Branched-Chain Amino Acid Negatively Regulates KLF15 Expression via PI3K-AKT Pathway.

Recent studies have linked branched-chain amino acid (BCAA) with numerous metabolic diseases. However, the molecular basis of BCAA's roles in metabolic regulation remains to be established. KLF15 (Krüppel-like factor 15) is a transcription factor and master regulator of glycemic, lipid, and amino acids metabolism. In the present study, we found high concentrations of BCAA suppressed KLF15 expression while BCAA starvation induced KLF15 expression, suggesting KLF15 expression is negatively controlled by BCAA.Interestingly, BCAA starvation induced PI3K-AKT signaling. KLF15 induction by BCAA starvation was blocked by PI3K and AKT inhibitors, indicating the activation of PI3K-AKT signaling pathway mediated the KLF15 induction. BCAA regulated KLF15 expression at transcriptional level but not post-transcriptional level. However, BCAA starvation failed to increase the KLF15-promoter-driven luciferase expression, suggesting KLF15 promoter activity was not directly controlled by BCAA. Finally, fasting reduced BCAA abundance in mice and KLF15 expression was dramatically induced in muscle and white adipose tissue, but not in liver. Together, these data demonstrated BCAA negatively regulated KLF15 expression, suggesting a novel molecular mechanism underlying BCAA's multiple functions in metabolic regulation

Selection and environmental adaptation along a path to speciation in the Tibetan frog Nanorana parkeri.

Tibetan frogs, Nanorana parkeri, are differentiated genetically but not morphologically along geographical and elevational gradients in a challenging environment, presenting a unique opportunity to investigate processes leading to speciation. Analyses of whole genomes of 63 frogs reveal population structuring and historical demography, characterized by highly restricted gene flow in a narrow geographic zone lying between matrilines West (W) and East (E). A population found only along a single tributary of the Yalu Zangbu River has the mitogenome only of E, whereas nuclear genes of W comprise 89-95% of the nuclear genome. Selection accounts for 579 broadly scattered, highly divergent regions (HDRs) of the genome, which involve 365 genes. These genes fall into 51 gene ontology (GO) functional classes, 14 of which are likely to be important in driving reproductive isolation. GO enrichment analyses of E reveal many overrepresented functional categories associated with adaptation to high elevations, including blood circulation, response to hypoxia, and UV radiation. Four genes, including DNAJC8 in the brain, TNNC1 and ADORA1 in the heart, and LAMB3 in the lung, differ in levels of expression between low- and high-elevation populations. High-altitude adaptation plays an important role in maintaining and driving continuing divergence and reproductive isolation. Use of total genomes enabled recognition of selection and adaptation in and between populations, as well as documentation of evolution along a stepped cline toward speciation

Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation

The Diffusion Probabilistic Model (DPM) has emerged as a highly effective generative model in the field of computer vision. Its intermediate latent vectors offer rich semantic information, making it an attractive option for various downstream tasks such as segmentation and detection. In order to explore its potential further, we have taken a step forward and considered a more complex scenario in the medical image domain, specifically, under an unsupervised adaptation condition. To this end, we propose a Diffusion-based and Prototype-guided network (DP-Net) for unsupervised domain adaptive segmentation. Concretely, our DP-Net consists of two stages: 1) Distribution Aligned Diffusion (DADiff), which involves training a domain discriminator to minimize the difference between the intermediate features generated by the DPM, thereby aligning the inter-domain distribution; and 2) Prototype-guided Consistency Learning (PCL), which utilizes feature centroids as prototypes and applies a prototype-guided loss to ensure that the segmentor learns consistent content from both source and target domains. Our approach is evaluated on fundus datasets through a series of experiments, which demonstrate that the performance of the proposed method is reliable and outperforms state-of-the-art methods. Our work presents a promising direction for using DPM in complex medical image scenarios, opening up new possibilities for further research in medical imaging

Diagnostic performance of metagenomic next-generation sequencing based on alveolar lavage fluid in unexplained lung shadows

Unexplained lung shadows are challenging in respiratory medicine, with both infectious and non-infectious etiologies. Lung biopsy is definitive but invasive, prompting a need for non-invasive alternatives. Metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) is emerging as a promising diagnostic tool. We retrospectively analyzed 105 patients with unexplained lung shadows, collecting general information, mNGS results from BALF, and clinical diagnosis. We evaluated mNGS's diagnostic performance by comparing with final diagnosis. mNGS showed good diagnostic performance in differentiating infectious from non-infectious causes. The specificity and accuracy for bacteria and fungi exceeded 90%, while the sensitivity and precision for fungi were lower than for bacteria. Atypical pathogens were frequently identified, especially in mixed infections. mNGS of BALF is efficient in diagnosing infectious and non-infectious causes of unexplained lung shadows. While effective for bacteria and fungi detection, the sensitivity and precision for fungi are lower. [Abstract copyright: Copyright © 2024. Published by Elsevier Inc.

FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. ... Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.Comment: This paper has been accepted by Information Fusion. Permission from Elsevier must be obtained for all other uses, in any current or future media. The final version is available at [doi:10.1016/J.INFFUS.2024.102540

CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Models

Patch robustness certification is an emerging kind of defense technique against adversarial patch attacks with provable guarantees. There are two research lines: certified recovery and certified detection. They aim to label malicious samples with provable guarantees correctly and issue warnings for malicious samples predicted to non-benign labels with provable guarantees, respectively. However, existing certified detection defenders suffer from protecting labels subject to manipulation, and existing certified recovery defenders cannot systematically warn samples about their labels. A certified defense that simultaneously offers robust labels and systematic warning protection against patch attacks is desirable. This paper proposes a novel certified defense technique called CrossCert. CrossCert formulates a novel approach by cross-checking two certified recovery defenders to provide unwavering certification and detection certification. Unwavering certification ensures that a certified sample, when subjected to a patched perturbation, will always be returned with a benign label without triggering any warnings with a provable guarantee. To our knowledge, CrossCert is the first certified detection technique to offer this guarantee. Our experiments show that, with a slightly lower performance than ViP and comparable performance with PatchCensor in terms of detection certification, CrossCert certifies a significant proportion of samples with the guarantee of unwavering certification.Comment: 23 pages, 2 figures, accepted by FSE 2024 (The ACM International Conference on the Foundations of Software Engineering

Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However, several challenges persist 1) RNNs struggle with centric feature aggregation and are sensitive to interfering pixels, 2) Transformers require significant computational resources and often underperform with limited HSI training samples, and 3) Current scanning methods for converting images into sequence-data are simplistic and inefficient. In response, this study introduces the innovative Mamba-in-Mamba (MiM) architecture for HSI classification, the first attempt of deploying State Space Model (SSM) in this task. The MiM model includes 1) A novel centralized Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2) A Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), a Semantic Token Learner (STL), and a Semantic Token Fuser (STF) for enhanced feature generation and concentration, and 3) A Weighted MCS Fusion (WMF) module coupled with a Multi-Scale Loss Design to improve decoding efficiency. Experimental results from three public HSI datasets with fixed and disjoint training-testing samples demonstrate that our method outperforms existing baselines and state-of-the-art approaches, highlighting its efficacy and potential in HSI applications

Myocardial Stunning-Induced Left Ventricular Dyssynchrony On Gated Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging

Objectives Myocardial stunning provides additional nonperfusion markers of coronary artery disease (CAD), especially for severe multivessel CAD. The purpose of this study is to assess the influence of myocardial stunning to the changes of left ventricular mechanical dyssynchrony (LVMD) parameters between stress and rest gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Patients and methods A total of 113 consecutive patients (88 males and 25 females) who had undergone both stress and rest 99mTc-sestamibi gated SPECT MPI were retrospectively enrolled. Suspected or known patients with CAD were included if they had exercise stress MPI and moderate to severe myocardial ischemia. Segmental scores were summed for the three main coronary arteries according to standard myocardial perfusion territories, and then regional perfusion, wall motion, and wall thickening scores were measured. Myocardial stunning was defined as both ischemia and wall dysfunction within the same coronary artery territory. Patients were divided into the stunning group (n=58) and nonstunning group (n=55). Results There was no significant difference of LVMD parameters between stress and rest in the nonstunning group. In the stunning group, phase SD and phase histogram bandwidth of contraction were significantly larger during stress than during rest (15.05±10.70 vs. 13.23±9.01 and 46.07±34.29 vs. 41.02±32.16, PP\u3c0.05). Conclusion Both systolic and diastolic LVMD parameters deteriorate with myocardial stunning. This kind of change may have incremental values to diagnose CAD