Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

This paper presents our Facial Action Units (AUs) recognition submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition

Submission of Evidence on Online Violence Against Women to the UN Special Rapporteur on Violence Against Women, its Causes and Consequences, Dr Dubravka Šimonović

Figure S1. B3GALNT2 levels determined by W.B. and ROC curve. a–c Relative mRNA expression of B3GALNT2 in HCC tumor tissues and normal liver tissues obtained from GSE76427, GSE36376, and TCGA-LIHC datasets. d Western blot analysis of B3GALNT2 levels in 24 pairs of HCC tissues. T HCC tumor tissue, N adjacent non-tumor tissue. e ROC curve analysis of the sensitivity and specificity for the predictive value of TNM model, B3GALNT2 expression, and the combination model. (TIFF 546 kb

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth label, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical features and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group sampling theory in medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation, and show that certain variance-reduction techniques are particularly beneficial in medical image segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on three benchmark datasets with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing medical image analysis tasks

Synthesis and Characterization of Pyrochlore Bi 2

Praseodymium doped Bi2Sn2O7 (BSO), as a visible-light responsive photocatalyst, was prepared by a hydrothermal method with different dopant contents. The as-prepared photocatalysts were investigated by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), N2 adsorption-desorption isotherm, X-ray photoelectron spectroscopy analysis (XPS), and UV-Vis diffuse reflectance spectroscopy (DRS). The photocatalytic activity of prepared catalysts was evaluated by the degradation of Rhodamine Bextra (RhB) and 2,4-dichlorophenol (2,4-DCP) in aqueous solution under visible light irradiation. It was found that Pr doping inhibited the growth of crystalline size and the as-prepared materials were small in size (10–20 nm). In our experiments, Pr-doped BSO samples exhibited enhanced visible-light photocatalytic activity compared to the undoped BSO, and the optimal dopant amount of Pr was 1.0 mol% for the best photocatalytic activity. On the basis of the calculated PL spectra, the mechanism of enhanced photocatalytic activity has been discussed

Ternary NiCoTi-layered double hydroxide nanosheets as a pH-responsive nanoagent for photodynamic/chemodynamic synergistic therapy

Combining photodynamic therapy (PDT) with chemodynamic therapy (CDT) has been proven to be a promising strategy to improve the treatment efficiency of cancer, because of the synergistic therapeutic effect arising between the two modalities. Herein, we report an inorganic nanoagent based on ternary NiCoTi-layered double hydroxide (NiCoTi-LDH) nanosheets to realize highly efficient photodynamic/chemodynamic synergistic therapy. The NiCoTi-LDH nanosheets exhibit oxygen vacancy-promoted electron-hole separation and photogenerated hole-induced O2-independent reactive oxygen species (ROS) generation under acidic circumstances, realizing in situ pH-responsive PDT. Moreover, due to the effective conversion between Co^{3+} and Co^{2+} caused by photogenerated electrons, the NiCoTi-LDH nanosheets catalyze the release of hydroxyl radicals (∙OH) from H2O2 through Fenton reactions, resulting in CDT. Laser irradiation enhances the catalyzed ability of the NiCoTi-LDH nanosheets to promote the ROS generation, resulting in a better performance than TiO_{2} nanoparticles at pH 6.5. In vitro and in vivo experimental results show conclusively that NiCoTi-LDH nanosheets plus irradiation lead to efficient cell apoptosis and significant inhibition of tumor growth. This study reports a new pH-responsive inorganic nanoagent with oxygen vacancy-promoted photodynamic/chemodynamic synergistic performance, offering a potentially appealing clinical strategy for selective tumor elimination

The core inflammatory factors in patients with major depressive disorder: a network analysis

