bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2)
Authors: Sinha Eva , Calvin Katherine V. , Bond‐Lamberty Ben , Drewniak Beth A. , Ricciuto Daniel M. , Sargsyan Khachik , Cheng Yanyan , Bernacchi Carl , Moore Caitlin E. 
Published: December 2022

AbstractPerennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few Earth System Models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Land Model to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high‐productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality, although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger Earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems.
Machine learning and hypothesis driven optimization of bull semen cryopreservation media
Authors: Tu Frankie , Bhat Maajid , Blondin Patrick , Vincent Patrick , Sharafi Mohsen , Benson James D. 
Published: December 2022

AbstractCryopreservation provides a critical tool for dairy herd genetics management. Due to widely varying inter- and within-bull post thaw fertility, recent research on cryoprotectant extender medium has not dramatically improved suboptimal post-thaw recovery in industry. This progress is stymied by the interactions between samples and the many components of extender media and is often compounded by industry irrelevant sample sizes. To address these challenges, here we demonstrate blank-slate optimization of bull sperm cryopreservation media by supervised machine learning. We considered two supervised learning models: artificial neural networks and Gaussian process regression (GPR). Eleven media components and initial concentrations were identified from publications in bull semen cryopreservation, and an initial 200 extender-post-thaw motility pairs were used to train and 32 extender-post-thaw motility pairs to test the machine learning algorithms. The median post-thaw motility after coupling differential evolution with GPR the increased from 52.6 ± 6.9% to 68.3 ± 6.0% at generations 7 and 17 respectively, with several media performing dramatically better than control media counterparts. This is the first study in which machine learning was used to determine the best combination of constituents to optimize bull sperm cryopreservation media, and provides a template for optimization in other cell types.
Effects of different applied voltages of irreversible electroporation on prostate cancer in a mouse model
Authors: Kim Hong Bae , Zeng Chu Hui , Kim Yunlim , Jeong Seung , Kim Song Hee , Kang Jeon Min , Park Yubeen , Won Dong-Sung , Kim Ji Won , Ryu Dae Sung , Lim Bumjin , Park Jung-Hoon 
Published: December 2022

AbstractAs a non-thermal ablation method, irreversible electroporation (IRE) has been widely investigated in the treatment of prostate cancer. However, no consensus has been achieved on the optimal parameters of IRE for prostate cancer. Since high voltage is known to carry risks of muscle contraction and patient discomfort, it is crucial to identify the minimum but effective and safer applied voltage to inhibit tumor growth. In this study, the effect of different applied voltages of IRE on prostate cancer was evaluated in BALB/c nude mice. Mathematical simulation and measurement of the actual ablation area revealed a larger ablation area at a higher voltage. In in vivo experiment, except for the three different voltages applied, all groups received identical electrical conditions: pulse number, 180 (20 groups × 9 pulses/group); pulse width, 100 µs; pulse interval, 2 ms; distance between the electrodes, 5 mm; and electrode exposure length, 15 mm. Whilst the tumor volume initially decreased in the 500 V (1000 V/cm) and 700 V (1400 V/cm) groups and subsequently increased, only a transient increase followed by a continuous decrease until the sacrifice was observed in the 900 V (1800 V/cm) group. This result demonstrated a lasting effect of a higher applied voltage on tumor growth inhibition. The histological, immunohistochemical, and western blot findings all confirmed IRE-induced apoptosis in the treatment groups. Taken together, 900 V seemed to be the minimum applied voltage required to reduce tumor growth, though subsequent studies are anticipated to further narrow the voltage intervals and lower the minimum voltage required for tumor inhibition.
Observation and simulation of atmospheric gravity waves exciting subsequent tsunami along the coastline of Japan after Tonga explosion event
Authors: Nishikawa Yasuhiro , Yamamoto Masa-yuki , Nakajima Kensuke , Hamama Islam , Saito Hiroaki , Kakinami Yoshihiro , Yamada Masumi , Ho Tung-Cheng 
Published: December 2022

AbstractTsunamis are commonly generated by earthquakes beneath the ocean floor, volcanic eruptions, and landslides. The tsunami following the Tonga eruption of 2022 is believed to have been excited by atmospheric pressure fluctuations generated by the explosion of the volcano. The first, fast-traveling tsunami was excited by Lamb waves; however, it has not been clarified observationally or theoretically which type of atmospheric fluctuations excited more prominent tsunami which followd. In this study, we investigate atmospheric gravity waves that possibly excited the aforementioned subsequent tsunami based on observations and atmosphere-ocean coupling simulations. The atmospheric fluctuations are classified as Lamb waves, acoustic waves, or gravity waves. The arrival time of the gravity wave and the simulation shows that the gravity wave propagated at a phase speed of 215 m/s, coinciding with the tsunami velocity in the Pacific Ocean, and suggesting that the gravity wave resonantly excited the tsunami (Proudman resonance). These observations and theoretical calculations provide an essential basis for investigations of volcano-induced meteotsunamis, including the Tonga event.
Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing
Authors: Khan Sulaiman , Khan Habib Ullah , Nazir Shah 
Published: December 2022

AbstractBig data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.
Search and rescue system-of-systems influence degree evaluation of aviation equipment based on simulation
Authors: Gao Yan , Liu Hu , Niu Fu , Tian Yongliang , Wang Jin , Cheng Wangchi 
Published: December 2022

AbstractSearch and rescue (SAR) is an important part of joint operations, and also one of the key supports for ensuring combat effectiveness. Aviation equipment is a major component of SAR action. Therefore, the SAR capability of aviation equipment has become the key to affecting the overall SAR action. This paper proposes the concept of the system of systems influence degree (SoSID) and conducts a scientific quantitative evaluation to quantitatively measure the effect of aviation equipment used in SAR. First, according to the characteristics of SAR action in threat environments, the SAR capability of aviation equipment is analyzed, and an indicator decomposition hierarchy model based on this SAR capability is proposed. Second, based on the above model, the DECIDE (destroy, execute, cost, implement, defend, evade) SoSID evaluation model is proposed. Third, a comparative test is designed and a sensitivity analysis is conducted based on the sobol power sensitivity (SPS) analysis method to obtain the index sensitivity of the SAR capability. The sensitivity is then ranked to obtain key indicators. Finally, we build a simulation test environment to obtain multiple test plans for comparison and verify the rationality of the index decomposition hierarchy model and the SoSID evaluation model as well as the effectiveness of the SPS analysis method through analysis of the simulation results.
Incidental nodal irradiation in patients with esophageal cancer undergoing (chemo)radiation with 3D-CRT or VMAT
Authors: Peschel David Paul , Düsberg Mathias , Peeken Jan C. , Kaiser Jan Christian , Borm Kai Joachim , Sommer Katharina , Combs Stephanie E. , Münch Stefan 
Published: December 2022

AbstractThe extent of elective nodal irradiation (ENI) in patients undergoing definitive chemoradiotherapy (dCRT) for esophageal squamous cell carcinoma (ESCC) remains unclear. The aim of this dosimetric study was to evaluate the extent of incidental nodal irradiation using modern radiation techniques. A planning target volume (PTV) was generated for 30 patients with node-negative esophageal carcinoma (13 cervical/upper third, 7 middle third, 10 lower third/abdomen). Thereby, no elective nodal irradiation (ENI) was intended. Both three-dimensional conformal radiotherapy (3D-CRT) and volumetric-modulated arc therapy (VMAT) treatment plans (50 Gy in 25 fractions) were calculated for all patients. Fifteen nodal stations were contoured according to the definitions of the AJCC and investigated in regard to dosimetric parameters. Compared to 3D-CRT, VMAT was associated with lower dose distribution to the organs at risk (lower Dmean, V20 and V30 for the lungs and lower Dmean and V30 for the heart). For both techniques, the median Dmean surpassed 40 Gy in 12 of 15 (80%) nodal stations. However, VMAT resulted in significantly lower Dmeans and equivalent uniform doses (EUD) compared to 3D-CRT for eight nodal stations (1L, 2L, 2R, 4L, 7, 8L, 10L, 15), while differences did not reach significance for seven nodal station (1R, 4R, 8U, 8M, 10R, 16). For dCRT of ESCC, the use of VMAT was associated with significantly lower median (incidental) doses to eight of 15 regional lymph node areas compared to 3D-CRT. However, given the small absolute differences, these differences probably do not impair (regional) tumor control rates.
Evaluation of center of mass estimation for obese using statically equivalent serial chain
Authors: Chebel Elie , Tunc Burcu 
Published: December 2022

