bluuebunny/crossref_metadata_2025 huggingface dataset:
Unsteady Flow Structure of Corner Separation in a Highly Loaded Compressor Cascade
Authors: Zhong Weibo , Liu Yangwei , Tang Yumeng
Published: September 2023

Corner separation is an inherently unsteady flow feature in an axial compressor cascade, and it significantly affects the aerodynamic performance of compressors. The flow field of a highly-loaded compressor cascade at the Mach number of 0.59 under the moderate separation condition is simulated based on delayed detached eddy simulation. Comparisons of averaged flow field and transient flow field show that the three-dimensional corner separation flow is highly unsteady and composed of fine-scale vortex structures. The classical recognition of corner separation structures is a consequence of time-averaging. To better understand the contribution of unsteady structures to the averaged flow structures, the evolutions of flow fields in time series and the power spectrums are analyzed. A dominant periodic flow fluctuation is caused by the development of separating vortices with a characteristic frequency around 3500Hz, or at a Strouhal number of 0.75. Further, energy scales and spatiotemporal features of these dominant unsteady behaviors are analyzed using proper orthogonal decomposition and dynamic mode decomposition methods. Results show that the low-frequency behaviors mainly caused by the passage vortex at lower-span regions govern large-scale changes of separation flow in size and intensity and act with a certain intermittency. The vortex developing mode around 3500Hz prevails at higher regions affected by the concentrated shedding vortex. As the separating vortices dissipate approaching the midspan, the effect of the vortex developing mode on axial velocity fluctuation is reduced, although it dominates the pressure fluctuation with good stability in the whole passage.
A Novel Multi-Fidelity Surrogate for Turbomachinery Design Optimization
Authors: Wang Qineng , Song Liming , Guo Zhendong , Li Jun , Feng Zhenping
Published: September 2023

The design optimization of turbomachinery is a challenging task as it involves expensive black-box problems. The sample-efficient multi-fidelity optimization (MFO) algorithm has been proposed as an efficient solution to this problem. By utilizing multi-fidelity surrogates (MFS), the MFO algorithm can use fewer high-fidelity samples aided by low-fidelity samples to establish an accurate surrogate model. However, when MFS is used in sequential sampling optimization, it has been observed that the final optimal solution obtained by single-fidelity optimization (SFO) is better than that of MFO, even though MFO performs better at the early stages. This can be attributed to the assumption of an even and nested distribution of samples, which is incorrect when using a sequential adding strategy. To address these issues, we propose a novel algorithm called multi-single-fidelity optimization (MSFO) to overcome the limitations of the conventional MFO procedures. In the surrogate establishment of MSFO, we use the density-based spatial clustering of applications with noise (DBSCAN) method to detect local areas where low-fidelity samples are no longer effective. A combination of both global MFS and local single-fidelity surrogate model, built using high-fidelity samples alone, is used to establish an ensemble, which improves the anti-interference ability of the algorithm against misleading low-fidelity data. The effectiveness of the MSFO algorithm is verified first on numerical benchmark functions. Then, the algorithm is used to optimize the aerodynamic profile of a turbine and the film cooling layout design of a turbine endwall. Here, high-fidelity sample sources are obtained from fine-mesh CFD simulations, whereas low-fidelity sample sources are obtained from the same simulations run on a coarser mesh. The results demonstrate that our MSFO algorithm performs significantly better than the conventional SFO and MFO processes, with a higher level of robustness. Therefore, the effectiveness of the MSFO algorithm is well demonstrated.
A Computationally Efficient Method for Predicting Tip Clearance Effects on the Natural Frequency of Submerged Discs and Inducers
Authors: Langston Cody , Wray Timothy , Brown Andrew
Published: September 2023

Structures submerged in fluids have a lower natural frequency due to the effective added mass of the attached fluid. Recent studies and experiments have shown that this fluid added mass effect is amplified as the gap between the structure and solid boundary closes. This tight tip clearance effect exists in many common engineering structures including inducers in turbopumps. Previous research has been performed to improve fluid/structure finite element models to predict these effects. These approaches can accurately predict the effect but are not viable for complex geometry and extremely tight tip clearances when meshing and excessive runtime problems occur. These challenges are particularly daunting in the design phase when a multitude of geometries and tip clearances may be considered. To remedy this, the tip clearance effect is first classified into three regions, one with an infinite boundary, an intermediate region, and one with a very tight gap. This paper uses finite element analyses of submerged discs to examine how model properties such as disc thickness affect these regions. The results of these parametric studies are then used to develop an equation using a sigmoid fit that is capable of predicting the natural frequency for all clearances which avoids additional finite element runs. This method is applied to an inducer geometry and compared to both the finite element and previous experimental results. The natural frequency predictions of this method are most accurate for inducer structural mode shapes where the blades are not in phase.
Controlling Rheological Properties of Hybrid Hydrogel Using Short Fiber for Extrusion-Based 3D Bioprinting Process
Authors: Tuladhar Slesha , Clark Scott , Habib MD Ahasan
Published: September 2023

Among various available 3D bioprinting techniques, extrusion-based three-dimensional (3D) bio-printing allows the deposition of cell-laden bio-ink, ensuring predefined scaffold architecture that may offer living tissue regeneration. With a combination of unique characteristics such as biocompatibility, less cell toxicity, and high-water content, natural hydrogels are a great candidate for bio-ink formulation for the extrusion-based 3D bioprinting process. However, due to its low mechanical integrity, hydrogel faces a common challenge in maintaining structural ty. To tackle this challenge, we characterized the rheological properties of a set of hybrid hydrogels composed of cellulose-derived nanofiber (TEMPO-mediated nano-fibrillated cellulose, TONFC), carboxymethyl cellulose (CMC) and commonly used alginate. A total of 46 compositions were prepared using higher (0.5% and 1.0%) and lower percentages (0.005% and 0.01%) of TONFC, 1%–4% of CMC, and 1%–4% of alginate to analyze the rheological properties. The shear thinning coefficients of n and K were determined for each composition from the flow diagram and co-related with the 3D printability. The ability to control rheological properties with various ratios of a nanofiber can help achieve a 3D bio-printed scaffold with defined scaffold architecture.
Multi-Resolution Quality Inspection for Additive Manufacturing
Authors: Yang Hui , Reijonen Joni , Revuelta Alejandro
Published: September 2023

Automated optical inspection (AOI) is increasingly advocated for in-situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (i.e., originally developed for document scanners and multi-function printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multi-scale lenses. Therefore, the objective of this paper is to design and develop an AOI system with contact image sensors for multi-resolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, recoater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the CT data of layerwise AM builds. The proposed framework of multi-resolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.
Thermal-Image-Enabled Additive Manufacturing Process Monitoring and Extrusion Trajectory Compensation
Authors: Duan Molong , Chen Siqi , Yang Yuexin
Published: September 2023

The additive manufacturing (AM) system today still suffers from low precision, low product stability, low automation level, and low data utilization. The urgent need for enhancements on these fronts leads to the various monitoring and control techniques for AM process. Currently, most existing AM monitoring and corresponding control methods are built on statistical models and used to refine the AM process under a parameter-tuning framework. They help regulate the steady-state or low-frequency behavior in AM process but do not address issues related to AM’s unsteady or dynamic behavior. These unsteady and dynamic behaviors are especially prevalent in high-speed, high-acceleration AM systems with moving parts (e.g., fused filament fabrication, directed energy deposition) and dynamic trajectory regions (e.g., corners). Therefore, in this paper, we proposed a monitoring and control solution that addresses printing inaccuracies with real-time thermal image monitoring. The method depends on a low-order analytical model describing the printed path boundary and an established non-contacting deposition direction. The thermal image observations are thus converted to the dynamic time-domain compensation signals to account for dynamic over-extrusion. The method is verified with a fused filament fabrication printer to alleviate its over-extrusion when printing corners.
Development of an Open-Source Low-Cost Modular Quad-Extrusion 3D Bioprinter
Authors: Zgeib Ralf , Wang Xiaofeng , Zaeri Ahmadreza , Zhang Fucheng , Cao Kai , Chang Robert
Published: September 2023

Advanced additive manufacturing (AM) technologies are being harnessed to capture the complex range and specificity of native tissue properties towards fully functional bioprinted tissue constructs. Such enabling technologies have been reported to recapitulate the complexity and heterogeneity of the native tissues. However, the challenges of cost and scalability hamper broad AM process adoption and implementation for fundamental research in the life sciences as well as for clinical end-use applications. In order to address the cost barrier to AM adoption, an open-source low-cost modular quad-extrusion multi-material 3D bioprinting system is developed herein to enable the fabrication of complex tissue constructs. The developed quad-extrusion bioprinter (QEB) is established with two divergent printing modes, namely in-air printing (IAP) and support bath printing (SBP), using gelatin methacryloyl as a model hydrogel bioink. Bioprinted performance outcomes are then measured for structural fidelity with benchmarking to the computer-aided design models. Moreover, biological outcomes are qualified by way of a LIVE/DEAD cell viability assay over a 3-day time course. In summary, the developed QEB is shown to be a robust platform that enables the scalable fabrication of multi-material complex tissue constructs at an accessible cost under $300, further closing the gap between developmental and clinical AM platforms.
3D Printing of Filefish Inspired Microscale Multifunctional Structure
Authors: He Qingqing , Tang Han , Zeng Yushun , Yang Yang
Published: September 2023

