The majority of cellular proteins are degraded by the 26S proteasome in eukaryotes. However, intrinsically disordered proteins (IDPs), which contain large portions of unstructured regions and are inherently unstable, are degraded via the ubiquitin-independent 20S proteasome. Emerging evidence indicates that plant IDP homeostasis may also be controlled by the 20S proteasome. Relatively little is known about the specific functions of the 20S proteasome and the regulatory mechanisms of IDP degradation in plants compared to other species because there is a lack of systematic protocols for in vitro assembly of this complex to perform in vitro degradation assays. Here, we present a detailed protocol of in vitro reconstitution assay of the 20S proteasome in Arabidopsis by modifying previously reported methods. The main strategy to obtain the 20S core proteasome here is to strip away the 19S regulatory subunits from the 26S proteasome. The protocol has two major parts 1) Affinity purification of 20S proteasomes from stable transgenic lines expressing epitope-tagged PAG1, an essential component of the 20S proteasome (Procedures A-D) and 2) an in vitro 20S proteasome degradation assay (Procedure E). We anticipate that these protocols will provide simple and effective approaches to study in vitro degradation by the 20S proteasome and advance the study of protein metabolism in plants.Cation-chloride cotransporters (CCCs) mediate the coupled, electroneutral symport of cations such as Na+ and/or K+ with chloride across membrane. Among CCCs family, K-Cl cotransporters (KCC1-KCC4) extrude intracellular Cl- by the transmembrane K+ gradient. In humans, these KCCs play vital roles in the physiology of the nervous system and kidney. However, mechanisms underlying the KCCs specific properties remain poorly understood, partly because purification of membrane proteins is challenging. Here, we present the protocol for purifying the full-length KCC1 from HEK293F cells used in our recent publication ( Liu et al., 2019 ). The procedure may be adapted for functional and structural studies.The linker of nucleoskeleton and cytoskeleton (LINC) complex is responsible for tethering the nucleus to the cytoskeleton, providing a pathway for the cell's nucleus to sense mechanical signals from the environment. Recently, we explored the role of the LINC complex in the development of glandular epithelial acini, such as those found in kidneys, breasts, and other organs. Acini developed with disrupted LINC complexes exhibited a loss of structural integrity, including filling of the lumen structures. As part of our investigation, we performed a mechanical indentation assay of LINC disrupted and undisrupted MDCK II cells using a micro-indentation instrument mounted above a laser-scanning confocal microscope. Through a combination of force measurements acquired from the micro-indentation instrument and contact area measurements taken from fluorescence images, we determined the average contact pressure at which the acini structure ruptured. Here, we provide a detailed description of the design of the micro-indentation instrument, as well as the experimental steps developed to perform these bio-indentation measurements. Furthermore, we discuss the data analysis steps necessary to determine the rupture pressure of the acini structures. While this protocol is focused on the indentation of individual glandular acini, the methods presented here can be adapted to perform a variety of mechanical indentation experiments for both 2D and 3D biological systems.Purpose We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels. Approach An encoder-decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert materials were acquired from a research photon-counting-detector CT. The Incept-net was trained with image patches ( 64 × 64    pixels ) extracted from the phantom data, as well as synthesized, random-shaped numerical insert materials. The whole CT images ( 512 × 512    pixels ) with the remaining real insert materials that were unseen in network training were used for testing.  https://www.selleckchem.com/products/lurbinectedin.html  Seven contrast-enhanced abdominal CT exams were used for preliminary evaluation of Incept-net generalizability over anatomical background. Mean absolute percentage error (MAPE) was used to evaluate CT number accuracy. Results Compared to commercial VMI software, Incept-net largely suppressed beam-hardening artifact and reduced noise (53%) in phantom study. Incept-net presented comparable CT number accuracy at higher-density ( P -value [0.0625, 0.999]) and improved it at lower-density inserts ( P - value = 0.0313 ) with overall MAPE Incept-net [2.9%, 4.6%]; commercial-VMI [6.7%, 10.9%]. In patient images, Incept-net suppressed beam-hardening artifact and reduced noise (up to 50%, P - value = 0.0156 ). Conclusion In this preliminary study, Incept-net presented the potential to improve low-energy VMI quality.Purpose The purpose of our study was to combine differences in radiomic features extracted from lung regions in the computed tomography (CT) scans of patients diagnosed with idiopathic pulmonary fibrosis (IPF) to identify associations with genetic variations and patient survival. Approach A database of CT scans and genomic data from 169 patients diagnosed with IPF was collected retrospectively. Six region-of-interest pairs (three per lung, positioned posteriorly, anteriorly, and laterally) were placed in each of three axial CT sections for each patient. Thirty-one features were used in logistic regression to classify patients' genetic mutation status; classification performance was evaluated through the area under the receiver operating characteristic (ROC) curve [average area under the ROC curve (AUC)]. Kaplan-Meier (KM) survival curve models quantified the ability of each feature to differentiate between survival curves based on feature-specific thresholds. Results Nine first-order texture features and one fractal feature were correlated with TOLLIP-1 (rs4963062) mutations (AUC 0.