bluuebunny/crossref_metadata_2025 huggingface dataset:
Design Strategy for Realizing Optimized Calibration Rod for an End Mill Cutter Based on Receptance Coupling Substructure Analysis
Authors: Tudu Anirban , Gunjan Shashi Bhushan , Devadula Sivasrinivasu
Published: August 2024

Chatter in machining caused by self-excited vibration limits the capability of high-speed spindles and results in poor surface finish and tool wear. Stability lobe diagram (SLD) is widely used to determine chatter-free machining parameters. The SLD for milling needs accurate measurement of frequency response function (FRF) at the cutting tool tip. However, asymmetry and complex geometry of end mills contribute to FRF data with low coherence. The impact testing is difficult due to the minimum area available on the cutting tool’s sharp edges for its excitation and attaching accelerometer. Hence, accurate FRF measurement is time-consuming. The problem gets more prominent when cutting tool rotates. To address this, a calibration rod that mimics the cutting tool is proposed. However, there are limited efforts in identifying optimal design of the calibration rod to capture tool’s dynamic behavior.

The present work aims to develops a design strategy for realizing a calibration rod for an end mill cutter. Towards this, a receptance coupling substructure analysis (RCSA) based approach is employed to evaluate the tool tip FRF for a given length (L) and diameter (D) of the calibration rod. The spindle with solid HSS cylinder of 10 mm diameter as substructure A and cylinder with varying L and D as substructure B were considered. The FRF of substructure A is evaluated by impact test, whereas for substructure B, is evaluated analytically. By minimizing the error between the FRFrcsa and FRFexp of the targeted end mill cutter, the optimal L and D for a calibration rod were obtained. The optimal rods are manufactured to validate the equivalency with the end mill cutter. Experimental validation showed close dynamic responses from the end mill and designed calibration rod. The proposed method’s performance is evaluated for two fluted and four fluted end mills. For the 10 mm four-fluted end mill cutter, the optimal L and D are obtained as 33.5 mm and 7mm, respectively. The evaluated natural frequencies for milling cutter and corresponding calibration rod are observed as 832Hz and 849 Hz, respectively. For 10 mm two-fluted end mill the optimized length and diameter are obtained as 28 mm and 7 mm. Corresponding natural frequencies for end mill and calibration rod are observed as 886 Hz and 888 Hz, respectively.
Particle Analysis From Heterogeneous Background With Deep Learning Tool
Authors: Alam Adeeb , Hassan Md Rafiul , Khoda Bashir
Published: August 2024

The performance of conventional image processing techniques is highly dependent on many parameters like image quality, light source, background surface texture, optimal threshold value and particle morphology. However, during intermediate stages of manufacturing processes (such as continuous deposition, coating, mixing, and transfer), complex backgrounds can arise from heterogeneous particle-substrate (HPS) systems. In such HPS environments, particles become integrated with substrates or suspended in liquid carriers or etching media, making them challenging to identify using traditional particle analysis tools and techniques. In response to this challenge, a deep learning object detection algorithm (YOLO) has been put into practical use. Initially, an HPS (heterogeneous particle-substrate) system was created using a wet-deposition particle transfer process that involved the immersion of poly-disperse particles on to a cylindrical substrate. By manipulating the capillary number in the wet-deposition process, four distinct HPS morphologies were captured, each characterized by variations in image heterogeneity. These morphologies were subsequently subjected to detailed analysis with neural network-based AI algorithm. The proposed artificial intelligence tool has demonstrated an impressive ability to identify and analyze poly-dispersed particles within HPS morphologies, achieving an accuracy rate of over 97%. We can evaluate the quality of sorting by calculating the particle size distribution using the proposed method and find the ideal process parameters for the particle transfer process. The results of this study, outlined in this paper, underscore the potential of deep learning as a particle analysis tool for in-situ applications, even in environments with heterogeneous backgrounds. This developed tool holds promise for various manufacturing processes, including semiconductor industries, high-density powder-based 3D printing, powder metallurgy, refractory coatings in harsh environments, and particle sorting, among others.
Sustainability of Gear Manufacturing With Dry Grinding
Authors: Cestone Lorenzo , Iqbal Hambal , Liverani Erica , Ascari Alessandro , Fortunato Alessandro
Published: August 2024

