bluuebunny/crossref_metadata_2025 huggingface dataset:
Process Planning for Hybrid Manufacturing: Ball Nose End Milling and Direct Energy Deposition of Stainless Steel
Authors: Hughes Zane Weldon , Siller Hector R.
Published: September 2023

Hybrid manufacturing can combine additive and machining processing to create parts that would otherwise require two or more machines and can provide many beneficial opportunities in the manufacturing industry. This paper details the creation and application of a generalized process plan for the hybrid manufacturing of AISI 316L stainless steel, using direct energy deposition (DED) and ball-nose end-mill machining; that includes the inspection and measurement of objects created by that hybrid manufacturing process plan. The proposed process plan progresses through the selection of substrate thickness, single-track, multi-track, and multi-layer depositions, then on to machining processing. A manufacturers’ recommended set and range of DED parameters were used to create a designed experiment that aided in the analysis of objects created in each of the DED process planning steps; those objects were then machined in the same enclosure using a set of machining parameters screened from industry recommendations for ball-nose milling of stainless steel, after which measurements were taken for surface roughness, some material characteristics, and for tool deterioration. The results, analyses, and discussions collected herein show that the proposed process plan can provide models for geometrical outputs for each step in the plan, some improvements in substrate stability, surface roughness, tool deterioration, and material porosity due to voids. Current research in hybrid manufacturing does not show generalized process planning influences. The process plan, as demonstrated by the work in this paper will help operators, designers, and researchers in the future by defining a generalized workflow that can be applied to other materials used in the form of hybrid manufacturing that combines DED with machining processes.
An Investigation on 3D Bio-Printed Scaffold Shape Fidelity Incubated in a Custom-Made Perfusion Bioreactor
Authors: Mankowsky Jack , Quigley Connor , Clark Scott , Habib MD Ahasan
Published: September 2023

Three-dimensional (3D) bioprinting is a promising technique for creating patient-specific 3D scaffolds of tissues or organs. An appropriate culturing process is critical to confirm encapsulated and seeded cells’ excellent viability and proliferation into scaffolds materials. Traditional stagnant cell culturing methods don’t ensure entering medium inside areas or passing through the scaffolds. To resolve this issue, we developed a customized perfusion bioreactor to supply the growth medium dynamically to the encapsulated or seeded cells. Our custom-designed bioreactor improves the in vivo stimuli and conditions, which may enhance cell viability and proliferation performance. A design of a dual medium tank was utilized allowing the replacement of already-used medium without interrupting perfusion. Accommodating an array of cassettes in a newly designed perfusion chamber allowed a wide range of scaffolds with various size and shapers to hold. In this paper, we explored fluid flow response on scaffolds fabricated with various material compositions with different viscosities. We fabricated scaffolds following a 00–900 deposition pattern with 8% Alginate, 4% Alginate-4% Carboxymethyl Cellulose (CMC), and 2% Alginate-6% CMC incubated, allowing a constant fluid flow for various periods such as 1, 2, 4, and 8 hours. The change of scaffolds fabricated with multiple material compositions was determined in terms of swelling rate, i.e., change of filament width, and material diffusion, i.e., comparison of dry material weight before and after incubation. This comparative study can assist in application-based materials selection suitable for incubating in a perfusion bioreactor.
FPGA-Based Edge Computing Framework: Modeling of Computation Task Scheduling
Authors: Tan Jianfei , Yang Hao , Zhao Chun , Zhang Lin
Published: September 2023

Cloud manufacturing is a service-oriented networked manufacturing paradigm, which integrates manufacturing resources and capabilities, to enable the intelligence and digitization of the whole life cycle from design to scrap of products. The manufacturing process of complex products requires the collaboration of a large number of heterogeneous devices, the devices accessed to a manufacturing cloud can generate a bulk of data and computing requirements. Traditional cloud computing can not meet the demand for timely and efficient task processing anymore. Edge computing is a computing paradigm that enables tasks to be processed close to the edge, in order to reduce the load on the cloud, and enhances the overall responsiveness of cloud manufacturing systems. However, the computing performance and computing resources on edge nodes are limited, which cannot meet the complex computing tasks. In this paper, a modeling method of task scheduling for FPGA-based edge computing framework is proposed to load balance the edge computing network by dynamic task scheduling and algorithm hardware-based acceleration. This framework builds a task model to describe task information. The task offloading rules are decided by task information and edge nodes states, then task data is subsequently sent to the target edge node. FPGAs are introduced in the edge nodes to enhance the computing performance, which avoids the task can not be processed due to the insufficient processing capacity of a single node, in order to make the cloud manufacturing system load balance. Finally, a case is passed to verify that the proposed framework can handle the task in a timely and efficient manner.
Single Shot 3D Shape Measurement of Non-Volatile Data Storage Devices
Authors: Balasubramaniam Badrinath , Li Beiwen
Published: September 2023

