Domain Wall Network: A Dual Solution for Gravitational Waves and Hubble Tension?

We search for stochastic gravitational wave background (SGWB) generated by domain wall networks in the Data Release-2 of Parkes Pulsar Timing Array and find that the observed strong common power-law process can be explained by domain wall networks for the wall tension $\sigma_{\textrm{DW}}\sim (29-414~\textrm{TeV})^3$ and the wall-decay temperature $T_d\sim 20-257~\textrm{MeV}$ at $68\%$ Credible Level. Interestingly, the same parameter region can largely alleviate the Hubble tension, if the free particles generated from domain wall networks further decay into dark radiation. This coincidence that a domain wall network can simultaneously account for the nano-Hertz SGWB and Hubble tension is robust, independent of domain wall parameters and applicable to observations by other pulsar timing array collaborations in general. On the other hand, assuming that the common power-law process is not due to domain wall networks, we can put stringent constraints on the wall tension and decay temperature.Comment: 14 pages, 9 figures, 4 table

Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits

The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits. However, analyzing functional imaging data of addiction and detecting functional addiction circuits still have challenges. We have developed a data-driven and end-to-end generative artificial intelligence(AI) framework to address these difficulties. The framework integrates dynamic brain network modeling and novel network architecture networks architecture, including temporal graph Transformer and contrastive learning modules. A complete workflow is formed by our generative AI framework: the functional imaging data, from neurobiological experiments, and computational modeling, to end-to-end neural networks, is transformed into dynamic nicotine addiction-related circuits. It enables the detection of addiction-related brain circuits with dynamic properties and reveals the underlying mechanisms of addiction

DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI

Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner. To be specific, the proposed DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and adversarial learning to efficiently generate high-fidelity SC through a few steps from fMRI. By designing the dual-channel multi-head spatial attention (DMSA) and graph convolutional modules, the symmetric graph generator first captures global relations among direct and indirect connected brain regions, then models the local brain region interactions. It can uncover the complex mapping relations between fMRI and structural connectivity. Furthermore, the spatially connected consistency loss is devised to constrain the generator to preserve global-local topological information for accurate intrinsic SC prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed model can effectively generate empirical SC-preserved connectivity from four-dimensional imaging data and shows superior performance in SC prediction compared with other related models. Furthermore, the proposed model can identify the vast majority of important brain regions and connections derived from the empirical method, providing an alternative way to fuse multimodal brain networks and analyze clinical disease.Comment: 12 page

Generative AI for brain image computing and brain network computing: a review

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial

A Novel CRYGD Mutation (p.Trp43Arg) Causing Autosomal Dominant Congenital Cataract in a Chinese Family

To identify the genetic defect associated with autosomal dominant congenital nuclear cataract in a Chinese family, molecular genetic investigation via haplotype analysis and direct sequencing were performed Sequencing of the CRYGD gene revealed a c.127T>C transition, which resulted in a substitution of a highly conserved tryptophan with arginine at codon 43 (p.Trp43Arg). This mutation co-segregated with all affected individuals and was not observed in either unaffected family members or in 200 normal unrelated individuals. Biophysical studies indicated that the p.Trp43Arg mutation resulted in significant tertiary structural changes. The mutant protein was much less stable than the wild-type protein, and was more prone to aggregate when subjected to environmental stresses such as heat and UV irradiation. © 2010 Wiley-Liss, Inc

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer

Emissions of volatile organic compounds (VOCs) from cooking and their speciation: A case study for Shanghai with implications for China

Cooking emission is one of sources for ambient volatile organic compounds (VOCs), which is deleterious to air quality, climate and human health. These emissions are especially of great interest in large cities of East and Southeast Asia. We conducted a case study in which VOC emissions from kitchen extraction stacks have been sampled in total 57 times in the Megacity Shanghai. To obtain representative data, we sampled VOC emissions from kitchens, including restaurants of seven common cuisine types, canteens, and family kitchens. VOC species profiles and their chemical reactivities have been determined. The results showed that 51.26% ± 23.87% of alkane and 24.33 ± 11.69% of oxygenated VOCs (O-VOCs) dominate the VOC cooking emissions. Yet, the VOCs with the largest ozone formation potential (OFP) and secondary organic aerosol potential (SOAP) were from the alkene and aromatic categories, accounting for 6.8–97.0% and 73.8–98.0%, respectively. Barbequing has the most potential of harming people's heath due to its significant higher emissions of acetaldehyde, hexanal, and acrolein. Methodologies for calculating VOC emission factors (EF) for restaurants that take into account VOCs emitted per person (EFperson), per kitchen stove (EFkitchen stove) and per hour (EFhour) are developed and discussed. Methodologies for deriving VOC emission inventories (S) from restaurants are further defined and discussed based on two categories: cuisine types (Stype) and restaurant scales (Sscale). The range of Stype and Sscale are 4124.33–7818.04 t/year and 1355.11–2402.21 t/year, respectively. We also found that Stype and Sscale for 100,000 people are 17.07–32.36 t/year and 5.61–9.95 t/year, respectively. Based on Environmental Kuznets Curve, the annual total amount of VOCs emissions from catering industry in different provinces in China was estimated, which was 5680.53 t/year, 6122.43 t/year, and 66,244.59 t/year for Shangdong and Guangdong provinces and whole China, respectively. Large and medium-scaled restaurants should be paid more attention with respect to regulation of VOCs

Evidence for heterogeneity in China’s progress against pulmonary tuberculosis: uneven reductions in a major center of ongoing transmission, 2005–2017

Abstract Background China contributed 8.9% of all incident cases of tuberculosis globally in 2017, and understanding the spatiotemporal distribution of pulmonary tuberculosis (PTB) in major transmission foci in the country is critical to ongoing efforts to improve population health. Methods We estimated annual PTB notification rates and their spatiotemporal distributions in Sichuan province, a major center of ongoing transmission, from 2005 to 2017. Time series decomposition was used to obtain trend components from the monthly incidence rate time series. Spatiotemporal cluster analyses were conducted to detect spatiotemporal clusters of PTB at the county level. Results From 2005 to 2017, 976,873 cases of active PTB and 388,739 cases of smear-positive PTB were reported in Sichuan Province, China. During this period, the overall reported incidence rate of active PTB decreased steadily at a rate of decrease (3.77 cases per 100,000 per year, 95% confidence interval (CI): 3.28–4.31) that was slightly faster than the national average rate of decrease (3.14 cases per 100,000 per year, 95% CI: 2.61–3.67). Although reported PTB incidence decreased significantly in most regions of the province, incidence was observed to be increasing in some counties with high HIV incidence and ethnic minority populations. Active and smear-positive PTB case reports exhibited seasonality, peaking in March and April, with apparent links to social dynamics and climatological factors. Conclusions While PTB incidence rates decreased strikingly in the study area over the past decade, improvements have not been equally distributed. Additional surveillance and control efforts should be guided by the seasonal-trend and spatiotemporal cluster analyses presented here, focusing on areas with increasing incidence rates, and updated to reflect the latest information from real-time reporting.https://deepblue.lib.umich.edu/bitstream/2027.42/152198/1/12879_2019_Article_4262.pd

Healthy cities initiative in China: Progress, challenges, and the way forward

Article discusses how China implemented the first phase of its National Healthy Cities pilot program from 2016-20. Authors recommend aligning the Healthy Cities initiative in China with strategic national and global level agendas such as Healthy China 2030 and the Sustainable Development Goals (SDGs) by providing an integrative governance framework to facilitate a coherent intersectoral program to systemically improve population health