Surfaces that exhibit anisotropy, widely found in nature, promote one-dimensional water control by relying on well-organized features. A comprehensive understanding of the wetting behavior of organic liquids, especially the movement of water and oil liquids on the surface, including the manipulation of water droplets, the separation of water and oil, and the cleaning of the ocean, are of great significance. Here, The filefish Navodon septentrionalis has an anisotropic oleophobicity underwater owing to its distinctive skin, which allows it to self-clean oil droplets from its body. Filefish skin has oriented microscaled hook-like spines arranged in an array pattern that contributes to this prominent wetting anisotropy. Considering the performance at a micron level and the variable arrangement, conventional approaches are ineffective in simulating the natural skin structure. Due to its capabilities of high productivity, cost-effectiveness, rapid production, as well as the ability to fabricate microstructures, additive manufacturing (AM), also known as 3D printing, has gained considerable attention in various academic fields. Specifically, we utilize a 3D printing method to fabricate biomimetic structures that resemble the arrangement of spines on the skin of filefish. In order to quantitatively evaluate the hydrophobicity of a 3D-printed surface, four different arrangements of spines were displayed on the surface, including the original surface arrangement, the surface with movement, the surface with rotation, as well as the surface that has both movement and rotation. Each surface arrangement has a level of surface hydrophobicity, with moving and rotating surfaces possessing the greatest level of hydrophobicity. This study provides an opportunity to investigate the wettability of biological surface structures with specific arrangements and offers good prospects for oleophobicity studies and a variety of subsequent applications such as water-oil separation, and oil collection devices.
Model Calibration in Inkjet Printing Process
Authors: Zuniga-Navarrete Christian , Zhou Chi , Sun Hongyue , Segura Luis Javier
Published: September 2023

Inkjet printing (IJP) is an additive manufacturing process capable to produce intricate functional structures. The IJP process performance and the quality of the printed parts are considerably affected by the deposited droplets’ volume. Obtaining consistent droplets volume during the process is difficult to achieve because the droplets are prone to variations due to various material properties, process parameters, and environmental conditions. Experimental (i.e., IJP setup observations) and computational (i.e., computational fluid dynamics (CFD)) analysis are used to study the droplets variability; however, they are expensive and computationally inefficient, respectively. The objective of this paper is to propose a framework that can perform fast and accurate droplet volume predictions for unseen IJP driving voltage regimes. A two-step approach is adopted: (1) an emulator is constructed from the physics-based droplet volume simulations to overcome the computational complexity and (2) the emulator is calibrated by incorporating the experimental IJP observations. In particular, a scaled Gaussian stochastic process (s-GaSP) is deployed for the emulation and calibration. The resulting surrogate model is able to rapidly and accurately predict the IJP droplets volume. The proposed methodology is demonstrated by calibrating the simulated data (i.e., CFD droplet simulations) emulator with experimental data from two distinct materials, namely glycerol and isopropyl alcohol.
Edge Cognitive Data Fusion: From In-Situ Sensing to Quality Characterization in Hybrid Manufacturing Process
Authors: Hoang Danny , Mannan Nasir , ElKharboutly Ruby , Chen Ruimin , Imani Farhad
Published: September 2023

While hybrid additive manufacturing offers improved material properties, increased design flexibility, and reduced production time, the presence of variation in the process (e.g., torque, current, power, and tool speed), together with the impact of tool degradation (e.g., spindles, holders, and cutters) alters the surface roughness and dimensional accuracy of fabricated parts. Recently, edge sensors are coupled with machine learning techniques (e.g., feature-based support vector machines and end-to-end deep neural networks) to connect process parameters with build quality. However, the lack of interpretable learning, poor sample efficiency, and low accuracy and precision limit their capabilities for reliable analysis. This paper introduces hyperdimensional computing (HDC) to fuse load, current, torque, command speed, control differential, power, and contour deviation which provides robust, sample-efficient, and explainable learning of quality characterization. Experimental results on a real-world hybrid 5-axis CNC Deckel-Maho-Gildemeister (DMG) machine show that HDC achieves a superior 90.5% accuracy in prediction of deviation for a 25.4 mm counterbore feature using multichannel data. Comparing the difference in results with ML methods such as support vector machines, logistic regression, multinomial Naive-Bayes, multilayer perceptron, and residual neural network, HDC outperforms by 55.8%, 28.6%, 53.1%, 11.6%, and 28.0%, respectively. The proposed HDC is shown to be effective for data fusion and training with relatively few iterations and eliminates the necessity of costly and long retraining in various manufacturing processes such as 3D printing and bio-fabrication.
Effect of In-Situ Laser Polishing on Microstructure, Surface Characteristics, and Phase Transformation of LPBF Martensitic Stainless Steel
Authors: Ali Majed , Almotari Abdalmageed , Al Gamal Anwar , Alafaghani Ala’aldin , Abedi Hossein , Qattawi Ala
Published: September 2023

