A novel trifunctional IgG-like bispecific antibody to inhibit HIV-1 infection and enhance lysis of HIV by targeting activation of complement

BACKGROUND: The complement system is not only a key component of innate immunity but also provides a first line of defense against invading pathogens, especially for viral pathogens. Human immunodeficiency virus (HIV), however, possesses several mechanisms to evade complement-mediated lysis (CoML) and exploit the complement system to enhance viral infectivity. Responsible for this intrinsic resistance against complement-mediated virolysis are complement regulatory membrane proteins derived from the host cell that inherently downregulates complement activation at several stages of the cascade. In addition, HIV is protected from complement-mediated lysis by binding soluble factor H (fH) through the viral envelope proteins, gp120 and gp41. Whereas inhibition of complement activity is the desired outcome in the vast majority of therapeutic approaches, there is a broader potential for complement-mediated inhibition of HIV by complement local stimulation. PRESENTATION OF THE HYPOTHESIS: Our previous studies have proven that the complement-mediated antibody-dependent enhancement of HIV infection is mediated by the association of complement receptor type 2 bound to the C3 fragment and deposited on the surface of HIV virions. Thus, we hypothesize that another new activator of complement, consisting of two dsFv (against gp120 and against C3d respectively) linked to a complement-activating human IgG1 Fc domain ((anti-gp120 × anti-C3d)-Fc), can not only target and amplify complement activation on HIV virions for enhancing the efficiency of HIV lysis, but also reduce the infectivity of HIV through blocking the gp120 and C3d on the surface of HIV. TESTING THE HYPOTHESIS: Our hypothesis was tested using cell-free HIV-1 virions cultivated in vitro and assessment of virus opsonization was performed by incubating appropriate dilutions of virus with medium containing normal human serum and purified (anti-gp120 × anti-C3d)-Fc proteins. As a control group, viruses were incubated with normal human serum under the same conditions. Virus neutralization assays were used to estimate the degree of (anti-gp120 × anti-C3d)-Fc lysis of HIV compared to untreated virus. IMPLICATIONS OF THE HYPOTHESIS: The targeted complement activator, (anti-gp120 × anti-C3d)-Fc, can be used as a novel approach to HIV therapy by abrogating the complement-enhanced HIV infection of cells

Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread

The Corona Virus Disease 2019 (COVID-19) is spreading all over the world. Quantitative analysis of the effects of various factors on the spread of the epidemic will help people better understand the transmission characteristics of SARS-CoV-2, thus providing a theoretical basis for governments to develop epidemic prevention and control strategies. This article uses public data sets from The Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE), Air Quality Open Data Platform, China Meteorological Data Network, and WorldPop website to construct experimental data. The epidemic situation is predicted by Dual-link BiGRU Network, and the relationship between epidemic spread and various feature factors is quantitatively analyzed by the Gauss-Newton iteration Method. The study found that population density has the greatest positive correlation to the spread of the epidemic among the selected feature factors, followed by the number of landing flights. The number of newly diagnosed daily will increase by 1.08% for every 1% of the population density, the number of newly diagnosed daily will increase by 0.98% for every 1% of the number of landing flights. The results of this study show that the control of social distance and population movement has a high priority in epidemic prevention and control strategies, and it can play a very important role in controlling the spread of the epidemic

Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method

The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact.</p

High-order tensor flow processing using integrated photonic circuits

Convolutional operation is a very efficient way to handle tensor analytics, but it consumes a large quantity of additional memory. Here, the authors demonstrate an integrated photonic tensor processor which directly handles high-order tensors without tensor-matrix transformation

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Abstract Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories