bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate Energy Consumption Prediction for Cleaner Fused Deposition Modeling (FDM)
Authors: Ferraro Vincenzo , Sciancalepore Corrado , Lutey Adrian Hugh Alexander 
Published: August 2024

Abstract
Additive Manufacturing (AM) has revolutionized rapid prototyping and mechanical production in a range of different fields, including e-mobility and aerospace, as well as personalized medical devices. In an era dominated by increasing environmental awareness, requirements to reduce CO2 emissions and climate change associated with manufacturing energy consumption are paramount, further to the development of clean energy systems. With the aim of achieving cleaner production, accurate prediction of energy consumption during additive manufacturing processes is the first step in obtaining tangible reductions in manufacturing-related emissions. Energy optimization not only promotes environmental sustainability, but also gives an additional competitive advantage to additive manufacturing processes in today’s market, bringing considerable benefits to these technologies. The present work provides an in-depth study into energy consumption during additive manufacturing, applying a novel and highly accurate methodology for evaluating electrical energy usage during Fused Deposition Modeling (FDM) as a test case for applying this approach to a wider range of additive manufacturing technologies such as Laser Powder Bed Fusion (LPBF), Selective Laser Sintering (SLS), Stereolithography (SLA) and others. FDM is renowned for its popularity in multiple sectors due to its versatility in achieving customized results at low cost with relative ease and safety. To date, the vast majority of scientific literature relating to additive manufacturing has focused on the mechanical properties of printed objects, topology optimization, support structures and energy consumption. Investigations into the energy consumption during additive manufacturing have mainly focused on the measurement and estimation of total energy consumption over the entire printing process based on general considerations and, in some cases, machine learning algorithms. The present study distinguishes itself from previous works by providing precise and detailed analysis of each individual step in the printing process. Experimental evaluation of each machine action was performed, allowing translation of the machine instructions (G-code) employed for printing an object into distinct energy contributions for each action during the process. This approach makes it possible to estimate the total energy consumption very accurately as the sum of all energy contributions, allowing an additional energy optimization parameter to be introduced. Such a detailed analysis allows the main drivers of energy consumption during FDM to be identified, leading to relatively simple solutions to dramatically reduce energy consumption during the process. The presented approach was developed as a test bed for detailed evaluation of energy consumption during additive manufacturing, with potential for application over a wide range of different technologies with similar approaches.
Disassembly Analysis to Promote Rare Earth Permanent Magnet Recovery From End-of-Life Electric Vehicle Motors
Authors: Maani Thomas , Deng Sidi , Sutherland John W. 
Published: August 2024

Abstract
Driven by growing decarbonization initiatives, looming energy security risks, and heightened environmental awareness, there is escalating attention devoted to clean energy technologies such as electric vehicles (EVs), and wind turbines. Data from the U.S. Environmental Protection Agency (EPA) in 2022 revealed that the transportation sector alone contributed a staggering 29% of the total greenhouse gas emissions in the United States. Consequently, EVs have emerged as a promising solution to achieve sustainable transportation. The global adoption of EVs has seen exponential growth, with sales projected to surpass 14 million in 2023. EVs heavily rely on rare earth permanent magnets (REPMs), specifically Neodymium-Iron-Boron (NdFeB) magnets. These magnets are integral components within the traction motors that drive EVs and are reliant on rare earth elements (REEs), notably Neodymium (Nd) and Dysprosium (Dy). Both Nd and Dy are critical elements vulnerable to supply chain risks, particularly in light of the projected surge in EV demand. Furthermore, the growing number of EVs will lead to a corresponding increase in vehicles reaching their end-of-life (EoL). To mitigate the supply chain vulnerabilities associated with REEs and effectively manage the impending waste generated by EoL EV components, circular economy strategies have been proposed. Consequently, efforts are underway to develop technologies and processes aimed at the circularization of EoL EV components like traction motors, through approaches such as reusing, remanufacturing, and recycling. The disassembly of EoL motors containing REPMs is an essential but challenging step in these operations. The difficulty arises from the unknown sizes and volumes of components and significant variations in transmission and motor designs across different vehicle types. The high costs associated with the disassembly process have made it a prominent research concern, particularly in the remanufacturing industry. In this study, we propose integrating disassembly techniques into value recovery plans for traction motors to reduce landfill waste and encourage the reuse and recycling of REPMs. The primary objective of this paper is to quantitatively assess the level of difficulty (using time as a metric) involved in the manual disassembly of EoL transmissions, focusing on the traction motor. The results of this disassembly evaluation will be used to provide recommendations for design and process improvements. Furthermore, the paper will explore the feasibility of automating specific disassembly procedures, drawing from the insights gained from the manual disassembly assessment. Monte Carlo simulations of the system will be employed to analyze disassembly times in various scenarios characterized by uncertainty, and opportunities to streamline the disassembly process will be investigated.
Quantifying High Density Polyethylene Flows in the United States Using Material Flow Analysis
Authors: Chatterjee Abheek , Mathur Nehika , Figola Daniel , Triebe Matthew J. , Hapuwatte Buddhika , Hartwell Ashley , Morris K. C. 
Published: August 2024