Metal additive manufacturing recently has made advances in terms of mechanical properties and microstructure. Martensitic stainless steel (15-5PH) is well known for its corrosion resistance, high stiffness, and tensile strength. Due to the poor surface roughness of additively manufactured parts, further surface treatment techniques are essential. Using Laser Powder Bed Fusion (LPBF) enables in-situ laser polishing that can be applied after the part is fabricated. This technique results in better surface roughness, an improvement in mechanical properties, and fine microstructure over the final layer. In this study, the influence of laser polishing on surface roughness, hardness, and phase transformation are characterized using microscopy, surface roughness test, Vickers hardness test, and X-ray diffraction (XRD) methods, respectively. The LPBF process is monitored using a thermal camera to understand the effect of thermal history on the surface quality and phase transformation after applying single and double passes of the laser polishing. The results indicate that high energy density results in rougher surfaces, a higher amount of retained austenite phase, and lower material hardness. Performing energy input with 2.27 J/mm2 under one laser pass has shown better values in terms of surface hardness and martensitic phases for martensitic stainless steel.
Degradation Modeling of a Robot Arm to Support Prognostics and Health Management
Authors: Kibira Deogratias , Qiao Guixiu
Published: September 2023

Robots are increasingly being adopted in manufacturing industries and this trend is projected to continue. However, robots, like all equipment, degrade once in operation and eventually fail. Yet today’s manufacturing systems are highly paced requiring high equipment availability. Tools and methods are being developed for monitoring, diagnostics, and prognostics to support maintenance activities. These tools require the presence of data representing both healthy and unhealthy states of the robot. Robot unhealthy state data is usually not available because robots are normally operated in a healthy state. A digital twin, which is a virtual real-time representation of a system, can support generating this data. This paper demonstrates the building of a digital twin of a robot workcell that inputs data from the real system. The most frequent robot degradations are identified as increased bearing friction and gear backlash, which are modeled in the digital twin. The digital twin is then used to generate data representing degraded states of the workcell, which are plotted against healthy state data to reveal patterns associated with the respective types of failure. The results show that modeling degradations in the digital twin can provide data which, when analyzed, can support prognostics and health management.
Improve Resistance Spot Weld Quality of Press Hardened Steel by Using Stepped Current Pulse and External Magnetic Field
Authors: Li Zhuo-Ran , Chen Ruiming , Xia Yujun , Qi Lin , Lou Ming , Li Yongbing
Published: September 2023

Press-hardened steel (PHS) with extremely high strength has wide applications in vehicle body manufacturing as an innovative lightweight material. However, the poor weldability of PHS results in low weld toughness and a high risk of interfacial fracture, posing significant challenges to the resistance spot welding (RSW) process. Magnetically assisted RSW (MA-RSW) process, which introduces an external magnetic field in the welding process to perform electromagnetic stirring, has been shown to effectively improve the weld toughness for high-strength steel, although it may increase the risk of expulsion. To further improve the weldability of PHS, this study proposes a new process called stepped-current pulses magnetically assisted resistance spot welding (SPMA-RSW), which combines the MA-RSW with the stepped-current pulses (SP) method to enlarge the weld lobe. The study systematically investigates nugget appearance, microstructure, microhardness, and mechanical properties by comparing traditional RSW, MA-RSW and SPMA-RSW. The result showed that the SPMA-RSW process would significantly increase the nugget size, inhibit the shrinkage voids, refine the grain size and harden the nugget region of PHS welds, resulting in increased lap-shear strength and a transition from a brittle interfacial (IF) mode to a ductile button pullout fracture (BPF) mode at the same heat input. Specifically, the peak load and energy absorption were increased by 32.3% and 84.2%, respectively. This work can help improve the weld quality and thermal efficiency of the RSW process for PHS.
Multi-Criteria Decision-Making for Optimal Manufacturing Configuration Selection Using an Object-Oriented Data Model and Mathematical Formalization
Authors: Torayev Agajan , Wang Zi , Martínez-Arellano Giovanna , Chaplin Jack C. , Sanderson David , Ratchev Svetan
Published: September 2023

Modern manufacturing systems often cannot efficiently cope with rapid changes in market demands. A main challenge is the inability of current manufacturing systems to utilize the capabilities and capacities of manufacturing resources efficiently due to inefficient data models and adaptation algorithms. To deal with transiently changing market demands, manufacturing systems often either under-provision or over-provision resources. However, both under-provisioning and over-provisioning incur unnecessary costs. The lack of sufficient resources leads to missing opportunities for new work, and over-provisioning incurs a cost by wasting investment, time, maintenance, and other unused resources.

