bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated identification of hip arthroplasty implants using artificial intelligence
Authors: Gong Zibo , Fu Yonghui , He Ming , Fu Xinzhe 
Published: July 2022

AbstractThe purpose of this study was to develop and evaluate the performance of deep learning methods based on convolutional neural networks (CNN) to detect and identify specific hip arthroplasty models. In this study, we propose a novel deep learning-based approach to identify hip arthroplasty implants’ design using anterior–posterior images of both the stem and the cup. We harness the pre-trained ResNet50 CNN model and employ transfer learning methods to adapt the model for the implants identification task using a total of 714 radiographs of 4 different hip arthroplasty implant designs. Performance was compared with the operative notes and crosschecked with implant sheets. We also evaluate the difference in performance of models trained with the images of the stem, the cup or both. The training and validation data sets were comprised of 357 stem images and 357 cup radiographs across 313 patients and included 4 hip arthroplasty implants from 4 leading implant manufacturers. After 1000 training epochs the model classified 4 implant models with very high accuracy. Our results showed that jointly using stem images and cup images did not improve the classification accuracy of the CNN model. CNN can accurately distinguish between specific hip arthroplasty designs. This technology could offer a useful adjunct to the surgeon in preoperative identification of the prior implant. Using stem images or cup images to train the CNN can both achieve effective identification accuracy, with the accuracy of the stem images being higher. Using stem images and cup images together is not more effective than using images from only one perspective.
Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data
Authors: Wang Zhiwen , Xi Bin , Yu Bingxiao , Zhou Junhui , Wang Min , Wang Changfeng , Liu Ruen 
Published: July 2022

AbstractThere is lacking research on risk factors and prediction models associated with Post-hemorrhagic hydrocephalus (PHH). Thus, this present study aimed to analyze the risk factors of PHH and establish a risk-scoring system through a large-scale study. A retrospective study of 382 patients with intracranial hemorrhage assessed age, history and diagnosis, Glasgow coma score (GCS), and fever time. After univariate and logistic regression analysis, a risk scoring system was established according to independent risk factors and evaluated using the area under the curve (AUC). Of the 382 patients, 133 (34.8%) had PHH, 43 (11.3%) received surgical treatment. Factor classification showed that age > 60 years old [odds ratio (OR): 0.347, II = 5 points], GCS < 5 (OR: 0.09, IV = 10 points), GCS 6‒8 (OR = 0.232, III = 6 points), fever time > 9 (OR: 0.202, III = 7 points), fever time 5–9 (OR: 0.341, II = 5 points), CSF-TP x time > 14,4000 group (OR: 0.267, IV = 6 points), and CSF-TP x time 9,601‒14,400 group (OR: 0.502, III = 3 points) were independent risk factors. The result of the receiver operating characteristic (ROC) prediction showed that AUC = 0.790 (0.744‒0.836). Low-risk (IV-VII), moderate (VIII-X), and high-risk group (XI-XIII) incidence of PHH were 11.76%, 50.55%, and 70.00% (p < 0.001), respectively. The coincidence rates in the validation cohort were 26.00%, 74.07%, and 100.0% (p < 0.001), respectively. AUC value was 0.860 (0.780‒0.941). The predictive model was conducive to determining the occurrence of PHH and facilitating early intervention.
Process optimization for the rapid conversion of calcite into hydroxyapatite microspheres for chromatographic applications
Authors: Ashokan Anbuthangam , Kumar T. S. Sampath , Jayaraman Guhan 
Published: July 2022

AbstractMicrosphere hydroxyapatite (HAp) is widely used in various biomedical and chromatographic applications. The work described in this manuscript focuses on a dissolution precipitation method for production of HAp microspheres. This method overcomes certain drawbacks of conventional preparation methods used for HAp preparation, which produce polydisperse particles and are time-consuming and expensive. In the present work, the calcium carbonate (calcite) particles were directly and rapidly converted into HAp microspheres using an inexpensive dissolution precipitation method. The effects of the reaction temperature, time, and mechanical stirring rates were studied, and the reaction parameters were optimized. As confirmed by the XRD studies, the higher reaction temperature and time promote complete HAp conversion, while calcite residues were observed for lower temperatures and times. SEM images show the influence of reaction parameters on the surface microstructure of the microspheres produced. It was observed that the HAp microspheres undergo disintegration at a higher stirring rate. The reaction parameters optimized in this work were ideal for preparing HAp microspheres. The resultant HAp particles were utilized as matrices for chromatographic separation of protein mixtures.
A novel magnetic compression technique for cystostomy in rabbits
Authors: Zhang Miaomiao , Mao Jianqi , Li Yixing , Xu Shuqin , Gai Jingci , Lan Ting , Wang Yirui , Ren Yuxiang , Shi Aihua , Lyu Yi , Yan Xiaopeng 
Published: July 2022