AbstractThe complex structure of the human body makes its center of mass (CoM) estimation very challenging. The typically used estimation methods usually suffer from large estimation errors when applied to bodies with structural differences. Thus, a reliable estimation method is of utmost importance. In this paper, we present a detailed evaluation of a subject-specific CoM estimation technique named Statically Equivalent Serial Chain (SESC) by investigating its estimation ability over two different groups of subjects (Fit and Obese) in comparison to the segmental analysis method. For this study, we used an IMU-based motion capture system and a force platform to record the joint angles and corresponding center of pressure (CoP) values of twenty-five participants while performing a series of static postures. The root-mean-square errors (RMSE) of SESC’s estimation for both groups showed close and lower mean values, whereas the segmental analysis method showed significantly larger RMSE values in comparison to SESC (p < 0.05). In addition, we used the Bland–Altman analysis to evaluate the agreement between the two techniques and the ground truth CoP, which showed the accuracy, precision, and reliability of SESC over both groups. In contrast, the segmental analysis method did not present neither accurate nor precise estimations, as our analysis revealed considerable fixed and proportional biases.
The prevalence of ocular Demodex folliculorum in 2253 young males
Authors: Ye Qian , Yan Weiming , Wang Yunpeng , Chen Meizhu 
Published: December 2022

AbstractTo investigate the prevalence and influencing factors of Demodex folliculorum (DF) in the eyelashes of healthy young males. An epidemiological cross-sectional prevalence study was conducted. We conducted visual acuity, eye-related examination, eyelash microscopic examination and DF count of recruits in Fujian Province in 2019. The presence of DF was analyzed according to age, keratorefractive surgery, annual household income, educational level, long-term residence, sleep time, time of using electronic products, smoking and drinking habit. A total of 2253 healthy young males (aged 17–24 years) were studied for the presence of DF within eyelash follicles. The total prevalence of DF was 20.73% in our study. Subjects with history of keratorefractive surgery had a statistically significant increase in the prevalence of DF (P < 0.001). The prevalence of DF was higher in subjects with ocular symptoms than in those without symptoms (P < 0.05). Factors such as the educational level and so on we analyzed had no significant correlation with the prevalence of DF (all P > 0.05). According to the multivariate logistic regression analysis, the history of keratorefractive surgery was the risk factors of DF infection (P < 0.001), and the risk of infection was 1.437 times higher in the population with the history of keratorefractive surgery than in the population without. There was no correlation between ocular discomfort and DF infection (P > 0.05). The prevalence of DF in eyelash follicles in healthy young males was relatively high. The history of keratorefractive surgery was an important risk factor for the infection.
Performance evaluation and financial viability analysis of grid associated 10 MWP solar photovoltaic power plant at UP India
Authors: Singh Shailendra , Alharthi Majed , Anand Abhishek , Shukla Amritanshu , Sharma Atul , Panchal Hitesh 
Published: December 2022

