bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temporal changes in metal concentrations in a polluted urban basin: the contribution of multivariate techniques to the evaluation of their recovery
Authors: Tripodi Mariel Alejandra , Cueto Gerardo Ruben , Muschetto Emiliano , Hancke Diego , Suárez Olga Virginia 
Published: December 2021

Abstract
In urban rivers in many fast-growing cities, heavy metals pollution is one of the major quality issues. Quantifying its recovery success seems to be a difficult but necessary challenge, in part, because a complex database is generally needed to assess water quality. The overall goal of this work was to evaluate temporal changes in metal concentrations in a polluted basin applying the AEM method and to determine the physicochemical and meteorological parameters associated with these changes using the RDA. We analyzed temporal changes of 15 sites located in the Matanza Riachuelo River Basin, one of the most polluted basins in the word. For this, we collected data of metal concentrations in surface water, physicochemical parameters of water and meteorological factors of eight years (2008 to 2015) in each site. The results of this work allowed make evident temporal patterns (at different scales) in metal concentrations and several factors associated with these patterns. Also, we found that the effects of physicochemical and meteorological factors in metal concentrations were dependent of each site, possibly related to the presence of different sources of pollution or characteristics of the river. Our study showed that the combination of AEM and RDA multivariate techniques is a useful tool for both detecting temporal trends in the concentrations of environmental pollutants, which are not easily detectable in highly degraded environments, and for evaluating factors associated with these changes. These techniques could be applied to different scenarios (aquatic and terrestrial) affected by the continuous advance of human activities.
Systematically Evaluating DOTATATE and FDG as PET Immuno-Imaging Tracers of Cardiovascular Inflammation
Authors: Toner Yohana C. , Ghotbi Adam A. , Naidu Sonum , Sakurai Ken , Leent Mandy M.T. , Jordan Stefan , Ordikhani Farideh , Amadori Letizia , Sofias Alexandros Marios , Fisher Elizabeth L. , Maier Alexander , Sullivan Nathaniel , Munitz Jazz , Senders Max L. , Mason Christian , Reiner Thomas , Soultanidis Georgios , Tarkin Jason M. , Rudd James H.F. , Giannarelli Chiara , Ochando Jordi , Pérez-Medina Carlos , Kjaer Andreas , Mulder Willem J.M. , Fayad Zahi A. , Calcagno Claudia 
Published: December 2021

Abstract
The somatostatin receptor 2-binding PET tracer DOTATATE is emerging as an alternative to 18F-FDG to assess cardiovascular inflammation. The strengths and weaknesses of each tracer and their different specificity for inflammatory cells still need to be fully elucidated. In this manuscript, we employed mouse and rabbit animal models of inflammation. In mice, 64Cu-DOTATATE’s pharmacokinetics and timed biodistribution were determined in control (C57BL/6) and atherosclerotic (Apoe−/−) mice by ex vivo gamma counting. In vivo PET/CT, combined with ex vivo flow cytometry and gamma counting, was used to evaluate the quantification of cardiovascular inflammation by 64Cu-DOTATATE and 18F-FDG and the tracers’ cellular specificity in control versus infarcted and atherosclerotic mice. In a translational PET/MRI rabbit study, we then compared DOTATATE labeled with short-lived radioisotope 68Ga and 18F-FDG for the assessment of aortic inflammation, combined with ex vivo radiometric assays and near-infrared imaging of macrophage burden. In infarcted mice, in vivo 64Cu-DOTATATE PET showed higher differential uptake than 18F-FDG between infarcted and remote myocardium (p=0.0286), and with respect to controls (p=0.0286; n=4-6). In atherosclerotic mice, 64Cu-DOTATATE PET aortic signal, but not 18F-FDG, was higher compared to controls (p=0.0286; n=4). In both models, 64Cu-DOTATATE demonstrated preferential accumulation in macrophages with respect to other myeloid cells, while 18F-FDG uptake was less cell-specific. The translational rabbit PET/MRI study showed significantly higher aortic accumulation of both 68Ga-DOTATATE and 18F-FDG in atherosclerotic compared to control animals (p=0.0002 and p=0.0159, respectively; n=10-32). In conclusion, we introduce a workflow integrating in vivo PET and ex vivo immunological and radioactivity counting assays to characterize DOTATATE and 18F-FDG as inflammation tracers in small animal models of cardiovascular disease. Our results support the use of DOTATATE to assess cardiovascular inflammation, as alternative and complement to 18F-FDG. In addition, our study establishes a comprehensive and robust framework for the thorough assessment and comparison of novel and validated PET immuno-tracers in the cardiovascular arena.
FK506 and Lactobacillus Acidophilus Ameliorate Acute Graft-Versus-Host Disease by Modulating the T Helper 17/Regulatory T-Cell Balance
Authors: Baek Jin-ah , Park Min-Jung , Kim Se-Young , Jhun JooYeon , Woo Jin Seok , Choi Jeong Won , Na Hyun Sik , Lee Soon Kyu , Choi Jong Young , Cho Mi-La 
Published: December 2021

Abstract
Background
Graft-versus-host disease (GvHD) is critical complication after allogeneic hematopoietic stem cell transplantation (HSCT). The immunosuppressants given to patients undergoing allogeneic HSCT disturb the microbiome and the host immune system, potentially leading to dysbiosis and inflammation, and may affect immune function and bone marrow transplantation. The intestinal microbiome is a target for the development of novel therapies for GvHD. Lactobacillus species are in widely used supplements to induce production of antimicrobial and anti-inflammatory factors.
Methods
We determined the effect of the combination of Lactobacillus acidophilus and FK506 on GvHD following major histocompatibility complex-mismatched bone marrow transplantation.
Results
The combination treatment suppressed IFN-γ and IL-17-producing T cell differentiation, but increased Foxp3+Treg differentiation and IL-10 production. Also, the combination treatment and combination treated-induced Treg cells modulated the proliferation of murine alloreactive T cells in vitro. Additionally, the combination treatment upregulated Treg-related genes—Nt5e, Foxp3, Ikzf2, Nrp1 and Itgb8—in murine CD4+-T cells. The combination treatment also alleviated GvHD clinically and histopathologically by controlling the effector T cell and Treg balance in vivo. Moreover, the combination treatment decreased Th17 differentiation significantly and significantly upregulated Foxp3 and IL-10 expression in peripheral blood mononuclear cells from healthy controls and liver transplantation (LT) patients.
Conclusions
Therefore, the combination of L. acidophilus and FK506 is effective and safe for patients undergoing allogeneic hematopoietic stem cell transplantation.
Prognostic Factors for Cancer Therapeutics-Related Cardiac Dysfunction in Breast Cancer Patients Treated with Current Anthracycline Regimens
Authors: Egashira Koichi , Sueta Daisuke , Tomiguchi Mai , Hidaka Kaori , Goto-Yamaguchi Lisa , Sueta Aiko , Komorita Takashi , Takae Masafumi , Oike Fumi , Fujisue Koichiro , Usuku Hiroki , Yamamoto Eiichiro , Matsushita Kenichi , Yamamoto Yutaka , Tsujita Kenichi 
Published: December 2021

Abstract
Background
Anthracycline therapies cause myocardial damage and the onset of heart failure, depending on their doses. We investigated prognostic factors for cancer therapeutics-related cardiac dysfunction (CTRCD) in patients receiving modern anthracycline therapies.
Methods
Of 472 breast cancer patients with complete data treated with anthracycline, 8 were diagnosed with CTRCD.
Results
Multivariate regression analyses revealed that the anthracycline cumulative dose, concomitant use of molecular targeted drugs and a prechemotherapy left ventricular ejection fraction < 50% were independent and significant predictors of the onset of CTRCD.
Conclusions
Even in the modern era, the anthracycline cumulative dose is an independent risk factor for the onset of CTRCD.
Texture Analysis of The Microstructure of Internal Curing Concrete Based On Image Recognition Technology
Authors: Guo Lixia , Wang Weikai , Zhong Ling , Guo Lei , Zhang Fangfang , Guo Yuhang 
Published: December 2021