AbstractMagnetic compression technique (MCT) is a popular new anastomosis method. In this paper, we aimed to explore the feasibility of use of MCT for performing cystotomy in rabbits. The parent magnets and daughter magnets for rabbit cystostomy were designed and manufactured according to the anatomical characteristics of rabbit lower urinary tract. Twelve female New Zealand rabbits were used as animal models. After anesthesia, a daughter magnet was inserted into the bladder through the urethra, and the parent magnet was placed on the body surface projection of the bladder over the abdominal wall. The two magnets automatically attract each other. Postoperatively, the state of magnets was monitored daily, and the time when the magnets fell off was recorded. Cystostomy with MCT was successfully performed in all twelve rabbits. The mean operation time was 4.46 ± 0.75 min. The magnets fell off from the abdominal wall after a mean duration of 10.08 ± 1.62 days, resulting in the formation of bladder fistula. Macroscopic and microscopic examination showed that the fistula was well formed and unobstructed. The junction between bladder and abdominal wall was tight and smooth. We provide preliminary experimental evidence of the safety and feasibility of cystostomy based on MCT.
Value-added fabrication of NiO-doped CuO nanoflakes from waste flexible printed circuit board for advanced photocatalytic application
Authors: Hossain Rumana , Nekouei Rasoul Khayyam , Al Mahmood Abdullah , Sahajwalla Veena 
Published: July 2022

AbstractThe disposal of electronic waste (e-waste) presents a number of environmental problems. However, there are great opportunities to use this problem waste as a source of value-added metals. These metals could be recovered and transformed for use in beneficial applications, such as the manufacture of nanomaterials for the generation of hydrogen through thermodynamic water-splitting. This study used microrecycling techniques to synthesise Nitrogen oxide (NiO) doped copper oxide (CuO) nanoflakes from waste flexible printed circuit boards (FPCBs) using microrecycling techniques. Several precise characterisation and experimental analysis were used to validate the synthesised nanoflakes’ phase purity, surface chemistry, morphology and optical properties. XRD analysis confirmed the nanoflakes produced in the system were predominantly Tenorite, CuO (98.5% ± 4.5) with a dopant of NiO (1.5% ± 0.1). The nanoflakes had a specific surface area of 115.703 m2/g and mesoporous structure with an average pore diameter of 11 nm. HRTEM analysis confirmed that the nanoflakes were not a single structure but assembled from 2D nanorods. The width of the nanorods varied from ∼ 10 to 50 nm, and the length from ∼ 30 to 80 nm. After rapid thermal processing, the photocurrent response of the synthesised material was assessed, revealing a higher photocurrent density (− 1.9 mA/cm2 at 0.6 V vs. reversible hydrogen electrode (RHE) under 1.5G AM). Mott Schottky analysis and electrochemical impedance spectroscopy showed that the synthesised nanomaterial had the potential thermodynamic water-splitting capability. These results were an encouraging indication of the promise of techniques which use e-waste to produce nanomaterials with valuable properties. This has the potential to both decrease problem waste and preserves dwindling natural resources.
Three-dimensional conditional generative adversarial network-based virtual thin-slice technique for the morphological evaluation of the spine
Authors: Nakamoto Atsushi , Hori Masatoshi , Onishi Hiromitsu , Ota Takashi , Fukui Hideyuki , Ogawa Kazuya , Masumoto Jun , Kudo Akira , Kitamura Yoshiro , Kido Shoji , Tomiyama Noriyuki 
Published: July 2022

AbstractVirtual thin-slice (VTS) technique is a generative adversarial network-based algorithm that can generate virtual 1-mm-thick CT images from images of 3–10-mm thickness. We evaluated the performance of VTS technique for assessment of the spine. VTS was applied to 4-mm-thick CT images of 73 patients, and the visibility of intervertebral spaces was evaluated on the 4-mm-thick and VTS images. The heights of vertebrae measured on sagittal images reconstructed from the 4-mm-thick images and VTS images were compared with those measured on images reconstructed from 1-mm-thick images. Diagnostic performance for the detection of compression fractures was also compared. The intervertebral spaces were significantly more visible on the VTS images than on the 4-mm-thick images (P < 0.001). The absolute value of the measured difference in mean vertebral height between the VTS and 1-mm-thick images was smaller than that between the 4-mm-thick and 1-mm-thick images (P < 0.01–0.54). The diagnostic performance of the VTS images for detecting compression fracture was significantly lower than that of the 4-mm-thick images for one reader (P = 0.02). VTS technique enabled the identification of each vertebral body, and enabled accurate measurement of vertebral height. However, this technique is not suitable for diagnosing compression fractures.
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions
Authors: Hinson Jeremiah S. , Klein Eili , Smith Aria , Toerper Matthew , Dungarani Trushar , Hager David , Hill Peter , Kelen Gabor , Niforatos Joshua D. , Stephens R. Scott , Strauss Alexandra T. , Levin Scott 
Published: July 2022

