bluuebunny/crossref_metadata_2025_split huggingface dataset:
A global time series of traffic volumes on extra-urban roads
Authors: van Strien Maarten J. , Grêt-Regamey Adrienne 
Published: May 2024

AbstractTraffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.
Comprehensive energy demand and usage data for building automation
Authors: Heer Philipp , Derungs Curdin , Huber Benjamin , Bünning Felix , Fricker Reto , Stoller Sascha , Niesen Björn 
Published: May 2024

AbstractBuildings are essential in satisfying our daily need for comfort (privacy, protection from weather, etc.) and are responsible for almost half of the world’s total energy consumption. Research at the interface of room comfort and energy efficiency is of critical societal importance. At the same time, there is a lack of publicly available data to optimize important building functions automatically. It is only through data-driven approaches that building automation becomes financially affordable and achieves widespread adoption. In this publication, measurement data from three buildings of the NEST platform are made publicly available. The dataset includes detailed information on energy consumption (electricity, heating, cooling, domestic hot water), building operation (set points, valve openings, windows), and occupant practice (e.g., presence, operation of blinds and kitchen, showering patterns). All data have been measured over four years and with a temporal resolution of 1 minute. This combination of information allows learning the function of different building types (office and residential) and thus addresses important research gaps.
Association of the CUN-BAE body adiposity estimator and other obesity indicators with cardiometabolic multimorbidity: a cross-sectional study
Authors: Chen Xuejiao , Geng Shuoji , Shi Zhan , Ding Jiacheng , Li Haojie , Su Donghai , Cheng Yulin , Shi Songhe , Tian Qingfeng 
Published: May 2024

AbstractCardiometabolic multimorbidity (CM), defined as the coexistence of two or three cardiometabolic disorders, is one of the most common and deleterious multimorbidities. This study aimed to investigate the association of Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) with the prevalence of CM. The data were obtained from the 2021 health checkup database for residents of the Electronic Health Management Center in Xinzheng, Henan Province, China. 81,532 participants aged ≥ 60 years were included in this study. Logistic regression models were used to estimate the odd ratios (ORs) and 95% confidence intervals (CIs) for CUN-BAE, BMI, WC, and WHtR in CM. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of different anthropometric indicators for CM. The multivariable-adjusted ORs (95% CIs) (per 1 SD increase) of CM were 1.799 (1.710–1.893) for CUN-BAE, 1.329 (1.295–1.364) for BMI, 1.343 (1.308–1.378) for WC, and 1.314 (1.280–1.349) for WHtR, respectively. Compared with BMI, WC and WHtR, CUN-BAE had the highest AUC in both males and females (AUC: 0.642; 95% CI 0.630–0.653 for males, AUC: 0.614; 95% CI 0.630–0.653 for females). CUN-BAE may be a better measure of the adverse effect of adiposity on the prevalence of CM than BMI, WC, and WHtR.
CSChighE-cadherinlow immunohistochemistry panel predicts poor prognosis in oral squamous cell carcinoma
Authors: Ortiz Rafael Carneiro , Amôr Nádia Ghinelli , Saito Luciana Mieli , Santesso Mariana Rodrigues , Lopes Nathália Martins , Buzo Rodrigo Fonseca , Fonseca Angélica Cristina , Amaral-Silva Gleyson Kleber , Moyses Raquel Ajub , Rodini Camila Oliveira 
Published: May 2024

AbstractIdentifying marker combinations for robust prognostic validation in primary tumour compartments remains challenging. We aimed to assess the prognostic significance of CSC markers (ALDH1, CD44, p75NTR, BMI-1) and E-cadherin biomarkers in OSCC. We analysed 94 primary OSCC and 67 metastatic lymph node samples, including central and invasive tumour fronts (ITF), along with clinicopathological data. We observed an increase in ALDH1+/CD44+/BMI-1- tumour cells in metastatic lesions compared to primary tumours. Multivariate analysis highlighted that elevated p75NTR levels (at ITF) and reduced E-cadherin expression (at the tumour centre) independently predicted metastasis, whilst ALDH1high exhibited independent predictive lower survival at the ITF, surpassing the efficacy of traditional tumour staging. Then, specifically at the ITF, profiles characterized by CSChighE-cadherinlow (ALDH1highp75NTRhighE-cadherinlow) and CSCintermediateE-cadherinlow (ALDH1 or p75NTRhighE-cadherinlow) were significantly associated with worsened overall survival and increased likelihood of metastasis in OSCC patients. In summary, our study revealed diverse tumour cell profiles in OSCC tissues, with varying CSC and E-cadherin marker patterns across primary tumours and metastatic sites. Given the pivotal role of reduced survival rates as an indicator of unfavourable prognosis, the immunohistochemistry profile identified as CSChighE-cadherinlow at the ITF of primary tumours, emerges as a preferred prognostic marker closely linked to adverse outcomes in OSCC.
Numerical simulation for MHD Oldroyd-B fluid flow with melting and slip effect
Authors: Dadheech Amit , Sharma Surbhi , Al-Mdallal Qasem 
Published: May 2024

