DNMT3a in the hippocampal CA1 is crucial in the acquisition of morphine self‐administration in rats

Drug‐reinforced excessive operant responding is one fundamental feature of long-lasting addiction‐like behaviors and relapse in animals. However, the transcriptional regulatory mechanisms responsible for the persistent drug‐specific (not natural rewards) operant behavior are not entirely clear. In this study, we demonstrate a key role for one of the de novo DNA methyltransferase, DNMT3a, in the acquisition of morphine self‐administration (SA) in rats. The expression of DNMT3a in the hippocampal CA1 region but not in the nucleus accumbens shell was significantly up‐regulated after 1‐ and 7‐day morphine SA (0.3 mg/kg/infusion) but not after the yoked morphine injection. On the other hand, saccharin SA did not affect the expression of DNMT3a or DNMT3b. DNMT inhibitor 5‐aza‐2‐deoxycytidine (5‐aza) microinjected into the hippocampal CA1 significantly attenuated the acquisition of morphine SA. Knockdown of DNMT3a also impaired the ability to acquire the morphine SA. Overall, these findings suggest that DNMT3a in the hippocampus plays an important role in the acquisition of morphine SA and may be a valid target to prevent the development of morphine addiction. Includes Supplemental informatio

DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing

Text-guided image editing faces significant challenges to training and inference flexibility. Much literature collects large amounts of annotated image-text pairs to train text-conditioned generative models from scratch, which is expensive and not efficient. After that, some approaches that leverage pre-trained vision-language models are put forward to avoid data collection, but they are also limited by either per text-prompt optimization or inference-time hyper-parameters tuning. To address these issues, we investigate and identify a specific space, referred to as CLIP DeltaSpace, where the CLIP visual feature difference of two images is semantically aligned with the CLIP textual feature difference of their corresponding text descriptions. Based on DeltaSpace, we propose a novel framework called DeltaEdit, which maps the CLIP visual feature differences to the latent space directions of a generative model during the training phase, and predicts the latent space directions from the CLIP textual feature differences during the inference phase. And this design endows DeltaEdit with two advantages: (1) text-free training; (2) generalization to various text prompts for zero-shot inference. Extensive experiments validate the effectiveness and versatility of DeltaEdit with different generative models, including both the GAN model and the diffusion model, in achieving flexible text-guided image editing. Code is available at https://github.com/Yueming6568/DeltaEdit.Comment: 17 pages. arXiv admin note: text overlap with arXiv:2303.0628

Thiazolidinediones Cause Cardiotoxicity via PPARγ- Independent Mechanism

Thiazolidinediones (TZDs), peroxisome proliferator-activated receptor gamma (PPARγ) agonists, are highly effective antidiabetic drugs that are widely used to treat type 2 diabetes mellitus (T2DM) due to their unique beneficial actions, such as a renoprotective effect, amelioration of glucose homeostasis, and blood pressure lowering, that other antidiabetic drugs do not have. Those beneficial actions, however, are shadowed by the increased risks of cardiovascular adverse events, including mitochondrial dysfunction, oxidative stress and myocardial energy deficiency, fluid retention, congestive heart failure, and myocardial infarction. Except PPARγ, TZDs also have affinity to numerous non-PPARγ targets in mitochondria, cytosol, and cytoplasm, including MitoNEET, mitochondrial pyruvate carrier, dehydrogenases involved in tricarboxylic acid cycle and electron transport, cytoplasmic ion channels, Na-K-pump, and other unknown enzymes. By binding to these targets, TZDs produce off-target effects and potentially increase cardiotoxicity. In this chapter, we review recent studies, both experimental and clinical, on the myocardial adverse effects associated with TZDs and their underlying mechanisms. We focus our review in large part on the relationship between these myocardial adverse effects and PPARγ

2-(4-Fluoro­phen­yl)quinoxaline

In the title compound, C14H9FN2, the dihedral angle between the benzene ring and the quinoxaline ring system is 22.2 (3)°. Any aromatic π–π stacking in the crystal must be very weak, with a minimum centroid–centroid separation of 3.995 (2) Å

Methyl 4-(5-meth­oxy-1H-indol-3-yl)benzoate

In the title compound, C17H15NO3, the dihedral angle between the benzene ring and the indole ring system is 22.5 (3)°. In the crystal, mol­ecules are linked by N—H⋯π and C—H⋯O inter­actions

Dual-agonist occupancy of orexin receptor 1 and cholecystokinin A receptor heterodimers decreases G-protein-dependent signaling and migration in the human colon cancer cell line HT-29

The orexin (OX1R) and cholecystokinin A (CCK1R) receptors play opposing roles in the migration of the human colon cancer cell line HT-29, and may be involved in the pathogenesis and pathophysiology of cancer cell invasion and metastasis. OX1R and CCK1R belong to family A of the G-protein-coupled receptors (GPCRs), but the detailed mechanisms underlying their functions in solid tumor development remain unclear. In this study, we investigated whether these two receptors heterodimerize, and the results revealed novel signal transduction mechanisms. Bioluminescence and Förster resonance energy transfer, as well as proximity ligation assays, demonstrated that OX1R and CCK1R heterodimerize in HEK293 and HT-29 cells, and that peptides corresponding to transmembrane domain 5 of OX1R impaired heterodimer formation. Stimulation of OX1R and CCK1R heterodimers with both orexin-A and CCK decreased the activation of Gαq, Gαi2, Gα12, and Gα13 and the migration of HT-29 cells in comparison with stimulation with orexin-A or CCK alone, but did not alter GPCR interactions with β-arrestins. These results suggest that OX1R and CCK1R heterodimerization plays an anti-migratory role in human colon cancer cells. [Abstract copyright: Copyright © 2017. Published by Elsevier B.V.

DFGC 2022: The Second DeepFake Game Competition

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).Comment: Accepted by IJCB 202