The value of electronic waste at present is estimated to increase rapidly year after year, and with rapid advances in electronics, shows no signs of slowing down. Storage devices such as SATA Hard Disks and Solid State Devices are electronic devices with high value recyclable raw materials which often goes unrecovered. Most of the e-waste currently generated, including HDDs, is either managed by the informal recycling sector, or is improperly landfilled with the municipal solid waste, primarily due to insufficient recovery infrastructure and labor shortage in the recycling industry. This emphasizes the importance of developing modern advanced recycling technologies such as robotic disassembly. Performing smooth robotic disassembly operations of precision electronics necessitates fast and accurate geometric 3D profiling to provide a quick and precise location of key components. Fringe Projection Profilometry (FPP), as a variation of the well-known structured light technology, provides both the high speed and high accuracy needed to accomplish this. However, Using FPP for disassembly of high-precision electronics such as hard disks can be especially challenging, given that the hard disk platter is almost completely reflective. Furthermore, the metallic nature of its various components make it difficult to render an accurate 3D reconstruction. To address this challenge, We have developed a single-shot approach to predict the 3D point cloud of these devices using a combination of computer graphics, fringe projection, and deep learning. We calibrate a physical FPP-based 3D shape measurement system and set up its digital twin using computer graphics. We capture HDD and SSD CAD models at various orientations to generate virtual training datasets consisting of fringe images and their point cloud reconstructions. This is used to train the U-NET which is then found efficient to predict the depth of the parts to a high accuracy with only a single shot fringe image. This proposed technology has the potential to serve as a valuable fast 3D vision tool for robotic re-manufacturing and is a stepping stone for building a completely automated assembly system.
Comparison of Reinforcement Learning Methods for Production Control in Discrete Manufacturing Systems
Authors: Yun Lingxiang , Wang Jingwen , Xiao Minkun , Li Lin
Published: September 2023

The widespread deployment of smart meters and communication technologies brings opportunities to improve the adaptability and flexibility of future manufacturing systems under growing complexity and uncertainty. As a result, data-driven approaches, especially Reinforcement Learning (RL) methods, are gaining wide attention in recent years. Although in the current literature, RL approaches show superiority over traditional methods in many applications, a comprehensive review and comparison of different RL methods and their use in discrete manufacturing system control under changing environments have not yet been established. This paper first provides a literature review of RL algorithms for decision-making in discrete manufacturing systems, and then systematically discusses the underlying mechanisms of four most commonly used RL algorithms. In addition, the performance of these RL algorithms is compared by solving a production control problem, which aims to maximize the profit under time-varying production costs. The comparison results show that the single-agent RL methods with discrete action space can provide satisfactory results in small-scale systems, while multi-agent RL algorithms are more suitable to solve problems in complex, large-scale systems to remit the curse of dimensionality. This research sheds light on the impacts of RL method selection on the search for high-quality solutions in manufacturing systems, which is envisioned to drive future research on RL-supported manufacturing systems.
Inhouse Multi-Material Nozzle System Design and Fabrication for 3D Bioprinting Process: Next Step
Authors: Quigley Connor , Hurd Warren , Clark Scott , Sarah Rokeya , Habib MD Ahasan
Published: September 2023

Three-dimensional (3D) bio-printing is a rapidly growing field attempting to recreate functional tissues for medical and pharmaceutical purposes. The printability of multiple materials encapsulating various living cells can take this emerging effort closer to tissue regeneration. In our earlier research, we designed a Y-like nozzle connector system capable of switching materials between more than one filament with continuous deposition. The device had a fixed switching angle, was made from plastic, and was suitable for one-time use. This paper presents the extension of our previously proposed nozzle system. We considered 30°, 45°, 60°, and 90° angles (vertical and tilted) between the two materials and chose stainless steel as a material to fabricate those nozzle connectors. The overall material switching time was recorded and compared to analyze the effects of those various angles. Our previously developed hybrid hydrogel (4% Alginate and 4% Carboxymethyl Cellulose, CMC) was used as a test material to flow through the nozzle system. These in-house fabricated nozzle connectors are reusable, easy to clean, and sterile, allowing smooth material transition and flow.
Tracking and Visualization of Benchtop Assembly Components Using a RGBD Camera
Authors: Maddipatla Yashwanth , Li Jiaqiong , Zheng Yi , Li Beiwen
Published: September 2023

The workforce training for part assembly processes usually depends on paper-based instructions, which may not be intuitive and could take a long time for a worker to be fully proficient. This paper aims to improve the efficiency of the training for the part assembly process with the help of an RGBD camera like Kinect. Essentially, the RGBD camera tracks the assembly bench, and an associated computer graphics interface provides the assembly instructions through a virtual model of the real part on the bench. The model is displayed on a computer, and its orientation is manipulated based on its real counterpart, which is tracked by the Kinect system in real-time. A U-net-based full-flow bidirectional fusion network model was trained for real-time 6D pose estimation. The training data was primarily captured with Kinect and Apple iPad Pro’s LiDAR. The captured data was processed in Meshlab and Matlab. The training data was also synthetically enriched so the machine learning model could recognize and track the parts in various environmental settings. The virtual model visualization interface is built with the Unity gaming engine and uses the 3D models of the assembly parts. Once set up, the system works seamlessly and reduces the time an operator has to spend assembling parts containing many circuit boards and other smaller components. Our experimental testing showed high accuracy in recognizing and tracking the assembly parts with the trained model.
Composites 4.0: Enabling the Modernization of Legacy Manufacturing Assets in South Carolina
Authors: Deshpande Amit Makarand , Keskar Gautami Girish , Pradeep Sai Aditya , Farahani Saeed , Pilla Srikanth
Published: September 2023

