FTFDNet: Learning to Detect Talking Face Video Manipulation with Tri-Modality Interaction

DeepFake based digital facial forgery is threatening public media security, especially when lip manipulation has been used in talking face generation, and the difficulty of fake video detection is further improved. By only changing lip shape to match the given speech, the facial features of identity are hard to be discriminated in such fake talking face videos. Together with the lack of attention on audio stream as the prior knowledge, the detection failure of fake talking face videos also becomes inevitable. It's found that the optical flow of the fake talking face video is disordered especially in the lip region while the optical flow of the real video changes regularly, which means the motion feature from optical flow is useful to capture manipulation cues. In this study, a fake talking face detection network (FTFDNet) is proposed by incorporating visual, audio and motion features using an efficient cross-modal fusion (CMF) module. Furthermore, a novel audio-visual attention mechanism (AVAM) is proposed to discover more informative features, which can be seamlessly integrated into any audio-visual CNN architecture by modularization. With the additional AVAM, the proposed FTFDNet is able to achieve a better detection performance than other state-of-the-art DeepFake video detection methods not only on the established fake talking face detection dataset (FTFDD) but also on the DeepFake video detection datasets (DFDC and DF-TIMIT).Comment: arXiv admin note: substantial text overlap with arXiv:2203.0517

Genome-Wide Network-Based Analysis of Colorectal Cancer Identifies Novel Prognostic Factors and an Integrative Prognostic Index

Background/Aims: Colorectal cancer (CRC) is one of leading cancers in both incidence and mortality rate. The 5-year survival rate varies considerably depending on the pathological stage of the tumor. Although prominent progress has been made through screening for survival-associated factors from a certain type of genetic or epigenetic modifications, few attempts have been made to apply a network-based approach in prognostic factor identification, which could prove valuable for a complex, multi-faceted disease such as CRC. Methods: In this study, a TCGA dataset of 379 CRC patients was subjected to a network-based analysis strategy consisting of multivariate regression, co-expression network and gene regulatory network analyses, and survival analyses. Both genetic and epigenetic aberrations, including those in gene expression and DNA methylation at specific sites, were screened for significant association with patient survival. A prognostic index (PI) integrating all potential prognostic factors was subsequently validated for its prognostic value. Results: A collection of six miRNAs, eleven mRNAs, and nine DNA methylation sites were identified as potential prognostic factors. The low- and high-risk patient groups assigned based on PI level showed significant difference in overall survival (hazard ratio = 1.32, 95% confidence interval 1.29-1.36, p < 0.0001). Patients in the low- and high-risk groups can be further divided into a total of four subgroups, based on pathological staging. In the two high-risk subgroups (PI > 0), there was significant different (Cox p < 0.0001) in OS between the earlier (stages I/II) and later stages (stages III/IV). However, in the two low-risk subgroups (PI < 0), earlier (stages I/II) and later stages (stages III/IV) showed no significant difference in OS (Cox p = 0.185). On the other hand, there were significant differences in OS between the low- and high-risk subgroups when both subgroups were of earlier stages (Cox p < 0.001) or of later stages (Cox p < 0.0001). Conclusion: The novel network-based, integrative analysis adopted in this study was efficient in screening for prognostic predictors. Along with PI, the set of 6 miRNAs, 11 mRNAs, and 9 DNA methylation sites could serve as the basis for improved prognosis estimation for CRC patients in future clinical practice

Defect identification in adhesive structures using multi-Feature fusion convolutional neural network

The interface-debonding defects of adhesive bonding structures may cause a reduction in bonding strength, which in turn affects the bonding quality of adhesive bonding samples. Hence, defect recognition in adhesive bonding structures is particularly important. In this study, a terahertz (THz) wave was used to analyze bonded structure samples, and a multi-feature fusion convolutional neural network (CNN) was used to identify the defect waveforms. The pooling method of the squeeze-and-excitation (SE) attention mechanism was optimized, defect feature weights were adaptively assigned, and feature fusion was conducted using automatic label net-works to segment the THz waveforms in the adhesive bonding area with fine granularity waveforms as an input to the multi-channel CNN. The results revealed that the speed of the THz waveform labeling with the automatic labeling network was 10 times higher than that with traditional methods, and the defect-recognition accuracy of the defect-recognition network constructed in this study was up to 99.28%. The F1-score was 99.73%, and the lowest pre-embedded defect recognition error rate of the generalization experiment samples was 0.27%

