Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and cellular signaling by modulating protein-protein interactions (PPIs). It alters binding affinities and interaction networks, thereby influencing biological processes and maintaining cellular homeostasis. Experimental validation of these effects is labor-intensive and expensive, highlighting the need for efficient computational approaches. We propose DeepPhosPPI, the first sequence-based deep learning framework for phosphorylation effects on PPIs prediction, which employs the pre-trained protein language model for feature embedding, with ProtBERT and ESM-2 as alternative backbone encoders. By combining attention-based convolutional neural network and Transformer models, DeepPhosPPI accurately predicts phosphorylation effects. The experimental results show that DeepPhosPPI consistently outperforms state-of-the-art methods in multiple tasks, including functional sites identification and regulatory effect classification.

Proteins commonly perform biological functions through protein-protein interactions (PPIs). The knowledge of PPI sites is imperative for the understanding of protein functions, disease mechanisms, and drug design. Traditional biological experimental methods for studying PPI sites still incur considerable drawbacks, including long experimental time and high labor costs. Therefore, many computational methods have been proposed for predicting PPI sites. However, achieving high prediction performance and overcoming severe data imbalance remain challenging issues. In this paper, we propose a new sequence-based deep learning model called CLPPIS (standing for CNN-LSTM ensemble based PPI Sites prediction). CLPPIS consists of CNN and LSTM components, which can capture spatial features and sequential features simultaneously. Further, it utilizes a novel feature group as input, which has 7 physicochemical, biophysical, and statistical properties. Besides, it adopts a batch-weighted loss function to reduce the interference of imbalance data. Our work suggests that the integration of protein spatial features and sequential features provides important information for PPI sites prediction. Evaluation on three public benchmark datasets shows that our CLPPIS model significantly outperforms existing state-of-the-art methods.

With the rising of demands for novel human-computer interaction approaches in the 2D plane, a number of intelligent devices come into being. For example, Microsoft Surface Dial supports simple clicks and rotations for the interaction with computer. However, these approaches are dedicated devices, and they might require batteries or have limited functions. In this paper, we propose RF-Dial to realize a light-weight, battery-free and functional 2D human-computer interaction solution via commercial off-the-shelf (COTS) passive RFID tags. What RF-Dial shines is that it can easily turn an ordinary object, e.g., a board eraser, into an intelligent interaction device. By deploying a tag array on the side face of the object together with a dipole tag on the top face, RF-Dial cannot only track the rigid motion of the object but also detect the touch gesture of a user on the surface of the object, including translation, rotation, click, press and hold, and swipe. To do the motion tracking, RF-Dial builds a phase-based model that captures the translation and the rotation of the tagged object simultaneously, by jointly exploiting the information of phase variations and the topology of the tag array. To detect the touch gesture, RF-Dial builds an RSSI-based model that uses the impact of the touching finger on the tag antenna’s impedance to estimate the touch position in real time, which is robust to environmental factors like position or orientation. We implemented a prototype of RF-Dial with commodity RFID devices. Extensive experiments show that RF-Dial achieves an accurate rigid motion tracking, with a small error of 0.6cm for the translation tracking, and a small error of 1.9 degrees for the rotation estimation. Besides, RF-Dial can also detect the touch gesture accurately, as the 90 percent of touch position errors are less than 2.09mm.

Currently, the logistic industry has introduced 3D reconstruction to monitor the package placement in the warehouse. Previous 3D reconstruction solutions mainly utilize computer vision or sensor-based methods, which are restricted to the line-of-sight or the battery life. Therefore, we propose a passive RFID-based solution, called RF-3DScan, to perform 3D reconstruction on tagged packages, including the package orientation and the package stacking. The basic idea is that a moving antenna can obtain RF-signals from the tags attached on packages with the 1D linear mobile scanning. Through extracting phase differences to build angle profiles for each tag, RF-3DScan derives their relative positions, further determines the package orientation and the coarse-grained package stacking. By simply performing the 2D scanning, RF-3DScan can provide the fine-grained package stacking determination. We implement a prototype system of RF-3DScan and evaluate its performance in real settings. Our experiment results show that RF-3DScan can achieve about 92.5 percent identification accuracy of the bottom face, and an average error about 4.08° of thethe rotation angle. For the package stacking, 1D scanning can achieve the comparable performance in comparison with 2D scanning.

