Graph Contrastive Learning with Implicit Augmentations

Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this paper, we introduce Implicit Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent space learned from a Variational Graph Auto-Encoder by reconstructing graph topological structure. Importantly, instead of explicitly sampling augmentations from latent distributions, we further propose an upper bound for the expected contrastive loss to improve the efficiency of our learning algorithm. Thus, graph semantics can be preserved within the augmentations in an intelligent way without arbitrary manual design or prior human knowledge. Experimental results on both graph-level and node-level tasks show that the proposed method achieves state-of-the-art performance compared to other benchmarks, where ablation studies in the end demonstrate the effectiveness of modules in iGCL

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.Comment: Paper under review. 16 pages, 11 figures, 4 table

Energy deficiency promotes rhythmic foraging behavior by activating neurons in paraventricular hypothalamic nucleus

BackgroundDysregulation of feeding behavior leads to a variety of pathological manifestations ranging from obesity to anorexia. The foraging behavior of animals affected by food deficiency is not fully understood.MethodsHome-Cage system was used to monitor the behaviors. Immunohistochemical staining was used to monitor the trend of neuronal activity. Chemogenetic approach was used to modify neuronal activity.ResultsWe described here a unique mouse model of foraging behavior and unveiled that food deprivation significantly increases the general activities of mice with a daily rhythmic pattern, particularly foraging behavior. The increased foraging behavior is potentiated by food cues (mouthfeel, odor, size, and shape) and energy deficit, rather than macronutrient protein, carbohydrate, and fat. Notably, energy deficiency increases nocturnal neuronal activity in paraventricular hypothalamic nucleus (PVH), accompanying a similar change in rhythmic foraging behavior. Activating neuronal activity in PVH enhances the amplitude of foraging behavior in mice. Conversely, inactivating neuronal activity in PVH decreases the amplitude of foraging behavior and impairs the rhythm of foraging behavior.DiscussionThese results illustrate that energy status and food cues regulate the rhythmic foraging behavior via PVH neuronal activity. Understanding foraging behavior provides insights into the underlying mechanism of eating-related disorders

Study on Mechanical Properties and Cracking Mode of Coal Samples under Compression–Shear Coupled Load Considering the Effect of Loading Rate

Under coupled compression–shear loading, the failure and instability behavior of inclined pillars is different from that of horizontal pillars. To enhance the reliability and accuracy of pillar strength design, the influence of different inclination angles and loading rates on mechanical property and the failure behavior of inclined pillar should be studied. In this paper, the combined compression and shear test (C-CAST) system was developed, and mechanical properties and macro failure behavior of coal samples under different inclination angles and loading rates were studied, and acoustic emission (AE) technology was used to determine the internal cracking mode of the sample. The results show that with the increase of inclination angle, the peak shear stress of coal sample increases gradually, while the peak axial stress and elastic modulus slightly increase first and then decrease, and reach the maximum value at an inclination angle of 5°. Within the inclination angle range of 0°–15°, with the increase of loading rate, the peak axial stress and elastic modulus of coal samples first increase and then decrease, while the loading rate corresponding to peak axial stress and elastic modulus decreases. Within the inclination angle range of 20°–25°, the peak axial stress and elastic modulus of the sample gradually decrease with the increase of loading rate. The failure mode of coal samples changes from tension-splitting failure (0°–5°), tension–shear composite failure (10°) to single shear failure (15°–25°). Meanwhile, the loading rate has little effect on the failure mode of coal samples, but has a significant effect on the failure degree. When the loading rate is 1.0 and 10 mm/min and the inclination angle ranges from 0°–5°, the proportion of tensile crack is significantly greater than that of the shear crack, and tensile failure is the main failure mode; when the inclination angle ranges from 10°–25°, the proportion of shear crack is more than 50% and increases gradually with the increase of inclination angle, and shear failure is the main failure mode. This law is consistent with the macroscopic failure mode of the sample

Ultrafine MoOx clusters anchored on g-C3N4 with nitrogen/oxygen dual defects for synergistic efficient O2 activation and tetracycline photodegradation