Abstract
A Circular Economy (CE) aims to reduce natural resource consumption, waste generation, and related detrimental environmental, social, and economic impacts by retaining valuable materials in the economy as long as possible. This research focuses on the implementation of CE for High-density polyethylene (HDPE) plastics. Recycled HDPE retains desirable properties such as high durability, flexibility, and corrosion resistance leading to a high demand for recycled HDPE by piping and other building and construction industries. Unfortunately, most HDPE comprising products are mismanaged at their end-of-use (EoU) resulting in significant HDPE stocks being incinerated, landfilled, or disposed of in water bodies. This mismanagement of EoU HDPE products contributes to the plastics waste crisis, leads to deleterious environmental and human health impacts, and further reinforces an unsustainable linear economy model. Therefore, improved plastic waste collection mechanisms are needed to keep the different plastic polymer waste streams segregated and to enable the implementation of efficient recovery processes, particularly for recyclable plastics such as HDPE. A challenge in developing end-of-use infrastructure for HDPE recovery is the knowledge gap related to the systematic accounting of HDPE flows.
Material Flow Analysis (MFA) provides the means to track the stocks and flows of a resource within a defined system. This paper investigates HDPE flows in the United States (US) economy and presents two static MFA models for the years 2015 and 2019. The HDPE recycling rates were found to be at 7.4% and 10.1% in the US in 2015 and 2019, respectively. Based on the available data, EoU packaging and container goods are identified as the primary source for recycled HDPE. Our analysis also revealed discrepancies in historical data about HDPE flow across different industry sectors and life-cycle stages. To overcome limitations associated with data unavailability, HDPE flows for the MFA models were computed based on assumptions discussed in the paper. The availability of more granular data, which includes HDPE flows through industry sectors and sub-sectors, would lead to a more thorough MFA. By providing the static MFA models of HDPE flows and stocks in 2015 and 2019 in the US economy, and identifying specific data collection needs, this investigation takes the first step towards a comprehensive characterization of HDPE flows in the US. Quantifying and characterizing HDPE flows is an important component of anticipating the availability of secondary HDPE feedstocks, scaling up recovery infrastructure adequately, and contributing to our overall understanding of the current and future secondary HDPE market dynamics.
Virtual Navigation-Guided Remote Control System for Pulmonary Puncture Localization Assistance
Authors: Wang Zhen , Hu Yingda , Xu Tian , Du Shilun , Lei Yong 
Published: August 2024

Abstract
Minimally invasive puncture surgery is widely used for tissue sampling, ablation, and drug delivery, but its success relies heavily on the surgeon’s experience. To address this, we present a remote surgical robot system utilizing virtual navigation for precise puncture procedures during lung surgeries. First, we developed a master-slave control system based on low CT artifacts and wearable positioning-assisted robot, providing safe and reliable guidance for lung puncture surgery. Second, a virtual navigation system, integrated with the needle-tissue interaction model, was developed to provide surgeons with visual guidance and tissue deformation compensation for assisting in puncture trajectory planning. Third, a registration method was introduced for swift calibration between the virtual and physical worlds. Ex vivo porcine lung puncture experiments were conducted to validate the accuracy and feasibility of the system. The results of 70 porcine lung punctures experiments demonstrated an average system positioning accuracy of 2.3 mm with a deviation of 1.4 mm, meeting clinical requirements. The experimental results demonstrate feasibility of the system in lung puncture surgery.
A series of vectors for inducible gene expression in multidrug-resistant
Acinetobacter baumannii
Authors: Intorcia Valerie , Sava Rosa L. , Schroeder Grace P. , Gebhardt Michael J. 
Published: August 2024