AbstractThis investigation reflects an examination of Oldroyd-B fluid flow over a permeable surface subjected to the effects of melting, slip effect, inclined magnetic field and chemical reactions. The governing equations are resolved using the bvp4c inbuilt MATLAB tool, the arithmetic computation for the momentum, thermal and concentration equations are executed. The results are exhibited graphically. Numerical outcomes are graphically depicted by aid of velocity, concentration, temperature profiles for several model variables. The achieved results exhibit a promising agreement with the previously established findings available in the open literature. The results obtained indicated that Deborah number $$\beta_{1}$$

β
1

reduces the momentum boundary layer thickness whereas Deborah number $$\beta_{2}$$

β
2

enhances the adjacent momentum boundary layer. Furthermore, temperature profile declined for melting parameter $$Me$$

Me

. The application of this study transcends various engineering disciplines, offering practical solutions and optimization opportunities in polymer processing, coating technologies, cooling systems, materials processing, biomedical and environmental engineering.
In-situ particle analysis with heterogeneous background: a machine learning approach
Authors: Alam Adeeb Ibne , Rahman Md Hafizur , Zia Akhter , Lowry Nate , Chakraborty Prabuddha , Hassan Md Rafiul , Khoda Bashir 
Published: May 2024

AbstractWe propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.
Experiences of stigma, discrimination and violence and their impact on the mental health of health care workers during the COVID-19 pandemic
Authors: Janoušková Miroslava , Pekara Jaroslav , Kučera Matěj , Kearns Pavla Brennan , Šeblová Jana , Wolfová Katrin , Kuklová Marie , Šeblová Dominika 
Published: May 2024

AbstractHealth care workers have been exposed to COVID-19 more than people in other professions, which may have led to stigmatization, discrimination, and violence toward them, possibly impacting their mental health. We investigated (1) factors associated with stigma, discrimination, and violence, (2) the association of stigma, discrimination, and violence with mental health, (3) everyday experiences of stigmatization, discrimination, and violence. We chose a combination of a quantitative approach and qualitative content analysis to analyze data collected at three time points: in 2020, 2021 and 2022. A higher age was associated with lower odds of experiencing stigma, discrimination, and violence, whereas female gender was related to more negative experiences. The intensity of exposure to COVID-19 was associated with greater experience with stigmatization, discrimination, and violence across all three years (for example in 2022: odds ratio, 95% confidence interval: 1.74, 1.18–2.55 for mild exposure; 2.82, 1.95–4.09 for moderate exposure; and 5.74, 3.55–9.26 for severe exposure, when compared to no exposure). Stigma, discrimination, and violence were most strongly associated with psychological distress in 2020 (odds ratio = 2.97, 95% confidence interval 2.27–3.88) and with depressive symptoms in 2021 (odds ratio = 2.78, 95% confidence interval 2.12–3.64). Attention should be given to the destigmatization of contagious diseases and the prevention of discrimination, violence, and mental health problems, both within workplaces and among the public.
A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG
Authors: Laskar Mostafizur Rahaman , Pratiher Sawon , Dutta Amit Kumar , Ghosh Nirmalya , Patra Amit 
Published: May 2024

AbstractExtracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately $$48\%$$

48
%

gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by $$\tilde{{\mathscr {O}}}\left( \frac{\kappa ^2 \text {poly}(\log {N})}{\varepsilon _P}\right)$$

O
~

κ
2

poly

(
log
N
)

ε
P

for condition number $$\kappa$$
κ
of the $$N\times N$$

N
×
N

filter matrix within precision $$\varepsilon _P$$

ε
P

. Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.
Incidence of resistance to ALS and ACCase inhibitors in Echinochloa species and soil microbial composition in Northern Italy
Authors: Cusaro Carlo Maria , Capelli Enrica , Picco Anna Maria , Brusoni Maura 
Published: May 2024

