Exploring Disentangled Content Information for Face Forgery Detection

Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily embeddable disentanglement framework for content information removal, and further propose a Content Consistency Constraint (C2C) and a Global Representation Contrastive Constraint (GRCC) to enhance the independence of disentangled features. Furthermore, we cleverly construct two unbalanced datasets to investigate the impact of the content bias. Extensive visualizations and experiments demonstrate that our framework can not only ignore the interference of content information, but also guide the detector to mine suspicious artifact traces and achieve competitive performance

Application and Development of CRISPR/Cas9 Technology in Pig Research

Pigs provide valuable meat sources, disease models, and research materials for humans. However, traditional methods no longer meet the developing needs of pig production. More recently, advanced biotechnologies such as SCNT and genome editing are enabling researchers to manipulate genomic DNA molecules. Such methods have greatly promoted the advancement of pig research. Three gene editing platforms including ZFNs, TALENs, and CRISPR/Cas are becoming increasingly prevalent in life science research, with CRISPR/Cas9 now being the most widely used. CRISPR/Cas9, a part of the defense mechanism against viral infection, was discovered in prokaryotes and has now developed as a powerful and effective genome editing tool that can introduce and enhance modifications to the eukaryotic genomes in a range of animals including insects, amphibians, fish, and mammals in a predictable manner. Given its excellent characteristics that are superior to other tailored endonucleases systems, CRISPR/Cas9 is suitable for conducting pig-related studies. In this review, we briefly discuss the historical perspectives of CRISPR/Cas9 technology and highlight the applications and developments for using CRISPR/Cas9-based methods in pig research. We will also review the choices for delivering genome editing elements and the merits and drawbacks of utilizing the CRISPR/Cas9 technology for pig research, as well as the future prospects

μ-Actetato-1:2κ2 O:O′-tribromido-2κ3 Br-(5,5,7,12,12,14-hexa­methyl-1,4,8,11-tetra­aza­cyclo­tetra­deca-1,7-diene-1κ4 N)dizinc(II)

In the title compound, [Zn2Br3(CH3COO)(C16H32N4)], one ZnII atom has a distorted square-planar coordination formed by the four macrocyclic N atoms with an acetate O atom in the apical position and the other ZnII atom has a tetra­hedral coordination environment formed by three Br atoms and one O acetate atom. The two ZnII atoms are linked by an acetate bridge. In the crystal, mol­ecules are linked into centrosymmetric dimers with graph-set motifs R 2 2(16) by an N—H⋯Br inter­action. The mol­ecular configuration is stabilized by an intra­molecular N—H⋯Br hydrogen bond

Applications of Animal Models in Researching Hepatitis A

Hepatitis diseases are remaining in the list of significant threats to human health. Human hepatitis viruses are basically classified into six major hepatotropic pathogens—hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), hepatitis E virus (HEV), and hepatitis G virus (HGV). Among these different forms of hepatotropic viruses, HAV as the leading cause of acute viral hepatitis is characterized as a kind of tiny ribonucleic acid virus that is linked to atopic disease. As we know, animal models have been instrumental in promoting understanding of complex host-virus interactions and boosting the advancement of immune therapies. So far, animal models such as nonhuman primates (NHPs) have enabled scientists to mimic and study the pathogenicities and host immune responses for hepatitis A infection. With the exception of chimpanzees and marmosets, animals like mice, pigs, guinea pigs, and tree shrews can also be selected as alternative animal models infected with HAV under laboratory conditions. In order to gain a better insight into hepatitis A pathogenesis and relevant contents, this chapter is mainly focused on the research progress in animal models of hepatitis A, and discusses the merits and demerits of these alternative models

Advances in understanding of health-promoting benefits of medicine and food homology using analysis of gut microbiota and metabolomics

The health-promoting benefits of medicine and food homology (MFH) are known for thousands of years in China. However, active compounds and biological mechanisms are unclear, greatly limiting clinical practice of MFH. The advent of gut microbiota analysis and metabolomics emerge as key tools to discover functional compounds, therapeutic targets, and mechanisms of benefits of MFH. Such studies hold great promise to promote and optimize functional efficacy and development of MFH-based products, for example, foods for daily dietary supplements or for special medical purposes. In this review, we summarized pharmacological effects of 109 species of MFH approved by the Health and Fitness Commission in 2015. Recent studies applying genome sequencing of gut microbiota and metabolomics to explain the activity of MFH in prevention and management of health consequences were extensively reviewed. We discussed the potentiality in future to decipher functional activities of MFH by applying metabolomics-based polypharmacokinetic strategy and multiomics technologies. The needs for personalized MFH recommendations and comprehensive databases have also been highlighted. This review emphasizes current achievements and challenges of the analysis of gut microbiota and metabolomics as a new avenue to understand MFH

Applications of CRISPR/Cas Technology to Research the Synthetic Genomics of Yeast

The whole genome projects open the prelude to the diversity and complexity of biological genome by generating immense data. For the sake of exploring the riddle of the genome, scientists around the world have dedicated themselves in annotating for these massive data. However, searching for the exact and valuable information is like looking for a needle in a haystack. Advances in gene editing technology have allowed researchers to precisely manipulate the targeted functional genes in the genome by the state-of-the-art gene-editing tools, so as to facilitate the studies involving the fields of biology, agriculture, food industry, medicine, environment and healthcare in a more convenient way. As a sort of pioneer editing devices, the CRISPR/Cas systems having various versatile homologs and variants, now are rapidly giving impetus to the development of synthetic genomics and synthetic biology. Firstly, in the chapter, we will present the classification, structural and functional diversity of CRISPR/Cas systems. Then we will emphasize the applications in synthetic genome of yeast (Saccharomyces cerevisiae) using CRISPR/Cas technology based on year order. Finally, the summary and prospection of synthetic genomics as well as synthetic biotechnology based on CRISPR/Cas systems and their further utilizations in yeast are narrated

Robust Framework for PET Image Reconstruction Incorporating System and Measurement Uncertainties

In Positron Emission Tomography (PET), an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts

Exploiting Magnetic Resonance Angiography Imaging Improves Model Estimation of BOLD Signal

The change of BOLD signal relies heavily upon the resting blood volume fraction () associated with regional vasculature. However, existing hemodynamic data assimilation studies pretermit such concern. They simply assign the value in a physiologically plausible range to get over ill-conditioning of the assimilation problem and fail to explore actual . Such performance might lead to unreliable model estimation. In this work, we present the first exploration of the influence of on fMRI data assimilation, where actual within a given cortical area was calibrated by an MR angiography experiment and then was augmented into the assimilation scheme. We have investigated the impact of on single-region data assimilation and multi-region data assimilation (dynamic cause modeling, DCM) in a classical flashing checkerboard experiment. Results show that the employment of an assumed in fMRI data assimilation is only suitable for fMRI signal reconstruction and activation detection grounded on this signal, and not suitable for estimation of unobserved states and effective connectivity study. We thereby argue that introducing physically realistic in the assimilation process may provide more reliable estimation of physiological information, which contributes to a better understanding of the underlying hemodynamic processes. Such an effort is valuable and should be well appreciated

Causal relationships between COVID-19 and osteoporosis: a two-sample Mendelian randomization study in European population

IntroductionThe causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study.MethodsWe conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR–Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran’s Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method.ResultsThe primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses.ConclusionThe results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent