bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computational Study of Lactucine and its Derivatives with Apoptosis Inducing Proteins of Various Pathways
Authors: Arya Mamta , Tiwari Apoorv , Singh Dev Bukhsh , Taj Gohar 
Published: August 2021

Abstract
In the present computational study, we found that lactucin 15-oxalate a chemical component present in the cichorium intybus (chicory) has a potential apoptosis inducing effect in human leukemia cancer cell and may acts as an anticancer agent. Lactucine and its derivatives were used as ligand molecules to trace out its binding interactions with proteins involved in apoptosis and these ligands were docked with different apoptosis inducing protein such as caspases, cytochrome C, apaf-1, CDKs, etc involved in cell cycle regulation. Among lactucine derivatives, the lactucin 15-oxalate showed virtuous affinity for the apoptosis inducing protein. In addition to this, the number of the hydrogen bonding was higher with lactucin 15-oxalate as compare to other derivatives which indicates its suitability as an anticancer agent. An apoptosis inducing agent if implemented for the treatment of leukemia cancer, then it can reduce the use of multidrug dose therapy and chemotherapeutic drugs. However, different in vitro as well as clinical trials are needed for further validation of the lactucin 15-oxalate.
Digital Simulation Assessments of the Sensitivity of Quantitative MRI for Detection of an Iron Oxide Nanoparticle Brain MRI Contrast Agent
Authors: Kim Joong , Wang Wen-Tung , Knutsen Andrew , Brody David 
Published: August 2021

Abstract
There have been substantial efforts to develop targeted exogenous MRI contrast agents to assess specific brain pathologies. In parallel with other efforts, it is important to assess the sensitivity of candidate MRI methods for detection of contrast agents. Here, we propose a digital simulation approach, which includes MR relaxation (R1 and R2) mapping and image co-registration. We simulated the effects of 3 nm iron oxide nanoparticles (IONPs) as a model contrast agent. Two independent relaxation maps acquired from the brain of the same subject were co-registered. The baseline subtraction between the two relaxation maps showed good agreement, demonstrating the high reproducibility of the method. Next, the second relaxation map was digitally altered (“seeded”) to simulate additional MR relaxation values corresponding to several concentrations of 3 nm IONPs in various locations. The maps of absolute differences between the first relaxation map and the digitally altered second relaxation maps were assessed for conspicuity. Results based on living mouse and human brains scanned at 9.4 T and 3.0 T respectively both indicated reliable conspicuity for signal equivalent to 0.06 mM IONP or higher. Overall, the digital simulation approach is a useful method to improve the development of MRI contrast agents and accompanying MRI methodologies.
A Metamaterial-Inspired Microwave Sensor for Dielectric Characterization of Organic Liquids and Solid Dielectric Substrates
Authors: CAO YUNHAO , Ruan Cunjun , Chen Kanglong , Zhang Xingyun 
Published: August 2021

Abstract
A microwave metamaterial-inspired sensor based on a 13×13 arrays of Asymmetric Electric Split-Ring Resonator (AESRR) is proposed for dielectric characterization of organic liquids and solid dielectric substrates with low permittivity. The sensor, excited by a pair of patch antennas and working at around 11.575 GHz, is fabricated using printed circuit board (PCB) technology. T-shape channel was integrated to the sensor by grooving in the FR-4 substrate which improved the integration and provided the feasibility of liquids detection. Seven liquids and four dielectric substrates are measured by this sensor. The measured results show the transmission frequency shifts from 11.575 GHz to 11.150 GHz as the liquid samples permittivity changes from 1 to 7 and the transmission frequency shifts from 11.575 GHz to 8.260 GHz as the solid substrates permittivity changes from 1 to 9. The measured results have proven the improved sensitivity and the larger frequency shift ∆f on material under test (MUTs) compared with the conventional reported sensor. The relative permittivity of liquid samples and solid samples can be fitted by establishing approximate models in CST, respectively. Two transcendental equations derived from measured results are proposed to predict the relative permittivity of liquid samples and solids samples. The accuracy and reliability of measured results and predicted results are numerically verified by comparing them with literature values. The proposed sensor has many advantages, such as low-cost, high-sensitivity, high-robustness, and extensive detecting range, which provided a great potential to be implemented in a lab-on-a-chip sensor system in the future.
Machine Learning Classifier Models Can Identify Delirium in Intensive Care Units
Authors: Hu Anmin , Li Hui-Ping , Li Zhen , Zhang Zhongjun , Zhong Xiong-Xiong 
Published: August 2021