Composites 4.0 is the implementation of Industry 4.0 concepts to plastics and composites manufacturing with the goal to overcome the complexities associated with these materials. Due to very complex process-structure-property relationships associated with plastics and composites, a wide range of process parameters need to be tracked and monitored. Furthermore, these parameters are often affected by the tool and machinery, human intervention, and variability. They should thus, be monitored by integrating intelligence and connectivity in manufacturing systems. Retrofitting of legacy manufacturing systems with modern sensing and control systems is emerging as a cost-effective approach as it circumvents the substantial investments needed to replace legacy equipment with expensive modern systems to enhance productivity.

The goal of the following study is to contribute to these retrofitting efforts by identifying the gap in the current state-of-the-art research, and the implementation level of Composites 4.0 capabilities in plastics and composites manufacturing. The study was conducted in two phases, first, a detailed review of the current state-of-the-art for Industry 4.0 in the manufacturing domain was conducted to understand the level of integration possible. It also helped gain insights into formulating the right questions for the composites manufacturing industry in South Carolina. Second, a survey of the plastics and composites manufacturing industries was performed based on these questions, which helped identify the needs of the industry and the gap in the implementation of Composites 4.0. The study focuses on three major composite manufacturing industries: injection molding, extrusion, and 3D printing of thermoplastic materials.

Through the survey, it was possible to identify areas and desired functionalities being targeted by the surveyed industries and concentrate research efforts to develop targeted solutions. After analyzing the survey responses, it was found that updating old protocols using manufacturer support and customized integration of cost-effective solutions like retrofit kits, edge gateways, and smart sensors were the best-suited solutions to modernize the equipment. Composites 4.0 is already being implemented for Preventive Maintenance (PM), Manufacturing Execution System (MES), and Enterprise Resource Planning (ERP) to some extent, and the focus is on process optimization and equipment downtime reduction. The inferences drawn from this study are being used to develop highly targeted, supplier-agnostic solutions to modernize legacy manufacturing assets.
Modeling and Characterizing a Novel Hybrid Infill Pattern for Additive Manufacturing
Authors: Roney Md Saidur Rahman , Ahsan AMM Nazmul
Published: September 2023

In this paper, a novel hybrid infill pattern is introduced denoted as Hexagonal Zigzag pattern and its mechanical properties are investigated through experiments. The novel pattern is island type with zigzag raster repeating in a regular periodic hexagonal pattern. The size of the hexagon and the infill density can be controlled by the quantity and spacing of the zigzag raster in each hexagon cell. A mathematical model on the relationship between these two raster parameters and their effect on infill density is established. This model allows the scope of manipulating the infill parameter values to customize the hexagon size and infill density. The mechanical properties of the proposed pattern are then compared with two existing infill patterns — honeycomb and zigzag — for 15.5% overall infill density through compression tests. The test results indicate that the proposed pattern has the highest absorbed energy density before densification compared to the two existing infill patterns. Besides, the compression strength of the proposed pattern gradually increases after 40% compression strain while the other patterns exhibit this phenomenon after 60% compression strain, which exhibits its improved resistance against crushing. Overall, the experimental investigation reveals that the proposed infill pattern yields comparable mechanical performance with improved energy absorption ability.
Cyber-Physical Trust Anchors in Additive Manufacturing: Secure, Low-Cost, and Educational
Authors: Maasberg Michele , Birch Brendan , Janes Daniel , Stor Kirsten , Ham Kyungmin , Butler Leslie G.
Published: September 2023

We report progress towards development of a cyber-physical trust anchor for additive manufacturing systems. The additive manufacturing commercial sector needs cyber-physical trust anchors to establish a secure supply chain, to detect counterfeiting and to ensure part provenance. However, the underlying technology of cyber-physical trust anchors requires optimization and spans several sectors ranging from mathematics, additive manufacturing, materials science, nondestructive evaluation, to cyber science. The fast and effective deployment of cyber-physical trust anchors requires an educational component. This project present a novel method for authenticating additively manufactured parts. Features are extracted using advanced X-ray imaging, transformed into unique identifiers, and bound with security features for cloud-based blockchain authentication. A plan for the low-cost and safe incorporation of cyber-physical trust anchor research in education is included. The anticipated outcome is an optimized trust anchor prototype and educational product suitable for interdisciplinary research and coursework to develop the workforce needed for cyber-secured physical supply chainsd.
A Numerical Investigation on Nanosecond Laser Shock Peening of Ti-6Al-4V
Authors: Matura Max , Aguilar Lesly , Ma Jianfeng , Jahan Muhammad
Published: September 2023