Abstract
Mechanical properties of internal curing concrete are greatly affected by its physical properties such as water content, cementing material content, porosity, and saturation. At the micro level, such impact is finally reflected in the surface texture of its materials. In this study, the image recognition technology was used to find that the internal curing concrete samples have significant micromorphology and texture features. A texture parameter–strength model was established based on the relationship between Tamura texture parameters, gray level co-occurrence matrix (GLCM) texture parameters, and the mechanical strength. Due to the characteristics of materials and the sensitivity of parameters, not all Tamura and GLCM texture parameters can effectively characterize the texture features of internal curing concrete materials. In terms of the Tamura texture, coarseness, regularity, and directionality are effective parameters to predict the compressive strength of the internal curing concrete. In terms of the GLCM texture, energy, correlation, entropy, and contrast are effective parameters to predict the compressive strength of the internal curing concrete. Correlations between each texture parameter and compressive strength follow different laws.
Development of an Algorithm To Differentiate Uterine Sarcoma From Fibroid Using MRI and LDH Levels
Authors: Suzuki Ayako , Kido Aki , Matsuki Mitsuru , Kotani Yasushi , Murakami Kosuke , Yamanishi Yukio , Numoto Isao , Nakai Hidekatsu , Otani Tomoyuki , Konishi Ikuo , Mandai Masaki , Matsumura Noriomi 
Published: December 2021

Abstract
This study aimed to establish an evaluation method for detecting uterine sarcoma with 100% sensitivity using MRI and serum LDH levels. One evaluator reviewed the MRI images and LDH values of a total of 1801 cases, including 36 cases of uterine sarcoma and 1765 cases of uterine fibroids. The reproducibility of the algorithm was also examined using a test set of 61 cases, including 14 cases of uterine sarcoma, by four evaluators with different imaging experience and abilities. From the MRI images and LDH values of 1801 cases of uterine sarcoma and uterine fibroid, we found that all sarcomas were included in the group with high T2WI and either high T1WI, unclear margin, or high LDH value. In addition, when cases with DWI were examined, all sarcomas had high DWI. Among the 36 sarcoma cases, the group with positive findings in T2WI, T1WI, margin, and serum LDH levels all had a poor prognosis (p = 0.015). The reproducibility of the algorithm was examined by four evaluators, and the sensitivity of sarcoma detection ranged from 71–93%. We established an algorithm that is not uterine sarcoma if tumors in the myometrium with low T2WI and DWI.
Multi-environment trials data analysis: An efficient biplot analysis approach
Authors: Woldemeskel Tarekegn Argaw , Fenta Brehanu Amsalu , Mekonnen Girum Azmach , Endalamaw Habtemariam Zegeye , Alemu Assefa Funga 
Published: December 2021

Abstract
The analysis of multi-environment trials (MET) data has a long history in plant breeding and agricultural research, with the earliest approaches being based on ANOVA methods. ANOVA-based biplot analysis has been used for a long time in analyzing MET data, and advances have been made employing different modeling approaches. This paper presents MET data analysis using mixed model approaches, and compares three methods of biplot analysis, namely genotype main effects plus genotype by environment interaction (GGE) analysis, factor analytic multiplicative mixed (FAMM) model analysis, and combined model analysis. Ten grain yield datasets from the national variety trial series conducted by the Ethiopian institute of agricultural research were used for this study. Our results revealed that spatial and FA model provide a significant improvement in analyzing MET data. This was demonstrated with evidence of heritability measure. We demonstrated that biplot analysis based on the approached of combined model analysis provides a substantial increase in the total percentage of genotype by environment (G×E) variance explained by the first two multiplicative components for both types of balanced and unbalanced datasets. Thus, by estimating the G×E mean values with the best linear unbiased predictions using spatial+FA (FAMM model analysis), and thereby conducting biplot analysis based on the combined model analysis, plant breeding and trial evaluation programs can have a more robust platform for evaluation of crop cultivars with greater confidence in discriminating superior cultivars across a range of environments.
Enhanced Local Patterns Using Deep Learning Techniques for ECG Based Identity Recognition System
Authors: Mostefai Lotfi , Mohamed Benouis , Mouloud Denai , Merzoug Bouhamdi 
Published: December 2021