Abstract
Purpose: The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of delirium among ICU patients.Methods: We developed a set of real-world data to enable the comparison of the reliability and accuracy of delirium prediction models from the MIMIC-III database, the MIMIC-IV database and the eICU Collaborative Research Database. Significance tests, correlation analysis, and factor analysis were used to individually screen 80 potential risk factors. The predictive algorithms were run using the following models: Logistic regression, naive Bayesian, K-nearest neighbors, support vector machine, random forest, and eXtreme Gradient Boosting. Conventional E-PRE-DELIRIC and eighteen models, including all-factor (AF) models with all potential variables, characteristic variable (CV) models with principal component factors, and rapid predictive (RP) models without laboratory test results, were used to construct the risk prediction model for delirium. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC) of tenfold cross-validation. The VIMs and SHAP algorithms, feature interpretation and sample prediction interpretation algorithms of the machine learning black box model were implemented.Results: A total of 78,365 patients were enrolled in this study, 22,159 of whom (28.28%) had positive delirium records. The E-PRE-DELIRIC model (AUC, 0.77), CV models (AUC, 0.77-0.93), CV models (AUC, 0.77-0.88) and RP models (AUC, 0.75-0.87) had discriminatory value. The random forest CV model found that the top five factors accounting for the weight of delirium were length of ICU stay, verbal response score, APACHE-III score, urine volume and hemoglobin. The SHAP values in the eXtreme Gradient Boosting CV model showed that the top three features that were negatively correlated with outcomes were verbal response score, urine volume, and hemoglobin; the top three characteristics that were positively correlated with outcomes were length of ICU stay, APACHE-III score, and alanine transaminase.Conclusion: Even with a small number of variables, machine learning has a good ability to predict delirium in critically ill patients. Characteristic variables provide direction for early intervention to reduce the risk of delirium.
Oral administration of E-type prostanoid (EP) 1 receptor antagonist suppresses carcinogenesis and development of prostate cancer via upregulation of apoptosis in an animal model
Authors: Miyata Yasuyoshi , Masato Masahito , Mitsunari Kensuke , Asai Akihiro , Nakamura Yuichiro , Araki Kyohei , Mukae Yuta , Matsuda Tsuyoshi , Harada Junki , Matsuo Tomohiro , Ohba Kojiro , Sakai Hideki , Kurata Hiroki 
Published: August 2021

Abstract
Prostaglandin E2 plays important roles in carcinogenesis and malignant potential of prostate cancer (PC) by binding to its specific receptors, E-type prostanoid (EP) receptors. However, anti-carcinogenic effects of EP receptor antagonist are not clear. In this study, feed with or without EP1 receptor antagonist were given to a mouse model of prostate cancer. The mice were sacrificed at 10, 15, 30, and 52 weeks of age. Apoptosis was evaluated by immunohistochemical analysis using cleaved caspase-3. The incidence of cancer in the experimental group was significantly lower than that in the control group at 15, 30, and 52 weeks of age. The percentage of poorly differentiated PC cells was significantly lower in the experimental group than in the control group at 30 and 52 weeks of age. The survival period in the experimental group was significantly longer than that in the control group, and the percentage of apoptotic cells in the experimental group was significantly higher than that in the control group at 15, 30, and 52 weeks of age. Thus, an EP1 receptor antagonist delayed PC progression via the upregulation of apoptosis. We suggest that EP1 receptor may be a novel chemopreventive agent for the development of PC.
Sleep Disturbance and Depressive Tendency in Bed Partners of Patients With Obstructive Sleep Apnea
Authors: Chu Chien-Ming , Wu Shao-Yun , Yu Chung-Chieh , Huang Chih-Yu 
Published: August 2021