AbstractDemand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80–0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation.
Identification of a guanine-specific pocket in the protein N of SARS-CoV-2
Authors: Rafael Ciges-Tomas J. , Franco María Luisa , Vilar Marçal 
Published: July 2022

AbstractThe SARS-CoV-2 nucleocapsid protein (N) is responsible for RNA binding. Here we report the crystal structure of the C-terminal domain (NCTD) in open and closed conformations and in complex with guanine triphosphate, GTP. The crystal structure and biochemical studies reveal a specific interaction between the guanine, a nucleotide enriched in the packaging signals regions of coronaviruses, and a highly conserved tryptophan residue (W330). In addition, EMSA assays with SARS-CoV-2 derived RNA hairpin loops from a putative viral packaging sequence showed the preference interaction of the N-CTD to RNA oligonucleotides containing G and the loss of the specificity in the mutant W330A. Here we propose that this interaction may facilitate the viral assembly process. In summary, we have identified a specific guanine-binding pocket in the N protein that may be used to design viral assembly inhibitors.
Deployed measurement-device independent quantum key distribution and Bell-state measurements coexisting with standard internet data and networking equipment
Authors: Berrevoets Remon C. , Middelburg Thomas , Vermeulen Raymond F. L. , Chiesa Luca Della , Broggi Federico , Piciaccia Stefano , Pluis Rene , Umesh Prathwiraj , Marques Jorge F. , Tittel Wolfgang , Slater Joshua A. 
Published: July 2022

AbstractThe forthcoming quantum Internet is poised to allow new applications not possible with the conventional Internet. The ability for both quantum and conventional networking equipment to coexist on the same fiber network would facilitate the deployment and adoption of coming quantum technology. Most quantum networking tasks, like quantum repeaters and the connection of quantum processors, require nodes for multi-qubit quantum measurements (often Bell-State measurements), and their real-world coexistence with the conventional Internet has yet to be shown. Here we field deploy a Measurement-Device Independent Quantum Key Distribution (MDI-QKD) system, containing a Bell-State measurement node, over the same fiber connection as multiple standard Internet Protocol (IP) data networks, between three nearby cities in the Netherlands. We demonstrate over 10 Gb/s classical data communication rates simultaneously with our next-generation QKD system, and estimate 200 GB/s of classical data transmission would be easily achievable without significantly affecting QKD performance. Moreover, as the system ran autonomously for two weeks, this shows an important step towards the coexistence and integration of quantum networking into the existing telecommunication infrastructure.
Climate variability and multi-decadal diatom abundance in the Northeast Atlantic
Authors: Edwards Martin , Beaugrand Gregory , Kléparski Loïck , Hélaouët Pierre , Reid Philip C. 
Published: July 2022

AbstractDiatoms are important contributors to marine primary production and the ocean carbon cycle. In the North Atlantic and its adjacent seas primary production is driven by diatoms that transfer a significant part of the produced energy to higher trophic levels and carbon to the deep ocean. Anthropogenic warming and climate variability will likely have important consequences for the productivity and spatial dynamics of these eukaryotic phytoplankton. Using multidecadal diatom abundance data (>60 years) for the Northeast Atlantic and the North Sea, we show significant spatial and temporal correlations over these scales between diatoms and climate variability. A general multidecadal trend is established where climate warming is increasing diatom populations in northerly systems but decreasing populations in more southerly systems. We discover major phase shifts in diatom abundance synchronous with multi-decadal trends in Atlantic climate variability that occurred after the mid-1990s.
Robust multi‐model controllers design without impulse in switching times for non‐linear vibrations suppression of sandwich plate
Authors: Zazerani Amir Amini , Faraji Alireza , Mohammadimehr Mahdi 
Published: July 2022

AbstractGenerally multi‐model controllers design has three stages: Decomposition of complicated non‐linear systems into local linear models, designing the local controllers, and composition of them in sub‐regions. In the present paper an algorithm is proposed in which all the three stages are simultaneously done to decrease the number of controllers. Determining a specific threshold based on non‐linear model characteristics is a good criterion for this purpose. The threshold depends on the knowledge of the system features and designing method, and the improper selection of the threshold will increase the number of local models and the complexity of the global controller structure. This non‐convex optimization problem is solved by the genetic algorithm whose cost function is defined by the complementary sensitivity function to guarantee the robust stability and optimal performance of the close loop the system, respectively. Another challenge in designing multi‐model controllers is the transient performance degradation when switching from one local model to another. In the present research, the problem is solved by the system input generated by the online controller error signal and the feedback coefficient of the offline controller. The proposed robust controller in the presence of the uncertainties is evaluated for vibration suppression of a sandwich plate.
Design of current detection LDO regulator with silicon controlled rectifier based electrostatic discharge protection circuit to provide optimized voltage and power for system‐on‐chip applications
Authors: Kwon Sang Wook , Koo Yong Seo 
Published: July 2022