Search for Higgs boson decays to two charm quarks at ATLAS

The Higgs boson, the mass mediator in the Standard Model (SM) of particle physics, was discovered at the Large Hadron Collider (LHC) in 2012. Since then, measuring the decay from the Higgs boson to fermions and validating the SM prediction has been one of the main physics goals of the LHC. The Higgs boson to bottom quark decay ($H\rightarrow b\overline{b}$) has been observed by the ATLAS collaboration during the LHC second data run (Run 2). Similarly, upper limits have been set on the probability of Higgs bosons decaying to charm quarks ($H\rightarrow c\overline{c}$) using ATLAS Run 2 data. In this talk, we present the latest ATLAS measurement of the Higgs boson decays to charm quarks using Higgs produced in association with vector bosons ($VH\rightarrow c\overline{c}$). Novel machine learning techniques are used to tag charm jets. This latest ATLAS Run 2 measurement sets a stringent upper limit on $VH\rightarrow c\overline{c}$. In addition, the measurement is combined with the latest $VH\rightarrow b\overline{b}$ analysis. The combination sets the most stringent ratio between the charm and bottom Yukawa coupling modifiers ($\kappa_{c}/\kappa_{b}$)

Terahertz Multiple Echoes Correction and Non-Destructive Testing Based on Improved Wavelet Multi-Scale Analysis

During terahertz (THz) non-destructive testing (NDT), multiple echoes from the sample interface reflection signals are mixed with the detection signals, resulting in signal distortion and affecting the accuracy of the THz NDT results. Combined with the frequency property of multiple echoes, an improved wavelet multi-scale analysis is put forth in this paper to correct multiple echoes, allowing the maximum retention of detailed signal information in contrast with the existing echo correction methods. The results showed that the improved wavelet multi-scale analysis enhanced the continuity and smoothness of the image at least twice in testing adhesive layer thickness, prevented missing judgments and misjudgments in identifying characteristic defects, and ensured accurate detection results. Hence, it is of great significance for evaluating the THz NDT results

Look\&Listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech Enhancement

Active speaker detection and speech enhancement have become two increasingly attractive topics in audio-visual scenario understanding. According to their respective characteristics, the scheme of independently designed architecture has been widely used in correspondence to each single task. This may lead to the representation learned by the model being task-specific, and inevitably result in the lack of generalization ability of the feature based on multi-modal modeling. More recent studies have shown that establishing cross-modal relationship between auditory and visual stream is a promising solution for the challenge of audio-visual multi-task learning. Therefore, as a motivation to bridge the multi-modal associations in audio-visual tasks, a unified framework is proposed to achieve target speaker detection and speech enhancement with joint learning of audio-visual modeling in this study.Comment: 13 pages, 8figure

Selective Behavior of Juvenile Brachymystax tsinlingensis Depends on Substrate Color, Light Intensity, and Light Color

Brachymystax tsinlingensis is a unique cold water fish locally distributed in China, belonging to Salmoniformes, Salmonidae and Brachymystax. It is mainly distributed in the mountain streams of the Qinling mountain range, including the Shitouhe River in the northern foothills, the Heihe River in the eastern foothills and the Taibaihe and Xushuihe rivers in the southern foothills. B. tsinlingensis has high sensitivity owing to demanding natural habitat conditions and special biological properties. In the past few decades, environmental pollution, a variety of human-caused threats, and reduced resources has caused drastic declines in the wild populations of B. tsinlingensis. In 1998, the species was listed as a second-class state-protected wild animal in the China Red Data Book of Endangered Animals. Due to environmental disruption and human impacts, wild numbers of this species have declined quickly. In China, researchers have focused on B. tsinlingensis conservation. Artificial propagation is one of the most effective methods to restore the natural populations of B. tsinlingensis. In recent years, initial breakthroughs in artificial propagation techniques have aided this species, but the fry survival rate remains relatively low. During artificial breeding experiments, we identified the light environment and substrate conditions that are important factors affecting the survival rate of fry. Fry behavioral selection of light and substrate characteristics was highly significant. The aim of this study was to identify the habitat preferences and associated behavior of B. tsinlingensis. Behavioral experiments were conducted on the progeny of B. tsinlingensis in response to the light environment and substrate color. In this study, we randomly selected healthy juveniles from the same offspring batch obtained through artificial propagation as the experimental fish. The fry total length ranged from 2.23–4.57 cm, with an average of (3.31±0.67) cm. Fry weight ranged from 0.21–0.77 g, with an average weight of (0.42±0.18) g. The experimental fish were not fed 2 h before initiating the experiment. We undertook a combination of individual tests and population tests to investigate three different behavioral selection experiments on juveniles: substrate color preference with the substrate colors of black, white, and blue; light intensity preference with the light intensity of dark (from 1 lx to 5 lx), transition area (from 5 lx to 10 lx), and illuminated area (from 10 lx to 25 lx); light color preference with the light colors of yellow, red, green, or blue. The statistical analysis of the percentage of residence time and the distribution number of experimental fish in each area, enabled analysis using a selective index for the different light intensities, different light colors, and different substrate colors. All analyses used Excel 2016 and SPSS (V 25.0) software, and the statistical values were expressed as the mean ± standard deviation. The results showed that the percentage of time the individuals resided in the black substrate area was significantly higher than that in the white or blue area (P 0.05), and the percentage of the population in the illuminated area was significantly lower than that in the dark area and the transition area (P 0.05). However, the percentage of individuals in the green light area was significantly lower than that in other areas (P < 0.05). The population had a negative tendency towards the green light, and fish displayed sudden acceleration when swimming through the green area in the light color selection experiment. Consequently, the population had a more pronounced avoidance than the individual experiments, this might be related to the mutual transmission of information when residing in clusters, and the speed of information transmission in groups encouraging individuals to avoid the adverse environment. Juvenile B. tsinlingensis preferred a black substrate, avoided green light, and their optimum illumination range was 1–10 lx. The results provide scientific guidance for environmental fry rearing and releasing of B. tsinlingensis

