Spatiotemporal Correlations between Water Footprint and Agricultural Inputs: A Case Study of Maize Production in Northeast China

To effectively manage water resources in agricultural production, it is necessary to understand the spatiotemporal variation of the water footprint (WF) and the influences of agricultural inputs. Employing spatial autocorrelation analysis and a geographically weighted regression (GWR) model, we explored the spatial variations of the WF and their relationships with agricultural inputs from 1998 to 2012 in Northeast China. The results indicated that: (1) the spatial distribution of WFs for the 36 major maize production prefectures was heterogeneous in Northeast China; (2) a cluster of high WFs was found in southeast Liaoning Province, while a cluster of low WFs was found in central Jilin Province, and (3) spatial and temporal differentiation in the correlations between the WF of maize production and agricultural inputs existed according to the GWR model. These correlations increased over time. Our results suggested that localized strategies for reducing the WF should be formulated based on specific relationships between the WF and agricultural inputs

Discontinuous cracking of TiN films on a steel substrate induced by an adhesive interlayer

The basic mechanisms governing the process of cracking of single-layer brittle films have been extensively explored through both simulations and experiments. However, the role that an adhesive interlayer plays in the cracking of the overlying brittle film remains unclear. By performing three-point bending experiments, we observed that the insertion of a 100 nm thick Ti interlayer changed the cracking behaviour of TiN films from a continuous pattern to a discontinuous pattern. The slight change in the microstructure of the film and the increase in film thickness arising from the addition of the Ti interlayer are unlikely to cause the observed cracking morphology. The combination of the different interface between the Ti and the steel substrate and the fracture of the Ti interlayer are responsible for the transition in the TiN film cracking morphologies.acceptedVersionLocked until 22.8.2020 due to copyright restrictions. This is an [Accepted Manuscript] of an article published by Taylor & Francis in [Philosophical Magazine Letters] on [22 Aug 2019], available at https://doi.org/10.1080/09500839.2019.165635

Single PW takes a shortcut to compound PW in US imaging

Reconstruction of ultrasound (US) images from radio-frequency data can be conceptualized as a linear inverse problem. Traditional deep learning approaches, which aim to improve the quality of US images by directly learning priors, often encounter challenges in generalization. Recently, diffusion-based generative models have received significant attention within the research community due to their robust performance in image reconstruction tasks. However, a limitation of these models is their inherent low speed in generating image samples from pure Gaussian noise progressively. In this study, we exploit the inherent similarity between the US images reconstructed from a single plane wave (PW) and PW compounding PWC). We hypothesize that a single PW can take a shortcut to reach the diffusion trajectory of PWC, removing the need to begin with Gaussian noise. By employing an advanced diffusion model, we demonstrate its effectiveness in US image reconstruction, achieving a substantial reduction in sampling steps. In-vivo experimental results indicate that our approach can reduce sampling steps by 60%, while preserving comparable performance metrics with the conventional diffusion model.Comment: Submitted to ISBI 202

Underwater instance segmentation: a method based on channel spatial cross-cooperative attention mechanism and feature prior fusion

In aquaculture, underwater instance segmentation methods offer precise individual identification and counting capabilities. However, due to the inherent unique optical characteristics and high noise in underwater imagery, existing underwater instance segmentation models struggle to accurately capture the global and local feature information of objects, leading to generally lower detection accuracy in underwater instance segmentation models. To address this issue, this study proposes a novel Channel Space Coordinates Attention (CSCA) attention module and a Channel A Prior Attention Fusion (CAPAF) feature fusion module, aiming to improve the accuracy of underwater instance segmentation. The CSCA module effectively captures local and global information by combining channel and spatial attention weight, while the CAPAF module optimizes feature fusion by removing redundant information through learnable parameters. Experimental results demonstrate significant improvements when these two modules are applied to the YOLOv8 model, with the [email protected] metric increasing by 3.2% and 2% on the UIIS underwater instance segmentation dataset. Furthermore, the instance segmentation accuracy is significantly improved on the UIIS and USIS10K datasets after these two modules are applied to other networks

