A Versatile Protein Scaffold Engineered for the Hierarchical Assembly of Robust and Highly Active Enzymes.

Scaffold proteins play immense roles in bringing enzymes together to enhance their properties. However, the direct fusion of scaffold with bulky guest enzymes may disrupt the assembly process or diminish catalytic efficiency. Most self-assembling protein scaffolds are engineered to form structures beforehand, and then carry guest proteins via different conjugation strategies in vitro.

ACformer: A unified transformer for arbitrary-frame image exposure correction.

Both the single-image exposure correction (SEC) methods and multi-image exposure fusion (MEF) methods aim to obtain a well-exposed image, but from different number of input image(s). Despite their promising performance on the specific SEC or MEF task, few of these methods explores the inherent correlation behind the same goal of the SEC or MEF tasks. In this paper, we propose to unify the SEC and MEF tasks into a unified task of "Arbitrary-Frame Exposure Correction" (AF-EC) with arbitrary number of input frames.

Deletion of BMP4 impairs trophoblast function and decidual macrophage polarization via autophagy leading to recurrent spontaneous abortion.

Bone morphogenetic protein 4 (BMP4) is widely involved in the regulation of cell proliferation and differentiation, but its role in Recurrent Spontaneous Abortion (RSA) remains unclear. RSA is a disease that affects roughly 1-2% of partner pairs, but its pathogenesis is still unclear. In recent years, many studies have focused on the role of decidual macrophages in RSA.

Elevated ITGA3 expression serves as a novel prognostic biomarker and regulates tumor progression in cervical cancer.

Patients with advanced and recurrent cervical cancer often lack satisfactory treatment outcomes. Thus, it is necessary to seek reliable biomarkers that provide the ability to identify the disease at an early stage and predict the patient prognosis, providing new strategies for the treatment of cervical cancer. The sequencing data of ITGA3 were retrieved from public datasets.

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

Objectives: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts.

Methods: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort.

Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features.

Background: Approximately 50% of breast mucinous carcinomas (MCs) are oval and have the possibility of being misdiagnosed as fibroadenomas (FAs). We aimed to identify the key features that can help differentiate breast MC with an oval shape from FA on ultrasonography (US).

Methods: Seventy-six MCs from 71 consecutive patients and 50 FAs with an oval shape from 50 consecutive patients were included in our study.

Diagnostic Efficacy of High-frequency Ultrasound (HFU) in Early Diagnosis of Congenital Hip Dysplasia.

Background: Hip dysplasia is one of the most prevalent disorders in children and one of the three primary congenital orthopedic deformities. Although there are numerous existing methods (e.g.

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.

Background: Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.

Methods: In total, 96 patients with LVNC were included and randomly divided into training and test cohorts.

BMP4 participates in the pathogenesis of PCOS by regulating glucose metabolism and autophagy in granulosa cells under hyperandrogenic environment.

Polycystic ovary syndrome (PCOS) is a complex reproductive endocrine disease characterized by ovulation dysfunction with multiple etiologies and manifestations, and it is widely believed that the disorders of hyper-androgen and glucose metabolism play a key role in its progression. There has been evidence that bone morphogenetic protein 4 (BMP4) is essential for the regulation of granulosa cells, but whether it regulates metabolism level of granulosa cells under hyperandrogenic environment remains unclear. In this study, Gene Expression Omnibus, clinical data and serum of PCOS patient were collected to detect androgen and BMP4 levels.

UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation.

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled.

CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation.

Generalization to previously unseen images with potential domain shifts is essential for clinically applicable medical image segmentation. Disentangling domain-specific and domain-invariant features is key for Domain Generalization (DG). However, existing DG methods struggle to achieve effective disentanglement.

One-Shot Weakly-Supervised Segmentation in 3D Medical Images.

Deep neural networks typically require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot and weakly-supervised learning are promising research directions that reduce labeling effort by learning a new class from only one annotated image and using coarse labels instead, respectively. In this work, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings.

TP53RK Drives the Progression of Chronic Kidney Disease by Phosphorylating Birc5.

Renal fibrosis is a common characteristic of various chronic kidney diseases (CKDs) driving the loss of renal function. During this pathological process, persistent injury to renal tubular epithelial cells and activation of fibroblasts chiefly determine the extent of renal fibrosis. In this study, the role of tumor protein 53 regulating kinase (TP53RK) in the pathogenesis of renal fibrosis and its underlying mechanisms is investigated.