Polyamines Disrupt the KaiABC Oscillator by Inducing Protein Denaturation

Polyamines are positively charged small molecules ubiquitously existing in all living organisms, and they are considered as one kind of the most ancient cellular components. The most common polyamines are spermidine, spermine, and their precursor putrescine generated from ornithine. Polyamines play critical roles in cells by stabilizing chromatin structure, regulating DNA replication, modulating gene expression, etc., and they also affect the structure and function of proteins. A few studies have investigated the impact of polyamines on protein structure and function previously, but no reports have focused on a protein-based biological module with a dedicated function. In this report, we investigated the impact of polyamines (putrescine, spermidine, and spermine) on the cyanobacterial KaiABC circadian oscillator. Using an established in vitro reconstitution system, we noticed that polyamines could disrupt the robustness of the KaiABC oscillator by inducing the denaturation of the Kai proteins (KaiA, KaiB, and KaiC). Further experiments showed that the denaturation was likely due to the induced change of the thermal stability of the clock proteins. Our study revealed an intriguing role of polyamines as a component in complex cellular environments and would be of great importance for elucidating the biological function of polyamines in future

Process Optimization of Enzyme-Assisted Extraction of Polysaccharides from Artificially-Cultivated Cordyceps cicadae and Its Kinetic, Thermodynamic and Antioxidant Activities Analysis

Objective: To optimize the enzyme-assisted extraction process of polysaccharides from artificially-cultivated Cordyceps cicadae was conducted in this study. Four different parameters were considered to evaluate the yield of polysaccharides including liquid to solid ratio, enzyme dosage, enzymatic temperature and extraction time. Methods: A four-factor-three-level experimental design was employed to establish a mathematical model by Box-Behnken method, and the scavenging capacity of polysaccharides against three radicals was examined. Afterward, Fick's second law was used to build the kinetic model for the extraction of polysaccharide from artificially-cultivated Cordyceps cicadae. The parameters including rate constants, relative extraction rate, and activation energy were employed to analyze the kinetic and thermodynamic features. Results: Based on the response surface analysis, the optimal extraction process was presented to be as following: Liquid to solid ratio 1:30 g/mL, enzyme dosage 1.6%, enzymatic digestion temperature 67 ℃ and extraction time 90 min. The polysaccharide yield under the above condition was 7.91%, which was close to the predicted value. Moreover, the results of antioxidant capacities indicated that the obtained crude polysaccharides under optimal conditions showed strong DPPH radical scavenging and hydroxyl radical scavenging with IC50 values for 0.60 and 0.54 mg/mL, respectively, and its ORAC value was 45.62 Trolox μmol/g, suggesting potent antioxidant activity in vitro. Conclusion：The study of enzyme-assisted extraction of artificially-cultivated Cordyceps cicadae flower polysaccharides provide theoretical support for the production of polysaccharide fractions from this kind of commercialized Cordyceps cicadae resources

Metabolomics Characterize the Differential Metabolic Markers between Bama Xiang Pig and Debao Pig to Identify Pork

The Bama Xiang pig (BM) is a unique pig species in Guangxi Province, China. Compared to other breeds of domestic pig, such as the Debao pig (DB), it is smaller in size, better in meat quality, resistant to rough feeding and strong in stress resistance. These unique advantages of Bama Xiang pigs make them of great edible value and scientific research value. However, the differences in muscle metabolites between Bama Xiang pigs (BM) and Debao pigs (DB) are largely unexplored. Here, we identified 214 differential metabolites between these two pig breeds by LC-MS. Forty-one such metabolites are enriched into metabolic pathways, and these metabolites correspond to 11 metabolic pathways with significant differences. In Bama pigs, the abundance of various metabolites such as creatine, citric acid, L-valine and hypoxanthine is significantly higher than in Debao pigs, while the abundance of other metabolites, such as carnosine, is significantly lower. Among these, we propose six differential metabolites: L-proline, citric acid, ribose 1-phosphate, L-valine, creatine, and L-arginine, as well as four potential differential metabolites (without the KEGG pathway), alanyl-histidine, inosine 2′-phosphate, oleoylcarnitine, and histidinyl hydroxyproline, as features for evaluating the meat quality of Bama pigs and for differentiating pork from Bama pigs and Debao pigs. This study provides a proof-of-concept example of distinguishing pork from different pig breeds at the metabolite level and sheds light on elucidating the biological processes underlying meat quality differences. Our pork metabolites data are also of great value to the genomics breeding community in meat quality improvement