High quality gears for automotive applications require a high level of geometrical accuracy and surface finish of the gear flank to improve the operational performance while reducing the noise during their lifecycle. Generally, the high-quality gears are produced using a final grinding process for surface finishing, this reduces the surface roughness and geometrical errors caused by the hobbing process and surface hardening techniques. Wet grinding is the most common method in conventional gear manufacturing for this final stage, however, it involves a massive amount of lubricant. Typically, oil or emulsion of oil and water are used as cooling fluids. As a result, every year tonnes of lubricant fluids are incinerated. This causes a significant environmental impact. Moreover, the use of lubricants possesses higher capital and operational costs and requires additional auxiliary equipment in the operation. Three grinding processes, which comprise the final finishing stage in gear production after the hobbing process were compared in this study. These processes include standard wet grinding, optimized wet grinding and dry grinding. In this study, five gears for each of the three process categories were tested under the same loading conditions and number of cycles i.e., 350 Nm and 1.5 million cycles respectively. The standard wet grinding was used as the reference process from Fiat Chrysler Automobiles (FCA) for this comparative study. After the torsional fatigue tests, the surface damages were measured and analysed on the gear flanks produced with each process category. Similarly, the damages for each gear’s flank surface, which resemble a micro-pitting type of damage, were compared and the contribution of the final grinding process was identified to evaluate the vulnerability induced on the component by these finishing processes. Similarly, microstructural analysis and Vickers hardness results further added valuable conclusions for the three process categories. It was concluded that the dry grinding process possesses similar performance with respect to the reference grinding process (FCA) and wet optimised process. Therefore, the feasibility of adopting the dry grinding process in the final stage of gear production is proposed considering the environmental impacts and additional cost of auxiliary equipment related to the use of lubricants. This research highlights the potential for innovative and eco-friendly practices within automotive industry, proposing a cleaner and more efficient gear production process.
Electrospinning Process Modeling: A Multi-Stage View
Authors: Zuniga-Navarrete Christian , Segura Stalin Jamil , Baidya Sabur , Narváez-Muñoz Christian , Toscano Juan Diego , Segura Luis Javier
Published: August 2024

Electrospinning is a versatile electrohydrodynamic-based manufacturing process that can produce micro/nanofibers from polymeric solutions (e.g., polylactide (PLA), polyacrylonitrile (PAN), etc.). It has a variety of applications, including tissue engineering, flexible electronics, filtering, etc. However, the final product performance is drastically affected by the diameter variation of the deposited micro/nanofibers, which is attributed to the process settings, material composition, and ambient conditions. Having a uniform and repeatable electrospun fibers is fundamental to enhancing product functionality, but this is challenging due to the intricate processes that the solutions undergo, from material preparation to product performance. Therefore, it is paramount to understand the different stages that the electrospinning experiences during micro/nanofiber fabrication. In particular, the electrospinning process can be discretized into several stages, namely, stable regimen (i.e., Taylor cone and jet formation), whipping instability, and fiber collection, where the individual stage’s behavior influences the others downstream. The objective of this study is to systematically model the mechanical properties of the non-woven mat via a two-stage simplified framework: (1) the micro/nano fibers’ diameter (i.e., fiber collection stage) is modeled considering process parameters (i.e., voltage, flow rate, and tip-to-collector distance) and in-situ data (i.e., streaming videos from stable regimen and ambient temperature and humidity signals) as predictors. (2) The mechanical properties (i.e., failure-load point) of the non-woven mats are modeled with significant factors of (1), predicted micro/nano fibers’ diameters in (1), and electrospinning manufacturing time (time to build the non-woven mat), as predictors. To achieve this goal, feature extraction via tensor train (TT) is performed to deal with the captured diameters from the stable regimen videos (in our previous work) and ambient temperature and humidity signals. Subsequently, lasso regression is deployed to model each stage in a sequential fashion, resulting in accurate micro/nano fibers’ diameters prediction and subsequent non-woven mat failure-load point prediction. In addition, the framework offers important interpretability that supports not only process understanding but also proper data acquisition recommendations. The framework is demonstrated by electrospinning a PAN-based polymeric solution (PAN 10%).
Dynamic Contact Force Alignment Strategy Against Abrasive Wear in Robotic Compliant Disc Grinding for Uniform Material Removal
Authors: Wang Jingyun , Yang Zeyuan , Yan Sijie , Cai Wenqi , Yang Yisheng
Published: August 2024

Maintaining consistent material removal is a significant concern in robotic compliant disc grinding (RCDG), particularly when confronted with the issue of progressive abrasive wear. To tackle this issue, the present work develops a material removal-based abrasive wear model, and a suitable dynamic contact force alignment strategy is implemented to compensate for wear losses, thus ensuring a uniform material removal process. First, the grinding trajectory and material removal behavior of abrasive grains characterized by random cone angles and heights are developed. Next, the abrasive wear model is presented, which reveals the relationship between uniform material removal and grinding parameters. Then, the contact force variation law, on the target of uniform material removal, is determined by compensating for the losses in material removal caused by abrasive wear. On this basis, a set of active force control devices is designed and employed, which consist of a PC interface, a 6-axis force sensor, and a high-speed motorized spindle, to realize the highly real-time dynamic adjustment of the contact force. Finally, both simulations and experiments were carried out, which indicated that the method proposed is capable of enhancing the machining consistency within a certain grinding time range and providing a more uniform material removal process.
Combining Physics-Based Modeling and Artificial Intelligence for the Optimization of Battery Manufacturing Processes
Authors: Franco Alejandro A. , Vijay Utkarsh , Saravanan Soorya
Published: August 2024

