TEINet: Towards an Efficient Architecture for Video Recognition

Efficiency is an important issue in designing video architectures for action recognition. 3D CNNs have witnessed remarkable progress in action recognition from videos. However, compared with their 2D counterparts, 3D convolutions often introduce a large amount of parameters and cause high computational cost. To relieve this problem, we propose an efficient temporal module, termed as Temporal Enhancement-and-Interaction (TEI Module), which could be plugged into the existing 2D CNNs (denoted by TEINet). The TEI module presents a different paradigm to learn temporal features by decoupling the modeling of channel correlation and temporal interaction. First, it contains a Motion Enhanced Module (MEM) which is to enhance the motion-related features while suppress irrelevant information (e.g., background). Then, it introduces a Temporal Interaction Module (TIM) which supplements the temporal contextual information in a channel-wise manner. This two-stage modeling scheme is not only able to capture temporal structure flexibly and effectively, but also efficient for model inference. We conduct extensive experiments to verify the effectiveness of TEINet on several benchmarks (e.g., Something-Something V1&V2, Kinetics, UCF101 and HMDB51). Our proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efficiency.Comment: Accepted by AAAI 202

Recognize Anything: A Strong Image Tagging Model

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM can recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision

Screening and identification of the dominant antigens of the African swine fever virus

African swine fever is a highly lethal contagious disease of pigs for which there is no vaccine. Its causative agent African swine fever virus (ASFV) is a highly complex enveloped DNA virus encoding more than 150 open reading frames. The antigenicity of ASFV is still unclear at present. In this study, 35 proteins of ASFV were expressed by Escherichia coli, and ELISA was developed for the detection of antibodies against these proteins. p30, p54, and p22 were presented as the major antigens of ASFV, positively reacting with all five clinical ASFV-positive pig sera, and 10 pig sera experimentally infected by ASFV. Five proteins (pB475L, pC129R, pE199L, pE184L, and pK145R) reacted well with ASFV-positive sera. The p30 induced a rapid and strong antibody immune response during ASFV infection. These results will promote the development of subunit vaccines and serum diagnostic methods against ASFV

Shufeng Jiedu Capsules Alleviate Lipopolysaccharide-Induced Acute Lung Inflammatory Injury via Activation of GPR18 by Verbenalin

Background/Aims: Acute respiratory tract infection (ARTI) is the most common reason for outpatient physician office visits. Although powerful and significant in the treatment of infections, antibiotics used for ARTI inappropriately have been an important contributor to antibiotic resistance. We previously reported that Shufeng Jiedu Capsule (SJC) can effectively amplify anti-inflammatory signaling during infection. In this study, we aimed to systematically explore its composition and the mechanism of its effects in ARTI. Methods: Pseudomonas aeruginosa (PAK) strain was used to generate a mouse model of ARTI, which were then treated with different drugs or compounds to determine the corresponding anti-inflammatory roles. High-performance liquid chromatography-quadrupole time of flight-tandem mass spectrometry. was conducted to detect the chemical compounds in SJC. RNAs from the lung tissues of mice were prepared for microarray analysis to reveal globally altered genes and the pathways involved after SJC treatment. Results: SJC significantly inhibited the expression and secretion of inflammatory factors from PAK-induced mouse lung tissues or lipopolysaccharide-induced peritoneal macrophages. Verbenalin, one of the bioactive compounds identified in SJC, also showed notable anti-inflammatory effects. Microarray data revealed numerous differentially expressed genes among the different treatment groups; here, we focused on studying the role of GPR18. We found that the anti-inflammatory role of verbenalin was attenuated in GPR18 knockout mice compared with wild-type mice, although no statistically significant difference was observed in the untreated PAK-induced mice types. Conclusion: Our data not only showed the chemical composition of SJC, but also demonstrated that verbenalin was a significant anti-inflammatory compound, which may function through GPR18

Discovery of [11C]MK-6884: a positron emission tomography (PET) imaging agent for the study of M4 muscarinic receptor positive allosteric modulators (PAMs) in neurodegenerative diseases