Photocatalytic O2 activation to generate reactive oxygen species is crucially important for purifying organic pollutants, yet remains a challenge due to poor adsorption of O2 and low efficiency of electron transfer. Herein, we demonstrate that ultrafine MoOx clusters anchored on graphitic carbon nitride (g-C3N4) with dual nitrogen/oxygen defects promote the photocatalytic activation of O2 to generate·O2− for the degradation of tetracycline hydrochloride (TCH). A range of characterization techniques and density functional theory (DFT) calculations reveal that the introduction of the nitrogen/oxygen dual defects and MoOx clusters enhances the O2 adsorption energy from −2.77 to −2.94 eV. We find that MoOx clusters with oxygen vacancies (Ov) and surface Ov-mediated Moδ+ (3 ≥ δ ≥ 2) possess unpaired localized electrons, which act as electron capture centers to transfer electrons to the MoOx clusters. These electrons can then transfer to the surface adsorbed O2, thus promoting the photocatalytic conversion of O2 to ·O2− and, simultaneously, realizing the efficient separation of photogenerated electron–hole pairs. Our fully-optimized MoOx/g-C3N4 catalyst with dual nitrogen/oxygen defects manifests outstanding photoactivities, achieving 79% degradation efficiency toward TCH within 120 min under visible light irradiation, representing nearly 7 times higher activity than pristine g-C3N4. Finally, based on the results of liquid chromatograph mass spectrometry and DFT calculations, the possible photocatalytic degradation pathways of TCH were proposed

Shank3 deficiency elicits autistic-like behaviors by activating p38α in hypothalamic AgRP neurons

Abstract Background SH3 and multiple ankyrin repeat domains protein 3 (SHANK3) monogenic mutations or deficiency leads to excessive stereotypic behavior and impaired sociability, which frequently occur in autism cases. To date, the underlying mechanisms by which Shank3 mutation or deletion causes autism and the part of the brain in which Shank3 mutation leads to the autistic phenotypes are understudied. The hypothalamus is associated with stereotypic behavior and sociability. p38α, a mediator of inflammatory responses in the brain, has been postulated as a potential gene for certain cases of autism occurrence. However, it is unclear whether hypothalamus and p38α are involved in the development of autism caused by Shank3 mutations or deficiency. Methods Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and immunoblotting were used to assess alternated signaling pathways in the hypothalamus of Shank3 knockout (Shank3 −/− ) mice. Home-Cage real-time monitoring test was performed to record stereotypic behavior and three-chamber test was used to monitor the sociability of mice. Adeno-associated viruses 9 (AAV9) were used to express p38α in the arcuate nucleus (ARC) or agouti-related peptide (AgRP) neurons. D176A and F327S mutations expressed constitutively active p38α. T180A and Y182F mutations expressed inactive p38α. Results We found that Shank3 controls stereotypic behavior and sociability by regulating p38α activity in AgRP neurons. Phosphorylated p38 level in hypothalamus is significantly enhanced in Shank3 −/−  mice. Consistently, overexpression of p38α in ARC or AgRP neurons elicits excessive stereotypic behavior and impairs sociability in wild-type (WT) mice. Notably, activated p38α in AgRP neurons increases stereotypic behavior and impairs sociability. Conversely, inactivated p38α in AgRP neurons significantly ameliorates autistic behaviors of Shank3 −/− mice. In contrast, activated p38α in pro-opiomelanocortin (POMC) neurons does not affect stereotypic behavior and sociability in mice. Limitations We demonstrated that SHANK3 regulates the phosphorylated p38 level in the hypothalamus and inactivated p38α in AgRP neurons significantly ameliorates autistic behaviors of Shank3 −/− mice. However, we did not clarify the biochemical mechanism of SHANK3 inhibiting p38α in AgRP neurons. Conclusions These results demonstrate that the Shank3 deficiency caused autistic-like behaviors by activating p38α signaling in AgRP neurons, suggesting that p38α signaling in AgRP neurons is a potential therapeutic target for Shank3 mutant-related autism