The rapid scaling up and process optimization of manufacturing are essential to keep up with current demand and reduce the cost of lithium-ion batteries. In particular, electrode and cell processing and optimization are key in ensuring reliable lithium-ion batteries. Here, we explain how our hybrid modeling approach, combining physics-based simulations and artificial intelligence, can be used to gain insights into performance-manufacturing relationships and optimize the battery cell manufacturing processes. This hybrid approach is a powerful tool that can simulate the various manufacturing steps from powder to power. With this approach, in combination with the acquisition of battery manufacturing pilot line data, it is possible to perform inverse design of the electrode microstructures and cells for optimal properties. A wide diversity of process parameters is taken into account, such as those in the slurry, its drying, and the resulting electrode calendering, together with the parameters associated with the electrolyte filling, the formation, and the electrochemical operation. We briefly discuss various works done under the umbrella of our ARTISTIC project initiative, demonstrating a comprehensive hybrid approach to pave the way toward digital twins of the manufacturing process of lithium-ion and (also) next-generation battery cells.
Aerosol Jet and Inkjet Printing of Electrical Interconnects for Die Level Packaging of Custom MEMS Devices
Authors: Sherehiy Andriy , Sassa Michael , Jackson Douglas , Sills Daniel , Ratnayake Dilan , Zhang Ruoshi , Yang Zhong , Walsh Kevin , Naber John , Popa Dan O.
Published: August 2024

In this study we propose an alternative, lowcost packaging method using both inkjet and aerosol jet printing to fabricate functional interconnects for custom die-level packages assembled on Printed Circuit Boards (PCB).

Our process involves manufacturing of the stacked structure with insulating and conducting layers, fabricated using inkjet, and aerosol jet printing, respectively. In the first part of the study, we characterized UV adhesive deposition on the standard resin coated PCB, with the help of a Nordson EFD Pico Pulse drop on demand inkjet printer. This was done in order to acquire the optimal parameters necessary for the fabrication of an insulating layer with a thickness around 100 μm.

In the second part of our study, we developed precise printing of silver conducting lines using OPTOMEC Decathlon, an aerosol jet printer, which enables realization of structures with features as small as 50 microns on non-planar surfaces. The insulating part of the MEMS/PCB structure serves as a ramp ensuring continuity of the aerosol jet printed connection between the PCB and Si die. A 1 cm × 1 cm Si chip with custom MEMS microrobot was used to demonstrate the feasibility and flexibility of our approach in practice. After the fabrication, our interconnects were evaluated for conductivity and repeatability. The packaging and inspection process was carried out using our unique robotic system, Nexus, which integrates additive manufacturing, robotic transport, and metrology. Nexus allowed precision control during printing and transition between the main steps of manufacturing process: material deposition, curing, sintering, and microscope inspection. The results show that we successfully fabricated printed interconnects between copper PCB pads and Si die cleanroom fabricated gold pads, whereas electrical characterization revealed resistances in the range 1 Ω – 10 Ω. Our approach can be utilized in manufacturing of electrical interconnects for custom devices on different substrates including traditional and flexible PCBs. Furthermore, applied printing techniques enable the use of other insulating or conducting inks and formation of the structures of custom geometries at wide range of scale – 20 μm to 1 mm.
Particle Inspection by Single Camera-Based 3D Stereoscopy
Authors: Lee Hyun Jae , Sung Gugyeong , Chun Heebum , Lee ChaBum
Published: August 2024

Surface inspection of semiconductor wafers plays a pivotal role in ensuring the quality and reliability of semiconductor products. Conventional wafer inspection technologies have harnessed various methodologies to enhance the precision of inspection systems. However, as the semiconductor industry relentlessly pushes the boundaries of technology, these conventional methods primarily designed for single-wafer surfaces confront novel challenges. The unyielding pursuit of shrinking critical dimensions (CD) has intensified the need for more measurements, underscoring the significance of precise and reliable inspection systems. In a landscape where the technology is requiring higher resolutions and precision, various inspection methods are developed. However, as the instrumentation become precise, the cost of the system increased. High-cost instruments increase the gap between the industry and research. For this reason, the low cost with high precision inspection method should be developed.

This paper introduces an innovative approach to particle inspection within semiconductor silicon wafers, employing single camera-based 3D stereoscopy reconstruction (SCSR). Conventional stereoscopy methods encounter limitations, primarily stemming from the dimensions of the cameras used. To surmount these limitations, we propose the utilization of a single-camera system for generating highly detailed depth maps of submicron-sized particles. For the precise measurement of small defects, a smaller baseline becomes a requirement. The single-camera stereoscopic method offers the flexibility to control the baseline, enabling the measurement of smaller defects in comparison to conventional methods. Furthermore, we leverage the inherent transparency of semiconductor wafers to infrared (IR) light. Through the use of a halogen lamp as the IR light source, the emitted light efficiently penetrates the wafer. Subsequently, Short-Wave Infrared (SWIR) cameras are employed to capture images, thereby revealing particle defects that would remain concealed from standard visible cameras.

