bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of maternal and neonatal survival exposed to humidifier disinfectants during perinatal periods: a case-series study
Authors: Lee Jang Hoon , Joh Joon Sung , Choi Seoheui 
Published: November 2023

AbstractA humidifier disinfectant (HD) has been prohibited by the government due to its serious effects on the human body. Several studies on the relationship between HD and lung diseases have been performed independently on children and adults. However, there have been no reports on the effects of HD exposure on pregnant women and their foetuses. Therefore, the present study was conducted to investigate the effects of HD exposure on the foetuses of women who encountered HD during pregnancy. A total of 56 cases were recruited from 2017 to 2019 through the Korea Environmental Industry & Technology Institute, and data obtained from the medical records included maternal date of birth, maternal date of death, maternal start and end date of HD exposure, maternal date of symptom onset, neonatal birthday, neonatal birthweight, gestational age, and neonatal survival status within 28 days. All data were retrospectively investigated through medical records. Of the 47 mothers, 20 (42.6%) mothers survived, and 27 (57.4%) mothers died. In the group of survivors, there was a shorter period of total HD use, period of HD use before pregnancy and period of HD use to onset of symptoms. Shorter durations of HD use resulted in higher survival rate of mothers. HD use caused an increase in gestational age surviving foetuses, and foetal mortality increased when clinical symptoms developed before birth.
Toward pulping process for enhancing the RS-black liquors as precursor of activated carbons for aqueous adsorbent purposes
Authors: Lotfy Vivian F. , Bao Zhichao , Zhou Xuesong , Basta Altaf H. , Fu Shiyu 
Published: November 2023

AbstractThis work deals with providing a green pulping process of rice straw with zero waste discharged, via valorization of its by-product as a promising precursor for production of carbon nanostructures. The carbon nanostructures (BL-CNSs) from rice straw pulping liquors (BLs) are prepared in one step with phosphoric acid activation. The carbon nanostructures (BL-CNSs) from rice straw pulping liquors (BLs) are prepared in one step with phosphoric acid activation. The optimal pulping approach for achieving effective adsorbent (BL-CNSs) of cationic and anionic dyes is recommended from using different BLs precursors resulting from different reagents (alkaline, neutral, and acidic reagents). The carbon precursors are characterized by elemental, thermal (TGA and DTG) and ATR FTIR analyses. While the impact of pulping route on performance of CNSs is evaluated by their adsorption of iodine, cationic dye and anionic dye, as well as ATR-FTIR, textural characterization, and SEM. The data of elemental analysis displayed a high Carbon content ranges from 57.85 to 66.69% suitable for CNSs preparation, while the TGA showed that Sulphur-containing BLs (Kraft, neutral sulfite and acidic sulfite) have higher degradation temperature and activation energies as compared with other BLs. The optimum BL-CNSs adsorbent is prepared from the disposed neutral sulfite black liquor, with the following characteristics: cationic dye adsorption capacity 163.9 mg/g, iodine value 336.9 mg/g and SBET 310.6 m2/g. While the Kraft-CNSs provided highest anionic adsorption (70.52 mg/g). The studies of equilibrium and kinetic adsorption of dyes showed that the adsorption equilibrium of all investigated BL-CNSs toward MB follow the Langmuir and mainly Freundlich models for BB adoption. Their adsorption kinetics are a good fit with the pseudo-second-order model. The textural characterization and SEM revealed the CNSs exhibit a mixture of mesoporous and microporous structure.
Association between infrapatellar fat pad ultrasound elasticity and anterior knee pain in patients with knee osteoarthritis
Authors: Satake Yoshinori , Izumi Masashi , Aso Koji , Ikeuchi Masahiko 
Published: November 2023

