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Soil nematodes are valuable bioindicators for the ecological status of soils. Nematode community properties are known to be altered by land-use intensity, to vary with seasonal dynamics, and to be affected by climate change. These external drivers also affect a range of structural, physical, and biochemical soil properties. However, it is unclear whether shifts in nematode community properties are the result of changing resource accessibility in the soil or whether these just co-occur. Here, we linked nematode community to microhabitat properties of intact soils and biochemical properties of bulk soils from a long-term field trial on land-use intensity (cropland vs. grassland) and simulated climate change (ambient vs. future climate). Soil samples were taken in two seasons (November vs. June) to capture a wide range of climatic conditions. The objective of the study was to investigate whether the resource accessibility imposed by microhabitat properties would regulate nematode communities and whether the strength of bottom-up regulation depended on climate change, land use intensification, seasonality and their interactions. Land-use and seasonality had clearly separable effects on nematode community composition. The coupling of physical microstructure properties with nematode community properties depended on land use. In cropland, nematode abundance was strongly associated with the features of the habitable pore space, such as nematode-specific porosity, pore connectivity, and particulate organic matter. Grassland nematode communities were independent of these measurable habitat properties and featured stronger co-occurrence networks. The effect of increased temperature and shifting precipitation patterns on nematode community properties were generally smaller, varied with land use and season, and were not linked to concomitant changes in microhabitat properties. Our findings indicate that characterizing microhabitat properties might be a promising approach to help

Image noise and motion artifacts greatly affect the quality of brain MRI and negatively influence downstream medical image analysis. Previous studies often focus on 2D methods that process each volumetric MR image slice-by-slice, thus losing important 3D anatomical information. Additionally, these studies generally treat image denoising and artifact correction as two standalone tasks, without considering their potential relationship, especially on low-quality images where severe noise and motion artifacts occur simultaneously. To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model. In the adaptive denoising model, we first design a novel noise level estimation strategy, and then adaptively reduce the noise through a U-Net backbone with feature normalization conditioning on the estimated noise variance. The anti-artifact model employs another U-Net for eliminating motion artifacts, incorporating a novel gradient-based loss function designed to maintain the integrity of brain anatomy during the motion correction process. These two models are iteratively employed for joint image denoising and artifact correction through an iterative learning framework. An early stopping strategy depending on noise level estimation is applied to accelerate the iteration process. The denoising model is trained with 9,544 T1-weighted MRIs with manually added Gaussian noise as supervision. The anti-artifact model is trained on 552 T1-weighted MRIs with motion artifacts and paired motion-free images. Experimental results on a public dataset and a clinical study suggest the effectiveness of JDAC in both tasks of denoising and motion artifact correction, compared with several state-of-the-art methods.

Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors. Numerous retrospective MRI harmonization techniques have demonstrated encouraging outcomes in reducing the site effects at the image level. However, existing methods generally suffer from high computational requirements and limited generalizability, restricting their applicability to unseen MRIs. In this paper, we design a novel disentangled latent energy-based style translation (DLEST) framework for unpaired image-level MRI harmonization, consisting of (a) site-invariant image generation (SIG), (b) site-specific style translation (SST), and (c) site-specific MRI synthesis (SMS). Specifically, the SIG employs a latent autoencoder to encode MRIs into a low-dimensional latent space and reconstruct MRIs from latent codes. The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style. By disentangling image generation and style translation in latent space, the DLEST can achieve efficient style translation. Our model was trained on T1-weighted MRIs from a public dataset (with 3,984 subjects across 58 acquisition sites/settings) and validated on an independent dataset (with 9 traveling subjects scanned in 11 sites/settings) in four tasks: histogram and feature visualization, site classification, brain tissue segmentation, and site-specific structural MRI synthesis. Qualitative and quantitative results demonstrate the superiority of our method over several state-of-the-arts.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used in multimodal analysis of neurodegenerative disorders. While MRI is broadly utilized in clinical settings, PET is less accessible. Many studies have attempted to use deep generative models to synthesize PET from MRI scans. However, they often suffer from unstable training and inadequately preserve brain functional information conveyed by PET. To this end, we propose a functional imaging constrained diffusion (FICD) framework for 3D brain PET image synthesis with paired structural MRI as input condition, through a new constrained diffusion model (CDM). The FICD introduces noise to PET and then progressively removes it with CDM, ensuring high output fidelity throughout a stable training phase. The CDM learns to predict denoised PET with a functional imaging constraint introduced to ensure voxel-wise alignment between each denoised PET and its ground truth. Quantitative and qualitative analyses conducted on 293 subjects with paired T1-weighted MRI and 18F-fluorodeoxyglucose (FDG)-PET scans suggest that FICD achieves superior performance in generating FDG-PET data compared to state-of-the-art methods. We further validate the effectiveness of the proposed FICD on data from a total of 1,262 subjects through three downstream tasks, with experimental results suggesting its utility and generalizability.