In experimental validation, we conducted a comparative analysis between visible cameras and SWIR cameras to ascertain the dimensions of particles on the wafer surface. We harnessed the capabilities of SCSR methods to create 3D depth maps. The results unequivocally demonstrate the exceptional potential of the single-camera stereoscopic method for particle inspection. In conclusion, our innovative approach harnesses single camera-based 3D stereoscopy, surmounting the limitations posed by traditional stereoscopy methods. By capitalizing on the transparency of semiconductor wafers to SWIR light, we have enabled the detection of particle defects that were previously elusive. The experimental validation underscores the promise of our method for inspecting particles on silicon wafers. Our approach promises to be a valuable asset to the semiconductor industry, where the pursuit of smaller CD and 3D technologies necessitates advanced inspection systems.
Deep Learning-Based Recognition of Manufacturing Components Using Augmented Reality for Worker Training of Assembly Tasks
Authors: Deshpande Sourabh , Aryal Manish Raj , Anand Sam
Published: August 2024

Current smart manufacturing computational tools and optimization methods leverage advances from artificial intelligence (AI), augmented / mixed/virtual reality, and predictive modeling to meet factory production goals. With significant developments in computer graphics tools, deep learning methods, and immersive experiences (metaverse), there is a need to study its seamless adaptation to AR-based manufacturing-specific scenarios, especially for worker training. In this work, we develop an augmented reality-based worker training framework aimed at helping novice technicians with complex machine component identification in several smart factory tasks and shop-floor training environments. A Deep-Learning (DL) semantic segmentation network U-Net, prominently applied in several manufacturing use cases, is enhanced for detailed pixel-level component identification. We formulated an automatic synthetic image generation algorithm that uses computer-aided design (CAD) models generated using Siemens NX to create images and automatic labels for training the neural network. These images are custom-rendered with variable lighting and randomized orientations to mimic real-life occlusions and improve data diversity for neural network training. In this approach, arbitrary textured materials are assigned to the 3D objects through Blender, an open-source computer graphics software using Python programming in the input scene environment. The background for these images is set from the multiple real-world environment scenes. A custom Python script integrated into Blender captures the image snapshots through the built-in camera system. It automatically separates render and mask images, thus saving time on the tedious manual labeling process. We pass the virtual images (both render and mask images) to the DL pipeline for training. After training the data through Pytorch and CUDA parallel computing, the Open Neural Network Exchange (ONNX) format output model is imported to the AR app. The application is developed using the Unity 3D game engine platform and deployed on Android. Specifically, the Unity Barracuda toolkit maps the input and output tensors as rendering textures and recognizes components in colored pixels. We present a prototype three-a stage automotive engine assembly case study to evaluate the proposed framework’s effectiveness. The engine components include tools (mallet, chisel, wrench), machine tooling equipment (nuts, bolts, Allen keys, screwdriver), and assembly equipment (engine piston, cylinder head, and camshaft). The 3D-printed components mimicking engine parts are identified in real-time along their exact pixel-level contours in the AR app. This framework can extend AR-based methods to visualize components in complex wiring, cluttered environments, and intricate assemblies on the factory floor, reducing the training time performed through conventional approaches and providing an interactive training approach through gamification.
An Active Machine Learning Approach for Spatial-Temporal Temperature Prediction of a Moving Heat Source
Authors: Heydari Mahtab , Tai Bruce
Published: August 2024

Moving heat source problems are commonly seen in many manufacturing applications, such as machining, laser cutting, welding, and additive manufacturing processes, while numerical modeling often takes time to analyze. A data-driven framework for predicting the spatial-temporal temperature response of a moving heat source has been developed to address the need for real-time temperature prediction of a moving heat source. This paper presents a method to reduce the training data requirements and improve the overall efficiency and accuracy of such a framework. This process, referred to as the boundary-focused training approach, is a type of active learning approach which allows for further scalability of the framework to large and/or complicated geometries by using a clustering approach to classify the spatial data according to differences in its thermal response. Then, a tandem learning approach is adopted to train a series of neural networks for each respective cluster. This boundary-focused training approach is implemented on two materials with different thermal diffusivities to evaluate the suitability of this framework in various contexts. The boundary-focused method showed improvement on the boundary-adjacent cases.
Reconfigurable Tooling for Customized Stretch Forming of Profiles
Authors: Ma Jun , Tronvoll Sigmund A. , Blindheim Jørgen , Ringen Geir , Welo Torgeir
Published: August 2024

Aluminum profiles are increasingly used in the manufacture of lightweight components for electric vehicles. Stretch forming is a key operation for producing extrusion-based components, owing to its volume production capabilities. However, traditional stretch forming is characterized by limited flexibility and high tooling costs, which makes it less attractive in today’s dynamic market demanding rapid adaptation solutions.