The measurement of receptor occupancy (RO) using positron emission tomography (PET) has been instrumental in guiding discovery and development of CNS directed therapeutics. We and others have investigated muscarinic acetylcholine receptor 4 (M4) positive allosteric modulators (PAMs) for the treatment of symptoms associated with neuropsychiatric disorders. In this article, we describe the synthesis, in vitro, and in vivo characterization of a series of central pyridine-related M4 PAMs that can be conveniently radiolabeled with carbon-11 as PET tracers for the in vivo imaging of an allosteric binding site of the M4 receptor. We first demonstrated its feasibility by mapping the receptor distribution in mouse brain and confirming that a lead molecule 1 binds selectively to the receptor only in the presence of the orthosteric agonist carbachol. Through a competitive binding affinity assay and a number of physiochemical properties filters, several related compounds were identified as candidates for in vivo evaluation. These candidates were then radiolabeled with 11C and studied in vivo in rhesus monkeys. This research eventually led to the discovery of the clinical radiotracer candidate [11C]MK-6884

Distributed meter data aggregation framework based on Blockchain and homomorphic encryption

A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber-physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real-time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non-trivial task. This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method

The influence of high temperature on cell damage and shoot survival rates of Plagiomnium acutum

Cell damage and shoot survival rates of Plagiomnium acutum under high temperatures were studied by immersing the moss in hot water in the light (wet) and in dark incubators in air (dry) at various constant temperatures for up to 8 h. At 35-40°C no moss cells were damaged (indicated by expansion, contraction, or breach of chloroplasts, total loss of green colour in cells, or protoplast of cell disintegrating), and all moss shoots survived. At 45°C, damage to both wet and dry moss cells, and death of moss shoots increased with exposure time. No samples survived prolonged exposure at temperatures of 50°C and higher, indicating that the lethal high temperature of Plagiomnium acutum is about 45°C. Mosses in air in a dark constant-temperature chamber required a longer time to exhibit cell damage and death, reaching 100% only at 50°C and above. Age of tissue, exemplified by position on stem, had no apparent effect on these responses

A Surface-Scattering-Based Composite Optical Waveguide Sensor for Aerosol Deposition Detection

Aerosol is a suspension of fine chemical or biological particles in the air, and it is harmful, easily causing air pollution, respiratory diseases, infrastructure corrosion, and poor visibility. Therefore, the development of advanced optical sensors for real-time detection of aerosol deposition is of great significance. In this work, a prism-coupled composite optical waveguide (COWG) sensor for aerosol deposition detection based on surface scattering is proposed and demonstrated theoretically and experimentally. The COWG consists of a single-mode slab glass waveguide locally covered with a tapered thin film of high-index metal oxide. The tapered film can greatly enhance the evanescent field through the adiabatic transition of the fundamental transverse electric (TE0) mode between the uncovered and film-covered regions, thereby enabling the COWG to serve as a simple yet highly sensitive evanescent-wave scattering sensor for sensitive detection of aerosol deposition. The COWG with a tapered layer of Ta2O5 was prepared by masked sputtering, aerosol salt particle deposition on the COWG was successfully detected, and the influence of surface water droplets on the COWG sensor performance was analyzed. The experimental results indicate that the sensitivity of the COWG is 30 times higher than that of the bare glass waveguide

Identify the Virus-like Models for COVID-19 as Bio-Threats: Combining Phage Display, Spectral Detection and Algorithms Analysis

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply the actual virus to study and research because of the highly infectious and pathogenic SARS-CoV-2. In this study, the virus-like models were designed and produced to replace the original virus as bio-threats. Three-dimensional excitation-emission matrix fluorescence and Raman spectroscopy were employed for differentiation and recognition among the produced bio-threats and other viruses, proteins, and bacteria. Combined with PCA and LDA analysis, the identification of the models for SARS-CoV-2 was achieved, reaching a correction of 88.9% and 96.3% after cross-validation, respectively. This idea might provide a possible pattern for detecting and controlling SARS-CoV-2 from the perspective of combining optics and algorithms, which could be applied in the early-warning system against COVID-19 or other bio-threats in the future