Structural evolution and characterization of organic-rich shale from macroscopic to microscopic resolution: The significance of tectonic activity

Shale gas exploration and development have taken significant strides in the relatively straightforward intra-basin stability zone and intra-basin weak deformation zone of marine shale in the Sichuan Basin, South China. In addition, the extra-basin strong tectonic modification zones have been actively explored. However, the results have been limited, which reveals the complexity of shale gas formation and preservation conditions in the context of multi-scale geological processes. These tectonic geological conditions have a significant impact on the shale gas content, while it has been difficult to figure out how tectonic deformation modifies reservoir structure and what specific mechanism causes shale gas content anomalies. Based on subjecting geologic samples to combined high-temperature and high-pressure experiments, this study summarizes the tectonic constraint mechanism of shale petrophysical structure evolution and its impact on shale gas storage, reveals the intrinsic connection and mechanism of shale pore-fracture and organic matter, inorganic mineral particle structure evolution and tectonic stress, and identifies the remodeling mechanism of the shale reservoir physical property change. The findings contribute to the theory of shale deformation and gas accumulation, as well as offer a scientific foundation for the exploration of marine shale gas in the complex tectonic zones outside the Sichuan Basin.Document Type: PerspectiveCited as: Gao, J., Li, X., Cheng, G., Luo, H., Zhu, H. Structural evolution and characterization of organic-rich shale from macroscopic to microscopic resolution: The significance of tectonic activity. Advances in Geo-Energy Research, 2023, 10(2): 84-90. https://doi.org/10.46690/ager.2023.11.0

Human Preference Score: Better Aligning Text-to-Image Models with Human Preference

Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .Comment: Accepted by ICCV 202

Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics. To address this issue, we introduce Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human preferences on images from a wide range of sources. HPD v2 comprises 798,090 human preference choices on 433,760 pairs of images, making it the largest dataset of its kind. The text prompts and images are deliberately collected to eliminate potential bias, which is a common issue in previous datasets. By fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a scoring model that can more accurately predict human preferences on generated images. Our experiments demonstrate that HPS v2 generalizes better than previous metrics across various image distributions and is responsive to algorithmic improvements of text-to-image generative models, making it a preferable evaluation metric for these models. We also investigate the design of the evaluation prompts for text-to-image generative models, to make the evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for text-to-image generative models using HPS v2, which includes a set of recent text-to-image models from the academic, community and industry. The code and dataset is available at https://github.com/tgxs002/HPSv2 .Comment: Revisio

Characterization of Coal Porosity for Naturally Tectonically Stressed Coals in Huaibei Coal Field, China

The enrichment of coalbed methane (CBM) and the outburst of gas in a coal mine are closely related to the nanopore structure of coal. The evolutionary characteristics of 12 coal nanopore structures under different natural deformational mechanisms (brittle and ductile deformation) are studied using a scanning electron microscope (SEM) and low-temperature nitrogen adsorption. The results indicate that there are mainly submicropores (2~5 nm) and supermicropores (<2 nm) in ductile deformed coal and mesopores (10~100 nm) and micropores (5~10 nm) in brittle deformed coal. The cumulative pore volume (V) and surface area (S) in brittle deformed coal are smaller than those in ductile deformed coal which indicates more adsorption space for gas. The coal with the smaller pores exhibits a large surface area, and coal with the larger pores exhibits a large volume for a given pore volume. We also found that the relationship between S and V turns from a positive correlation to a negative correlation when S>4 m2/g, with pore sizes <5 nm in ductile deformed coal. The nanopore structure (<100 nm) and its distribution could be affected by macromolecular structure in two ways. Interconversion will occur among the different size nanopores especially in ductile deformed coal

The impact of gene polymorphism and hepatic insufficiency on voriconazole dose adjustment in invasive fungal infection individuals

Voriconazole (VRZ) is a broad-spectrum antifungal medication widely used to treat invasive fungal infections (IFI). The administration dosage and blood concentration of VRZ are influenced by various factors, posing challenges for standardization and individualization of dose adjustments. On the one hand, VRZ is primarily metabolized by the liver, predominantly mediated by the cytochrome P450 (CYP) 2C19 enzyme. The genetic polymorphism of CYP2C19 significantly impacts the blood concentration of VRZ, particularly the trough concentration (Ctrough), thereby influencing the drug’s efficacy and potentially causing adverse drug reactions (ADRs). Recent research has demonstrated that pharmacogenomics-based VRZ dose adjustments offer more accurate and individualized treatment strategies for individuals with hepatic insufficiency, with the possibility to enhance therapeutic outcomes and reduce ADRs. On the other hand, the security, pharmacokinetics, and dosing of VRZ in individuals with hepatic insufficiency remain unclear, making it challenging to attain optimal Ctrough in individuals with both hepatic insufficiency and IFI, resulting in suboptimal drug efficacy and severe ADRs. Therefore, when using VRZ to treat IFI, drug dosage adjustment based on individuals’ genotypes and hepatic function is necessary. This review summarizes the research progress on the impact of genetic polymorphisms and hepatic insufficiency on VRZ dosage in IFI individuals, compares current international guidelines, elucidates the current application status of VRZ in individuals with hepatic insufficiency, and discusses the influence of CYP2C19, CYP3A4, CYP2C9, and ABCB1 genetic polymorphisms on VRZ dose adjustments and Ctrough at the pharmacogenomic level. Additionally, a comprehensive summary and analysis of existing studies’ recommendations on VRZ dose adjustments based on CYP2C19 genetic polymorphisms and hepatic insufficiency are provided, offering a more comprehensive reference for dose selection and adjustments of VRZ in this patient population

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments

JourneyDB: A Benchmark for Generative Image Understanding

While recent advancements in vision-language models have had a transformative impact on multi-modal comprehension, the extent to which these models possess the ability to comprehend generated images remains uncertain. Synthetic images, in comparison to real data, encompass a higher level of diversity in terms of both content and style, thereby presenting significant challenges for the models to fully grasp. In light of this challenge, we introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images within the context of multi-modal visual understanding. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images, each paired with the corresponding text prompts that were employed in their creation. Furthermore, we additionally introduce an external subset with results of another 22 text-to-image generative models, which makes JourneyDB a comprehensive benchmark for evaluating the comprehension of generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension in relation to both content and style interpretation. These benchmarks encompass prompt inversion, style retrieval, image captioning, and visual question answering. Lastly, we evaluate the performance of state-of-the-art multi-modal models when applied to the JourneyDB dataset, providing a comprehensive analysis of their strengths and limitations in comprehending generated content. We anticipate that the proposed dataset and benchmarks will facilitate further research in the field of generative content understanding. The dataset is publicly available at https://journeydb.github.io.Comment: Accepted to the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023