In this research, a new 3D reconfigurable multi-piece tooling for a recently developed novel flexible stretch forming process was proposed to improve the flexibility. The new tooling concept is realized by dividing the unified 3D die into multiple adjustable pieces. Each unit includes an adjustable “bottom holder” and an adjustable “L-shape” bracket insert with a curved surface installed on the bottom holder. By controlling the positions of the array of L-shape brackets and holders, a wide range of reconfigurable tooling curvatures can be achieved for customized 3D forming. Using rectangular, hollow AA6082 profiles in T4 temper, the new tooling strategy has been verified, demonstrating its good capability for producing different part configurations. Looking forward, this reconfigurable tooling concept has demonstrated great potential in terms of dimensional accuracy and springback compensation in stretch forming, enabling customized, cost-effective, and rapid development of aluminum extrusion components.
Brief Paper: A Preliminary Study on the Dynamics of Modulated Turning (MT) With Spindle Speed Variation (SSV)
Authors: Ozdemir Mert , Sencer Burak
Published: August 2024

This paper proposes a novel strategy to enhance dynamic stability of turning processes. It presents a novel assistive strategy, which combines Spindle Speed Variation (SSV) and Sinusoidal Tool Modulations to dramatically improve chatter stability of high-speed turning. Chatter vibrations are a type of self-exiting vibrations that originate due to the dynamic flexibilities in the machine/workpiece/tool. Once chatter is triggered, it rapidly grows to destroy the surface finish, harms the tool and even the machine tool components. Chatter is the most limiting factor restricting productivity and attenable material removal rates (MRR) in most high-speed turning operations. A well-known strategy to improve chatter stability in turning is to use SSV. Continuously varying the spindle speed helps disturb and weaken the regenerative effect (regenerations) and thus improve chatter stability. Most recently, it is also reported that adding sinusoidal tool modulations also help improve chatter stability. This process is called the modulated turning (MT), and sinusoidal tool modulations cause the tool to disengage from the workpiece (cut) repeatedly introducing time for the regeneration effect to die out. This paper, for the first time, proposes to utilize sinusoidal tool modulations and SSV at the same time to assist and improve chatter stability of turning even further. The semi-discrete time domain approach is utilized to analyze chatter stability of this newly created turning process. It is observed, that jointly using tool modulations and SSV provides greater asymptotic chatter stability margins enabling average 10∼20% greater material removal rates to be achieved. Furthermore, it modifies existing stability lobes and helps create additional lobes, which may be utilized to maximize material removal rate at other desired target spindle speeds. Overall, joint application of SSV and tool modulations provide greater stability in turning.
Interior three-dimensional acoustic modeling and modal analysis using wavelet-based finite-element approach
Authors: Sun Zexi , Jin Guoyong , Ye Tiangui , Chen Yukun , Song Kaiyao
Published: August 2024

This paper introduces two-dimensional (2D) and 3D acoustic modeling and modal analysis using the wavelet finite-element method (WFEM). Governed by the Helmholtz equation, the acoustic domain is parameterized and analyzed using the scaling functions of B-spline wavelets, which facilitates the construction of elements with varying numbers of nodes via multi-resolution analysis. The wavelet-based shape functions provide a semi-orthogonal basis that enables rapid searching for approximate solutions in Lebesgue spaces, thereby offering significantly reduced interpolation errors and computational burden. Numerical examples are considered using WFEM, comprising a 2D acoustic problem involving a tube for predicting acoustic pressure and eigenfrequency investigations, and 3D acoustic problems involving a cubic room and an L-shaped room for capturing acoustic characteristics. The results are compared with those of (i) standard FEM with the same mesh and (ii) analytical solutions. Importantly, WFEM demonstrates stability by being insensitive to internal mesh size variations, indicating that B-spline wavelet elements have minimal effects on the numerical results. Furthermore, B-spline wavelet elements effectively control the pollution (dispersion) error of numerical methods when imposing Neumann boundary conditions in the high-frequency range, and they reduce interpolation errors caused by polynomial interpolation in the low-frequency domain.
The host protease KLK5 primes and activates spike proteins to promote human betacoronavirus replication and lung inflammation
Authors: Kim Hyunjoon , Kang Yeonglim , Kim Semi , Park Dongbin , Heo Seo-Young , Yoo Ji-Seung , Choi Isaac , N Monford Paul Abishek , Ahn Jae-Woo , Yang Jeong-Sun , Bak Nayeon , Kim Kyeong Kyu , Lee Joo-Yeon , Choi Young Ki
Published: August 2024

Coronaviruses rely on host proteases to activate the viral spike protein, which facilitates fusion with the host cell membrane and the release of viral genomic RNAs into the host cell cytoplasm. The distribution of specific host proteases in the host determines the host, tissue, and cellular tropism of these viruses. Here, we identified the kallikrein (KLK) family member KLK5 as a major host protease secreted by human airway cells and exploited by multiple human betacoronaviruses. KLK5 cleaved both the priming (S1/S2) and activation (S2′) sites of spike proteins from various human betacoronaviruses in vitro. In contrast, KLK12 and KLK13 displayed preferences for either the S2′ or S1/S2 site, respectively. Whereas KLK12 and KLK13 worked in concert to activate SARS-CoV-2 and MERS-CoV spike proteins, KLK5 by itself efficiently activated spike proteins from several human betacoronaviruses, including SARS-CoV-2. Infection of differentiated human bronchial epithelial cells (HBECs) with human betacoronaviruses induced an increase in KLK5 that promoted virus replication. Furthermore, ursolic acid and other related plant-derived triterpenoids that inhibit KLK5 effectively suppressed the replication of SARS-CoV, MERS-CoV, and SARS-CoV-2 in HBECs and mitigated lung inflammation in mice infected with MERS-CoV or SARS-CoV-2. We propose that KLK5 is a pancoronavirus host factor and a promising therapeutic target for current and future coronavirus-induced diseases.
CGRP inhibits SARS-CoV-2 infection of bronchial epithelial cells, and its pulmonary levels correlate with viral clearance in critical COVID-19 patients
Authors: Barbosa Bomfim Caio César , Génin Hugo , Cottoignies-Callamarte Andréa , Gallois-Montbrun Sarah , Murigneux Emilie , Sams Anette , Rosenberg Arielle R. , Belouzard Sandrine , Dubuisson Jean , Kosminder Olivier , Pène Frédéric , Terrier Benjamin , Bomsel Morgane , Ganor Yonatan
Published: August 2024

Upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), patients with critical coronavirus disease 2019 (COVID-19) present with life-threatening respiratory distress, pulmonary damage, and cytokine storm. One unexplored component in COVID-19 is the neuropeptide calcitonin gene-related peptide (CGRP), which is highly abundant in the airways and could converge in multiple aspects of COVID-19-related pulmonary pathophysiology. Whether CGRP affects SARS-CoV-2 infection directly remains elusive. We show that in critical COVID-19 patients, CGRP is increased in both plasma and lungs. Importantly, CGRP pulmonary levels are elevated in early SARS-CoV-2-positive patients and restored to baseline upon subsequent viral clearance in SARS-CoV-2-negative patients. We further show that CGRP and its stable analog SAX directly inhibit infection of bronchial Calu-3 epithelial cells with SARS-CoV-2 Omicron and Alpha variants in a dose-dependent manner. Both pre- and post-infection treatments with CGRP and/or SAX are enough to block SARS-CoV-2 productive infection of Calu-3 cells. CGRP-mediated inhibition occurs via activation of the CGRP receptor and involves down-regulation of both SARS-CoV-2 entry receptors at the surface of Calu-3 cells. Together, we propose that increased pulmonary CGRP mediates beneficial viral clearance in critical COVID-19 patients by directly inhibiting SARS-CoV-2 propagation. Hence, CGRP-based interventions could be harnessed for management of COVID-19.

IMPORTANCE

The neuropeptide CGRP is highly abundant in the airways. Due to its immunomodulatory, vasodilatory, and anti-viral functions, CGRP could affect multiple aspects of COVID-19-related pulmonary pathophysiology. Yet, the interplay between CGRP and SARS-CoV-2 during COVID-19 remains elusive. Herein, we show that pulmonary levels of CGRP are increased in critical COVID-19 patients, at an early stage of their disease when patients are SARS-CoV-2-positive. Upon subsequent viral clearance, CGRP levels are restored to baseline in SARS-CoV-2-negative patients. We further show that pre- and post-infection treatments with CGRP directly inhibit infection of Calu-3 bronchial epithelial cells with SARS -CoV-2, via activation of the CGRP receptor leading to decreased expression of both SARS-CoV-2 entry receptors. Together, we propose that increased pulmonary CGRP is beneficial in COVID-19, as CGRP-mediated inhibition of SARS-CoV-2 infection could contribute to viral clearance in critical COVID-19 patients. Accordingly, CGRP-based formulations could be useful for COVID-19 management.
Draft genomes of one
Staphylococcus haemolyticus
and five
Staphylococcus lugdunensis
strains isolated from catheterized urine samples of females
Authors: Appleberry Helen , Anjum Haaris , Cage Taleah , Jarm Kayla , Khan Haashir , Proctor Lizzie , Saroca Junelle , Wolfe Alan J. , Putonti Catherine , Kula Alex
Published: August 2024

Although
Staphylococcus haemolyticus
and
Staphylococcus lugdunensis
are members of the normal human flora, they also can cause infection. Here, we present the draft genomes of five strains of
S. lugdunensis
and one strain of
S. haemolyticus
isolated from transurethral catheterized urine samples from different females experiencing lower urinary tract symptoms.
New
Cydia pomonella
granulovirus strain with high entomopathogenic activity
Authors: Tsygichko Aleksandra A. , Asaturova Anzhela M. , Vasiliev Gennady V. , Klimenko Alexandra I. , Lashin Sergey A. , Lakhova Tatiana N.
Published: August 2024

We report a genome of CpGV from the bioresource collection of the Federal Research Center of Biological Plant Protection “State Collection of Entomoacariphages and Microorganisms.” Its sequence is 123,862 bp. The genome under study demonstrates a degree of similarity of more than 99% with reference
NC_002816
from the NCBI RefSeq database.
Oxford Nanopore long read-based shotgun metagenomic data sets of simulated bacterial communities originating from fresh spinach and surface water
Authors: Chen Zhao , Grim Christopher J. , Ramachandran Padmini , Meng Jianghong
Published: August 2024

Oxford Nanopore long reads of simulated bacterial communities from fresh spinach and surface water were generated (R9.4.1+SQK-LSK109 and R10.4+SQK-LSK112; 0.5, one, and two million reads).
Salmonella enterica
serotype Heidelberg, Montevideo, or Typhimurium was included alone or in combination in the spinach community, while the water community harbored
Pseudomonas aeruginosa
.
Ruminal microbial metagenomes and host transcriptomes shed light on individual variability in the growth rate of lambs before weaning: the regulated mechanism and potential long-term effect on the host
Authors: Hu Fan , Cheng Yan , Fan Bing , Li Wei , Ye Bingsen , Wu Zhiwu , Tan Zhiliang , He Zhixiong
Published: August 2024

