Indexing TNF-α gene expression using a gene-targeted reporter cell line

<p>Abstract</p> <p>Background</p> <p>Current cell-based drug screening technologies utilize randomly integrated reporter genes to index transcriptional activity of an endogenous gene of interest. In this context, reporter expression is controlled by known genetic elements that may only partially capture gene regulation and by unknown features of chromatin specific to the integration site. As an alternative technology, we applied highly efficient gene-targeting with recombinant adeno-associated virus to precisely integrate a luciferase reporter gene into exon 1 of the HeLa cell tumor necrosis factor-alpha (<it>TNF-α</it>) gene. Drugs known to induce <it>TNF-α </it>expression were then used to compare the authenticity of gene-targeted and randomly integrated transcriptional reporters.</p> <p>Results</p> <p><it>TNF-α</it>-targeted reporter activity reflected endogenous <it>TNF-α </it>mRNA expression, whereas randomly integrated <it>TNF-α </it>reporter lines gave variable expression in response to transcriptional and epigenetic regulators. 5,6-Dimethylxanthenone-4-acetic acid (DMXAA), currently used in cancer clinical trials to induce <it>TNF-α </it>gene transcription, was only effective at inducing reporter expression from <it>TNF-α </it>gene-targeted cells.</p> <p>Conclusion</p> <p>We conclude that gene-targeted reporter cell lines provide predictive indexing of gene transcription for drug discovery.</p

Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments are conducted using the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time

Compositional Mining of Multiple Object API Protocols through State Abstraction

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments

Establishment of a Reverse Genetics System for Studying Human Bocavirus in Human Airway Epithelia

Human bocavirus 1 (HBoV1) has been identified as one of the etiological agents of wheezing in young children with acute respiratory-tract infections. In this study, we have obtained the sequence of a full-length HBoV1 genome (including both termini) using viral DNA extracted from a nasopharyngeal aspirate of an infected patient, cloned the full-length HBoV1 genome, and demonstrated DNA replication, encapsidation of the ssDNA genome, and release of the HBoV1 virions from human embryonic kidney 293 cells. The HBoV1 virions generated from this cell line-based production system exhibits a typical icosahedral structure of approximately 26 nm in diameter, and is capable of productively infecting polarized primary human airway epithelia (HAE) from the apical surface. Infected HAE showed hallmarks of lung airway-tract injury, including disruption of the tight junction barrier, loss of cilia and epithelial cell hypertrophy. Notably, polarized HAE cultured from an immortalized airway epithelial cell line, CuFi-8 (originally derived from a cystic fibrosis patient), also supported productive infection of HBoV1. Thus, we have established a reverse genetics system and generated the first cell line-based culture system for the study of HBoV1 infection, which will significantly advance the study of HBoV1 replication and pathogenesis.This work was supported by PHS R21 grant AI085236 and PHS R01 grant AI070723 from NIAID (J Qiu) and PHS R01 grant HL108902 from NHLBI (J Engelhardt)

Long Non-Coding RNA XLOC_006753 Promotes the Development of Multidrug Resistance in Gastric Cancer Cells Through the PI3K/AKT/mTOR Signaling Pathway

Background/Aims: The development of multidrug resistance (MDR), which results in disease recurrence and metastasis, is a crucial obstacle to successful chemotherapy for patients with gastric cancer (GC). Long non-coding RNAs (lncRNAs) have been found to play various roles in cancer. This study aimed to investigate the effect of XLOC_006753 on the development of MDR in GC cells. Methods: The expression levels of XLOC_006753 in GC patients and MDR GC cell lines (SGC-7901/5-FU and SGC-7901/DDP cell line) were assessed by qRT-PCR. Statistical analyses were conducted to determine the relationship between XLOC_006753 expression and clinical features and to assess the prognostic value of XLOC_006753 for overall survival and progression-free survival. Then, a CCK-8 assay was used to detect cell proliferation ability and chemosensitivity. Flow cytometry was used to detect cell cycle and cell apoptosis. A wound-healing assay and transwell assay were used to detect cell migration. The expression of markers for MDR, G1/S transition, epithelial–mesenchymal transition (EMT) and PI3K/ AKT/mTOR signaling pathway were examined by western blot. Results: XLOC_006753 was highly expressed in GC patients and MDR GC cell lines (SGC-7901/5-FU and SGC-7901/DDP cell lines), and its high expression was positively associated with metastasis, TNM stage, tumor size, and poor survival in GC patients. Moreover, XLOC_006753 was an independent prognostic biomarker of overall survival and progression-free survival for gastric cancer patients. Knocking down XLOC_006753 in the two MDR GC cell lines significantly inhibited cell proliferation, cell viability, cell cycle G1/S transition, and migration. XLOC_006753 knockdown also promoted apoptosis. Furthermore, western blots showed that XLOC_006753 knockdown decreased some markers of MDR, G1/S transition, and EMT expression, while increasing caspase9 expression and inhibiting the PI3K/AKT/mTOR signaling pathway in SGC-7901/5-FU and SGC-7901/DDP cells. Conclusion: High expression of XLOC_006753 promoted the development of MDR, which was activated by the PI3K/AKT/mTOR pathway in GC cells

The Role of Acidity, Viscosity, and Morphology on Atmospheric Aerosol Physicochemical Properties and Impacts

