Geochronological, geochemical and Nd-Hf-Os isotopic fingerprinting of an early Neoproterozoic arc-back-arc system in South China and its accretionary assembly along the margin of Rodinia

This research was jointly funded by the NSFC project (40825009 and 40830319), Closure of Eastern Paleotethys Ocean and assembly of South China continents (41190073) of Major NSFC Program (41190070) Reconstruction of East Asian blocks in Pangea and the State Key Laboratory of Continental Dynamics, Northwest University (BJ081331). PAC acknowledges support from the University of St Andrews and NERC (NE/J021822/1).U-Pb geochronology along with elemental and Nd-Hf-Os isotopic data from the earliest Neoproterozoic metabasic rocks within the Cathaysia Block of the South China Block (SCB) constrain the tectonic setting and paleogeography of the block within the Rodinia supercontinent. The metabasic rocks give zircon U-Pb ages of 969-984 Ma, epsilon(Hf)(t) values of +1.8 to +15.3 and Hf model ages of 0.92-1.44 Ga. They are subalkaline basalts that can be geochemically classified into four groups. Group 1 has low Nb contents (1.24-4.33 ppm), highly positive epsilon(Nd)(t) values (+4.3 to +5.2), and REE and multi-elemental patterns similar to fore-arc MORB-type basalt. Group 2 has Nb contents ranging from 3.13 ppm to 6.48 ppm, epsilon(Nd)(t) of +3.1 to +6.2, low Re and Os contents and high initial Os isotopic ratios, and displays an E-MORB geochemical signature. Group 3 has Nb = 7.18-29.87 ppm, Nb/La = 0.60-1.40, Nb/U = 5.0-37, Ce/Pb = 1.1-6.6, epsilon(Nd)( t) = +2.9 to +7.0, Re-187/Os-188 = 5.87-8.87 and gamma Os (t) = 178-772, geochemically resembling to the Pickle Nb-enriched basalt. Group 4 has strong LREE/HREE and HREE fractionation and high epsilon(Nd)(t) values (+2.3 to +5.6), and is characterized by similar element patterns to arc volcanic rocks. Serpentinites coeval to Group 4 show Os-187/Os-188 of 0.1143-0.1442 and gamma Os (t) of -7.8 to +0.1. Groups 1 and 2 are interpreted to originate from the N-MORB and E-MORB-like sources with the addition of an arc-like component, genetically linked to fore- and back-arc settings, respectively. Groups 3 and 4 show inputs of newly subduction-derived melt and fluid in the wedge source. These geochronological and geochemical signatures fingerprint the development of an earliest Neoproterozoic (similar to 970 Ma) arc-back-arc system along the Wuyi-Yunkai domain of the Cathaysia Block. Regional relationships indicate that the Wuyi-Yunkai arc-back-arc system was one of a series of separate convergent margin settings, which included the Shuangxiwu (similar to 970-880 Ma) and Jiangnan (similar to 870-820 Ma) systems that developed in the SCB. The formation and closure of these arc-back-arc systems resulted in the northwestwardly episodic amalgamation of various pieces of the Yangtze and Cathaysia to finally form the SCB. These signatures require the SCB to occupy an exterior accretionary orogen along the periphery of Rodinia during 990-820 Ma, rather than to have formed through Mesoproterozoic Sibao orogenesis within the interior of Rodinia. (c) 2013 Elsevier B.V. All rights reserved.Peer reviewe

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201

Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network

Recently, the transformation of standard dynamic range TV (SDRTV) to high dynamic range TV (HDRTV) is in high demand due to the scarcity of HDRTV content. However, the conversion of SDRTV to HDRTV often amplifies the existing coding artifacts in SDRTV which deteriorate the visual quality of the output. In this study, we propose a dual inverse degradation SDRTV-to-HDRTV network DIDNet to address the issue of coding artifact restoration in converted HDRTV, which has not been previously studied. Specifically, we propose a temporal-spatial feature alignment module and dual modulation convolution to remove coding artifacts and enhance color restoration ability. Furthermore, a wavelet attention module is proposed to improve SDRTV features in the frequency domain. An auxiliary loss is introduced to decouple the learning process for effectively restoring from dual degradation. The proposed method outperforms the current state-of-the-art method in terms of quantitative results, visual quality, and inference times, thus enhancing the performance of the SDRTV-to-HDRTV method in real-world scenarios.Comment: 10 page

Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation

Out-of-distribution (OOD) generalization is a favorable yet challenging property for deep neural networks. The core challenges lie in the limited availability of source domains that help models learn an invariant representation from the spurious features. Various domain augmentation have been proposed but largely rely on interpolating existing domains and frequently face difficulties in creating truly "novel" domains. Humans, on the other hand, can easily extrapolate novel domains, thus, an intriguing question arises: How can neural networks extrapolate like humans and achieve OOD generalization? We introduce a novel approach to domain extrapolation that leverages reasoning ability and the extensive knowledge encapsulated within large language models (LLMs) to synthesize entirely new domains. Starting with the class of interest, we query the LLMs to extract relevant knowledge for these novel domains. We then bridge the gap between the text-centric knowledge derived from LLMs and the pixel input space of the model using text-to-image generation techniques. By augmenting the training set of domain generalization datasets with high-fidelity, photo-realistic images of these new domains, we achieve significant improvements over all existing methods, as demonstrated in both single and multi-domain generalization across various benchmarks. With the ability to extrapolate any domains for any class, our method has the potential to learn a generalized model for any task without any data. To illustrate, we put forth a much more difficult setting termed, data-free domain generalization, that aims to learn a generalized model in the absence of any collected data. Our empirical findings support the above argument and our methods exhibit commendable performance in this setting, even surpassing the supervised setting by approximately 1-2\% on datasets such as VLCS.Comment: Preprint. Paper under revie

