back electromotive force(back-EMF), power-angle characteristics, efficiency and power factor of SIPMSM are analyzed by establishing 3-dimensional finite element model(3-D-FED) of SIPMSM with stator skewed slot and straight slot. It is shown that the comprehensive performance of optimized SIPMSM is improved as confirmed by finite element analysis and analytical calculation results

Elucidating thermochemical pretreatment effectiveness of different particle-size switchgrass for cellulosic ethanol production

Effects of switchgrass particle sizes (\u3c0.25 mm, 0.5–1.0 mm, and 2.0–4.0 mm) on the effectiveness of H2SO4 and NaOH pretreatments were investigated. As particle size increased, glucan, xylan, and lignin contents in raw switchgrass augmented from 30.32% to 32.02%, 18.44% to 19.03%, and 14.78% to 15.33%, respectively. Glucan and xylan (58.54–60.94% and 18.55–20.01%) contents in NaOH pretreated switchgrass and their recoveries (91.95–94.69% and 47.91–52.31%) increased. The highest glucan content (55.76%) and recovery (79.72%) in H2SO4 pretreated switchgrass were reached by middle particle size. The lowest (59.39% for H2SO4 and 58.99% for NaOH) and highest (65.23% for H2SO4 and 66.15% for NaOH) CrI values were obtained from middle and small particle sizes, respectively. SEM images and FTIR spectra showed no visible variations in microstructures and chemical bonds among different particle sizes under the same pretreatment conditions. On the basis of pretreated switchgrass, the highest ethanol concentration and efficiency were reached by big particle size for H2SO4 pretreated (7.03 g/L and 49.28%) switchgrass, while they were achieved by small particle size for NaOH pretreated (11.68 g/L and 72.37%) switchgrass. The highest ethanol yield based on raw switchgrass was attained by big particle size for untreated (29.54%), middle particle size for H2SO4 pretreated (30.60%), and small particle size for NaOH pretreated (62.36%) switchgrass. These findings indicate that the optimal ethanol conversion performance is the result of the interaction between the pretreatment method and biomass particle size