Ti-6Al-4V is the most commonly used titanium alloy in aerospace, automotive, and biomedical industries because of its excellent strength, low modulus of elasticity, high corrosion resistance, good weldability and heat treatability. In this study, numerical investigation is conducted to assess the effect of pulse energy level and operating temperature on residual stress induced on Ti-6Al-4V workpiece during nanosecond laser shock peening with 5 ns laser pulse width. The commercial FEM software package ABAQUS is employed to conduct FEM simulation of single shot laser peening of Ti-6Al-4V. Totally, eight laser pulse energy levels and six operating temperatures are selected. The FEM model for laser peening can provide insightful guidance for selecting optimal peening parameters and operating temperatures.
Real-Time Prediction of Pores and Their Lifespan in Laser Melting Using Transfer Learning
Authors: Valizadeh Mahsa , Clark Samuel , Fezzaa Kamel , Wolff Sarah
Published: September 2023

Additive manufacturing, a layer-by-layer method to build up parts, has gained serious attention lately; however, there are part qualification and certification bottlenecks due to various defects observed in printed parts. To overcome this issue, deep learning, a fast-growing technology, is leveraged to detect anomalies automatically. Due to the nature of deep learning algorithms, which require thousands of samples to be trained, transfer learning is offered. In this study, laser melting of 316L stainless steel to provide proof-of-concept data and training for transfer learning, with the goal to scale up the results of laser melting to proper metal laser-based additive manufacturing technologies like laser powder bed fusion and directed energy deposition. In this study, four pre-trained state-of-the-art algorithms on ImageNet are employed to be trained for additively manufactured part defect detection while having a limited dataset. The results demonstrated that pre-trained EfficientNetB7 and InceptionV3 algorithms showed promising results, successfully classifying the test data with an accuracy of more than 94%.
Real-Time Monitoring and Gaussian Process-Based Estimation of the Melt Pool Profile in Direct Energy Deposition
Authors: Lyu Jiaqi , Akhavan Javid , Mahmoud Youmna , Xu Ke , Vallabh Chaitanya Krishna Prasad , Manoochehri Souran
Published: September 2023

A comprehensive understanding of the melt pool behavior during directed energy deposition (DED) has become essential in identifying process anomalies and controlling the process quality. Previous studies focused on predicting the melt pool characteristics by solely using the process parameters, in this study we use real-time melt pool images to predict the melt pool characteristics. A CMOS camera is used to capture coaxial images of the melt pool during the deposition of single-track prints to improve the prediction model.

Multiple regression models are trained and compared to estimate the melt pool profile (width, depth, and height) as a function of process parameters (namely, the laser power, the powder feed rate, and the scanning speed) and features from the ellipse fitting of the real-time melt pool images. A novel image processing algorithm is proposed to extract the major axis, minor axis, and tilt. The sensitivity analysis demonstrated that combining process parameters and coaxial images can improve the prediction performance. The Gaussian Process regression showed the best performance among all the employed regression models.
Part Surface Inspection Through Semi-Supervised Learning to Overcome Limited Data Challenge
Authors: Zhou Qianyu , Tang Jiong
Published: September 2023

Surface inspection plays an important role in manufacturing of parts with complicated geometry. Specifically, the timely detection of minor defects, e.g., dent and scratches, in small-sized parts such as engine blades before acceptance is usually the final step of quality assurance. Current practice, however, mainly relies on human decision which is subjective and labor intense. In this research we propose an automated, image-based inspection system that utilizes robotic automation to acquire images of a part being inspected and employs machine learning to facilitate decision making. The data acquisition aims at high-resolution images that can thoroughly reflect the surface features of parts. While deep learning is leveraged, a series of advancements are designed to overcome the challenges of limited data and especially limited number of labels (i.e., parts with defects) during the initial training process. We synthesize a semi-supervised learning framework, building upon the residual neural network (ResNet) as well as the deep convolutional generative adversarial network (DCGAN), to extract features from the ground truth data and synthetic data. The performance boost of synthetic samples is investigated with different dataset sizes and validated.
Finite Element Dynamic Model and Vibration Signal Simulation of Rolling Bearing With Local Faults
Authors: Wang Tianhe , Chen Lei , Lu Hong , Wang Shaojun , Li Zhangjie , Zhang Wei , Mei Jiangnuo
Published: September 2023

The lack of fault samples is a key problem that restricts various fault diagnosis methods to engineering applications. However, the acquisition of measured fault samples requires the construction and commissioning of an experimental bench, which has high material and time costs. And the fault signals other than the fault components cannot be obtained. The reducer’s ball bearing 6008 serves as the object in this study. Based on the ANSYS/LS-DYNA software, this paper takes the cosine value of the simulated and measured vibration acceleration signal as the similarity measurement standard to modify the important parameters such as the radial load of the finite element model, and finally establishes the 3D explicit finite element dynamic model of the healthy bearing. After adding some fault geometry models, a variety of bearing finite element models with local faults are obtained, and the simulated fault vibration acceleration signal is obtained through the acceleration response calculation, so as to solve the problem of lack of fault samples. The fault characteristic frequency of the simulation signal can be precisely located via envelope spectrum analysis. And the simulation results can play an auxiliary role in bearing fault diagnosis in the absence of fault samples.
Complex Shaped Detection and Reconstruction Algorithm Considering Uncertain Factors
Authors: Zhang Jun , Lu Hong , Wu Zidong , Tan Yanglei , Liu Meng , Li Dingzhong , Niu Yuxi , Wang Shaojun
Published: September 2023