IntroductionThe symptoms of major depressive disorder (MDD) vary widely. Psycho-neuro-inflammation has shown that MDD’s inflammatory factors can accelerate or slow disease progression. This network analysis study examined the complex interactions between depressed symptoms and inflammatory factors in MDD prevention and treatment.MeasuresWe gathered participants’ inflammatory factor levels, used the Hamilton Depression Scale (HAMD-17), and network analysis was used to analyzed the data. Network analysis revealed the core inflammatory (nodes) and their interactions (edges). Stability and accuracy tests assessed these centrality measures’ network robustness. Cluster analysis was used to group persons with similar dimension depressive symptoms and examine their networks.ResultsInterleukin-1β (IL-1β) is the core inflammatory factor in the overall sample, and IL-1β—interleukin-4 (IL-4) is the strongest correlation. Network precision and stability passed. Network analysis showed significant differences between Cluster 1 (with more severe anxiety/somatization and sleep disruption) and Cluster 3 (with more severe retardation and cognitive disorders), as well as between Cluster 2 (with more severe anxiety/somatization, sleep disruption and body weight) and Cluster 3. IL-1β is the core inflammatory factor in Cluster 1 and Cluster 2, while tumor necrosis factor alpha (TNF-α) in Cluster 3.ConclusionIL-1β is the central inflammatory factor in the network, and there is heterogeneity in the core inflammatory factor of MDD with specific depressive dimension symptoms as the main manifestation. In conclusion, inflammatory factors and their links should be prioritized in future theoretical models of MDD and may provide new research targets for MDD intervention and treatment

Application of a broad range lytic phage LPST94 for biological control of salmonella in foods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Salmonella, one of the most common food-borne pathogens, is a significant public health and economic burden worldwide. Lytic phages are viable alternatives to conventional technologies for pathogen biocontrol in food products. In this study, 40 Salmonella phages were isolated from environmentally sourced water samples. We characterized the lytic range against Salmonella and among all isolates, phage LPST94 showed the broadest lytic spectrum and the highest lytic activity. Electron microscopy and genome sequencing indicated that LPST94 belongs to the Ackermannviridae family. Further studies showed this phage is robust, tolerating a wide range of pH (4–12) and temperature (30–60◦C) over 60 min. The efficacy of phage LPST94 as a biological control agent was evaluated in various food products (milk, apple juice, chicken breast, and lettuce) inoculated with non-typhoidal Salmonella species at different temperatures. Interestingly, the anti-Salmonella efficacy of phage LPST94 was greater at 4◦C than 25◦C, although the efficacy varied between different food models. Adding phage LPST94 to Salmonella inoculated milk decreased the Salmonella count by 3 log10 CFU/mL at 4◦C and 0.84 to 2.56 log10 CFU/mL at 25◦C using an MOI of 1000 and 10000, respectively. In apple juice, chicken breast, and lettuce, the Salmonella count was decreased by 3 log10 CFU/mL at both 4◦C and 25◦C after applying phage LPST94 at an MOI of 1000 and 10,000, within a timescale of 48 h. The findings demonstrated that phage LPST94 is a promising candidate for biological control agents against pathogenic Salmonella and has the potential to be applied across different food matrices

Aberrant Dynamic Functional Connectivity of Posterior Cingulate Cortex Subregions in Major Depressive Disorder With Suicidal Ideation

Accumulating evidence indicates the presence of structural and functional abnormalities of the posterior cingulate cortex (PCC) in patients with major depressive disorder (MDD) with suicidal ideation (SI). Nevertheless, the subregional-level dynamic functional connectivity (dFC) of the PCC has not been investigated in MDD with SI. We therefore sought to investigate the presence of aberrant dFC variability in PCC subregions in MDD patients with SI. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 31 unmedicated MDD patients with SI (SI group), 56 unmedicated MDD patients without SI (NSI group), and 48 matched healthy control (HC) subjects. The sliding-window method was applied to characterize the whole-brain dFC of each PCC subregion [the ventral PCC (vPCC) and dorsal PCC (dPCC)]. In addition, we evaluated associations between clinical variables and the aberrant dFC variability of those brain regions showing significant between-group differences. Compared with HCS, the SI and the NSI groups exhibited higher dFC variability between the left dPCC and left fusiform gyrus and between the right vPCC and left inferior frontal gyrus (IFG). The SI group showed higher dFC variability between the left vPCC and left IFG than the NSI group. Furthermore, the dFC variability between the left vPCC and left IFG was positively correlated with Scale for Suicidal Ideation (SSI) score in patients with MDD (i.e., the SI and NSI groups). Our results indicate that aberrant dFC variability between the vPCC and IFG might provide a neural-network explanation for SI and may provide a potential target for future therapeutic interventions in MDD patients with SI