bluuebunny/crossref_metadata_2025_split huggingface dataset:
Freshwater spreading far offshore the Japanese coast
Authors: Wagawa Taku , Igeta Yosuke , Sakamoto Kei , Takeuchi Marika , Okuyama Shinobu , Abe Shoko , Yabe Itsuka 
Published: June 2024

AbstractRiver discharge to the ocean influences the transport of salts and nutrients and is a source of variability in water mass distribution and the elemental cycle. Recently, using an underwater glider, we detected thick, low-salinity water offshore for the first time, probably derived from coastal waters, in the central-eastern Sea of Japan, whose primary productivity is comparable to that of the western North Pacific. Thereafter, we aimed to investigate the offshore advection and diffusion of coastal water and its variability and assess their impact. We examined the effects of river water discharge on the flow field and biological production. Numerical experiments demonstrated that low-salinity water observed by the glider in spring was discharged from the Japanese coast to offshore regions. The water is discharged offshore because of its interaction with mesoscale eddies. A relationship between the modeled low-salinity water transport to the offshore region and the observed chlorophyll-ain the offshore region was also observed, indicating the influence of river water on offshore biological production. This study contributes to understanding coastal-offshore water exchange, ocean circulation, elemental cycles, and biological production, which are frontiers in the Sea of Japan and throughout the world.
Possible adverse events of imidazole antifungal drugs during treatment of vulvovaginal candidiasis: analysis of the FDA Adverse Event Reporting System
Authors: Zhou Tianyu , Chen Chongze , Chen Xiaowei , Wang Bin , Sun Feng , Li Wanfang , Liu Dong , Jin Hongtao 
Published: June 2024

AbstractAzole antifungal drugs are commonly used to treat vulvovaginal candidiasis (VVC). The nephrotoxicity and developmental toxicity of azole drugs have not been systematically analyzed in the real world. We used the FDA Adverse Event Reporting System (FAERS) to investigate the adverse events (AEs) associated with imidazole therapy for VVC. FAERS data (from quarter 1 2004 to quarter 3 2022) were retrieved using OpenVigil 2.1, and AEs were retrieved and standardized according to the Medical Dictionary for Regulatory Activities (MedDRA). In the top 10 System Organ Class (SOC), all four drugs have been found to have kidney and urinary system diseases and pregnancy. We found significant signals, including clotrimazole [bladder transitional cell carcinoma, (report odds ratio, ROR = 291.66)], [fetal death, (ROR = 10.28)], ketoconazole[nephrogenic anemia (ROR = 22.1)], [premature rupture of membranes (ROR = 22.91 46.45, 11, 3)], Miconazole[hematuria (ROR = 19.03)], [neonatal sepsis (ROR = 123.71)], [spontaneous abortion (ROR = 5.98)], Econazole [acute kidney injury (ROR = 4.41)], [spontaneous abortion (ROR = 19.62)]. We also discovered new adverse reactions that were not reported. Therefore, when using imidazole drugs for treatment, it is necessary to closely monitor the patient's renal function, pay attention to the developmental toxicity of the fetus during pregnancy, and be aware of potential adverse reactions that may occur.
Transfer learned deep feature based crack detection using support vector machine: a comparative study
Authors: Bhalaji Kharthik K. S. , Onyema Edeh Michael , Mallik Saurav , Siva Prasad B. V. V. , Qin Hong , Selvi C. , Sikha O. K. 
Published: June 2024

AbstractTechnology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.
The effect of mobility reductions on infection growth is quadratic in many cases
Authors: Paltra Sydney , Bostanci Inan , Nagel Kai 
Published: June 2024

AbstractStay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to $$70\% \cdot 70\% = 49\%$$

70
%
·
70
%
=
49
%

of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.
Circulating C-reactive protein levels as a prognostic biomarker in breast cancer across body mass index groups
Authors: Holm J. B. , Baggesen E. , Cronin-Fenton D. , Frystyk J. , Bruun J. M. , Christiansen P. , Borgquist S. 
Published: June 2024