ABSTRACT

The continued emergence of antibiotic resistance among bacterial pathogens remains a significant challenge. Indeed, the enhanced antibiotic resistance profiles of contemporary pathogens often restrict the number of suitable molecular tools that are available. We have constructed a series of plasmids that confer resistance to two infrequently used antibiotics with variants of each plasmid backbone incorporating several regulatory control systems. The regulatory systems include both commonly used systems based on the
lac-
and arabinose-controlled promoters found in
Escherichia coli
, as well as less frequently used systems that respond to tetracycline/anhydrotetracycline and toluic acid. As a test case, we demonstrate the utility of these plasmids for regulated and tunable gene expression in a multidrug-resistant (MDR) isolate of
Acinetobacter baumannii
, strain AB5075-UW. The plasmids include derivatives of a freely replicating, broad-host-range plasmid allowing for inducible gene expression as well as a set of vectors for introducing genetic material at the highly conserved Tn7-attachment site. We also modified a set of CRISPR-interference plasmids for use in MDR organisms, thus allowing researchers to more readily interrogate essential genes in currently circulating clinical isolates. These tools will enhance molecular genetic analyses of bacterial pathogens in situations where existing plasmids cannot be used due to their antibiotic resistance determinants or lack of suitable regulatory control systems.

IMPORTANCE

Clinical isolates of bacterial pathogens often harbor resistance to multiple antibiotics, with
Acinetobacter baumannii
being a prime example. The drug-resistance phenotypes associated with these pathogens represent a significant hurdle to researchers who wish to study modern isolates due to the limited availability of plasmid tools. Here, we present a series of freely replicating and Tn7-insertion vectors that rely on selectable markers to less frequently encountered antibiotics, apramycin, and hygromycin. We demonstrate the utility of these plasmid tools through a variety of experiments looking at a multidrug-resistant strain of
A. baumannii
, strain AB5075. Strain AB5075 is an established model strain for present-day
A. baumannii
, due in part to its genetic tractability and because it is a representative isolate of the globally disseminated multidrug-resistant clade of
A. baumannii
, global clone 1. In addition to the drug-selection markers facilitating use in strains resistant to more commonly used antibiotics, the vectors allow for controllable expression driven by several regulatory systems, including isopropyl β-D-1-thiogalactopyranoside (IPTG), arabinose, anhydrotetracycline, and toluic acid.
A retrospective observational study of mNGS test utilization to examine the role of diagnostic stewardship at two academic medical centers
Authors: Shean Ryan C. , Garrett Elizabeth , Malleis James , Lieberman Joshua A. , Bradley Benjamin T. 
Published: August 2024

ABSTRACT

Given the cost and unclear clinical impact of metagenomic next-generation sequencing (mNGS), laboratory stewardship may improve utilization. This retrospective observational study examines mNGS results from two academic medical centers employing different stewardship approaches. Eighty mNGS orders [54 cerebrospinal fluid (CSF) and 26 plasma] were identified from 2019 to 2021 at the University of Washington (UW), which requires director-level approval for mNGS orders, and the University of Utah (Utah), which does not restrict ordering. The impact of mNGS results and the relationship to traditional microbiology orders were evaluated. Nineteen percent (10/54) of CSF and 65% (17/26) of plasma studies detected at least one organism. Compared to CSF results, plasma results more frequently identified clinically significant organisms (31% vs 7%) and pathogens not detected by traditional methods (12% vs 0%). Antibiotic management was more frequently impacted by plasma versus CSF results (31% vs 4%). These outcome measures were not statistically different between study sites. The number and cumulative cost of traditional microbiology tests at UW were greater than Utah for CSF mNGS testing (UW: 46 tests, $6,237; Utah: 26 tests, $2,812;
P
< 0.05) but similar for plasma mNGS (UW: 31 tests, $3,975; Utah: 21 tests, $2,715;
P
= 0.14). mNGS testing accounted for 30%–50% of the total microbiology costs. Improving the diagnostic performance of mNGS by stewardship remains challenging due to low positivity rates and difficulties assessing clinical impact. From a fiscal perspective, stewardship efforts should focus on reducing testing in low-yield populations given the high costs of mNGS relative to overall microbiology testing expenditures.

IMPORTANCE
Metagenomic next-generation sequencing (mNGS) stewardship practices remain poorly standardized. This study aims to provide actionable insights for institutions that seek to reduce the unnecessary usage of mNGS. Importantly, we highlight that clinical impact remains challenging to measure without standardized guidelines, and we provide an actual cost estimate of microbiology expenditures on individuals undergoing mNGS.
Interpolation of microbiome composition in longitudinal data sets
Authors: Peleg Omri , Borenstein Elhanan 
Published: August 2024