AbstractThe increasing amount of weeds surviving herbicide represents a very serious problem for crop management. The interaction between microbial community of soil and herbicide resistance, along with the potential evolutive consequences, are still poorly known and need to be investigated to better understand the impact on agricultural management. In our study, we analyzed the microbial composition of soils in 32 farms, located in the Northern Italy rice-growing area (Lombardy) with the aim to evaluate the relationship between the microbial composition and the incidence of resistance to acetolactate synthase (ALS) and acetyl-CoA carboxylase (ACCase) inhibiting herbicides inEchinochloaspecies. We observed that the coverage of weeds survived herbicide treatment was higher than 60% in paddy fields with a low microbial biodiversity and less than 5% in those with a high microbial biodiversity. Fungal communities showed a greater reduction in richness thanBacteria. In soils with a reduced microbial diversity, a significant increase of some bacterial and fungal orders (i.e.Lactobacillales,MalassezialesandDiaporthales) was observed. Interestingly, we identified two different microbial profiles linked to the two conditions: high incidence of herbicide resistance (H-HeR) and low incidence of herbicide resistance (L-HeR). Overall, the results we obtained allow us to make hypotheses on the greater or lesser probability of herbicide resistance occurrence based on the composition of the soil microbiome and especially on the degree of biodiversity of the microbial communities.
Markerless 3D kinematics and force estimation in cheetahs
Authors: da Silva Zico , Shield Stacey , Hudson Penny E. , Wilson Alan M. , Nicolls Fred , Patel Amir 
Published: May 2024

AbstractThe complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah (Acinonyx jubatus) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs, where it is essential that the methods used are remote and do not constrain the animal’s motion. In this work, we use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We call this approach kinetic full trajectory estimation (K-FTE). We validate the method on a dataset comprising synchronised video and force plate data. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94% PCK using the nose-to-eye(s) length segment as a threshold), while the estimates produce an average root-mean-square error of 171.3N ($$\approx$$
≈
17.16% of peak force during stride) for the estimated ground reaction force when compared against the force plate data. While the joint torques cannot be directly validated against ground truth data, as no such data is available for cheetahs, the estimated torques agree with previous studies of quadrupeds in controlled settings. These results will enable deeper insight into the study of animal locomotion in a more natural environment for both biologists and roboticists.
Differential expression of angiogenesis-related genes ‘VEGF’ and ‘angiopoietin-1’ in metastatic and EMAST-positive colorectal cancer patients
Authors: Torshizi Esfahani Amir , Mohammadpour Somayeh , Jalali Pooya , Yaghoobi Alireza , Karimpour Raana , Torkamani Soha , Pardakhtchi Ali , Salehi Zahra , Nazemalhosseini-Mojarad Ehsan 
Published: May 2024

AbstractAbnormal angiogenesis leads to tumor progression and metastasis in colorectal cancer (CRC). This study aimed to elucidate the association between angiogenesis-related genes, including VEGF-A, ANGPT-1, and ANGPT-2 with both metastatic and microsatellite alterations at selected tetranucleotide repeats (EMAST) subtypes of CRC. We conducted a thorough assessment of the ANGPT-1, ANGPT-2, and VEGF-A gene expression utilizing publicly available RNA sequencing and microarray datasets. Then, the experimental validation was performed in 122 CRC patients, considering their disease metastasis and EMAST+/− profile by using reverse transcription polymerase chain reaction (RT-PCR). Subsequently, a competing endogenous RNA (ceRNA) network associated with these angiogenesis-related genes was constructed and analyzed. The expression level of VEGF-A and ANGPT-2 genes were significantly higher in tumor tissues as compared with normal adjacent tissues (P-value < 0.001). Nevertheless, ANGPT-1 had a significantly lower expression in tumor samples than in normal colon tissue (P-value < 0.01). We identified a significantly increased VEGF-A (P-value = 0.002) and decreased ANGPT-1 (P-value = 0.04) expression in EMAST+ colorectal tumors. Regarding metastasis, a significantly increased VEGF-A and ANGPT-2 expression (P-value = 0.001) and decreased ANGPT-1 expression (P-value < 0.05) were established in metastatic CRC patients. Remarkably, co-expression analysis also showed a strong correlation between ANGPT-2 and VEGF-A gene expressions. The ceRNA network was constructed by ANGPT-1, ANGPT-2, VEGF-A, and experimentally validated miRNAs (hsa-miR-190a-3p, hsa-miR-374c-5p, hsa-miR-452-5p, and hsa-miR-889-3p), lncRNAs (AFAP1-AS1, KCNQ1OT1 and MALAT1), and TFs (Sp1, E2F1, and STAT3). Network analysis revealed that colorectal cancer is amongst the 82 significant pathways. We demonstrated a significant differential expression of VEGF-A and ANGPT-1 in colorectal cancer patients exhibiting the EMAST+ phenotype. This finding provides novel insights into the molecular pathogenesis of colorectal cancer, specifically in EMAST subtypes. Yet, the generalization of in silico findings to EMAST+ colorectal cancer warrants future experimental investigations. In the end, this study proposes that the EMAST biomarker could serve as an additional perspective on CMS4 biology which is well-defined by activated angiogenesis and worse overall survival.
Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance
Authors: Zheng Weihui , Tang Xi , Dong Jinyun , Feng Jianguo , Chen Min , Zhu Xin 
Published: May 2024

AbstractRadioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.