In this work, we optimize manufacturing configurations using the known capabilities and capacities of manufacturing equipment. In particular, the work provides an object-oriented data model that complements a manufacturing systems’ semantic model and the mathematical formalization of the data model for the multi-demand satisfaction problem.

The proposed methodology is validated by optimizing the planning for small-box hinged product assemblies in aerospace manufacturing. This product family comprises rudders and elevators, which share commonalities in functionality, size, and build philosophy. Therefore, they can be assembled in a reconfigurable environment with common pick and place, drilling, fastening, and inspection procedures.
Stable Matching With Contracts for a Dynamic Two-Sided Manufacturing-as-a-Service (MaaS) Marketplace
Authors: Pahwa Deepak , Dur Umut , Starly Binil
Published: September 2023

Two-sided manufacturing-as-a-service (MaaS) marketplaces connect clients requesting manufacturing services to suppliers providing those services. The matching mechanism that allocates clients’ orders to suppliers is a key design parameter of the marketplace platform. The platform might perform an allocation to maximize its revenue or optimize for social welfare of all participants. However, individual participants might not get maximum value from their match and reject it to form matches themselves (called blocking groups), thereby bypassing the platform. This paper considers the bipartite matching problem in MaaS marketplaces in a dynamic environment and proposes two matching solutions that limit the formation of blocking groups. Matching is based on non-strict, incomplete and interdependent preferences of participants over contracts, enabling negotiations between both sides. Simulations are used to test the mechanisms in a 3D printing services marketplace and evaluate the impact of stability on its performance. It is found that stable matching results in small degradation in social welfare of the marketplace but significantly better outcomes in terms of stability. Unstable matchings introduce anarchy into the marketplace with participants rejecting its allocation leading to performance poorer than stable matchings.
Regression-Based Surrogate Model for Rapid Prediction of Temperature Evolution in a Microscale Selective Laser Sintering System
Authors: Grose Joshua , Annaluru Ramakrishna , Foong C. S. , Cullinan Michael
Published: September 2023

Existing metal Additive Manufacturing (AM) tools suffer from limitations on the minimum feature sizes of their producible parts. The Microscale Selective Laser Sintering (μ-SLS) system directly addresses this restriction with a minimum part resolution on the order of a single micrometer. However, the production of parts at the micrometer scale is unreliable due to unwanted heat transfer in the nanoparticle powder bed. As a result, Finite Element thermal models are developed to predict the temperature evolution within the particle bed during sintering. These thermal models are not only computationally expensive, but also must be integrated into an iterative control framework to optimize the digital mask used to selectively control laser power, making it critical to enable quick temperature predictions. Therefore, this paper proposes a regression-based Machine Learning model to facilitate accurate and rapid predictions of the thermal evolution within the μ-SLS particle bed and subsequent optimization of the system input parameters. The regression model presented in this work uses an “Element-by-Element” approach, where machine learning models are trained on individual finite elements to learn the relationship between the conditions experienced by the element at the current timestep and the element’s temperature at the subsequent timestep. An existing bed-scale thermal model of the μ-SLS system is used to generate element by element training data for various regression models. Key features for each element include the temperature and applied volumetric heat generation of the selected element and its surrounding elements at the current timestep. Additional features are included that represent the spatial distribution of laser energy relative to the selected element. This feature vector is used to predict the temperature of the given element at the next timestep. Using this dataset, a feed-forward Artificial Neural Network with 3 hidden layers is used to predict the temperature evolution of a 2D powder-bed over a 2 second sintering window with high accuracy.
Perceived Constraints to Pickleball Participation Among Black Older Adults
Authors: Casper Jonathan M. , Kim Amy Chan Hyung , Bocarro Jason N.
Published: September 2023

Pickleball offers sociopsychological and physical activity benefits for older adults but lacks racial diversity. The purpose of this study was to identify constraints to pickleball participation with Black older adults (65+ years) as well as examine differences based on physical activity and sex. A Qualtrics panel included Black older adults (N = 292) who have heard of pickleball and are physically able to play but have not played. Results found Knowledge, Accessibility, Interpersonal, and Interest were the most salient constraints overall. Multivariate analysis of variance found that those who report low physical activity had significantly higher Interpersonal, Psychological, Costs, and Perceived Racism constraints. Additionally, females report significantly higher Knowledge, Psychological, and Cost constraints compared to males. The results further the theoretical application of constraints to physical activity research and provide insights into practitioner implications to grow the sport of pickleball for Black older adults.
Flight risks
Authors: Cohen Jon
Published: September 2023