Atmospheric aerosol plays a critical role in Earth’s climate by scattering or absorbing solar radiation, acting as cloud condensation and ice nuclei, and impacting air quality and public health. The physicochemical properties of aerosols dictate their climate and health impacts yet are challenging to measure accurately and quantitatively due to the complex nature of atmospheric aerosol. Specifically, the chemical composition, size, morphology, acidity, and viscosity have great interparticle variation. Methods enabling detailed quantitative investigation of individual aerosol properties are needed to understand the chemical transformation and climate effects of atmospheric aerosol. In this dissertation, atmospherically-relevant aerosol particles were examined using various state-of-the-art microspectroscopic techniques to measure the acidity, morphology, and viscosity of individual submicron particles, allowing better prediction of the climate and health impacts. The acidity of aerosol is a critical property that affects the chemistry and composition of the atmosphere. However, there are challenges with quantifying aerosol acidity in individual particles due to the extremely small volumes of fine aerosol particles that have limited pH measurements. A novel single-particle acidity measurement was explored using the degradation of a pH-sensitive polymer. Submicron particles of known pH values (0 or 6) were deposited on a polymer thin film to erode the film. Particles were then rinsed off and the degradation of the polymer was characterized using atomic force microscopy (AFM) and Raman microspectroscopy. Acidic particles (pH=0) caused the polymer to degrade while near neutral particles (pH =6) did not. As particle size decreased, polymer degradation increased, indicating an increase in aerosol acidity at smaller particle diameters. To further understand the impacts of aerosol acidity on the formation and evolution of secondary organic aerosol (SOA), inorganic sulfate particles with varying acidities (pH 1, 2, 3, and 5) reacted with gaseous isoprene-derived epoxydiols (IEPOX) for a range of times (30, 60, and 120 minutes). The morphology and chemical composition were systematically characterized at a single-particle level using AFM with photothermal infrared spectroscopy (AFM-PTIR) and Raman microspectroscopy. Core-shell morphology of SOA particles was observed under acidic conditions after the IEPOX uptake and increasing aerosol acidity led to an increase in SOA viscosity and higher yield of organosulfates. These physicochemical properties have the potential to significantly alter the climate properties of the SOA particles. To examine the effects of physicochemical properties in more complex atmospheric particles, the morphology and viscosity of submicron SOA from four different volatile organic compounds precursors (α-pinene, β-caryophyllene, isoprene, and toluene) were characterized before and after exposure to IEPOX. Dramatic morphological modifications were observed after the reactive uptake of IEPOX. SOA derived from α-pinene and β-caryophyllene were less viscous after IEPOX reactive uptake, while the viscosities did not change for isoprene and toluene-derived SOA. Additionally, a new glass transition temperature measurement was developed to reveal the viscosity of individual particles. The glass transition temperatures of atmospheric particles were measured for the first time under ambient atmospheric conditions using AFM-PTIR with thermal analysis. The methods developed in this dissertation and their application to the study of atmospheric aerosol yield a wide range of possibilities to connect the physicochemical properties of aerosol particles with their chemical transformation processes and climate effects. Such characterization of individual submicron SOA particles provides new insights into the multiphase atmospheric processes and the ice nucleation/cloud formation of complex particles in the atmosphere.PHDEnvironmental Health SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169919/1/lziying_1.pd

Effect of Acidity on Ice Nucleation by Inorganic–Organic Mixed Droplets

Aerosol acidity significantly influences heterogeneous chemical reactions and human health. Additionally, acidity may play a role in cloud formation by modifying the ice nucleation properties of inorganic and organic aerosols. In this work, we combined our well-established ice nucleation technique with Raman microspectroscopy to study ice nucleation in representative inorganic and organic aerosols across a range of pH conditions (pH −0.1 to 5.5). Homogeneous nucleation was observed in systems containing ammonium sulfate, sulfuric acid, and sucrose. In contrast, droplets containing ammonium sulfate mixed with diethyl sebacate, poly(ethylene glycol) 400, and 1,2,6-hexanetriol were found to undergo liquid–liquid phase separation, exhibiting core–shell morphologies with observed initiation of heterogeneous freezing in the cores. Our experimental findings demonstrate that an increased acidity reduces the ice nucleation ability of droplets. Changes in the ratio of bisulfate to sulfate coincided with shifts in ice nucleation temperatures, suggesting that the presence of bisulfate may decrease the ice nucleation efficiency. We also report on how the morphology and viscosity impact ice nucleation properties. This study aims to enhance our fundamental understanding of acidity’s effect on ice nucleation ability, providing context for the role of acidity in atmospheric ice cloud formation

An Energy Harvester Coupled with a Triboelectric Mechanism and Electrostatic Mechanism for Biomechanical Energy Harvesting

Energy-harvesting devices based on a single energy conversion mechanism generally have a low output and low conversion efficiency. To solve this problem, an energy harvester coupled with a triboelectric mechanism and electrostatic mechanism for biomechanical energy harvesting is presented. The output performances of the device coupled with a triboelectric mechanism and electrostatic mechanism were systematically studied through principle analysis, simulation, and experimental demonstration. Experiments showed that the output performance of the device was greatly improved by coupling the electrostatic induction mechanisms, and a sustainable and enhanced peak power of approximately 289 &mu;W was produced when the external impedance was 100 M&Omega;, which gave over a 46-fold enhancement to the conventional single triboelectric conversion mechanism. Moreover, it showed higher resolution for motion states compared with the conventional triboelectric nanogenerator, and can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Furthermore, it can charge a capacitor of 10 &mu;F to 3 V within 2 min and light up 16 LEDs. On this basis, a self-powered access control system, based on gait recognition, was successfully demonstrated. This work proposes a novel and cost-effective method for biomechanical energy harvesting, which provides a more convenient choice for human motion status monitoring and can be widely used in personnel identification systems