AbstractVarious levels of circuits and systems in the ultra‐low nanometer fabrication process should be considered to implement a system‐on‐chip (SOC) application to reduce power consumption. This letter is intended to propose a current detection structure that supplies and discharges additional current along the load current to provide the optimized voltage and power for SOC applications. The proposed electrostatic discharge (ESD) protection circuit placed on the power line and I/O has improved the reliability by preventing the direct or indirect destruction of the internal integrated circuit (IC) due to ESD. In addition, the proposed low drop out (LDO) regulator for the low‐voltage applications has combined an ESD protection circuit to secure the high reliability of the IC. The proposed LDO regulator is implemented with a 0.18‐μm BCD process and is kept an undershoot voltage of 23 mV and an overshoot voltage of 25 mV for a load current of 200 mA.
Addressing Methamphetamine Use in Primary Care: Provider Perspectives
Authors: Dunn Julia , Yuan Mina , Ramírez Fernando , Chokron Garneau Hélène , Brown-Johnson Cati , Breland Haley , Antonini Valerie , Larkins Sherry , Rawson Richard , McGovern Mark 
Published: July 2022

Introduction
The opioid epidemic has evolved into a combined stimulant epidemic, with escalating stimulant and fentanyl-related overdose deaths. Primary care providers are on the frontlines grappling with patients’ methamphetamine use. Although effective models exist for treating opioid use disorder in primary care, little is known about current clinical practices for methamphetamine use.

Methods
Six semistructured group interviews were conducted with 38 primary care providers. Interviews focused on provider perceptions of patients with methamphetamine use problems and their care. Data were analyzed using inductive and thematic analysis and summarized along the following dimensions: (1) problem identification, (2) clinical management, (3) barriers and facilitators to care, and (4) perceived needs to improve services.

Results
Primary care providers varied in their approach to identifying and treating patient methamphetamine use. Unlike opioid use disorders, providers reported lacking standardized screening measures and evidence-based treatments, particularly medications, to address methamphetamine use. They seek more standardized screening tools, Food and Drug Administration–approved medications, reliable connections to addiction medicine specialists, and more training. Interest in novel behavioral health interventions suitable for primary care settings was also noteworthy.

Conclusions
The findings from this qualitative analysis revealed that primary care providers are using a wide range of tools to screen and treat methamphetamine use, but with little perceived effectiveness. Primary care faces multiple challenges in effectively addressing methamphetamine use among patients singularly or comorbid with opioid use disorders, including the lack of Food and Drug Administration–approved medications, limited patient retention, referral opportunities, funding, and training for methamphetamine use. Focusing on patients’ medical issues using a harm reduction, motivational interviewing approach, and linkage with addiction medicine specialists may be the most reasonable options to support primary care in compassionately and effectively managing patients who use methamphetamines.
ComBat Harmonization: Empirical Bayes versus Fully Bayes Approaches
Authors: Reynolds Maxwell , Chaudhary Tigmanshu , Torbati Mahbaneh Eshaghzadeh , Tudorascu Dana L. , Batmanghelich Kayhan ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: July 2022

AbstractStudying small effects or subtle neuroanatomical variation requires large-scale sample size data. As a result, combining neuroimaging data from multiple datasets is necessary. Variation in acquisition protocols, magnetic field strength, scanner build, and many other non-biologically related factors can introduce undesirable bias into studies. Hence, harmonization is required to remove the bias-inducing factors from the data. ComBat is one of the most common methods applied to features from structural images. ComBat models the data using a hierarchical Bayesian model and uses the empirical Bayes approach to infer the distribution of the unknown factors. The empirical Bayes harmonization method is computationally efficient and provides valid point estimates. However, it tends to underestimate uncertainty. This paper investigates a new approach, fully Bayesian ComBat, where Monte Carlo sampling is used for statistical inference. When comparing fully Bayesian and empirical Bayesian ComBat, we found Empirical Bayesian ComBat more effectively removed scanner strength information and was much more computationally efficient. Conversely, fully Bayesian ComBat better preserved biological disease and age-related information while performing more accurate harmonization on traveling subjects. The fully Bayesian approach generates a rich posterior distribution, which is useful for generating simulated imaging features for improving classifier performance in a limited data setting. We show the generative capacity of our model for augmenting and improving the detection of patients with Alzheimer’s disease. Posterior distributions for harmonized imaging measures can also be used for brain-wide uncertainty comparison and more principled downstream statistical analysis. Code for our new fully Bayesian ComBat extension is available athttps://github.com/batmanlab/BayesComBat.