AbstractObesity and systemic inflammation are associated with breast cancer (BC) outcomes. Systemic inflammation is increased in obesity. We examined the association between C-reactive protein (CRP) and disease-free survival (DFS) and overall survival (OS) overall, and according to body mass index (BMI). We assembled a cohort of women with BC (stage I–III) seen at Aarhus University Hospital between 2010 and 2020 who donated blood at BC diagnosis (N = 2673). CRP levels were measured and divided into quartiles. We followed patients from surgery to recurrence, contralateral BC, other malignancy, death, emigration, or end-of-follow-up. We used Cox regression to estimate hazard ratios (HRs) with 95% confidence intervals (95% CIs) to compare outcomes across CRP quartiles, overall and stratified by BMI (normal-weight (18.5 ≤ BMI < 25 kg/m2), overweight (25 ≤ BMI < 30 kg/m2), and obesity (BMI ≥ 30 kg/m2)). During follow-up, 368 events (212 recurrences, 38 contralateral BCs, and 118 deaths) occurred (median follow-up 5.55 years). For DFS, high CRP (CRP ≥ 3.19 mg/L) was associated with an increased risk of events (HRadj:1.62 [95% CI = 1.14–2.28]). In BMI-stratified analyses, high CRP was associated with elevated risk of events in normal-weight and overweight (HRadj:1.70 [95% CI = 1.09–2.66]; HRadj:1.75 [95% CI = 1.08–2.86]), but in obesity, the estimate was less precise (HRadj:1.73 [95% CI = 0.78–3.83]). For OS, high CRP was associated with increased risk of death (HRadj:2.47 [95% CI = 1.62–3.76]). The association was strong in normal-weight and overweight (HRadj:3.66 [95% CI = 1.95–6.87]; HRadj:1.92 [95% CI = 1.06–3.46]), but less clear in obesity (HRadj:1.40 [95% CI = 0.64–3.09]). To sum up, high CRP levels at BC diagnosis were associated with inferior prognosis in early BC irrespective of BMI, although less clear in patients with obesity.
Neutrophil NLRP3 promotes cardiac injury following acute myocardial infarction through IL-1β production, VWF release and NET deposition in the myocardium
Authors: Heger Lukas A. , Schommer Nicolas , Van Bruggen Stijn , Sheehy Casey E. , Chan William , Wagner Denisa D. 
Published: June 2024

AbstractNLRP3 inflammasome has been implicated in neutrophil polarization and extrusion of neutrophil extracellular traps (NETs) in vitro and facilitates secretion of Il1-beta (IL-1β). Permanent ligation of the left anterior descending artery was used to induce MI in WT and NLRP3−/− mice as well as in NLRP3−/− recipient mice transfused with either WT or NLRP3−/− neutrophils. NLRP3 deficiency reduced infarct size to roughly a third of WT heart injury and preserved left ventricular (LV) function at 12 h after MI as assessed by echocardiography and triphenyltetrazolium chloride staining of live tissue. Transfusion of WT but not NLRP3−/− neutrophils after MI increased infarct size in NLRP3−/− mice and significantly reduced LV function. The key features of myocardial tissue in WT neutrophil transfused recipients were increased H3Cit-positive deposits with NET-like morphology and increased tissue levels of IL-1β and plasma levels of von Willebrand Factor (VWF). Flow cytometry analysis also revealed that neutrophil NLRP3 increased the number of labeled and transfused neutrophils in the bone marrow of recipient mice following MI. Our data suggest a key role for neutrophil NLRP3 in the production of IL-1β and deposition of NETs in cardiac tissue exacerbating injury following MI. We provide evidence for a link between neutrophil NLRP3 and VWF release likely enhancing thromboinflammation in the heart. Neutrophil NLRP3 deficiency conferred similar cardioprotective effects to general NLRP3 deletion in MI rendering anti-neutrophil NLRP3 therapy a promising target for early cardioprotective treatment.
Coarse-graining Hamiltonian systems using WSINDy
Authors: Messenger Daniel A. , Burby Joshua W. , Bortz David M. 
Published: June 2024

AbstractWeak form equation learning and surrogate modeling has proven to be computationally efficient and robust to measurement noise in a wide range of applications including ODE, PDE, and SDE discovery, as well as in coarse-graining applications, such as homogenization and mean-field descriptions of interacting particle systems. In this work we extend this coarse-graining capability to the setting of Hamiltonian dynamics which possess approximate symmetries associated with timescale separation. A smooth $$\varepsilon$$
ε
-dependent Hamiltonian vector field $$X_\varepsilon$$