AbstractThis study investigates whether infrapatellar fat pad (IPFP) elasticity is associated with anterior knee pain in patients with knee osteoarthritis (KOA). The IPFP elasticity of 97 patients with KOA (Kellgren and Lawrence [KL] grades of the femorotibial and patellofemoral joints ≥ 2 and ≤ 2, respectively), aged 46–86 years, was evaluated via shear wave speed using ultrasound elastography. The patients were divided into two groups according to the presence or absence of anterior knee pain. Univariate analyses were used to compare patient age, sex, femorotibial KL grade, magnetic resonance imaging findings (Hoffa, effusion synovitis, bone marrow lesion scores, and IPFP size), and IPFP elasticity between the groups. Multivariate logistic regression analyses were subsequently performed using selected explanatory variables. IPFP elasticity was found to be associated with anterior knee pain in the univariate (p = 0.007) and multivariate (odds ratio: 61.12, 95% CI 1.95–1920.66; p = 0.019) analyses. Anterior knee pain is strongly associated with stiffer IPFPs regardless of the femorotibial KL grade, suggesting that ultrasound elastography is useful for the diagnosis of painful IPFP in patients with KOA.
Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm
Authors: Ju Rui-Yang , Cai Weiming 
Published: November 2023

AbstractHospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application “Fracture Detection Using YOLOv8 App” to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.
Wideband 1-bit reconfigurable transmission metasurface unit cell design in Ka-band with polarization hold and conversion
Authors: Huang Tongxing , Fu Wenjie , Lu Dun , Pan Yibo , Wang Minxing , Yan Yang 
Published: November 2023

AbstractIn this paper, a wideband transmission unit cell is proposed for programmable metasurfaces operating in the Ka-band. The unit cell features a compact period of only 2.91 mm, corresponding to 0.34 λ0 at the center frequency of 35 GHz. A receiving layer, consisting of a patch loaded with two PIN diodes, is utilized to achieve 1-bit phase modulation, while a U-shaped patch serves as the transmitting layer to enable selection of linear polarization hold or conversion. Based on the multi-resonance principle, the proposed unit cell exhibits broadband behavior, as demonstrated by simulation results under periodic boundary conditions, which indicate a 3 dB transmission bandwidth of 29.4–40 GHz (30.5%). Two unit cells were fabricated and tested in a standard waveguide, with the minimum insertion loss of the two states tested being 1.2 dB and 3 dB bandwidths of 30.1–31.2 GHz and 33.5–38.5 GHz, respectively. The maximum 180° phase error is 10°, indicating the high quality of the proposed unit cell.
Detecting age-related changes in skeletal muscle mechanics using ultrasound shear wave elastography
Authors: Ateş Filiz , Marquetand Justus , Zimmer Manuela 
Published: November 2023

AbstractAging leads to a decline in muscle mass and force-generating capacity. Ultrasound shear wave elastography (SWE) is a non-invasive method to capture age-related muscular adaptation. This study assessed biceps brachii muscle (BB) mechanics, hypothesizing that shear elastic modulus reflects (i) passive muscle force increase imposed by length change, (ii) activation-dependent mechanical changes, and (iii) differences between older and younger individuals. Fourteen healthy volunteers aged 60–80 participated. Shear elastic modulus, surface electromyography, and elbow torque were measured at five elbow positions in passive and active states. Data collected from young adults aged 20–40 were compared. The BB passive shear elastic modulus increased from flexion to extension, with the older group exhibiting up to 52.58% higher values. Maximum elbow flexion torque decreased in extended positions, with the older group 23.67% weaker. Significant effects of elbow angle, activity level, and age on total and active shear elastic modulus were found during submaximal contractions. The older group had 20.25% lower active shear elastic modulus at 25% maximum voluntary contraction. SWE effectively quantified passive and activation-dependent BB mechanics, detecting age-related alterations at rest and during low-level activities. These findings suggest shear elastic modulus as a promising biomarker for identifying altered muscle mechanics in aging.
Development and prospective validation of postoperative pain prediction from preoperative EHR data using attention-based set embeddings
Authors: Liu Ran , Gutiérrez Rodrigo , Mather Rory V. , Stone Tom A. D. , Santa Cruz Mercado Laura A. , Bharadwaj Kishore , Johnson Jasmine , Das Proloy , Balanza Gustavo , Uwanaka Ekenedilichukwu , Sydloski Justin , Chen Andrew , Hagood Mackenzie , Bittner Edward A. , Purdon Patrick L. 
Published: November 2023

AbstractPreoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0–10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.
A study of generative large language model for medical research and healthcare
Authors: Peng Cheng , Yang Xi , Chen Aokun , Smith Kaleb E. , PourNejatian Nima , Costa Anthony B. , Martin Cheryl , Flores Mona G. , Zhang Ying , Magoc Tanja , Lipori Gloria , Mitchell Duane A. , Ospina Naykky S. , Ahmed Mustafa M. , Hogan William R. , Shenkman Elizabeth A. , Guo Yi , Bian Jiang , Wu Yonghui 
Published: November 2023