Abstract
Snoring, apnea, and arousal are commonly observed during sleep in patients with obstructive sleep apnea (OSA), and these nocturnal symptoms frequently disturb their bed partners. We aimed to evaluate the sleep disturbance and depressive tendency in the bed partners of patients with OSA. A cross-sectional, prospective study was conducted. A total of 136 patients with OSA and their bed partners were recruited. We analyzed the demographic data of both patients with OSA and bed partners and the polysomnography parameters of patients with OSA. The sleep quality of bed partners was assessed using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI), and depressive symptoms were evaluated using the Center for Epidemiologic Studies Depression Scale (CES-D). The mean apnea–hypopnea index (AHI) was 43.5/h among all participants (76.5% male). Among bed partners, the mean PSQI score was 7.8 and the mean CES-D score was 15.4. The prevalence of chronic disease was significantly higher in bed partners with sleep disturbance (PSQI > 5) and depressive tendency (CES-D ≥ 16). The AHI and snore index of patients with OSA were not associated with bed partners’ sleep disturbance and depressive tendency, which were stratified according to PSQI (> 5 and ≤ 5) and CES-D (≥ 16 and < 16), respectively. The CES-D score was positively correlated with the PSQI score in the bed partners of patients with OSA (r = 0.426, p < 0.001). Bed partners tended to have sleep disturbance, which was unrelated to the severity of AHI and snoring in patients with OSA. Poor sleep quality may cause depressive tendency and chronic disease in the bed partners of patients with OSA.
Equiprobable Symbolization Pattern Entropy for Time Series Complexity Measurement
Authors: Wang Fuyi , Zhang Leo Yu 
Published: August 2021

Abstract
In order to more effectively mine the structural features in time series, while simplifying the complexity of time series analysis, equiprobable symbolization pattern entropy (EPSPE) based on time series symbolization combined with sliding window technology is proposed in this paper. Firstly, time series are implemented symbolic procession according to the equal probability distribution of the original data, which greatly simplifies the difficulty of analyzing the signal on the premise of small loss of precision to the original signal. Then, sliding window technique is used to obtain a finite number of different symbolic patterns, and the pattern pairs are determined by calculating the conversion between the symbolic patterns. Next, the conversion frequency between symbolized patterns is counted to calculate the probability of the pattern pairs, thus estimating the complexity measurement of complex signals. The results of test using the Logistic system with different parameters show that compared with multiscale sample entropy(MSE), EPSPE can more concisely and intuitively reflect the structural characteristics of time series. Finally, EPSPE is used to investigate the natural wind field signals collected at an outdoor space in which nine high precision two-dimensional (2D) ultrasonic anemometers are deployed in line with 1m interval. The values of EPSPE show consistent increase or decrease trend with the spatial regular arrangement of the nine anemometers. While the results of MSE are irregular, and cannot accurately predict the spatial deployment relationship of nine 2D ultrasonic anemometers.
Association and Transcription Analyses Identify Candidate Susceptibility Genes for Rice Blast Resistance Breeding
Authors: Xu Yuchen , Liu Ming-hao , Liu Yanchen , Peng Shasha , Hu Pei , Wang Dan , Yan Shuangyong , Zheng Wenjin , Liu Shiming , Xiao Yinghui , Wang Guo-Liang , Kang Houxiang 
Published: August 2021

Abstract
Traditional rice blast resistance breeding largely depends on utilizing the typical resistance (R) genes. The lack of durable R genes prompts rice breeders to find new source of resistance. The susceptibility (S) genes could be the potentially useful targets for genetic engineering using the cutting-edging genome-editing technologies. However, how to identify S genes is challenging. Here, by combining genome-wide association study and transcriptional analysis, we identified the candidate genes in the rice genome whose expression levels were negatively associated with rice blast resistance. We initially identified five candidate genes by qRT-PCR and comparison of their sequences among the rice blast resistant and susceptible varieties. The obtained results showed that the polymorphisms in 3’ un-translated regions (3’-UTR) of two genes, Os02g39360 and Os08g29170, named RNG1 and RNG3, respectively, were tightly associated with their expression levels and rice blast susceptibility. Subsequently, the negatively association between the expression levels and blast resistance was confirmed using the 3K rice population. Meanwhile, the frequency of the resistance genotype of these two genes was significantly decreased during the first step of rice domestication from wild rice to landrace rice. In contrast, the frequency was increased during the second step of domestication from landrace rice to modern cultivars. Nucleotide diversity analysis found an obvious selective sweep in the surrounding loci of RNG3, means that RNG3 has been artificially selected. The results obtained in this study provide new targets for further gene functional analysis as well as genome editing-based rice blast resistance breeding.
Newborn Screening for Methylmalonic Acidemia/propionic Acidemia: Systematic Evaluation of a Two-pronged Approach Based on MS/MS and UPLC-MS/MS
Authors: Zhou Wei , Song Jinxiu , Li Huizhong , Ji Zhe , Yin Xin , Gu Maosheng 
Published: August 2021