ABSTRACT

The human gut microbiome significantly impacts health, prompting a rise in longitudinal studies that capture microbiome samples at multiple time points. Such studies allow researchers to characterize microbiome changes over time, but importantly, also present major analytical challenges due to incomplete or irregular sampling. To address this challenge, longitudinal microbiome studies often employ various interpolation methods, aiming to infer missing microbiome data. However, to date, a comprehensive assessment of such microbiome interpolation techniques, as well as best practice guidelines for interpolating microbiome data, is still lacking. This work aims to fill this gap, rigorously implementing and systematically evaluating a large array of interpolation methods, spanning several different categories, for longitudinal microbiome interpolation. To assess each method and its ability to accurately infer microbiome composition at missing time points, we used three longitudinal microbiome data sets that follow individuals over a long period of time and a leave-one-out approach. Overall, our analysis demonstrated that the K-nearest neighbors algorithm consistently outperforms other methods in interpolation accuracy, yet, accuracy varied widely across data sets, individuals, and time. Factors such as microbiome stability, sample size, and the time gap between interpolated and adjacent samples significantly influenced accuracy, allowing us to develop a model for predicting the expected interpolation accuracy at a missing time point. Our findings, combined, suggest that accurate interpolation in longitudinal microbiome data is feasible, especially in dense cohorts. Furthermore, using our predictive model, future studies can interpolate data only in time points where the expected interpolation accuracy is high.

IMPORTANCE
Since missing samples are common in longitudinal microbiome dataset due to inconsistent collection practices, it is important to evaluate and benchmark different interpolation methods for predicting microbiome composition in such samples and facilitate downstream analysis. Our study rigorously evaluated several such methods and identified the K-nearest neighbors approach as particularly effective for this task. The study also notes significant variability in interpolation accuracy among individuals, influenced by factors such as age, sample size, and sampling frequency. Furthermore, we developed a predictive model for estimating interpolation accuracy at a specific time point, enhancing the reliability of such analyses in future studies. Combined, our study, thus, provides critical insights and tools that enhance the accuracy and reliability of data interpolation methods in the growing field of longitudinal microbiome research.
Acetylation of SAMHD1 at lysine 580 is crucial for blocking HIV-1 infection
Authors: Bulnes-Ramos Angel , Schott Kerstin , Rabinowitz Jesse , Luchsinger Charlotte , Bertelli Cinzia , Miyagi Eri , Yu Corey H. , Persaud Mirjana , Shepard Caitlin , König Renate , Kim Baek , Ivanov Dmitri N. , Strebel Klaus , Diaz-Griffero Felipe 
Published: August 2024

ABSTRACT

In humans, sterile alpha motif (SAM) domain- and histidine–aspartic acid (HD) domain-containing protein 1 (SAMHD1) is a dNTPase enzyme that prevents HIV-1 infection in non-cycling cells, such as differentiated THP-1 cells and human primary macrophages. Although phosphorylation of threonine 592 (T592) in SAMHD1 is recognized as the primary regulator of the ability to prevent HIV-1 infection, the contributions of SAMHD1 acetylation to this ability remain unknown. Mass spectrometry analysis of SAMHD1 proteins derived from cycling and non-cycling THP-1 cells, primary cycling B cells, and primary macrophages revealed that SAMHD1 is preferentially acetylated at lysine residues 354, 494, and 580 (K354, K494, and K580). In non-cycling cells, SAMHD1 is preferentially acetylated at K580, suggesting that this post-translational modification may contribute to the ability of SAMHD1 to block HIV-1 infection. Consistent with this finding, we found that mutations in K580 disrupted the ability of SAMHD1 to block HIV-1 infection without affecting the ability of SAMHD1 to deplete cellular dNTP levels. Gene editing of SAMHD1 in macrophage-like cells revealed that an intact K580 is required for HIV-1 restriction. This finding suggests that K580 acetylation in SAMHD1 is essential for blocking HIV-1 infection. More importantly, we found that a larger proportion of SAMHD1 featuring K580 acetylation could be detected in human primary macrophages when compared to human primary monocytes. In agreement, we found that SAMHD1 is acetylated during the monocyte-to-macrophage differentiation process. This finding agrees with the idea that the blockade of HIV-1 infection in macrophages requires SAMHD1 acetylation.

IMPORTANCE
The natural inhibitor of HIV-1, sterile alpha motif (SAM) domain- and histidine–aspartic acid (HD) domain-containing protein 1 (SAMHD1), plays a pivotal role in preventing HIV-1 infection of macrophages and dendritic cells, which are vital components of the immune system. This study unveils that SAMHD1 undergoes post-translational modifications, specifically acetylation at lysines 354, 494, and 580. Our research underscores the significance of these modifications, demonstrating that acetylation at residue K580 is indispensable for SAMHD1's efficacy in blocking HIV-1 infection. Notably, K580 is found in a critical regulatory domain of SAMHD1, highlighting acetylation as a novel layer of SAMHD1 regulation for HIV-1 restriction in humans. A comprehensive understanding of the regulation mechanisms governing this anti-HIV-1 protein is crucial for leveraging nature's defense mechanisms against HIV-1 and could pave the way for innovative therapeutic strategies.