AbstractThere are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians’ Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.
Systematic investigation of recipient cell genetic requirements reveals important surface receptors for conjugative transfer of IncI2 plasmids
Authors: Allard Nancy , Collette Arianne , Paquette Josianne , Rodrigue Sébastien , Côté Jean-Philippe 
Published: November 2023

AbstractBacterial conjugation is a major horizontal gene transfer mechanism. While the functions encoded by many conjugative plasmids have been intensively studied, the contribution of recipient chromosome-encoded genes remains largely unknown. Here, we analyzed the genetic requirement of recipient cells for conjugation of IncI2 plasmid TP114, which was recently shown to transfer at high rates in the gut microbiota. We performed transfer assays with ~4,000 single-gene deletion mutants of Escherichia coli. When conjugation occurs on a solid medium, we observed that recipient genes impairing transfer rates were not associated with a specific cellular function. Conversely, transfer assays performed in broth were largely dependent on the lipopolysaccharide biosynthesis pathway. We further identified specific structures in lipopolysaccharides used as recipient cell surface receptors by PilV adhesins associated with the type IVb accessory pilus of TP114. Our strategy is applicable to study other mobile genetic elements and understand important host cell factors for their dissemination.
Durotaxis and negative durotaxis: where should cells go?
Authors: Ji Congcong , Huang Yuxing 
Published: November 2023

AbstractDurotaxis and negative durotaxis are processes in which cell migration is directed by extracellular stiffness. Durotaxis is the tendency of cells to migrate toward stiffer areas, while negative durotaxis occurs when cells migrate toward regions with lower stiffness. The mechanisms of both processes are not yet fully understood. Additionally, the connection between durotaxis and negative durotaxis remains unclear. In this review, we compare the mechanisms underlying durotaxis and negative durotaxis, summarize the basic principles of both, discuss the possible reasons why some cell types exhibit durotaxis while others exhibit negative durotaxis, propose mechanisms of switching between these processes, and emphasize the challenges in the investigation of durotaxis and negative durotaxis.
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity
Authors: Ochiai Toshiki , Inukai Tensei , Akiyama Manato , Furui Kairi , Ohue Masahito , Matsumori Nobuaki , Inuki Shinsuke , Uesugi Motonari , Sunazuka Toshiaki , Kikuchi Kazuya , Kakeya Hideaki , Sakakibara Yasubumi 
Published: November 2023

AbstractThe structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.
Cu(I)-thioether coordination complexes based on a chiral cyclic β-amino acid ligand
Authors: Lee Jihee , Kim Jaewook , Jo Hongil , Lim Danim , Hong Jungwoo , Gong Jintaek , Ok Kang Min , Lee Hee-Seung 
Published: November 2023

AbstractCoordination complexes, particularly metalloproteins, highlight the significance of metal-sulfur bonds in biological processes. Their unique attributes inspire efforts to synthetically reproduce these intricate metal-sulfur motifs. Here, we investigate the synthesis and characterization of copper(I)-thioether coordination complexes derived from copper(I) halides and the chiral cyclic β-amino acid trans-4-aminotetrahydrothiophene-3-carboxylic acid (ATTC), which present distinctive structural properties and ligand-to-metal ratios. By incorporating ATTC as the ligand, we generated complexes that feature a unique chiral conformation and the capacity for hydrogen bonding, facilitating the formation of distinct geometric structures. Through spectroscopic analyses and density functional theory (DFT) calculations, we studied the complexes’ optical properties, including their emission bands and variable second-harmonic generation (SHG) efficiencies, which vary based on the halide used. Our findings underscore the potential of the ATTC ligand in creating unusual coordination complexes and pave the way for further investigations into their potential applications, particularly within materials science.
An integrated wavemeter based on fully-stabilized resonant electro-optic frequency comb
Authors: Niu Rui , Wan Shuai , Li Wenjian , Wang Pi-Yu , Sun Fang-Wen , Bo Fang , Liu Junqiu , Guo Guang-Can , Dong Chun-Hua 
Published: November 2023