Insights on the NF-κB system in polycystic ovary syndrome, attractive therapeutic targets.

The nuclear factor κappa B (NF-κB) signaling plays a well-known function in inflammation and regulates a wide variety of biological processes. Low-grade chronic inflammation is gradually considered to be closely related to the pathogenesis of Polycystic ovary syndrome (PCOS). In this review, we provide an overview on the involvement of NF-κB in the progression of PCOS particularly, such as hyperandrogenemia, insulin resistance, cardiovascular diseases, and endometrial dysfunction.

IL-27 attenuates IL-23 mediated inflammatory arthritis.

Interleukin 27 has both pro-inflammatory and anti-inflammatory properties in autoimmunity. The anti-inflammatory effects of IL-27 are linked with inhibition of Th17 differentiation but the IL-27 effect on myeloid cells is less studied. Herein we demonstrate that IL-27 inhibits IL-23-induced inflammation associated not only with Th17 cells but also with myeloid cell infiltration in the joints and splenic myeloid populations of CD11b GR1 and CD3CD11bCD11cGR1 cells.

SR: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites.

In clinical practice, it is desirable for medical image segmentation models to be able to continually learn on a sequential data stream from multiple sites, rather than a consolidated dataset, due to storage cost and privacy restrictions. However, when learning on a new site, existing methods struggle with a weak memorizability for previous sites with complex shape and semantic information, and a poor explainability for the memory consolidation process. In this work, we propose a novel Shape and Semantics-based Selective Regularization ( [Formula: see text]) method for explainable cross-site continual segmentation to maintain both shape and semantic knowledge of previously learned sites.

First Report of radish tubers rot caused by in China.

Radish ( L.) is a widely consumed vegetable in China. However, radish is susceptible to diseases, which limits its yield and development in Harbin, China.

Effects of stay-at-home orders on skill requirements in vacancy postings.

The COVID-19 pandemic and containment policies have had profound economic impacts on the labor market. Stay-at-home orders (SAHOs) implemented across most of the United States changed the way of people worked. In this paper, we quantify the effect of SAHO durations on skill demands to study how firms adjust labor demand within occupation.

A fast two-stage algorithm for non-negative matrix factorization in smoothly varying data.

This article reports the study of algorithms for non-negative matrix factorization (NMF) in various applications involving smoothly varying data such as time or temperature series diffraction data on a dense grid of points. Utilizing the continual nature of the data, a fast two-stage algorithm is developed for highly efficient and accurate NMF. In the first stage, an alternating non-negative least-squares framework is used in combination with the active set method with a warm-start strategy for the solution of subproblems.

Interleukin-23 Regulates Inflammatory Osteoclastogenesis via Activation of CLEC5A(+) Osteoclast Precursors.

Objective: To investigate the role of interleukin-23 (IL-23) in pathologic bone remodeling in inflammatory arthritis.

Methods: In this study we investigated the role of IL-23 in osteoclast differentiation and activation using in vivo gene transfer techniques in wild-type and myeloid DNAX-activation protein 12-associating lectin-1 (MDL-1)-deficient mice, and by performing in vitro and in vivo osteoclastogenesis assays using spectral flow cytometry, micro-computed tomography analysis, Western blotting, and immunoprecipitation.

Results: Herein, we show that IL-23 induces the expansion of a myeloid osteoclast precursor population and supports osteoclastogenesis and bone resorption in inflammatory arthritis.

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

Background And Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accurate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.

Comprehensive Analysis of Transcriptomics and Genetic Alterations Identifies Potential Mechanisms Underlying Anthracycline Therapy Resistance in Breast Cancer.

Anthracycline is a mainstay of treatment for breast cancer patients because of its antitumor activity. However, anthracycline resistance is a critical barrier in treating breast cancer. Thus, it is of great importance to uncover the molecular mechanisms underlying anthracycline resistance in breast cancer.

BraVRG, a novel protein of Brassica rapa, is induced by vernalization and promotes flowering in Arabidopsis thaliana.

Plant flowering is an important economical characteristic for the transformation from vegetative growth to reproductive growth, especially for biennial crops. Additionally, bolting or flowering time is more important for vegetable plants due to their different harvest organs, such as flower for cauliflower and broccoli and leafy heads for cabbage and Chinese cabbage. The flowering time of Arabidopsis thaliana has six classical regulated pathways, and some key regulated genes are identified in Brassicaceae crops.