bluuebunny/crossref_metadata_2025_split huggingface dataset:
An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel
Authors: Sun Shoukun , Xu Fei , Cai Lu , Salvato Daniele , Dilemma Fidelma , Capriotti Luca , Xian Min , Yao Tiankai 
Published: December 2023

AbstractGaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.
Self-reported energy use behaviour changed significantly during the cost-of-living crisis in winter 2022/23: insights from cross-sectional and longitudinal surveys in Great Britain
Authors: Huebner Gesche M. , Hanmer Clare , Zapata-Webborn Ellen , Pullinger Martin , McKenna Eoghan James , Few Jessica , Elam Simon , Oreszczyn Tadj 
Published: December 2023

AbstractThe winter of 2022/23 has seen large increases in energy prices and in the cost of living in many countries around the world, including Great Britain. Here, we report the results of two surveys, combining cross-sectional and longitudinal analysis, in a sample of about 5400 British households. One survey was conducted early in 2023, the other when participants had signed up to an ongoing research study in the past five years. Thermostat settings were about 1°C lower during the cost-of-living crisis than before, and householders were more likely to turn the heating off when the home was unoccupied. The effort to save energy increased compared to pre-cost-of-living-crisis levels. Using the in-home display more in the cost-of-living crisis than before correlated with greater effort to save energy, supporting the notion that displaying energy data can be a useful tool for energy reductions. Finding it difficult to keep comfortably warm in the home and struggling with meeting heating costs were linked to lower wellbeing, strengthening evidence links between cold, damp, and hard-to-heat homes and negative mental health outcomes. About 40% of respondents lowered the flow temperature of the boiler which might imply that highly tailored information campaigns can be effective in changing behaviour.
Reservoir impoundment-triggered seismicity in Brazil: the case of M4.0 Nova Ponte earthquake
Authors: Raza Haris , Kivi Iman Rahimzadeh , França George Sand , Vilarrasa Victor 
Published: December 2023

AbstractReservoir-triggered seismicity commonly occurs as a result of reservoir impoundment. In particular, the Nova Ponte reservoir triggered a series of earthquakes, including the 1998 M4.0 earthquake, which represents the second-largest earthquake triggered by reservoir impoundment in Brazil. The earthquake occurred after prolonged seismic activity following reservoir impoundment starting in 1993. After more than two decades, the mechanisms governing these earthquakes and their relation with the spatiotemporal evolution of the seismic events are still poorly understood. Here, we explain the causal mechanisms of the two largest earthquakes: an initial response M3.5 in 1995 and the delayed M4.0 in 1998. To this end, we numerically simulate the poromechanical subsurface response to reservoir impoundment using a 3D model that includes three geological layers down to 10 km depth. From the proposed potential nodal planes of the 1995 M3.5 earthquake, we show that the earthquake has most likely occurred on a vertical, E–W-oriented strike-slip fault with a reverse-displacement component. Deviatoric stresses generated by the water column loading on the surface, superimposed by undrained pore pressure enhancement in deep low-permeability layers can explain the fault reactivation. We find that for the 1998 M4.0 earthquake to occur, conductive flow pathways with permeability as high as 6.6·10−15 m2 should exist to transmit pore pressure to a deep critically oriented fault. Our analysis raises the importance of accounting for coupled poromechanical mechanisms controlling fault stability, hydromechanical properties of different rock layers, and realistic shape of the reservoir to accurately assess the potential for reservoir-triggered seismicity. We conclude that reliable forecasting models require accurate subsurface characterization before reservoir filling to enable managing the associated reservoir-triggered seismicity.
An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma
Authors: Ali Shah Asghar , Shaker Ayesha Sher Ali , Jabbar Sohail , Abbas Qaisar , Al-Balawi Talal Saad , Celebi M. Emre 
Published: December 2023

AbstractWhen the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews’s correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.
Proposing new path-planning metrics for operating rovers on Mars
Authors: Muñoz Pablo , Bellutta Paolo , R-Moreno Maria D. 
Published: December 2023

AbstractThe on-ground operation of Mars rovers is a complex task that requires comprehensive planning in which path planning plays a fundamental role. The selection of paths has to be carefully chosen considering the scientific objectives, terrain, energy, and safety. In this regard, operators are assisted by path-planning algorithms that generate candidate paths based on cost functions. Distance traveled has always been considered one of the primary criteria when comparing paths. Other metrics such as the run-time to generate the solution or the number of expanded nodes are common measures considered in the literature. However, we want to analyze if those metrics provide useful information in challenging and partially known terrain. In this paper, we will review those metrics using two-path planning algorithms on real Mars maps. Based on our experience operating Mars rovers, we propose new metrics for assessing paths in real-world applications.
Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy
Authors: Brenes David , Kortum Alex , Coole Jackson , Carns Jennifer , Schwarz Richard , Vohra Imran , Richards-Kortum Rebecca , Liu Yuxin , Cai Zhenjian , Sigel Keith , Anandasabapathy Sharmila , Gaisa Michael , Chiao Elizabeth 
Published: December 2023

AbstractAnal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidence of anal cancer in the United States has recently increased, with diagnosis and treatment hampered by high loss-to-follow-up rates. Novel methods for the automated, real-time diagnosis of AIN 2+ could enable "see and treat" strategies, reducing loss-to-follow-up rates. A previous retrospective study demonstrated that the accuracy of a high-resolution microendoscope (HRME) coupled with a deep learning model was comparable to expert clinical impression for diagnosis of AIN 2+ (sensitivity 0.92 [P = 0.68] and specificity 0.60 [P = 0.48]). However, motion artifacts and noise led to many images failing quality control (17%). Here, we present a high frame rate HRME (HF-HRME) with improved image quality, deployed in the clinic alongside a deep learning model and evaluated prospectively for detection of AIN 2+ in real-time. The HF-HRME reduced the fraction of images failing quality control to 4.6% by employing a high frame rate camera that enhances contrast and limits motion artifacts. The HF-HRME outperformed the previous HRME (P < 0.001) and clinical impression (P < 0.0001) in the detection of histopathologically confirmed AIN 2+ with a sensitivity of 0.91 and specificity of 0.87.
Development of a quantum dot-based lateral flow immunoassay strip for rapid and sensitive detection of SARS-CoV-2 neutralizing antibodies
Authors: Wang Xirong , Shao Shulin , Ye Huan , Li Sen , Gu Bing , Tang Bo 
Published: December 2023

AbstractTo a certain extent, the development and vaccination of COVID-19 vaccine have reduced the alarming rate of transmission speed and mortality rate. At present, vaccine coverage is quite high in countries around the world. Since individual differences are unavoidable, it is necessary to assess the efficacy of the vaccine in each vaccinated person in order to reflect the protective effect of the vaccine in different populations. In this study, we developed a novel COVID-19 neutralizing antibody detection kit combining lateral flow immunochromatography and novel quantum dot technology with 85.23% sensitivity, 92.50% specificity. The novel QD-ICA could achieve an accurate detection of SARS-CoV-2 neutralizing antibodies with 10 minutes, two steps, small equipment size, and broad testing application, suggesting its capability to assess vaccine effectiveness on a large scale in areas of world that currently affected by the pandemic.
Downregulation of osteoprotegerin in colorectal cancer cells promotes liver metastasis via activating tumor-associated macrophage
Authors: Hirata Wataru , Itatani Yoshiro , Masui Hideyuki , Kawada Kenji , Mizuno Rei , Yamamoto Takamasa , Okamoto Takuya , Ogawa Ryotaro , Inamoto Susumu , Maekawa Hisatsugu , Okamura Ryosuke , Kiyasu Yoshiyuki , Hanada Keita , Okamoto Michio , Nishikawa Yasuyo , Sugimoto Naoko , Tamura Takuya , Hatano Etsuro , Sakai Yoshiharu , Obama Kazutaka 
Published: December 2023

AbstractOsteoprotegerin (OPG) is a secreted cytokine that functions as a decoy receptor for receptor activator of nuclear factor kappa-B (RANK) ligand (RANKL). Anti-RANKL treatment for bone metastasis has been widely accepted for solid tumors. However, the mechanism of OPG-RANKL-RANK signaling in systemic colorectal cancer (CRC) metastasis remains unclear. In this study, we investigated the relevance and function of OPG expression in CRC liver metastasis. First, we performed in silico analysis using The Cancer Genome Atlas public database and found that lower OPG expression in CRC was associated with poor overall survival. Immunohistochemistry analyses using resected specimen from patients with CRC in our institute confirmed the result. Patient-matched primary CRC and liver metastases showed a significant downregulation of OPG expression in metastatic lesions. In CRC cell lines, OPG expression did not suppress cell proliferation and migration. However, OPG expression inhibited macrophage migration by suppressing the RANKL-RANK pathway. Moreover, in vivo mouse liver metastasis models showed that OPG expression in CRC cells suppressed liver metastases. In addition, treatment with an anti-RANKL neutralizing antibody also suppressed liver metastases. These results showed that downregulation of OPG expression in CRC cells promotes liver metastasis by activating tumor-associated macrophage, which can become a candidate for targeted therapy with anti-RANKL neutralizing antibody for CRC liver metastasis.
In silico investigation on sensing of tyramine by boron and silicon doped C60 fullerenes
Authors: Pattanaik S. , Vishwkarma A. K. , Yadav T. , Shakerzadeh E. , Sahu D. , Chakroborty S. , Tripathi P. K. , Zereffa E. A. , Malviya J. , Barik A. , Sarankar S. K. , Sharma P. , Upadhye V. J. , Wagadre S. 
Published: December 2023

AbstractThe present communication deals with the adsorption of tyramine neurotransmitter over the surface of pristine, Boron (B) and Silicon (Si) doped fullerenes. Density functional theory (DFT) calculations have been used to investigate tyramine adsorption on the surface of fullerenes in terms of stability, shape, work function, electronic characteristics, and density of state spectra. The most favourable adsorption configurations for tyramine have been computed to have adsorption energies of − 1.486, − 30.889, and − 31.166 kcal/mol, respectively whereas for the rest three configurations, it has been computed to be − 0.991, − 6.999, and − 8.796 kcal/mol, respectively. The band gaps for all six configurations are computed to be 2.68, 2.67, 2.06, 2.17, 2.07, and 2.14 eV, respectively. The band gap of pristine, B and Si doped fullerenes shows changes in their band gaps after adsorption of tyramine neurotransmitters. However, the change in band gaps reveals more in B doped fullerene rather than pristine and Si doped fullerenes. The change in band gaps of B and Si doped fullerenes leads a change in the electrical conductivity which helps to detect tyramine. Furthermore, natural bond orbital (NBO) computations demonstrated a net charge transfer of 0.006, 0.394, and 0.257efrom tynamine to pristine, B and Si doped fullerenes.
Immune response to zinc oxide inhalation in metal fume fever, and the possible role of IL-17f
Authors: Szűcs-Somlyó Éva , Lehel József , Májlinger Kornél , Tóth Fruzsina , Jerzsele Ákos , Kővágó Csaba 
Published: December 2023

AbstractMetal fume fever (MFF) is a work-related disease caused by the inhalation of metal particles, including zinc oxide. Chronic asthma may develop as a long-term consequence of exposure, particularly for welders and metal workers who are most at risk. In this study, we investigated the effects of ZnO fume inhalation on multiple inflammation-related cytokine- and cytokine receptor genes in mice from lung and lymph node samples, to explore the role of these in the pathogenesis of MFF. In our experiments, the animals were treated with a sub-toxic amount of ZnO fume for 4 h a day for 3 consecutive days. Sampling occurred 3 and 12 h post-treatment. We are the first to demonstrate that ZnO inhalation causes extremely increased levels of IL-17f gene expression at both sampling time points, in addition to increased gene expression rates of several other interleukins and cytokines, such as IL-4, IL-13, CXCL5, CSF-3, and IFN-γ. Our animal experiment provides new insights into the immunological processes of early metal fume fever development. IL-17f plays a crucial role in connecting immunological and oxidative stress events. The increased levels of IL-4 and IL-13 cytokines may explain the development of long-term allergic asthma after exposure to ZnO nanoparticles, which is well-known among welders, smelters, and metal workers.
Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment
Authors: Bolla Gergo , Berente Dalida Borbala , Andrássy Anita , Zsuffa Janos Andras , Hidasi Zoltan , Csibri Eva , Csukly Gabor , Kamondi Anita , Kiss Mate , Horvath Andras Attila 
Published: December 2023

AbstractMild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78–87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80–84% accuracy for our local and 74–82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.
Prevalence and antibiotics resistance status of Salmonella in raw meat consumed in various areas of Lahore, Pakistan
Authors: Fatima Aiman , Saleem Maira , Nawaz Shahid , Khalid Linta , Riaz Saba , Sajid Imran 
Published: December 2023

AbstractThis study reports the prevalence and antibiotics resistance status of Salmonella detected in raw meat from Lahore, Pakistan. Overall, N = 111 meat samples, were collected from local markets. Salmonella was recovered from 57 (51.35%) samples, including 45.83% of poultry, 60% of buffalo, 64.28% of cow, and 60% of goat meat samples. The predominant Salmonella strains were Salmonella enterica serovars; Typhimurium (45.4%), Typhi (27.2%), and Enteritidis (18.1%), identified by VITEK system and 16S rRNA gene sequencing. The isolates exhibited high resistance to Erythromycin (100%), Cefepime (98.24%), Colistin (94.73%), Azithromycin (92.98%), Tetracycline (87.71%), Polymyxin B (84.21%), Ciprofloxacin (84.21%), Trimethoprim-Sulfamethoxazole (80.70%), Nalidixic Acid (80.70%), Kanamycin (78.94%), Chloramphenicol (77.19%), Streptomycin (71.92%) and Ampicillin (64.91%). While the isolates exhibited more susceptibility to Meropenem (75.43%) and Amikacin (73.68%). N = 8 strains were designated as Multidrug Resistant (MDR) and N = 3 as Extensively Drug-Resistant (XDR) Salmonella. The PCR-based detection of resistance genes revealed the presence of blaTEM-1 gene (100%), catA1 gene (64%), and gyrA gene (18%). The whole genome sequencing (WGS) of two selected strains and subsequent downstream analysis confirmed the strains as MDR and XDR Salmonella enterica serovar Typhi. The study showed that raw meat consumed in Lahore carries a significantly high number of drug-resistant Salmonella.
An environmental analysis of public UAP sightings and sky view potential
Authors: Medina R. M. , Brewer S. C. , Kirkpatrick S. M. 
Published: December 2023

AbstractSightings of unidentified flying objects (UFOs) or unidentified anomalous phenomena (UAP) have been reported throughout history. Given the potential security and safety risks they pose, as well as scientific curiosity, there is increasing interest in understanding what these sighting reports represent. We approach this problem as an important one of the human experience and that can be examined through a geographical lens: what local factors may increase or decrease the number of sighting reports? Using a Bayesian regression method, we test hypotheses based on variables representing sky view potential (light pollution, tree canopy, and cloud cover) and the potential for objects to be present in the sky (aircraft and military installations). The dependent variable includes over 98,000 publicly reported UAP sightings in the conterminous United States during the 20-year period from 2001 to 2020. The model results find credible correlations between variables that suggest people see more “phenomena” when they have more opportunity to. This analysis is one of few investigations of UAP sighting reports at a national scale providing context to help examine individual reports. Given that these objects are labeled unidentifiable in the personal sense, there are many natural and/or human based explanations worth exploring.
Optimizing time, cost, and carbon in construction: grasshopper algorithm empowered with tournament selection and opposition-based learning
Authors: Pham Vu Hong Son , Vo Duy Phuoc , Nguyen Dang Nghiep Trinh 
Published: December 2023

AbstractThe global construction industry plays a pivotal role, yet its unique characteristics pose distinctive challenges. Each construction project, marked by its individuality, substantial value, intricate scale, and constrained adaptability, confronts crucial limitations concerning time and cost. Despite contributing significantly to environmental concerns throughout construction activities and infrastructure operations, environmental considerations remain insufficiently addressed by project managers. This research introduces an improved rendition of the muti-objective grasshopper optimization algorithm (MOGOA), termed eMOGOA, as a novel methodology to tackle time, cost, and carbon dioxide emission trade-off problems (TCCP) in construction project management. To gauge its efficacy, a case study involving 29 activities is employed. eMOGOA amalgamates MOGOA, tournament selection (TS), and opposition-based learning (OBL) techniques to enhance the performance of the original MOGOA. The outcomes demonstrate that eMOGOA surpasses other optimization algorithms, such as MODA, MOSMA, MOALO and MOGOA when applied to TCCP. These findings underscore the efficiency and relevance of the eMOGOA algorithm within the realm of construction project management.