Weaning weight is a reflection of management during the breastfeeding phase and will influence animal performance in subsequent phases, considered important indicators within production systems. The aims of this study were as follows: (i) to investigate variability in the growth rate among individual lambs from ewes rearing single or twin lambs fed with two different diets and (ii) to explore the molecular mechanisms regulating the growth rate and the potential long-term effects on the host. No significant change in lamb average daily gain (ADG) was observed in litter size and diet treatment, and there were large variations among individual lambs (ranging from 0.13 to 0.41 kg/day). Further analysis was conducted on serum amino acids, rumen fermentation characteristics, rumen metagenomics and transcriptome, and hepatic transcriptome of lambs with extremely high (HA;
n
= 6) and low (LA;
n
= 6) ADG. We observed significant increases in serum lysine, leucine, alanine, and phenylalanine in the HA group. The metagenome revealed that the HA group presented a higher rumen propionate molar proportion via increasing gene abundance in the succinate pathway for propionate synthesis. For the rumen transcriptome, higher expressed gene sets in the HA group were mainly related to rumen epithelial growth, including cytokine–cytokine receptor interaction, Jak-STAT signaling pathway, and adherens junction. For the liver transcriptome, the upregulated KEGG pathways in the HA group were primarily associated with fatty acid degradation, glyoxylate and dicarboxylate metabolism, cholesterol metabolism, and the immune system. This research suggests that preweaning lambs with high ADG may benefit from rumen development and enhanced liver metabolic and immune function.

IMPORTANCE

There is accumulating evidence indicating that the early-life rumen microbiome plays vital roles in rumen development and microbial fermentation, which subsequently affects the growth of young ruminants. The liver is also vital to regulate the metabolism and distribution of nutrients. Our results demonstrate that lambs with high average daily gain (ADG) enhanced microbial volatile fatty acid (VFA) metabolism toward rumen propionate and serum amino acid (AA) production to support host growth. The study highlights that high ADG in the preweaning period is beneficial for the rumen development and liver energy metabolism, leading to better growth later in life. Overall, this study explores the molecular mechanisms regulating the growth rate and the potential long-term effects of increased growth rate on the host metabolism, providing fundamental knowledge about nutrient manipulation in pre-weaning.
The core microbiome of cultured Pacific oyster spat is affected by age but not mortality
Authors: Cho Anna , Finke Jan F. , Zhong Kevin X. , Chan Amy M. , Saunders Rob , Schulze Angela , Warne Snehal , Miller Kristina M. , Suttle Curtis A.
Published: August 2024

The Pacific oyster is the most widely cultured shellfish worldwide, but production has been affected by mortality events, including in hatcheries that supply the seed for growers. Several pathogens cause disease in oysters, but in many cases, mortality events cannot be attributed to a single agent and appear to be multifactorial, involving environmental variables and microbial interactions. As an organism’s microbiome can provide resilience against pathogens and environmental stressors, we investigated the microbiomes in cohorts of freshly settled oyster spat, some of which experienced notable mortality. Deep sequencing of 16S rRNA gene fragments did not show a significant difference among the microbiomes of cohorts experiencing different mortality levels, but revealed a characteristic core microbiome comprising 74 taxa. Irrespective of mortality, the relative abundance of taxa in the core microbiomes changed significantly as the spat aged, yet remained distinct from the microbial community in the surrounding water. The core microbiome was dominated by bacteria in the families
Rhodobacteraceae
,
Nitrosomonadaceae
,
Flavobacteriaceae, Pirellulaeceae,
and
Saprospiraceae
. Within these families, 14 taxa designated as the “Hard-Core Microbiome” were indicative of changes in the core microbiome as the spat aged. The variability in diversity and richness of the core taxa decreased with age, implying niche occupation. As well, there was exchange of microbes with surrounding water during development of the core microbiome. The shift in the core microbiome demonstrates the dynamic nature of the microbiome as oyster spat age.

IMPORTANCE

The Pacific oyster (
Magallana gigas
, also known as
Crassostrea gigas
) is the most widely cultivated shellfish and is important to the economy of many coastal communities. However, high mortality of spat during the first few days following metamorphosis can affect the seed supply to oyster growers. Here, we show that the microbiome composition of recently settled oyster spat experiencing low or high mortality was not significantly different. Instead, development of the core microbiome was associated with spat aging and was partially driven by dispersal through the water. These findings imply the importance of early-stage rearing conditions for spat microbiome development in aquaculture facilities. Furthermore, shellfish growers could gain information about the developmental state of the oyster spat microbiome by assessing key taxa. Additionally, the study provides a baseline microbiome for future hypothesis testing and potential probiotic applications on developing spat.
Nuclear reorganization by NPM1-mediated phase separation triggered by adenovirus core protein VII
Authors: Genoveso Michelle Jane , Okuwaki Mitsuru , Kato Kohsuke , Nagata Kyosuke , Kawaguchi Atsushi
Published: August 2024