AbstractThe main aim of this simulation work is to assess the financial possibility analysis of 10 MWP grid-associated solar photovoltaic (PV) power plants in seven cities i.e. Lucknow, Agra, Meerut, Gorakhpur, Kanpur, Allahabad, and Varanasi of Uttar Pradesh (UP) state of India with the RETScreen Software. The presented research work demonstrates the method of selection of profitable locations for solar PV power plants according to financial viability indicators. It is found that Allahabad city is the most profitable site with values of 16,686 MWh of electricity exported to the grid (EEG), US$20,896.30/year of electricity export revenue (EER), 9.4 years of simple payback period (SPP), 7.7 years of equity payback period (EPP), 19545.9 tCO2/year of GHG emission reduction, US$3492.82/year of the annual life cycle savings (ALCS), 1.5 benefit-cost (B-C) ratio, US$27394.59 of net present value (NPV), 16.5% internal rate of return on equity (IRR-equity), 12.3% modified internal rate of return on equity (MIRR-equity), 5.4% internal rate of return on assets (IRR-assets), and 7% modified internal rate of return on assets (MIRR-assets). The second most profitable site is found in Gorakhpur city and Varanasi city is found the least profitable site. The other two cities, Agra and Kanpur are not found suitable because of the negative values of NPV and ALCS.
Diarylation of thiazolopyrimidines by laccase and their in vitro evaluation as antitumor agents
Authors: Shahedi Mansour , Shahani Rojina , Habibi Zohreh , Yousefi Maryam , Brask Jesper , Minaei-Tehrani Arash , Samadi Fatemeh Yazdi , Mohammadi Mehdi 
Published: December 2022

AbstractA mild and efficient method was developed for the synthesis of new derivatives of thiazolo[3,2-a] pyrimidin-3(2H)-ones from available starting materials based on the oxidation of catechols to ortho-quinone by Myceliophthora thermophila laccase (Novozym 51,003) and 1,4-addition of active methylene carbon to these in situ generated intermediates in moderate to good yields (35–93%). The structure of the products was confirmed through 1H NMR, 13C NMR, HMBC, HSQC, DEPT-135, and mass spectroscopy techniques. These novel compounds were evaluated as active antitumor agents against human colorectal adenocarcinoma and liver adenocarcinoma cell lines. All compounds displayed potent inhibition activities against the HT-29 cell line with IC50 values of 9.8–35.9 µM, superior to the positive control doxorubicin, and most showed potent anticancer activities against the HepG2 cell line.
Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals
Authors: Sorkhi Maryam , Jahed-Motlagh Mohammad Reza , Minaei-Bidgoli Behrouz , Daliri Mohammad Reza 
Published: December 2022

AbstractAchieving an efficient and reliable method is essential to interpret a user’s brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.
Fabrication and characterization of polycaprolactone/cellulose acetate blended nanofiber mats containing sericin and fibroin for biomedical application
Authors: Barnthip Naris , Teeka Jantima , Kantha Puripat , Teepoo Siriwan , Damjuti Watchara 
Published: December 2022

AbstractPolycaprolactone/cellulose acetate blended nanofiber mats containing sericin and fibroin were fabricated by electrospinning process to study the effect of sericin and fibroin on the physical and structural properties, wettability, degradability, elastic modulus, cell adhesion, and cell cytotoxicity of the electrospun nanofibers. Polycaprolactone/cellulose acetate solution was prepared with different percentage ratio of sericin and fibroin to be the running solution. Nanofibers were spun at fixed solution flow rate, flying distance, and operating voltage. The diameter of the obtained nanofibers linearly increases with the increasing of the sericin ratio. The derivative structures of polycaprolactone, cellulose acetate, sericin, and fibroin of the obtained nanofibers were confirmed by FTIR analysis. All acquired nanofibers show superhydrophilicity with adequate time of degradation for L-929 cell adhesion and growth. More elasticity is gained when the sericin ratio decreases. Moreover, all fibers containing sericin/fibroin reveal more elasticity, cell adhesion, and cell growth than that with only polycaprolactone/cellulose acetate. Greater cell adhesion and growth develop when the sericin ratio is lower. All the fabricated nanofibers are low toxic to the cells. Fibers with a mixture of sericin and fibroin at 2.5:2.5 (% w/v) are the most promising and suitable for further clinical development due to their good results in each examination. The novelty found in this study is not only making more value of the sericin, silk industrial waste, and the fibroin, but also getting the preferable biomaterials, scaffold prototype, with much greater mechanical property and slower degradation, which are required and appropriate for cell attachment and proliferation of cell generation process, compared to that obtaining from polycaprolactone/cellulose acetate or sericin/fibroin nanofibers.