Coactivators in PPAR-Regulated Gene Expression

Peroxisome proliferator-activated receptor (PPAR)α, β (also known as δ), and γ function as sensors for fatty acids and fatty acid derivatives and control important metabolic pathways involved in the maintenance of energy balance. PPARs also regulate other diverse biological processes such as development, differentiation, inflammation, and neoplasia. In the nucleus, PPARs exist as heterodimers with retinoid X receptor-α bound to DNA with corepressor molecules. Upon ligand activation, PPARs undergo conformational changes that facilitate the dissociation of corepressor molecules and invoke a spatiotemporally orchestrated recruitment of transcription cofactors including coactivators and coactivator-associated proteins. While a given nuclear receptor regulates the expression of a prescribed set of target genes, coactivators are likely to influence the functioning of many regulators and thus affect the transcription of many genes. Evidence suggests that some of the coactivators such as PPAR-binding protein (PBP/PPARBP), thyroid hormone receptor-associated protein 220 (TRAP220), and mediator complex subunit 1 (MED1) may exert a broader influence on the functions of several nuclear receptors and their target genes. Investigations into the role of coactivators in the function of PPARs should strengthen our understanding of the complexities of metabolic diseases associated with energy metabolism

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

Purpose – This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach – The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings – The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value – The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability

Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation

BackgroundDisulfidptosis is a newly identified variant of cell death characterized by disulfide accumulation, which is independent of ATP depletion. Accordingly, the latent influence of disulfidptosis on the prognosis of lung adenocarcinoma (LUAD) patients and the progression of tumors remains poorly understood.MethodsWe conducted a multifaceted analysis of the transcriptional and genetic modifications in disulfidptosis regulators (DRs) specific to LUAD, followed by an evaluation of their expression configurations to define DR clusters. Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). Patients were subsequently stratified into high and low DS cohorts based on median DS values. We then performed an exhaustive comparison between these cohorts, focusing on somatic mutations, clinical attributes, tumor microenvironment, and treatment responsiveness. Finally, we empirically validated the biological implications of a critical gene, KYNU, through assays in LUAD cell lines.ResultsWe identified two DR clusters and there were great differences in overall survival (OS) and tumor microenvironment. We selected the "Least Absolute Shrinkage and Selection Operator (LASSO) + Random Survival Forest (RFS)" algorithm to develop a DS based on the average C-index across different cohorts. Our model effectively stratified LUAD patients into high- and low-DS subgroups, with this latter demonstrating superior OS, a reduced mutational landscape, enhanced immune status, and increased sensitivity to immunotherapy. Notably, the predictive accuracy of DS outperformed the published LUAD signature and clinical features. Finally, we validated the DS expression using clinical samples and found that inhibiting KYNU suppressed LUAD cells proliferation, invasiveness, and migration in vitro.ConclusionsThe DR-based scoring system that we developed enabled accurate prognostic stratification of LUAD patients and provides important insights into the molecular mechanisms and treatment strategies for LUAD

Self-Assembly of a Virus-Mimicking Nanostructure System for Efficient Tumor-Targeted Gene Delivery

This is the published version, also available here: http://dx.doi.org/10.1089/10430340252792594.Molecular therapy, including gene therapy, is a promising strategy for the treatment of human disease. However, delivery of molecular therapeutics efficiently and specifically to the target tissue remains a significant challenge. A human transferrin (Tf)-targeted cationic liposome-DNA complex, Tf-lipoplex, has shown high gene transfer efficiency and efficacy with human head and neck cancer in vitro and in vivo (Xu, L., Pirollo, K.F., Tang, W.H., Rait, A., and Chang, E.H. Hum. Gene Ther. 1999;10:2941-2952). Here we explore the structure, size, formation process, and structure-function relationships of Tf-lipoplex. We have observed Tf-lipoplex to have a highly compact structure, with a relatively uniform size of 50-90 nm. This nanostructure is novel in that it resembles a virus particle with a dense core enveloped by a membrane coated with Tf molecules spiking the surface. More importantly, compared with unliganded lipoplex, Tf-lipoplex shows enhanced stability, improved in vivo gene transfer efficiency, and long-term efficacy for systemic p53 gene therapy of human prostate cancer when used in combination with conventional radiotherapy. On the basis of our observations, we propose a multistep self-assembly process and Tf-facilitated DNA cocondensation model that may provide an explanation for the resultant small size and effectiveness of our nanostructural Tf-lipoplex system

Ultrasound-guided microwave ablation in the treatment of recurrent primary hyperparathyroidism in a patient with MEN1: a case report

BackgroundMultiple endocrine neoplasia type 1 (MEN1) is an inherited endocrine syndrome caused by the mutation in the tumor suppressor gene MEN1. The recurrence rate of primary hyperparathyroidism (PHPT) in patients with MEN1 after parathyroidectomy remains high, and the management of recurrent hyperparathyroidism is still challenging.Case presentationWe reported a 44-year-old woman with MEN1 combined with PHPT who was diagnosed through genetic screening of the patient and her family members. After parathyroidectomy to remove one parathyroid gland, the patient suffered from persistent high levels of serum calcium and parathyroid hormone, which returned to normal at up to 8 months after ultrasound-guided microwave ablation (MWA) for bilateral parathyroid glands, suggesting an acceptable short-term prognosis.ConclusionUltrasound-guided MWA for parathyroid nodules may be an effective therapeutic strategy for recurrent PHPT in MEN1 patients