Uncertain factors such as environmental noise and light source fluctuation will cause the loss of important features of the detection object, which will interfere with the detection and reconstruction accuracy. Therefore, accurate extraction of groove information is the key to ensure the quality of subsequent processes when detection technology is applied to steel structure grooves in ships, buildings, transportation and other fields. This paper designs a complex shaped detection and reconstruction algorithm based on linear structured light. This algorithm performs the visually assisted localization of the ROI area of the welding groove in a complex environment, and completes the extraction of the characteristic area of the welding groove and the interpolation of missing parts based on the slope method. According to the obtained information, the digital modeling analysis of each groove section feature is carried out, and the 3D model is reconstructed based on the feature information fitting. The extraction of geometric characteristics of single V-groove and the reconstruction of the 3D model are realized, and the effectiveness of the extracted geometric characteristics is verified. On this basis, the subsequent welding is completed well, which verifies that the algorithm can effectively reduce the influence of the lack of groove characteristics caused by uncertain factors.
Quality Prediction and Model Explanation of Resistance Spot Welding Process Under Varying Fit-Up Conditions
Authors: Lyu Tianle , Xia Yunjun , Li Yongbing , Wang Songlin
Published: September 2023

This paper focuses on the challenge of online predicting resistance spot welding (RSW) quality, which is affected by the fit-up condition that inevitably varies in production due to assembly deviation but is rarely considered in previous research. To address this gap, this paper develops online prediction models for RSW quality based on multi-sensing signal features. Four widely-used machine learning regression models, namely multiple linear regression (MLR), Gaussian process regression (GPR), support vector regression (SVR), and multilayer perceptron regression (MLPR) were trained on datasets with varying fit-up conditions. The models’ adaptabilities to input signals and their generalizations to different fit-up conditions were assessed. Specifically, SHAP values were adopted to determine the contribution of each signal feature for model interpretation, and the t-SNE method was utilized to explain the influence of fit-up conditions on the model performance. The results demonstrated that the GPR model has the best prediction performance, with an accuracy of 92.2% and RMSE of 0.591 mm. Among the 26 features, welding current, heat input, and average electrode displacement exhibit the highest universality for varying conditions. When only limited signals are allowed as input, resistance together with displacement signals can achieve the best prediction. However, the model generalizations to varying conditions are low, particularly to the edge proximity condition. Therefore, it is important to minimize the discrepancy between the training and test datasets to improve the model generalization.
Machine Learning-Based Modeling of Electric-Field-Assisted Direct Ink Writing (EDIW) Process
Authors: Chen Yinong , Deshpande Anupam Ajit , Joyee Erina Baynojir , Pan Yayue
Published: September 2023

Direct ink writing (DIW) is an extrusion-based additive manufacturing technology. It has gained wide attentions in both industry and research because of its simple design and versatile platform. In electric-field-assisted Direct Ink Writing (eDIW) processes, an external electric field is added between the nozzle and the printing substrate to manipulate the ink-substrate wetting dynamics and therefore optimize the ink printability. eDIW was found effective in printing liquids that are typically difficult to print in the conventional DIW processes. In this paper, an eDIW process modeling system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms for predicting printing results and trace width are compared and discussed. The capabilities, applications and limitations of the presented machine learning-based modeling approach are presented.
An evolutionary epigenetic clock in plants
Authors: Yao N. , Zhang Z. , Yu L. , Hazarika R. , Yu C. , Jang H. , Smith L. M. , Ton J. , Liu L. , Stachowicz J. J. , Reusch T. B. H. , Schmitz R. J. , Johannes F.
Published: September 2023

Molecular clocks are the basis for dating the divergence between lineages over macroevolutionary timescales (~10
5
to 10
8
years). However, classical DNA-based clocks tick too slowly to inform us about the recent past. Here, we demonstrate that stochastic DNA methylation changes at a subset of cytosines in plant genomes display a clocklike behavior. This “epimutation clock” is orders of magnitude faster than DNA-based clocks and enables phylogenetic explorations on a scale of years to centuries. We show experimentally that epimutation clocks recapitulate known topologies and branching times of intraspecies phylogenetic trees in the self-fertilizing plant
Arabidopsis thaliana
and the clonal seagrass
Zostera marina
, which represent two major modes of plant reproduction. This discovery will open new possibilities for high-resolution temporal studies of plant biodiversity.
Aromatic nitrogen scanning by
ipso
-selective nitrene internalization
Authors: Pearson Tyler J. , Shimazumi Ryoma , Driscoll Julia L. , Dherange Balu D. , Park Dong-Il , Levin Mark D.
Published: September 2023