Migratory birds efficiently ferry pathogens around the world. As a warming climate reshapes their journeys, infectious disease experts are on guard for new threats to humans
NASA delivers bounty of asteroid samples to Earth
Authors: Voosen Paul
Published: September 2023

Bennu’s rocks could reveal origin of organic molecules and Solar System evolution
Molecular ‘sponges’ could be hydrogen fuel tanks
Authors: Service Robert F.
Published: September 2023

Cheap chemical cages could expand the use of the clean-burning gas as an industrial fuel
Examining the activity of cefepime-taniborbactam against
Burkholderia cepacia
complex and
Burkholderia gladioli
isolated from cystic fibrosis patients in the United States
Authors: Mojica Maria F. , Zeiser Elise T. , Becka Scott A. , LiPuma John J. , Six David A. , Moeck Greg , Papp-Wallace Krisztina M.
Published: September 2023

The novel clinical-stage β-lactam-β-lactamase inhibitor combination, cefepime-taniborbactam, demonstrates promising activity toward many Gram-negative bacteria producing class A, B, C, and/or D β-lactamases. We tested this combination against a panel of 150
Burkholderia cepacia
complex (Bcc) and
Burkholderia gladioli
strains. The addition of taniborbactam to cefepime shifted cefepime minimum inhibitory concentrations toward the provisionally susceptible range in 59% of the isolates tested. Therefore, cefepime-taniborbactam possessed similar activity as first-line agents, ceftazidime and trimethoprim-sulfamethoxazole, supporting further development.
The plant pathogenic bacterium
Candidatus
Liberibacter solanacearum induces calcium-regulated autophagy in midgut cells of its insect vector
Bactericera trigonica
Authors: Sarkar Poulami , Jassar Ola , Ghanim Murad
Published: September 2023

Autophagy plays an important role against pathogen infection in many organisms; however, little has been done with regard to vector-borne plant and animal pathogens, that sometimes replicate and cause deleterious effects in their vectors.
Candidatus
Liberibacter solanacearum (CLso) is a fastidious gram-negative phloem-restricted plant pathogen and vectored by the carrot psyllid,
Bactericera trigonica
. The plant disease caused by this bacterium is called carrot yellows and has recently gained much importance due to worldwide excessive economical losses. Here, we demonstrate that calcium ATPase, cytosolic calcium, and most importantly Beclin-1 have a role in regulating autophagy and its association with Liberibacter inside the psyllid. The presence of CLso generates reactive oxygen species and induces the expression of detoxification enzymes in the psyllid midguts, a main site for bacteria transmission. CLso also induces the expression of both sarco/endoplasmic reticulum Ca2+pump (SERCA) and 1,4,5-trisphosphate receptors (ITPR) in midguts, resulting in high levels of calcium in the cellular cytosol. Silencing these genes individually disrupted the calcium levels in the cytosol and resulted in direct effects on autophagy and subsequently on Liberibacter persistence and transmission. Inhibiting Beclin1-phosphorylation through different calcium-induced kinases altered the expression of autophagy and CLso titers and persistence. Based on our results obtained from the midgut, we suggest the existence of a direct correlation between cytosolic calcium levels, autophagy, and CLso persistence and transmission by the carrot psyllid.

IMPORTANCE

Plant diseases caused by vector-borne Liberibacter species are responsible for the most important economic losses in many agricultural sectors. Preventing these diseases relies mostly on chemical sprays against the insect vectors. Knowledge-based interference with the bacteria-vector interaction remains a promising approach as a sustainable solution. For unravelling how Liberibacter exploits molecular pathways in its insect vector for transmission, here, we show that the bacterium manipulates calcium levels on both sides of the endoplasmic reticulum membrane, resulting in manipulating autophagy. Silencing genes associated with these pathways disrupted the calcium levels in the cytosol and resulted in direct effects on autophagy and Liberibacter transmission. These results demonstrate major pathways that could be exploited for manipulating and controlling the disease transmission.
Understanding the Ries impact structure subsurface from high-resolution seismic data
Authors: McCall Naoma , Gulick Sean P.S. , Karro Kaidi , Jõeleht Argo , Wilk Jakob , Pösges Gisela
Published: September 2023