Nowadays, the demand for novel approaches of 2D human-computer interaction has enabled the emergence of a number of intelligent devices, such as Microsoft Surface Dial. Surface Dial realizes 2D interactions with the computer via simple clicks and rotations. In this paper, we propose RF-Dial, a battery-free solution for 2D human-computer interaction based on RFID tag arrays. We attach an array of RFID tags on the surface of an object, and continuously track the translation and rotation of the tagged object with an orthogonally deployed RFID antenna pair. In this way, we are able to transform an ordinary object like a board eraser into an intelligent HCI device. According to the RF-signals from the tag array, we build a geometric model to depict the relationship between the phase variations of the tag array and the rigid transformation of the tagged object, including the translation and rotation. By referring to the fixed topology of the tag array, we are able to accurately extract the translation and rotation of the tagged object during the moving process. Moreover, considering the variation of phase contours of the RF-signals at different positions, we divide the overall scanning area into the linear region and non-linear region in regard to the relationship between the phase variation and the tag movement, and propose tracking solutions for the two regions, respectively. We implemented a prototype system and evaluated the performance of RF-Dial in the real environment. The experiments show that RF-Dial achieved an average accuracy of 0. 6cm in the translation tracking, and an average accuracy of 1.9°in the rotation tracking.

Nowadays, novel approaches of 3D human-computer interaction have enabled the capability of manipulating in the 3D space rather than 2D space. For example, Microsoft Surface Pen leverages the embedded sensors to sense the 3D manipulations, such as inclining the pen to get bolder handwriting. In this paper, we propose RF-Brush, a battery-free and light-weight solution for 3D human-computer interaction based on RFID, by simply attaching a linear RFID tag array onto the linear shaped object like a brush. RF-Brush senses the 3D orientation and 2D movement of the linear shaped object, when the human subject is drawing with this object in the 3D space. Here, the 3D orientation refers to the relative orientation of the linear shaped object to the operating plane, whereas the 2D movement refers to the moving trace in the 2D operating plane. In this way, we are able to transform an ordinary linear shaped object like a brush or pen to an intelligent HCI device. Particularly, we build two geometric models to depict the relationship between the RF-signal and the 3D orientation as well as 2D movement, respectively. Based on the geometric model, we propose the linear tag array-based HCI solution, implemented a prototype system, and evaluated the performance in real environment. The experiments show that RF-Brush achieves an average error of 5.7° and 8.6° of elevation and azimuthal angle, respectively, and an average error of 3.8cm and 4.2cm in movement tracking along X-axis and Y-axis, respectively. Moreover, RF-Brush achieves 89% in letter recognition accuracy.

Nowadays, 3D reconstruction has been introduced in monitoring the package placement in logistic industry-related applications. Existing 3D econstruction methods are mainly based on computer vision or sensor-based approaches, which are limited by the line-of-sight or battery life constraint. In this paper, we propose RF-3DScan to perform 3D reconstruction on tagged packages via passive RFID, by attaching multiple reference tags onto the surface of the packages. The basic idea is that by moving the antenna along straight lines within a constrained 2-dimensional space, the antenna obtains the RF-signals of the reference tags attached on the packages. By extracting the phase differences to build the angle profile for each tag, RF-3DScan can compare the angle profiles of the different reference tags and derive their relative positions, then further determine the package orientation and stacking for 3D reconstruction. We implement RF- 3DScan and evaluate its performance in real settings. The experiment results show that the average identification accuracy of the bottom face is about 92.5%, and the average estimation error of the rotation angle is about 4.08o.