Nitrogen scanning in aryl fragments is a valuable aspect of the drug discovery process, but current strategies require time-intensive, parallel, bottom-up synthesis of each pyridyl isomer because of a lack of direct carbon-to-nitrogen (C-to-N) replacement reactions. We report a site-directable aryl C-to-N replacement reaction allowing unified access to various pyridine isomers through a nitrene-internalization process. In a two-step, one-pot procedure, aryl azides are first photochemically converted to 3
H
-azepines, which then undergo an oxidatively triggered C2-selective cheletropic carbon extrusion through a spirocyclic azanorcaradiene intermediate to afford the pyridine products. Because the
ipso
carbon of the aryl nitrene is excised from the molecule, the reaction proceeds regioselectively without perturbation of the remainder of the substrate. Applications are demonstrated in the abbreviated synthesis of a pyridyl derivative of estrone, as well as in a prototypical nitrogen scan.
An exhaustive multiple knockout approach to understanding cell wall hydrolase function in
Bacillus subtilis
Authors: Wilson Sean A. , Tank Raveen K. J. , Hobbs Jamie K. , Foster Simon J. , Garner Ethan C.
Published: September 2023

Most bacteria are surrounded by their cell wall, containing a highly cross-linked protective envelope of peptidoglycan. To grow, bacteria must continuously remodel their wall, inserting new material and breaking old bonds. Bond cleavage is performed by cell wall hydrolases, allowing the wall to expand. Understanding the functions of individual hydrolases has been impeded by their redundancy: single knockouts usually present no phenotype. We used an exhaustive multiple-knockout approach to determine the minimal set of hydrolases required for growth in
Bacillus subtilis
. We identified 42 candidate hydrolases. Strikingly, we were able to remove all but two of these genes in a single strain; this “∆40” strain shows only a mild reduction in growth rate, indicating that none of the 40 hydrolases are necessary for growth. The ∆40 strain does not detectably shed old wall, suggesting that turnover is not essential for growth. The remaining hydrolases in the ∆40 strain are LytE and CwlO, previously shown to be synthetically lethal. Either can be removed in ∆40, indicating that either hydrolase alone is sufficient for cell growth. Screening of environmental conditions and biochemistry revealed that LytE activity is inhibited by Mg
2+
and that RlpA-like proteins may stimulate LytE activity. Together, these results suggest that the only essential function of cell wall hydrolases in
B
.
subtilis
is to enable cell growth by expanding the wall and that LytE or CwlO alone are sufficient for this function. These experiments introduce the ∆40 strain as a tool to study hydrolase activity and regulation in
B. subtilis
.

IMPORTANCE

In order to grow, bacterial cells must both create and break down their cell wall. The enzymes that are responsible for these processes are the target of some of our best antibiotics. Our understanding of the proteins that break down the wall— cell wall hydrolases—has been limited by redundancy among the large number of hydrolases many bacteria contain. To solve this problem, we identified 42 cell wall hydrolases in
Bacillus subtilis
and created a strain lacking 40 of them. We show that cells can survive using only a single cell wall hydrolase; this means that to understand the growth of
B. subtilis
in standard laboratory conditions, it is only necessary to study a very limited number of proteins, simplifying the problem substantially. We additionally show that the ∆40 strain is a research tool to characterize hydrolases, using it to identify three “helper” hydrolases that act in certain stress conditions.
Species and condition shape the mutational spectrum in experimentally evolved biofilms
Authors: Hu Guohai , Wang Yue , Liu Xin , Strube Mikael Lenz , Wang Bo , Kovács Ákos T.
Published: September 2023

Laboratory experimental evolution provides a powerful tool for studying microbial adaptation to different environments. To understand the differences and similarities of the dynamic evolutionary landscapes of two model species from the
Bacillus
genus as they adapt to abiotic and biotic surfaces, we revived the archived population samples from our four previous experimental evolution studies and performed longitudinal whole-population genome sequencing. Surprisingly, higher number of mutations, higher genotypic diversity, and higher evolvability were detected in the biotic conditions with smaller population size. Different adaptation strategies were observed in different environments within each species, with more diversified mutational spectrum detected in biotic conditions. The insertion sequences of
Bacillus thuringiensis
are critical for its adaptation to the plastic bead-attached biofilm environment, but insertion sequence mobility was a general phenomenon in this species independent of the selection condition. Additionally, certain parallel evolution has been observed across species and environments, particularly when two species adapt to the same environment at the same time. Furthermore, our results suggest that the population size might be an important driver of evolution. Together, these results provide the first comprehensive mutational landscape of two bacterial species’ biofilms that is adapted to an abiotic and biotic surface.