Recent evidence has revealed that the reorganization of nuclear domains is largely mediated by liquid-liquid phase separation (LLPS). During viral infection, numerous nuclear domains undergo significant changes through LLPS for and against the replication of the virus. However, the regulatory mechanism of LLPS in response to viral infection and its detailed functions in viral replication remain unclear. In this study, we found that the activity of the nucleolar protein NPM1, a remodeling factor for the chromatin-like structure of adenovirus DNA, to induce LLPS is required for deposition of adenovirus core protein VII in a subnuclear domain, the virus-induced post-replication (ViPR) body, in the late phases of infection. The interaction between NPM1 and protein VII was responsible for initiating LLPS. The inhibition of LLPS by 1,6-hexanediol treatment resulted in the dispersion of protein VII from the ViPR bodies. These findings suggest that protein VII accumulates in the ViPR bodies in concert with the LLPS formation of NPM1 triggered by protein VII. After photobleaching of EGFP-NPM1 in the ViPR bodies, EGFP-NPM1 showed a relatively fast recovery half-time, indicating the fluid-like properties of NPM1 in this compartment. Importantly, NPM1 depletion decreased the genome packaging in the viral capsids, possibly owing to the formation of a defective adenovirus core. This study highlights the dynamic interplay between viral pathogens and the host nucleus for the reorganization of membrane-less compartments that facilitate their replication.

IMPORTANCE

In this study, we explored how adenoviruses utilize a process known as liquid-liquid phase separation (LLPS) to enhance their replication. We focused on a cellular chromatin remodeling protein, NPM1, which plays a crucial role in nucleolar formation through LLPS. NPM1 facilitates LLPS by interacting with adenovirus protein VII, effectively accumulating protein VII into membrane-less compartments called virus-induced post-replication bodies. NPM1 functions as a molecular chaperone of protein VII to assemble viral chromatin by transferring protein VII to viral DNA. Remarkably, when NPM1 was depleted, this process was disrupted, decreasing viral genome packaging. These findings shed light on a critical aspect of virus-host interactions, illustrating how adenovirus utilizes NPM1-mediated LLPS activity. Our findings provide valuable insights into the dynamic interplay between viruses and the host nucleus.
PmrB Y358N, E123D amino acid substitutions are not associated with colistin resistance but with phylogeny in
Escherichia coli
Authors: Butters Alyssa , Jovel Juan , Gow Sheryl , Liljebjelke Karen , Waldner Cheryl , Checkley Sylvia L.
Published: August 2024

Colistin resistance in
Escherichia coli
is of public health significance for its use to treat multidrug-resistant Gram-negative infections. Amino acid variations in PmrB have been implicated in colistin resistance in
E. coli
. In this cross-sectional study, 288 generic
E. coli
isolates from surveillance of broiler chicken and feedlot cattle feces, retail meat, wastewater, and well water were whole-genome sequenced. Phylogroup designation and screening for two amino acid substitutions in PmrB putatively linked to colistin resistance (Y358N, E123D) were performed
in silico
. Three additional data sets of publicly available
E. coli
assemblies were similarly scrutinized: (i)
E. coli
isolates from studies identifying the Y358N or E123D substitutions, (ii) colistin-susceptible
E. coli
isolates reported in the literature, and (iii) a random sampling of 14,700
E. coli
assemblies available in the National Center for Biotechnology Information public database. Within all data sets, ≥95% of phylogroup B1 and C isolates have the PmrB Y358N variation. The PmrB E123D amino acid substitution was only identified in phylogroup B2 isolates, of which 94%–100% demonstrate the substitution. Both PmrB amino acid variations were infrequent in other phylogroups. Among published colistin susceptible isolates, colistin minimum inhibitory concentrations (MICs) were not higher in isolates bearing the E123D and Y358N amino acid variations than in isolates without these PmrB substitutions. The E123D and Y358N PmrB amino acid substitutions in
E. coli
appear strongly associated with phylogroup. The previously observed associations between Y358N and E123D amino acid substitutions in PmrB and colistin resistance in
E. coli
may be spurious.

IMPORTANCE

Colistin is a critical last-resort treatment for extensively drug-resistant Gram-negative infections in humans. Therefore, accurate identification of the genetic mechanisms of resistance to this antimicrobial is crucial to effectively monitor and mitigate the spread of resistance. Examining over 16,000 whole-genome sequenced
Escherichia coli
isolates, this study identifies that PmrB E123D and Y358N amino acid substitutions previously associated with colistin resistance in
E. coli
are strongly associated with phylogroup and are alone not sufficient to confer a colistin-resistant phenotype. This is a critical clarification, as both substitutions are identified as putative mechanisms of colistin resistance in many publications and a common bioinformatic tool. Given the potential spurious nature of initial associations of these substitutions with colistin resistance, this study's findings emphasize the importance of appropriate experimental design and consideration of relevant biological factors such as phylogroup when ascribing causal mechanisms of resistance to chromosomal variations.