Reactions between cyano radical and aromatic hydrocarbons are believed to be important pathways for the formation of aromatic nitriles in the interstellar medium (ISM) including those identified in the Taurus molecular cloud (TMC-1). Aromatic nitriles might participate in the formation of polycyclic aromatic nitrogen containing hydrocarbons (PANHs) in Titan's atmosphere. Here, ab initio kinetics simulations reveal a high efficiency of $\rm \sim10^{-10}~cm^{3}~s^{-1}$ and the competition of the different products of 30-1800 K and $10^{-7}$-100 atm of the CN + toluene reaction. In the star-forming region of TMC-1 environment, the product yields of benzonitrile and tolunitriles for CN reacting with toluene may be approximately 17$\%$ and 83$\%$, respectively. The detection of main products, tolunitriles, can serve as proxies for the undetected toluene in the ISM due to their much larger dipole moments. The competition between bimolecular and unimolecular products is extremely intense under the warmer and denser PANH forming region of Titan's stratosphere. The computational results show that the fractions of tolunitriles, adducts, and benzonitrile are 19$\%$-68$\%$, 15$\%$-64$\%$ and 17$\%$, respectively, at 150-200 K and 0.0001-0.001 atm (Titan's stratosphere). Then, benzonitrile and tolunitriles may contribute to the formation of PANHs by consecutive $\rm C_{2}H$ additions. Kinetic information of aromatic nitriles for the CN + toluene reaction calculated here helps to explain the formation mechanism of polycyclic aromatic hydrocarbons (PAHs) or PANHs under different interstellar environments and constrains corresponding astrochemical models.

Water-dispersible conjugated polymer nanoparticles (CPNs) have demonstrated great capabilities in biological applications, such as in vitro cell/subcellular imaging and biosensing, or in vivo tissue imaging and disease treatment. In this review, we summarized the recent advances of CPNs used for tumor imaging and treatment during the past five years. CPNs with different structures, which have been applied to in vivo solid tumor imaging (fluorescence, photoacoustic, and dual-modal) and treatment (phototherapy, drug carriers, and synergistic therapy), are discussed in detail. We also demonstrated the potential of CPNs as cancer theranostic nanoplatforms. Finally, we discussed current challenges and outlooks in this field. © 2023 American Chemical Society.

Human pose analysis has garnered significant attention within both the research community and practical applications, owing to its expanding array of uses, including gaming, video surveillance, sports performance analysis, and human-computer interactions, among others. The advent of deep learning has significantly improved the accuracy of pose capture, making pose-based applications increasingly practical. This paper presents a comprehensive survey of pose-based applications utilizing deep learning, encompassing pose estimation, pose tracking, and action recognition.Pose estimation involves the determination of human joint positions from images or image sequences. Pose tracking is an emerging research direction aimed at generating consistent human pose trajectories over time. Action recognition, on the other hand, targets the identification of action types using pose estimation or tracking data. These three tasks are intricately interconnected, with the latter often reliant on the former. In this survey, we comprehensively review related works, spanning from single-person pose estimation to multi-person pose estimation, from 2D pose estimation to 3D pose estimation, from single image to video, from mining temporal context gradually to pose tracking, and lastly from tracking to pose-based action recognition. As a survey centered on the application of deep learning to pose analysis, we explicitly discuss both the strengths and limitations of existing techniques. Notably, we emphasize methodologies for integrating these three tasks into a unified framework within video sequences. Additionally, we explore the challenges involved and outline potential directions for future research., Comment: 47 pages