IMPORTANCE

Biofilm formation is a vital factor for the survival and adaptation of bacteria in diverse environmental niches. Experimental evolution combined with the advancement of whole-population genome sequencing provides us a powerful tool to understand the genomic dynamic of evolutionary adaptation to different environments, such as during biofilm development. Previous studies described the genetic and phenotypic changes of selected clones from experimentally evolved
Bacillus thuringiensis
and
Bacillus subtilis
that were adapted under abiotic and biotic biofilm conditions. However, the full understanding of the dynamic evolutionary landscapes was lacking. Furthermore, the differences and similarities of adaptive mechanisms in
B. thuringiensis
and
B. subtilis
were not identified. To overcome these limitations, we performed longitudinal whole-population genome sequencing to study the underlying genetic dynamics at high resolution. Our study provides the first comprehensive mutational landscape of two bacterial species’ biofilms that is adapted to an abiotic and biotic surface.
Microbial biogeography of pit mud from an artificial brewing ecosystem on a large time scale: all roads lead to Rome
Authors: Mei Jun-Lan , Chai Li-Juan , Zhong Xiao-Zhong , Lu Zhen-Ming , Zhang Xiao-Juan , Wang Song-Tao , Shen Cai-Hong , Shi Jin-Song , Xu Zheng-Hong
Published: September 2023

The fermentation process of strong-flavor
Baijiu
occurs in an underground cellar surrounded by mud, where grains are decomposed and generate new flavor substances. Pit mud microbiome plays a pivotal role in the formation of
Baijiu
flavor, which is positively correlated with cellar age. Previous research on the pit mud microbiome was often conducted for a specific distillery, making it hard to draw a general conclusion to decipher the pit mud microecosystem in such a unique brewing environment. Hence, in this study, we attempt to unravel community formation mechanisms through research in pit mud microbial biogeography based on 12 independent bacterial studies comprising qualified data sets of 302 samples clearly marked with cellar age from five major producing provinces. We found cellar age explained more variations in the bacterial community than geographical region. The bacterial community displayed evident dominant species alternations on a large time scale.
Lactobacillus
was found to be the dominant species in young pit mud (<10 years), while
Caproiciproducens
dominated in aged pit mud. Null model analysis revealed that as pit mud aged, the influence of stochastic and deterministic processes in the bacterial community shifted, and the relative contribution of homogeneous selection increased. Positive correlations dominated the bacterial co-occurrence networks, and the network complexity decreased and stability increased with cellar age. These findings shed new light on the formation of pit mud microbiomes under the impact of long-term batch-to-batch continuous brewing, providing a basis for the evaluation and production of high-quality pit mud.

IMPORTANCE

Baijiu
is a typical example of how humans employ microorganisms to convert grains into new flavors. Mud cellars are used as the fermentation vessel for strong-flavor
Baijiu
(SFB) to complete the decomposition process of grains. The typical flavor of SFB is mainly attributed to the metabolites of the pit mud microbiome. China has a large number of SFB-producing regions. Previous research revealed the temporal profiles of the pit mud microbiome in different geographical regions. However, each single independent study rarely yields a thorough understanding of the pit mud ecosystem. Will the pit mud microbial communities in different production regions exhibit similar succession patterns and structures under the impact of the brewing environment? Hence, we conducted research in pit mud microbial biogeography to uncover the impact of specific environment on the microbial community over a long time scale.
Tracing the evolving dynamics and research hotspots of microbiota and immune microenvironment from the past to the new era
Authors: Huang Runzhi , Yao Yuntao , Tong Xirui , Wang Lei , Qian Weijin , Lu Jianyu , Zhang Wei , Liu Yifan , Wang Siqiao , Xian Shuyuan , Zhu Yushu , Huang Jie , Guo Xinya , Gu Minyi , Lv Hanlin , Bi Wenshuai , Meng Chenwei , Chang Zhengyan , Zhang Jie , Xu Dayuan , Ji Shizhao
Published: September 2023

Gut microbiota can regulate many physiological processes within gastrointestinal tract and other distal sites. Dysbiosis may not only influence chronic diseases like the inflammatory bowel disease (IBD), metabolic disease, tumor and its therapeutic efficacy, but also deteriorate acute injuries. This article aims to review the documents in this field and summarize the research hotspots as well as developing processes. Gut microbiota and immune microenvironment-related documents from 1976 to 2022 were obtained from the Web of Science Core Collection database. Bibliometrics was used to assess the core authors and journals, most contributive countries and affiliations together with hotspots in this field and keyword co-occurrence analysis. Data were visualized to help comprehension. Nine hundred and twelve documents about gut microbiota and immune microenvironment were retrieved, and the annual publications increased gradually. The most productive author, country, and affiliation were “Zitvogel L,” USA and “UNIV TEXAS MD ANDERSON CANC CTR,” respectively. FRONTIERS IN IMMUNOLOGY, CANCERS, and INTERNATIONAL JOURNAL OF MOLECULAR SCIENCE were the periodicals with most publications. Keyword co-occurrence analysis identified three clusters, including gut microbiota, inflammation, and IBD. Combined with the visualized analysis of documents and keyword co-occurrence as well as literature reading, we recognized three key topics of gut microbiota: cancer and therapy; immunity, inflammation and IBD; acute injuries and metabolic diseases. This article revealed researches on gut microbiota and immune microenvironment were growing. More attention should be given to the latest hotspots like gut microbiota, inflammation, IBD, cancer and immunotherapy, acute traumas, and metabolic diseases.