Abstract
Background: An improved second-tier test is needed to reduce the false-positive rate of newborn screening (NBS) for inborn metabolic disorders in Xuzhou, China.Methods: We designed an expanded second-tier assay using newborn dried blood spots (DBSs). Analytical and clinical performance were evaluated in 53 newborns with methylmalonic acidemia (MMA) or propionic acidemia (PA) reported by the Xuzhou Maternity and Child Health Care Hospital NBS program. Additionally, we analyzed NBS data regarding seasonal variation of metabolites, birth weight and gestational age to improve the identification of true positive MMA/PA individuals.Results: Among the 53 MMA/PA individuals assessed, two pathogenic or likely pathogenic (P/LP) variants in an MMA/PA-associated gene were identified in 46 patients, and a pathogenic variant and a variant of unknown significance (VUS) were identified in 7 patients. No such variants were detected in MMA/PA false-positive individuals or healthy controls. Ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based analysis of the initial NBS metabolic profile correctly identified MMA/PA individuals and reduced the initial NBS false-positive rate by 98.86%. MMA/PA false-positive infants in Xuzhou, China, were most likely to be summer-born.Conclusion: We established a two-pronged approach to reduce false positives by nearly 99% and provided a novel NBS strategy. Challenges in neonate metabolic testing and DNA variant interpretation regarding season, birth weight and pregnancy status remain for this Chinese population.
Joint Radiomics Analysis on 18F-FDG-PET-MRI of Primary Tumor to Predict the Risk of Synchronous Distant Metastasis in Pancreatic Ductal Adenocarcinoma
Authors: Gao Jing , Bai Yaya , Miao Fei , Huang Xinyun , Schwaiger Markus , Rominger Axel , Li Biao , Zhu Hui , Lin xiaozhu , Shi Kuangyu 
Published: August 2021

Abstract
PurposeThis study explores the potential of joint radiomics analysis of PET-MRI of primary pancreatic ductal adenocarcinoma (PDAC) tumor in predicting the risk of synchronous distant metastasis (SDM).MethodsTwo cohorts of PDAC patients were collected including a development cohort (n=66) receiving separate 18F-FDG-PET/CT and multi-sequence MRI, and a external test cohort (n=25) receiving hybrid PET/MR. All of these patients were confirmed with SDM after imaging. Radiomics features of primary PDAC tumors were selected and models were built for PET, MRI, and PET-MRI from the development cohort. A radiomics nomogram was constructed by combining independent clinical indicators. The developed radiomics nomogram was independently evaluated on the test cohort. ResultsThe area under the curve (AUC) values of PET, MRI, and joint PET-MRI models were 0.89, 0.86, and 0.94 in the training set and 0.77, 0.67, and 0.77 in the test set. The radiomics nomogram combing the joint PET-MRI radiomics signature, age, and CA19-9 level had good calibration and high discrimination capacity with maximum AUC value (0.93). The decision curve analysis (DCA) confirmed the radiomics nomogram had clinical usefulness. The evaluation on the independent test cohort showed that the accuracy, sensitivity, specificityand AUC values of radiomics nomogram were 84.0%, 78.6%, 90.9% and 0.85.ConclusionThe robust and effective prediction of the risk of SDM for the preoperative PDAC patients confirmed the potential of the radiomics analysis on PET/MR. The radiomics information in primary tumor may provide complementary and alerting hints for cancer staging.
Mimicking skyglow at ecosystem level in a large-scale lake enclosure facility
Authors: Jechow Andreas , Schreck Günther , Kyba Christopher C. M. , Berger Stella A. , Bistarelli Lukas Thuile , Bodenlos Matthias , Gessner Mark O. , Grossart Hans-Peter , Kupprat Franziska , Nejstgaard Jens C. , Pansch Andreas , Penske Armin , Sachtleben Michael , Shatwell Tom , Singer Gabriel A. , Stephan Susanne , Walles Tim J. W. , Wollrab Sabine , Zielinska-Dabkowska Karolina M. , Hölker Franz 
Published: August 2021