X
ε

possesses an approximate symmetry if the limiting vector field $$X_0=\lim _{\varepsilon \rightarrow 0}X_\varepsilon$$

X
0

=

lim

ε
→
0

X
ε

possesses an exact symmetry. Such approximate symmetries often lead to the existence of a Hamiltonian system of reduced dimension that may be used to efficiently capture the dynamics of the symmetry-invariant dependent variables. Deriving such reduced systems, or approximating them numerically, is an ongoing challenge. We demonstrate that WSINDy can successfully identify this reduced Hamiltonian system in the presence of large perturbations imparted in the $$\varepsilon >0$$

ε
>
0

regime, while remaining robust to extrinsic noise. This is significant in part due to the nontrivial means by which such systems are derived analytically. WSINDy naturally preserves the Hamiltonian structure by restricting to a trial basis of Hamiltonian vector fields. The methodology is computationally efficient, often requiring only a single trajectory to learn the global reduced Hamiltonian, and avoiding forward solves in the learning process. In this way, we argue that weak-form equation learning is particularly well-suited for Hamiltonian coarse-graining. Using nearly-periodic Hamiltonian systems as a prototypical class of systems with approximate symmetries, we show that WSINDy robustly identifies the correct leading-order system, with dimension reduced by at least two, upon observation of the relevant degrees of freedom. While our main contribution is computational, we also provide a contribution to the literature on averaging theory by proving that first-order averaging at the level of vector fields preserves Hamiltonian structure in nearly-periodic Hamiltonian systems. This provides theoretical justification for our approach as WSINDy’s computations occur at the level of Hamiltonian vector fields. We illustrate the efficacy of our proposed method using physically relevant examples, including coupled oscillator dynamics, the Hénon–Heiles system for stellar motion within a galaxy, and the dynamics of charged particles.
Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation
Authors: Han Xiuying 
Published: June 2024

Abstract
A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint’s understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences.
Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study
Authors: Wang Yingwen , Fu Weijia , Zhang Yuejie , Wang Daoyang , Gu Ying , Wang Weibing , Xu Hong , Ge Xiaoling , Ye Chengjie , Fang Jinwu , Su Ling , Wang Jiayu , He Wen , Zhang Xiaobo , Feng Rui 
Published: June 2024

AbstractArtificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.
Railway track settlement patterns and control measures for multi-tunnel construction underneath a station track group: a case study
Authors: Li Pinpin , Lu Feng , Huang Haiyun , Qiu Wenge 
Published: June 2024

AbstractThis paper is based on the proximity engineering project of the Baishiyi tunnel group passing under the Chongqing West Station track group. Considering the train load and the spatial relationship of the tunnel and track groups, the settlement patterns, horizontal displacement, and differential settlement of the tunnel–strata–tracks system during the excavation process are studied through theoretical calculations and numerical simulation methods. The results indicate that the tunnel vault, strata, and track settlement deformation patterns are similar. Throughout the tunnel construction process, the tracks underwent uplift, settlement, and eventually stabilized. The settlement trough formed by the excavation of the three tunnels below the track group has an impact range of 25–145 m. Between 35 and 75 m, the differential settlement of the double track gradually increases with excavation. As the tunnel face reaches 75 m, the track differential settlement gradually converges and tends to stabilize. To minimize the impact of underpass tunnel construction on track groups, it is recommended to use a combination of full-section hole grouting and surface reinforcement grouting for ground reinforcement. Additionally, optimizing the construction parameters, including the step length and primary support closure time, and strengthening the locking anchor can further reduce the impact.
LLIN Evaluation in Uganda Project (LLINEUP)–effects of a vector control trial on Plasmodium infection prevalence and genotypic markers of insecticide resistance in Anopheles vectors from 48 districts of Uganda
Authors: Lynd Amy , Gonahasa Samuel , Staedke Sarah G. , Oruni Ambrose , Maiteki-Sebuguzi Catherine , Hancock Penelope A. , Knight Erin , Dorsey Grant , Opigo Jimmy , Yeka Adoke , Katureebe Agaba , Kyohere Mary , Hemingway Janet , Kamya Moses R. , McDermott Daniel , Lucas Eric R. , Donnelly Martin J. 
Published: June 2024

