Antibody responses in COVID-19 patients

Measuring virus-specific antibody responses to emerging pathogens is a well-established and highly useful tool to diagnose such infections, understand interactions between the immune system and pathogens, and provide potential clues for the development of vaccines or therapeutic agents against such pathogens. Since the beginning of 2020, the discovery of SARS-CoV-2 as the emerging virus responsible for the COVID-19 pandemic has provided new insight into the complexity of antibody responses to this dangerous virus. The current review aims to sort out diverse and sometimes seemingly confusing findings to put together a cohesive understanding on the profile of antibody responses elicited in COVID-19 patients

The dynamics of immunoglobulin V-gene usage and clonotype expansion in mice after prime and boost immunizations as analyzed by NGS

In the current study, an improved NGS approach was developed to study the B-cell repertoire evolution in a simple mouse immunization model including only two DNA immunizations. The combination of 5\u27RACE and Ion Torrent long reads enabled unbiased immunoglobulin repertoire analysis even from small amounts of peripheral mouse blood. The B-cell population expanded by the vaccine displayed a relatively strong clonality. Upon priming with the first vaccine dose, we observed a consistent pattern of V-segment gene and CDR3 usage (public specificities). Interestingly, this pattern diversified with the second dose of immunization -it was relatively different in individual mice in spite of having received the same vaccine regimen (private specificities). Nevertheless, there were several instances in which the same public V-segment genes and CDR3s that were expanded after the first dose were further amplified after the second immunization. Taken together, it appears that the major clonotypes expanded by vaccination were originally a homogeneous subset that later diversified after a second dose leading to diverse private clonal compositions in different mice. These results established a new platform valuable to perform longitudinal analyses of the Ig germline gene usage and clonotype evolution throughout an immunization regimen in a commonly used animal model

Di-μ-hydroxido-bis­({2,2′-[propane-1,3-diylbis(nitrilo­methyl­idyne)]diphenolato}iron(III)) dimethyl­formamide disolvate

The structure of the title compound, [Fe2(C17H16N2O2)2(OH)2]·2C3H7N, consists of centrosymmetric dimeric units in which crystallographically equivalent FeIII ions are doubly bridged by hydroxide groups. Each FeIII center in the complex has a six-coordinated distorted cis-FeN2O4 octa­hedral geometry

HIV-1 did not contribute to the 2019-nCoV genome

When a new pathogen that causes a global epidemic in humans, one key question is where it comes from. This is especially important for a zoonotic infectious disease that jumps from animals to humans. Knowing the origin of such a pathogen is critical to develop means to block further transmission and to develop vaccines. Discovery of the origin of a newly human pathogen is a sophisticated process that requires extensive and vigorous scientific validations and generally takes many years, such as the cases for HIV-1, SARS and MERS. Unfortunately, before the natural sources of new pathogens are clearly defined, conspiracy theories that the new pathogens are man-made often surface as the source. However, in all cases, such theories have been debunked in history

Gas-bearing prediction of deep reservoir based on DNN embeddings

The extraction of gas-bearing information from the deeply underground reservoir is extremely difficult due to the weak seismic response and complicated gas distribution characteristics. To predict gas-bearing reservoirs efficiently, we developed a deep neural network (DNN) embedding-based gas-bearing prediction scheme. First, the cepstrum coefficient that is sensitive to hydrocarbons is computed using the raw seismic data. A DNN model inspired by the x-vector in speech recognition is designed, comprising the long short-term memory (LSTM) networks and two fully connected (FC) networks, stacked from the bottom to the top layer. Then, the cepstrum features are fed into the DNN for training and testing, and DNN embedding is extracted from the top layers after optimized network parameters are determined. Finally, the gas-bearing probability of the reservoir is predicted by calculating the cosine distance between pairs of DNN embeddings. When applied to synthetic seismic data, the proposed method offers greater than 90% accuracy at SNR > 3 dB. Besides, the predicted result applied in deep carbonate reservoirs in China’s Sichuan Basin is in basic agreement with the actual situation, demonstrating the certain feasibility of the proposed scheme

The wide utility of rabbits as models of human diseases

Studies using the European rabbit Oryctolagus cuniculus contributed to elucidating numerous fundamental aspects of antibody structure and diversification mechanisms and continue to be valuable for the development and testing of therapeutic humanized polyclonal and monoclonal antibodies. Additionally, during the last two decades, the use of the European rabbit as an animal model has been increasingly extended to many human diseases. This review documents the continuing wide utility of the rabbit as a reliable disease model for development of therapeutics and vaccines and studies of the cellular and molecular mechanisms underlying many human diseases. Examples include syphilis, tuberculosis, HIV-AIDS, acute hepatic failure and diseases caused by noroviruses, ocular herpes, and papillomaviruses. The use of rabbits for vaccine development studies, which began with Louis Pasteur\u27s rabies vaccine in 1881, continues today with targets that include the potentially blinding HSV-1 virus infection and HIV-AIDS. Additionally, two highly fatal viral diseases, rabbit hemorrhagic disease and myxomatosis, affect the European rabbit and provide unique models to understand co-evolution between a vertebrate host and viral pathogens

The role of foam in improving the workability of sand : insights from DEM

Foam as a soil conditioner can transform the mechanical properties of the excavated natural muck and lubricate the interface between the cutting tools and muck, thus reducing the tools’ wear and promoting the efficiency of earth pressure balance (EPB) shield tunneling. This paper aims to explore the meso-mechanism of foam in improving the workability of sand by combining discrete element modeling (DEM) with experimental investigations of slump tests. A “sand-foam” mixture DEM model was generated by simplifying the sand grains and foam as individual particles with different properties. The particle-scale simulated parameters were calibrated based on a series of experimental observations. The effects of foam on the inter-particle contact distribution and the evolution of contact forces during the slumping process were investigated in detail through numerical modeling. It was found that injecting foam into sand specimens could increase the coordination number and the contact number around sand grains. Although the force transmission pattern changes from “sand-sand” into the coexistence of “sand-foam”, “sand-sand” and “foam-foam” contacts, the magnitude of contact forces transferred by foam particles is significantly lower than that by sand particles. The presence of foam reduces contact-scale frictional strength and thus reduces the stability of the microstructures of sand. In addition, the normal direction of inter-particle contact force deflects from the vertical to the horizontal and the magnitude of contact force decreases significantly with the influence of foam

Exact forced torsional vibration solution of a shaft with multiple discontinuities and arbitrary boundary conditions

In this paper, the method based on Laplace transform and Fourier transform and their inverse transforms is developed to give an exact solution to the forced torsional vibration of a shaft subjected to multiple inertias, multiple elastic supports, arbitrary boundary conditions and arbitrary excitation forces. Two simple cases are used to show in detail how this developed method can obtain an exact analytical solution to the forced torsional vibration of shaft and the results are compared with Eigenfunction Expansion Method and Finite Element Method (FEM) to demonstrate the accuracy and effectiveness of the developed method. Two more complex cases are investigated to further show the superiority of the developed method over FEM in highly efficient and accurate. Finally, using the developed method, the effects of parameters on forced torsional vibration response of shaft are discussed, including the stiffness, the location of elastic supports and the time interval of impact loading. The developed method can provide a reliable theoretical base not only for analysis and fault diagnosis of a shaft system in engineering signal testing projects but also for the verification of other numerical and analytical methods