Abstract
Light pollution is an environmental stressor of global extent that is growing exponentially in area and intensity. Artificial skyglow, a form of light pollution with large range, is hypothesized to have environmental impact at ecosystem level. However, testing the impact of skyglow at large scales and in a controlled fashion under in situ conditions has remained elusive so far. Here we present the first experimental setup to mimic skyglow at ecosystem level outdoors in an aquatic environment. Spatially diffuse and homogeneous surface illumination that is adjustable between 10 mlx and 10 lx, resembling rural to urban skyglow levels, was achieved with white light-emitting diodes at a large-scale lake enclosure facility. The illumination system was enabled by optical modeling with Monte-Carlo raytracing and validated by measurements. Our method can be adapted to other outdoor and indoor skyglow experiments, urgently needed to understand the impact of skyglow on ecosystems.
A comparative account of biogenic synthesis of silver nanoparticles using in-house potential probiotics and their antimicrobial activity against challenging antibiotic resistant pathogens
Authors: SHARMA SHAKSHI , SHARMA NIVEDITA , KAUSHAL NEHA 
Published: August 2021

Abstract
The present work focusses on development of a safe, inexpensive, and more accessible source for biosynthesis of silver nanoparticles. Four different in-house probiotic isolates i.e., Lactobacillus pentosus S6, Lactobacillus plantarum F22, Lactobacillus crustorum F11 and Lactobacillus paraplantarum KM1 isolated from different food sources, were used in the current study to check their ability to synthesize silver nanoparticles. All the probiotics synthesized silver nanoparticles shows maximum Surface Plasmon Resonance (SPR) at a peak of 450 nm, which confirms the formation of silver nanoparticles. Scanning Electron Microscopy (SEM) analysis identified the shape and distribution of silver nanoparticles. Transmission Electron Microscopy (TEM) revealed the average size of synthesized nanoparticles in the range of 10-50 nm, with smallest size of 5 nm for silver nanoparticles synthesized by L. crustorum F11. Further, Fourier-transform infrared spectroscopy (FTIR) detected the presence of different functional groups responsible for reduction of silver ion to form silver nanoparticles. The antimicrobial activity of these AgNP was also found to be effective against different bacterial and fungal pathogens viz. antibiotic resistant Staphylococcus aureus, Bacillus cereus, Listeria monocytogenes , Pythium aphanidermatum , Fusarium oxysporum and Phytopthora parasitica . However, L.crustorum F11 synthesized AgNP showed maximum inhibition against all the bacterial and fungal pathogens, with highest against S. aureus (20 0.61mm) and F. oxysporum (23 ). Findings from this study provide a durable and ecofriendly method for the biosynthesis of silver nanoparticles, having strong antimicrobial activity against different multidrug resistant microorganisms.
Gut microbial diversity and composition of Indian white shrimp (Penaeus indicus)
Authors: Patil Prasanna Kumar , TN Vinay , Ghate Sudeep Darbhe , Baskaran Viswanathan , Avunje Satheesha 
Published: August 2021

Abstract
The endemic Indian white shrimp (Penaeus indicus) is an economically important shrimp species, distributed in the Indo-West Pacific region. Knowledge of gut microbial composition helps in dietary interventions to ensure improved health and production. Here we analyzed V3-V4 hypervariable regions of the 16S rRNA gene to examine intestinal microbiota in the wild and farmed P. indicus. The study revealed Proteobacteria, Fusobacteria, Tenericutes, and Bacteroidetes, were the dominant phyla in both the groups; though the differences in relative abundance were evident. The dominant genera in case of the wild group were Photobacterium (29.5%) followed by Propionigenium (13.9%), Hypnocyclicus (13.7%) and Vibrio (11.1%); while Vibrio (46.5%), Catenococcus (14%), Propionigenium (10.3%) and Photobacterium (8.7%) were dominant in the farmed group. The results of the study suggest the role of environment on the relative abundance of gut bacteria. This is the first report characterizing gut microbial diversity in P. indicus, which can be used to understand the role of gut microbiota in health, nutrition, reproduction, and growth.