Text Guided Image Editing with Automatic Concept Locating and Forgetting

With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing. However, a persistent challenge remains in seamlessly incorporating objects into images based on textual instructions, without relying on extra user-provided guidance. Text and images are inherently distinct modalities, bringing out difficulties in fully capturing the semantic intent conveyed through language and accurately translating that into the desired visual modifications. Therefore, text-guided image editing models often produce generations with residual object attributes that do not fully align with human expectations. To address this challenge, the models should comprehend the image content effectively away from a disconnect between the provided textual editing prompts and the actual modifications made to the image. In our paper, we propose a novel method called Locate and Forget (LaF), which effectively locates potential target concepts in the image for modification by comparing the syntactic trees of the target prompt and scene descriptions in the input image, intending to forget their existence clues in the generated image. Compared to the baselines, our method demonstrates its superiority in text-guided image editing tasks both qualitatively and quantitatively

HS-Diffusion: Learning a Semantic-Guided Diffusion Model for Head Swapping

Image-based head swapping task aims to stitch a source head to another source body flawlessly. This seldom-studied task faces two major challenges: 1) Preserving the head and body from various sources while generating a seamless transition region. 2) No paired head swapping dataset and benchmark so far. In this paper, we propose an image-based head swapping framework (HS-Diffusion) which consists of a semantic-guided latent diffusion model (SG-LDM) and a semantic layout generator. We blend the semantic layouts of source head and source body, and then inpaint the transition region by the semantic layout generator, achieving a coarse-grained head swapping. SG-LDM can further implement fine-grained head swapping with the blended layout as condition by a progressive fusion process, while preserving source head and source body with high-quality reconstruction. To this end, we design a head-cover augmentation strategy for training and a neck alignment trick for geometric realism. Importantly, we construct a new image-based head swapping benchmark and propose two tailor-designed metrics (Mask-FID and Focal-FID). Extensive experiments demonstrate the superiority of our framework. The code will be available: https://github.com/qinghew/HS-Diffusion

Association between atherogenic index of plasma and depression in individuals with different glucose metabolism status

BackgroundThe atherogenic index of plasma (AIP) has been implicated in various disease processes, yet its relationship with depression, particularly in the context of differing glucose metabolism status, remains underexplored. This study aimed to investigate the association between AIP and depression in middle-aged and older adults with varying glucose metabolism profiles.MethodsData were derived from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011 and 2018, encompassing 7,723 participants aged 45 years and above. Depression was defined using a cutoff score of ≥12 on the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). The primary outcome of interest was incident depression. Logistic regression and restricted cubic spline (RCS) models were applied to assess the relationship between baseline AIP levels and depression risk across distinct glucose metabolism categories.ResultsElevated AIP was strongly associated with increased odds of depression. In fully adjusted models, a graded relationship was observed, with higher quartiles of AIP corresponding to greater depression risk. Participants in the highest AIP quartile (Q4) had significantly increased odds of depression (odds ratio [OR]: 3.36, 95% confidence interval [CI]: 2.67-4.24, P < 0.001) compared to those in the lowest quartile (Q1). Furthermore, RCS analyses revealed a significant positive association between AIP and incident depression among individuals with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (P < 0.001), whereas no such association was found in participants with normal glucose regulation (NGR) (P = 0.086). These findings suggest that glucose metabolism status modifies the relationship between AIP and depression risk.ConclusionHigher baseline AIP levels are significantly associated with an increased risk of depression in middle-aged and older adults, with distinct effects modulated by glucose metabolism status. These results highlight the potential utility of AIP as a biomarker for depression risk and suggest that metabolic health should be considered in the development of targeted strategies for depression prevention and intervention

Recent advances in the role of mesenchymal stem cells as modulators in autoinflammatory diseases

Mesenchymal stem cells (MSCs), recognized for their self-renewal and multi-lineage differentiation capabilities, have garnered considerable wide attention since their discovery in bone marrow. Recent studies have underscored the potential of MSCs in immune regulation, particularly in the context of autoimmune diseases, which arise from immune system imbalances and necessitate long-term treatment. Traditional immunosuppressive drugs, while effective, can lead to drug tolerance and adverse effects, including a heightened risk of infections and malignancies. Consequently, adjuvant therapy incorporating MSCs has emerged as a promising new treatment strategy, leveraging their immunomodulatory properties. This paper reviews the immunomodulatory mechanisms of MSCs and their application in autoimmune diseases, highlighting their potential to regulate immune responses and reduce inflammation. The immunomodulatory mechanisms of MSCs are primarily mediated through direct cell contact and paracrine activity with immune cells. This review lays the groundwork for the broader clinical application of MSCs in the future and underscores their significant scientific value and application prospects. Further research is expected to enhance the efficacy and safety of MSCs-based treatments for autoimmune diseases