The Ries impact structure (southern Germany) formed ca. 15 Ma and is 22–26 km in diameter, making it one of the youngest and best-preserved mid-size terrestrial impact craters, yet the subsurface has not been studied with modern geophysics. We present the first high-resolution seismic profiles of the Ries impact structure; the profiles show discontinuous intra-basement reflectors and a central crater floor without a significant central topographic high. The inner crystalline ring sits adjacent to, not on top of, the crater terrace zone. These morphologies indicate that during the crater modification stage, the rebounding central uplift at Ries rose and then collapsed without the continued outward motion required to form a fully developed peak ring. The Ries impact structure may be best considered a transitional complex crater form between a central-peak crater and a peak-ring crater as documented on the Moon and other rocky planets. A series of high-amplitude, discontinuous, topographically influenced reflectors overlying the basement implies that the suevite within the crater basin was emplaced via lateral transport.
Effects of antituberculosis treatment on pregnancy outcomes in infertile women with genital tuberculosis: a systematic review
Authors: Flores-Lovon Kevin , Soriano-Moreno David R , Medina-Ramirez Sebastian A , Fernandez-Guzman Daniel , Caira-Chuquineyra Brenda , Fernandez-Morales Jared , Tuco Kimberly G , Turpo-Prieto John , Alave Jorge , Goicochea-Lugo Sergio
Published: September 2023

Objectives

To evaluate the efficacy of antituberculosis therapy on pregnancy outcomes in infertile women with genital tuberculosis.

Design

Systematic review.

Data sources

We searched in PubMed/MEDLINE, CENTRAL and EMBASE up to 15 January 2023. Additionally, we manually search the reference lists of included studies.

Eligibility criteria

We included randomised controlled trials (RCT), non-RCTs (non-RCT) and cohort studies that evaluated the effects of antituberculosis treatment on pregnancy outcomes in infertile women with genital tuberculosis compared with not receiving antituberculosis treatment or receiving the treatment for a shorter period.

Data extraction and synthesis

Two independent reviewers extracted data. We used Cochrane Risk of Bias 1.0 and Risk Of Bias In Non-randomised Studies tools for risk of bias assessment and meta-analysis was not performed. We used Grading of Recommendations, Assessment, Development and Evaluations approach to assess the certainty of the evidence.

Results

Two RCTs and one non-RCT were included. The antituberculosis regimens were based on isoniazid, rifampicin, pyrazinamide and ethambutol for 6–12 months. In women without structural damage, very low certainty of evidence from one RCT showed that the antituberculosis treatment may have little to no effect on pregnancy, full-term pregnancy, abortion or intrauterine death and ectopic pregnancy, but the evidence is very uncertain. In women with structural damage, very low certainty of evidence from one non-RCT showed that the antituberculosis treatment may reduce the pregnancy rate (297 fewer per 1000, 95% CI −416 to −101), but the evidence is very uncertain. In addition, very low certainty of evidence from one RCT compared a 9-month vs 6-month antituberculosis treatment regimen showed similar effects between the schemes, but the evidence is very uncertain. Two RCTs reported that no adverse events of antituberculosis treatment were noted or were similar in both groups.

Conclusion

The effect of antituberculosis treatment on pregnancy outcomes in infertile women with genital tuberculosis is very uncertain.

PROSPERO registration number

CRD42022273145.
NEUROCANTRIAL: study protocol for a randomised controlled trial of a pain neuroscience education programme in adults with cancer pain
Authors: Ordoñez-Mora Leidy Tatiana , Rosero Ilem D , Morales-Osorio Marco Antonio , Guil Rocío , Quintero Jordan Giancarlo , Agudelo Jimenez Julian Alberto , Gonzalez-Ruiz Katherine , Avila-Valencia Juan Carlos
Published: September 2023

Introduction

Pain is the second most frequent symptom reported in patients with cancer among the main reasons for consultation. The incorporation of educational modalities in pain intervention processes has been suggested. This study aims to examine the efficacy of neuroscience pain education (PNE) in relation to pain, biopsychosocial variables and functional capacity in comparison with conventional treatment. It is hypothesised that an intervention based on PNE achieves better outcomes than conventional management, in terms of pain, biopsychosocial variables and functional capacity in adults with oncological pain.

Methods and analysis

This will be a single-blind, controlled clinical trial in which two groups will be compared using changes in pain as the primary measure. The first group will receive conventional treatment in addition to PNE, and participants will undergo an intervention of nine sessions (one session per week, for a total of 9 weeks), each lasting 30–40 min. Specifically, these sessions will teach biophysiological elements using metaphors that allow the adoption of these concepts related to pain. The second group will receive conventional treatment (pharmacological treatment). For this study, a sample size calculation was made based on the background, where 80 adults with oncological pain were required. An initial evaluation will be made to establish the baseline characteristics, and then, according to the randomisation, the assignment to the PNE group or the control group will be made and the two groups will be summoned again. Ten weeks later, for the intervention evaluation, the statistical analysis plan will be established by protocol and by intention-to-treat.

Ethics and dissemination

This protocol complies with all ethical considerations. The authors commit to presenting the study’s results. This study was approved by the ethics committees of the Universidad Santiago de Cali, Clínica de Occidente/Angiografía de Occidente. The authors commit to presenting the study’s results.