AbstractPyrethroid bednets treated with the synergist piperonyl butoxide (PBO) offer the possibility of improved vector control in mosquito populations with metabolic resistance. In 2017–2019, we conducted a large-scale, cluster-randomised trial (LLINEUP) to evaluate long-lasting insecticidal nets (LLINs) treated with a pyrethroid insecticide plus PBO (PBO LLINs), as compared to conventional, pyrethroid-only LLINs across 104 health sub-districts (HSDs) in Uganda. In LLINEUP, and similar trials in Tanzania, PBO LLINs were found to provide greater protection against malaria than conventional LLINs, reducing parasitaemia and vector density. In the LLINEUP trial, we conducted cross-sectional household entomological surveys at baseline and then every 6 months for two years, which we use here to investigate longitudinal changes in mosquito infection rate and genetic markers of resistance. Overall, 5395 female Anopheles mosquitoes were collected from 5046 households. The proportion of mosquitoes infected (PCR-positive) with Plasmodium falciparum did not change significantly over time, while infection with non-falciparum malaria decreased in An. gambiae s.s., but not An. funestus. The frequency of genetic markers associated with pyrethroid resistance increased significantly over time, but the rate of change was not different between the two LLIN types. The knock-down resistance (kdr) mutation Vgsc-995S declined over time as Vgsc-995F, the alternative resistance mutation at this codon, increased. Vgsc-995F appears to be spreading into Uganda. Distribution of LLINs in Uganda was previously found to be associated with reductions in parasite prevalence and vector density, but here we show that the proportion of infective mosquitoes remained stable across both PBO and non-PBO LLINs, suggesting that the potential for transmission persisted. The increased frequency of markers of pyrethroid resistance indicates that LLIN distribution favoured the evolution of resistance within local vectors and highlights the potential benefits of resistance management strategies.Trial registration: This study is registered with ISRCTN, ISRCTN17516395. Registered 14 February 2017, http://www.isrctn.com/ISRCTN17516395.
Peracetic acid treatment of squid eggs infected with parasitic copepod (Ikanecator primus gen. et sp. nov.)
Authors: Zoral Mehmet Arif , Lajbner Zdenek , Zifcakova Lucia , Miller Jonathan 
Published: June 2024

AbstractHaving been successfully bred in semi-intensive and intensive aquaculture systems, oval squids of the Sepioteuthis lessoniana species complex are emerging as promising candidates for research and industry. Nevertheless, information about pathogens and diseases that may affect squid aquaculture remains sparse. In this study, we identify new parasitic copepod species that causes squid mortality and decreases squid hatching rates, and we also offer a solution to eliminate the pathogen during incubation of squid eggs. The newly discovered copepod Ikanecator primus gen. et sp. nov. was identified on oval squid eggs for the first time using both morphological and molecular diagnostic markers. In the genomes of the copepod and associated microbiome, we identified multiple genes for enzymes involved in cephalopod eggshell degradation in genomes of the copepod and associated microbiome. Furthermore, we conducted experiments to assess efficacy of peracetic acid in inhibiting the I. primus gen. et sp. nov. both in vitro and in vivo using immersion treatment. We established that a 2-min exposure to a concentration of 250 μl/L of peracetic acid containing product (PAA-product; 35 mg/L PAA and 15 mg/L H2O2) inhibited the development of nauplii in vitro. All parasites exposed to a concentration of 500 μl/L of PAA-product (70 mg/L PAA and 30 mg/L H2O2) were eliminated within two minutes. On top of this, the immersion treatment with 500 μl/L of PAA-product (70 mg/L PAA and 30 mg/L H2O2) improved survival of squid embryos and increased size of squid hatchlings compared with control and the immersion treatment with 125 μl/L of PAA-product (17.5 mg/L PAA and 7.5 mg/L H2O2) and the immersion treatment with 250 μl/L of PAA-product (35 mg/L PAA and 15 mg/L H2O2). These findings suggest that PAA holds a great potential as inhibitor and controller of parasitic copepod infections and for overall health management in cephalopod culture.