AbstractOptical frequency combs provide a powerful tool for precise measurement of the optical frequency, holding significant importance in fields such as spectroscopy, optical communication and optical clock. The frequency stability of the comb line determines the precision of the frequency measurement, but the delicate interplay between high precision, low power consumption and integration still needs to be optimized. To this aim, here we demonstrate a frequency measurement scheme based on a fully stabilized electro-optic comb, in which the pump laser frequency and repetition rate are independently locked to the atomic transition and microwave signal. The measurement precision of the demonstrated wavemeter can reach sub-kHz-level, and the parallel measurement of multiple wavelengths can be performed. Therefore, by combining the technical scheme reported here with advanced integrated functional devices, our system is expected to provide a feasible solution for chip-scale frequency precision measurement and reference.
Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers
Authors: Khan Sumeer Ahmad , Maillo Alberto , Lagani Vincenzo , Lehmann Robert , Kiani Narsis A. , Gomez-Cabrero David , Tegner Jesper 
Published: November 2023

AbstractThe rise of single-cell genomics is an attractive opportunity for data-hungry machine learning algorithms. The scBERT method, inspired by the success of BERT (‘bidirectional encoder representations from transformers’) in natural language processing, was recently introduced by Yang et al. as a data-driven tool to annotate cell types in single-cell genomics data. Analogous to contextual embedding in BERT, scBERT leverages pretraining and self-attention mechanisms to learn the ‘transcriptional grammar’ of cells. Here we investigate the reusability beyond the original datasets, assessing the generalizability of natural language techniques in single-cell genomics. The degree of imbalance in the cell-type distribution substantially influences the performance of scBERT. Anticipating an increased utilization of transformers, we highlight the necessity to consider data distribution carefully and introduce a subsampling technique to mitigate the influence of an imbalanced distribution. Our analysis serves as a stepping stone towards understanding and optimizing the use of transformers in single-cell genomics.
Anticounterfeiting tags based on randomly oriented MoSx clusters enabled by capillary and Marangoni flow
Authors: Moon Changgyun , Pujar Pavan , Gandla Srinivas , So Byungjun , Lee Sunjong , Kim Dongmin , Kwon Kon-Woo , Kim Sunkook 
Published: November 2023

AbstractRecent advances in anticounterfeiting technology highlight the importance of functional materials with innovative processing methods for achieving robust anticounterfeiting tags. Here, we report a fabrication approach that enables the formation of randomly oriented MoSxor MoOxclusters of unpredictable shapes for use as anticounterfeiting tags. We exploit the combined effects of capillary and Marangoni flows to drive solute in a pre-deposited wet film that produces random clusters, creating tags. The reading of these tags is achieved using high-speed confocal laser microscopy, which allows for the extraction of complex topographies in seconds. The digitization of the tags involves converting height profiles into grayscale images, followed by the application of mathematical tools and the von Neumann debiasing technique. The extracted keys are subjected to the NIST statistical test, which consists of six tests to confirm the randomness of the generated keys. The recent advancements in anticounterfeiting technology highlight the importance of functional materials with innovative processing methods to achieve robust anticounterfeiting tags.
Petrological evidence for deep subduction of organic carbon to subarc depths
Authors: Hu Han , Zhang Lifei , Lan Chunyuan , Liu Zhicheng 
Published: November 2023

AbstractThe significance of subducted organic carbon for the deep carbon cycle has been demonstrated by the presence of ultra-deep diamonds and arc emissions. However, there is no convincing evidence that organic carbon can be subducted to subarc depths. This study provides the evidence for deep subduction of sedimentary organic carbon to mantle depths through petrological observation, Raman and isotopic analyses of ultrahigh-pressure (UHP) coesite- and graphite-bearing pelitic schists. The analyzed graphite shows light δ13C values (−24.7 to −22.5‰), indicating a sedimentary organic carbon source. Petrological characteristics of graphite co-existing with coesite in garnet and P–T calculations suggest that organic graphite was subducted to the subarc depths exceeding 90 km. This research represents the petrological evidence for the deep subduction of organic carbon to mantle depths and may provide insight into the origin of diamonds with light δ13C values and contribute as key evidence for the Lomagundi event.