IMPORTANCE

Gut microbiota can regulate many physiological processes within gastrointestinal tract and other distal sites. Dysbiosis may not only influence chronic diseases like inflammatory bowel disease (IBD), metabolic disease, tumor and its therapeutic efficacy, but also deteriorate acute injuries. While the application of bibliometrics in the field of gut microbiota and immune microenvironment still remains blank, which focused more on the regulation of the gut microbiota on the immune microenvironment of different kinds of diseases. Here, we intended to review and summarize the presented documents in gut microbiota and immune microenvironment field by bibliometrics. And we revealed researches on gut microbiota and immune microenvironment were growing. More attention should be given to the latest hotspots like gut microbiota, inflammation, IBD, cancer and immunotherapy, acute traumas, and metabolic diseases.
HutZ is required for efficient heme utilization and contributes to the pathogenicity of
Avibacterium paragallinarum
Authors: Huo Caiyun , Jiao Lijiao , Li Guiping , Li Donghai , Lin Wutong , Sun Yingjian , Sun Huiling
Published: September 2023

Avibacterium paragallinarum
is the pathogen that causes infectious coryza, a highly contagious respiratory disease that brings a serious threat to chickens. Heme utilization systems play an important role in bacterial adversity adaptation and pathogenicity, and our previous report found the presence of heme utilization (HutZ) in
Av. paragallinarum
. However, little is known about the function of HutZ in
Av. paragallinarum
. In this study, the HutZ mutant strain of
Av. paragallinarum
was successfully developed and identified by PCR and western blot analysis. Mutation of HutZ significantly retards bacterial growth under reduced iron conditions, indicating the regulatory role of HutZ on growth and iron acquisition. Notably, the HutZ mutant strain had slower growth than the wild-type strain when heme was provided as the sole source of iron; thus, HutZ is crucial for heme utilization in
Av. paragallinarum
. Moreover, the HutZ mutant strain exhibited a markedly compromised tolerance to acid stress compared to the wild-type strain. Pathogenicity analysis showed that mutation of HutZ significantly weakened the ability of bacteria to invade and reproduce in host macrophage cells
in vitro
. Furthermore, the HutZ mutation could significantly decrease the bacterial virulence in chickens, which displayed lower morbidity and milder clinical symptoms. Hence, this is the first study to demonstrate in-depth the essential roles of HutZ on iron homeostasis and pathogenesis of
Av. paragallinarum
, which provides novel insight into advances of new prophylactic vaccines against this kind of bacteria.

Importance

Heme utilization (HutZ) protein has been characterized as an important heme-degrading enzyme that is critical for the cleavage of heme to biliverdin
via
verdoheme and can release iron to be used by bacteria. The interaction between HutZ and
Av. paragallinarum
is still unknown. Here, we unraveled the role of HutZ on the growth, iron acquisition, heme utilization, and resistance to acidic stress in
Av. paragallinarum
. We also uncovered the importance of HutZ for the success of
Av. paragallinarum
infection and provided new clues to the pathogenesis strategies of this organism. This work constitutes a relevant step toward an understanding of the role of HutZ protein as a master virulence factor. Therefore, this study is of great importance for understanding the mechanisms underlying
Av. paragallinarum
virulence and may contribute to therapeutic applications.
Investigating weathering signatures in terrestrial muds: Can climatic signatures be separated from provenance?
Authors: Demirel-Floyd Cansu , Soreghan Gerilyn S. , Webb Nina D.S. , Roche Autumn , Joo Young Ji , Hall Brenda , Levy Joseph S. , Elwood Madden Andrew S. , Elwood Madden Megan E.
Published: September 2023

Siliciclastic muds (clay- and silt-sized sediment) concentrate physical and chemical weathering products. However, both rock composition and climate can affect the mineralogy and geochemistry of these sediments. We quantitatively assessed the influence of provenance and climate on muds collected from end-member climates to test, which, if any, of these potential weathering signatures are indicative of climate in fine-grained, fluvial sediments. Granulometry, mineralogy, and geochemistry of the studied muds indicated that provenance and mineral sorting hinder interpretation of (paleo)climate signals. These issues also affect chemical index of alteration (CIA) values, as well as mafic-felsic-weathering (MFW), Al2O3−(CaO* + Na2O)−K2O (A-CN-K), and Al2O3−(CaO* + Na2O + K2O)−(FeOT + MgO) (A-CNK-FM) ternary plots, decreasing their utility as paleoclimate proxies. CaO content is heavily weighted within the calculations, resulting in even felsic-sourced sediment commonly plotting as mafic owing to the relative enrichment in CaO from preferential sorting of Ca-rich minerals into the mud-sized fraction during transport. These results cast doubt on the indiscriminate use of CIA values and ternary plots for interpreting chemical weathering and paleoclimate within muds, particularly from glacial systems.

Most notably, the positive correlations between CIA and climatic parameters (mean annual temperature and mean annual precipitation) diminished when sediments that had formed in nonglacial settings were filtered out from the data sets. This implies that CIA may only be applicable when used in nonglacial systems in which the composition of the primary source material is well constrained—such as soil/paleosol profiles. Within this end-member climate data set, CIA was only useful in discriminating hot-humid climates.