Chen Chu,1,* Lixiao Zhou,1,* Heran Xie,1,* Zijie Pei,2 Mengyue Zhang,1 Mengqi Wu,1 Shaohui Zhang,3 Luqi Wang,4 Chunfang Zhao,5 Lei Shi,6,7 Ning Zhang,1 Yujie Niu,6,7 Yuxin Zheng,8 Rong Zhang1,7,*1Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, People’s Republic of China; 2Department of Pathology, Medical School, China Three Gorge University, Yichang, 443002, People’s Republic of China; 3Department of Experimental Center, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, People’s Republic of China; 4Riodiology, The Third Hospital of Hebei Medical University, Shijiazhuang 050051, People’s Republic of China; 5Histology and Embryology, Hebei Medical University, Shijiazhuang 050017, People’s Republic of China; 6Occupational Health and Environmental Health, Hebei Medical University, Shijiazhuang 050017, People’s Republic of China; 7Hebei Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang, People’s Republic of China; 8Department of Toxicology, Public Health College, Qingdao University, Qingdao, 266000, People’s Republic of China*These authors contributed equally to this workBackground: Recent years, there occurs heavy haze pollution in northern China during wintertime. The potential influence of airborne particulate matter (PM) on human health attracts great concern. The fuel-derived PM in the inhalable size range is dominated by aggregates of nanoparticles of Carbon black (CB). However, there are still lack of evidences especially regarding long-term exposure to explain the chronic effects of nanoscaled CB and the relative mechanism.Purpose: The objective of this study was to identify the potential mechanism of chronic effects of nanoscale CB. The systemic toxicity, immune suppression or activity and local toxicity were evaluated.Methods: 32 rats were divided into 2 groups: 30 mg/m3 CB exposure (nose only, 90 d, 6

Chen Chu,1,* Lixiao Zhou,1,* Heran Xie,1,* Zijie Pei,2 Mengyue Zhang,1 Mengqi Wu,1 Shaohui Zhang,3 Luqi Wang,4 Chunfang Zhao,5 Lei Shi,6,7 Ning Zhang,1 Yujie Niu,6,7 Yuxin Zheng,8 Rong Zhang1,7,*1Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, People’s Republic of China; 2Department of Pathology, Medical School, China Three Gorge University, Yichang, 443002, People’s Republic of China; 3Department of Experimental Center, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, People’s Republic of China; 4Riodiology, The Third Hospital of Hebei Medical University, Shijiazhuang 050051, People’s Republic of China; 5Histology and Embryology, Hebei Medical University, Shijiazhuang 050017, People’s Republic of China; 6Occupational Health and Environmental Health, Hebei Medical University, Shijiazhuang 050017, People’s Republic of China; 7Hebei Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang, People’s Republic of China; 8Department of Toxicology, Public Health College, Qingdao University, Qingdao, 266000, People’s Republic of China*These authors contributed equally to this workBackground: Recent years, there occurs heavy haze pollution in northern China during wintertime. The potential influence of airborne particulate matter (PM) on human health attracts great concern. The fuel-derived PM in the inhalable size range is dominated by aggregates of nanoparticles of Carbon black (CB). However, there are still lack of evidences especially regarding long-term exposure to explain the chronic effects of nanoscaled CB and the relative mechanism.Purpose: The objective of this study was to identify the potential mechanism of chronic effects of nanoscale CB. The systemic toxicity, immune suppression or activity and local toxicity were evaluated.Methods: 32 rats were divided into 2 groups: 30 mg/m3 CB exposure (nose only, 90 d, 6