Trial registration number

NCT05581784.
Efficacy and safety of oropharyngeal muscle strength training on poststroke oropharyngeal dysphagia: a systematic review and meta-analysis
Authors: Gao Minxing , Xu Lingyuan , Wang Xin , Yang Xiaoqiu , Wang Ying , Wang Heying , Song Jinan , Zhou Fenghua
Published: September 2023

Objectives

To investigate how oropharyngeal muscle strength training affected the safety and performance of swallowing in patients with poststroke oropharyngeal dysphagia.

Design

Systematic review and meta-analysis.

Data sources

Cochrane Central Register of Controlled of Trials, Web of Science, PubMed, Embase databases and ClinicalTrials.gov were systematically searched, for publications in English, from database inception to December 2022.

Eligibility criteria

Studies comparing the effect of oropharyngeal muscle strength training with conventional dysphagia therapy in patients with poststroke. Penetration-Aspiration Scale (PAS) and Functional Oral Intake Scale (FOIS) were assessed as the main outcomes.

Data extraction and synthesis

Two researchers independently screened the literature, extracted data and evaluated the quality of the included studies, with disagreements resolved by another researcher. The Cochrane risk-of-bias tool was used to assess the risk of bias. Review Manager V.5.3 was employed for the meta-analysis. Random effect models were used for meta-analysis.

Results

Seven studies with 259 participants were included in this meta-analysis. The results showed that oropharyngeal muscle strength training could reduce PAS score compared with conventional dysphagia therapy (mean difference=−0.98, 95% CI −1.34 to −0.62, p<0.0001, I2=28%). The results also showed that oropharyngeal muscle strength training could increase FOIS score (mean difference=1.04, 95% CI 0.55 to 1.54, p<0.0001, I2=0%) and the vertical displacement of the hyoid bone (mean difference=0.20, 95% CI 0.01 to 0.38, p=0.04, I2=0%) compared with conventional dysphagia therapy.

Conclusion

In patients with poststroke oropharyngeal dysphagia, oropharyngeal muscle strength training can improve swallowing safety and performance.

PROSPERO registration number

CRD42022302471.
Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency
Authors: Beaney Thomas , Clarke Jonathan , Salman David , Woodcock Thomas , Majeed Azeem , Barahona Mauricio , Aylin Paul
Published: September 2023

Objectives

To determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis.

Design

Retrospective cohort study.

Setting

GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset.

Participants

All patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019.

Primary and secondary outcome measures

The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme.

Results

3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, p<0.0001). There was significant variation in code frequency by GP which was not explained by patient sociodemographics. We found significant associations with patient sociodemographics, with a trend towards higher coding rates in people living in areas of higher deprivation for both QOF and non-QOF conditions. Code frequency was lower for conditions with follow-up time in 2020, associated with the onset of the COVID-19 pandemic.

Conclusions

The frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.
Association between psychosocial work-related factors at midlife and arterial stiffness at older age in a prospective cohort of 1736 white-collar workers
Authors: Massamba Victoria K , Talbot Denis , Milot Alain , Trudel Xavier , Dionne Clermont E , Vézina Michel , Mâsse Benoit , Gilbert-Ouimet Mahée , Dagenais Gilles R , Pearce Neil , Brisson Chantal
Published: September 2023

Objective

Arterial stiffness and exposure to psychosocial work-related factors increase the risk of developing cardiovascular disease. However, little is known about the relationship between psychosocial work-related factors and arterial stiffness. We aimed to examine this relationship.

Design

Prospective cohort study.

Setting

Public organisations in Quebec City, Canada.

Participants

The study included 1736 white-collar workers (women 52%) from 19 public organisations.

Primary and secondary outcome measures

Association between psychosocial work-related factors from the job strain and effort–reward imbalance (ERI) models assessed at study baseline (1999–2001) with validated instruments and arterial stiffness assessed using carotid–femoral pulse wave velocity at follow-up, on average 16 years later (2015–2018). Generalised estimating equations were used to estimate differences in arterial stiffness between exposed and unexposed participants. Subgroup analyses according to sex, age, blood pressure (BP), cardiovascular risk score and employment status were conducted.

Results

Among participants with high diastolic BP (≥90 mm Hg) at baseline, aged 47 on average, those exposed to high job strain had higher arterial stiffness (1.38 m/s (95% CI: 0.57 to 2.19)) at follow-up, 16 years later, following adjustment for a large set of potential confounders. The trend was similar in participants with high systolic BP (≥140 mm Hg) exposed to high job strain (0.84 m/s (95% CI: −0.35 to 2.03)). No association was observed for ERI in the total sample and counterintuitive associations were observed in subgroup analyses.

Conclusions

Job strain may have a long-term deleterious effect on arterial stiffness in people with high BP. Interventions at midlife to reduce job strain may mitigate arterial stiffness progression.