2,4-Dihydr­oxy-N′-(3,4,5-trimethoxy­benzyl­idene)benzohydrazide

In the title compound, C17H18N2O6, the mol­ecule is slightly twisted, with a dihedral angle of 18.1 (2)° between the two benzene rings. In the crystal structure, mol­ecules are linked into a network by inter­molecular N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds. An intra­molecular O—H⋯O hydrogen bond is also present

Improved Analytic Solution of Black Hole Superradiance

The approximate solution of the Klein-Gordon equation for a real scalar field of mass $\mu$ in the geometry of a Kerr black hole obtained by Detweiler \cite{Detweiler:1980uk} is widely used in the analysis of the stability of black holes as well as the search of axion-like particles. In this work, we confirm a missing factor $1/2$ in this solution, which was first identified in Ref.~\cite{Pani:2012bp}. The corrected result has strange features that put questions on the power-counting strategy. We solve this problem by adding the next-to-leading order (NLO) contribution. Compared to the numerical results, the NLO solution reduces the percentage error of the LO solution by a factor of 2 for all important values of $r_g \mu$. Especially the percentage error is $\lesssim 10\%$ in the region of $r_g\mu \lesssim 0.35$. The NLO solution also has a compact form and could be used straightforwardly.Comment: 5 pages, 3 figures, Accepted by PR

Next-to-leading-order solution to Kerr-Newman black hole superradiance

The superradiant instabilities of Kerr-Newman black holes with charged or uncharged massive spin-0 fields are calculated analytically to the next-to-leading order in the limit of $\alpha\sim r_g \mu \ll 1$. A missing factor of $1/2$ in the previous leading-order result is identified. The next-to-leading order result has a compact form and is in good agreement with existing numerical calculations. The percentage error increases with $\alpha$, from a few percent for $\alpha\sim 0.1$ to about $50\%$ for $\alpha\sim 0.4$. Massive neutral scalars too heavy to be produced with Kerr black hole superradiance may exist in the superradiant region of Kerr-Newman black holes.Comment: 11 pages, 5 figures, 2 tables. Matches Physical Review D versio

Neural Point-based Volumetric Avatar: Surface-guided Neural Points for Efficient and Photorealistic Volumetric Head Avatar

Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications. However, existing methods often struggle to model challenging facial regions (e.g., mouth interior, eyes, hair/beard), resulting in unrealistic and blurry results. In this paper, we propose {\fullname} ({\name}), a method that adopts the neural point representation as well as the neural volume rendering process and discards the predefined connectivity and hard correspondence imposed by mesh-based approaches. Specifically, the neural points are strategically constrained around the surface of the target expression via a high-resolution UV displacement map, achieving increased modeling capacity and more accurate control. We introduce three technical innovations to improve the rendering and training efficiency: a patch-wise depth-guided (shading point) sampling strategy, a lightweight radiance decoding process, and a Grid-Error-Patch (GEP) ray sampling strategy during training. By design, our {\name} is better equipped to handle topologically changing regions and thin structures while also ensuring accurate expression control when animating avatars. Experiments conducted on three subjects from the Multiface dataset demonstrate the effectiveness of our designs, outperforming previous state-of-the-art methods, especially in handling challenging facial regions

(E)-N′-(2-Bromo­benzyl­idene)-2-fluoro­benzohydrazide

The title compound, C14H10BrFN2O, adopts an E geometry about the C=N bond. The dihedral angle between the mean planes of the two benzene rings is 81.5 (6)°. In the crystal, mol­ecules are linked through inter­molecular N—H⋯O hydrogen bonds, forming chains running along the b axis

Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection.Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients.Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients.Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature

Background: Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy. Method: In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) analysis and systematically correlated with genomic characteristics and clinicopathological features of IDC patients. Furthermore, an immune signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A random forest algorithm was applied to identify the most important somatic gene mutations associated with the constructed immune signature. A nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed by multivariate Cox regression. Results: The IDC were clustered into low immune infiltration, intermediate immune infiltration, and high immune infiltration by the immune landscape. The high infiltration group had a favorable survival probability compared with that of the low infiltration group. The low-risk score subtype identified by the immune signature was characterized by T cell-mediated immune activation. Additionally, activation of the interferon-α response, interferon-γ response, and TNF-α signaling via the NFκB pathway was observed in the low-risk score subtype, which indicated T cell activation and may be responsible for significantly favorable outcomes in IDC patients. A random forest algorithm identified the most important somatic gene mutations associated with the constructed immune signature. Furthermore, a nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed, revealing that the immune signature was an independent prognostic biomarker. Finally, the relationship of VEGFA, PD1, PDL-1, and CTLA-4 expression with the immune infiltration landscape and the immune signature was analyzed to interpret the responses of IDC patients to immunotherapy. Conclusion: Taken together, we performed a comprehensive evaluation of the immune landscape of IDC and constructed an immune signature related to the immune landscape. This analysis of TME immune infiltration landscape has shed light on how IDC respond to immunotherapy and may guide the development of novel drug combination strategies

Sulforaphane induces adipocyte browning and promotes glucose and lipid utilization

Scope: Obesity is closely related to the imbalance of white adipose tissue storing excess calories, and brown adipose tissue dissipating energy to produce heat in mammals. Recent studies revealed that acquisition of brown characteristics by white adipocytes, termed “browning,” may positively contribute to cellular bioenergetics and metabolism homeostasis. The goal was to investigate the putative effects of natural antioxidant sulforaphane (1-isothiocyanate-4-methyl-sulfonyl butane; SFN) on browning of white adipocytes. Methods and Results: 3T3-L1 mature white adipocytes were treated with SFN for 48 h, and then the mitochondrial content, function, and energy utilization were assessed. SFN was found to induce 3T3-L1 adipocytes browning based on the increased mitochondrial content and activity of respiratory chain enzymes, whereas the mechanism involved the upregulation of nuclear factor E2-related factor 2/ sirtuin1/ peroxisome proliferator-activated receptor gamma coactivator 1 alpha signaling. SFN enhanced uncoupling protein 1 expression, a marker for brown adipocyte, leading to the decrease in cellular ATP. SFN also enhanced glucose uptake and oxidative utilization, lipolysis and fatty acid oxidation in 3T3-L1 adipocytes. Conclusion: SFN-induced browning of white adipocytes enhanced the utilization of cellular fuel, and the application of SFN is a promising strategy to combat obesity and obesity-related metabolic disorder

The Time Course of Age-related Emotional Preference in Task-irrelevant Affective Processing

Studies of the age-related positivity effect have demonstrated that older adults have a generalized preference to positive stimuli or avoidance to negative stimuli compared with younger adults. However, it remains unclear when and how this positive effect occurs in task-irrelevant affective processing in the aging brain. The present study investigated age-related emotional preference in one task-irrelevant affective stimuli processing by event-related brain potentials (ERPs) measurement with a specific focus on the time course of older adults' emotional processing and regulation. Younger and older adults completed a modified oddball task in which the deviant stimuli were affective faces. In the relatively early time window, the brain activities were not modulated by emotional valence in younger adults, yet the sad stimuli elicited a larger P3a than the happy and neutral ones in older adults. In the late time window, the sad stimuli elicited a larger positive slow wave than the happy stimuli in younger adults. Contrarily, at the later processing stage older adults' valence differences were eliminated. In general, we found time course differences in how older adults processed task-irrelevant affective stimuli compared, with the young, and an age-related positivity effect occurred in the late time window, manifested as a negativity preference in younger and no preferences in older adults. These results provided evidence for supporting socioemotional selectivity theory from an ERP approach