Abstract
Electrocardiogram (ECG) signals have distinct features of the electrical activity of the heart which are unique among individuals and have recently emerged as a potential biometric tool for human identification. The paper attempts to address the problem of ECG identification based on non-fiducial approach using unsupervised classifier and a Deep Learning approaches. This work investigates the ability of local binary pattern to extract the significant pattern/feature that describes the heartbeat activity for each person’s ECG and the use of staked autoencoders and deep belief network to further enhance the extracted features and classify them based on their heartbeat activity. The proposed approach is validated using experimental datasets from two publicly available databases MIT-BIH Normal Sinus Rhythm and ECG-ID and the results demonstrate the effectiveness of this approach for ECG-based human authentication.
Carbene and Photocatalyst-Catalyzed Decarboxylative Radical Coupling of Carboxylic Acids and Acyl Imidazoles to Form Ketones
Authors: Ren Shi-Chao , Yang Xing , Mondal Bivas , Mou Chengli , Tian Weiyi , Jin Zhichao , Chi Yonggui Robin 
Published: December 2021

Abstract
The carbene and photocatalyst co-catalyzed radical coupling of acyl electrophile and a radical precursor is emerging as attractive method for ketone synthesis. However, previous reports mainly limited to prefunctionalized radical precursors and two-component coupling. Herein, an N-heterocyclic carbene and photocatalyst catalyzed decarboxylative radical coupling of carboxylic acids and acyl imidazoles is disclosed, in which the carboxylic acids were directly used as radical precursors. The acyl imidazoles could also be generated in situ by reaction of a carboxylic acid with CDI thus furnishing a formally decarboxylative coupling of two carboxylic acids. In addition, the reaction was successfully extended to three-component coupling by using alkene as a third coupling partner via a radical relay process. The mild conditions, operational simplicity, and use of carboxylic acids as the reacting partners make our method a powerful strategy for construction of complex ketones from readily available starting materials, and late-stage modification of natural products and medicines.
New Mixed Solutions Generated By Velocity Resonance In The (2+1)-Dimensional Sawada-Kotera Equation
Authors: Qi Zequn , Chen Qingqing , Wang Miaomiao , Li Biao 
Published: December 2021

Abstract
Based on the mixed solutions of the (2+1)-dimensional Sawada-Kotera equation, the collisions among lump waves, line waves, and breather waves are studied in this paper. By introducing new constraints, the lump wave does not collide with other waves forever. Under the condition of velocity resonance, the soliton molecules consisting of a lump wave, a line wave and any number of breather waves are derived for the first time. In particular, the interaction of a line wave and a breather wave will generate two breathers under certain conditions, which is very interesting. Additionally, the method can also be extended to other (2+1)-dimensional integrable equations.
Improving the understanding of the influencing factors on sea level on various time scales based on wavelet coherence and partial wavelet coherence
Authors: Song Chao , Chen Xiaohong , Xia Wenjun 
Published: December 2021

Abstract
The relationship between sea level change and a single climate indicator has been widely discussed. Despite this, few studies focused on the relationship between monthly mean sea level (MMSL) and several key impact factors, including CO2 concentration, sea ice area, and sunspots, on various time scales. In addition, research on the independent relationship between climate factors and sea level on various time scales is lacking, especially when the dependence of climate factors on Niño 3.4 is excluded. Based on this, we use wavelet coherence (WC) and partial wavelet coherence (PWC) to establish a relationship between MMSL and its influencing factors. The WC results show that the influence of climate indices on MMSL has strong regional characteristics. Sunspots affect MMSL on a scale of more than 64 months. The influence of the sea ice area on MMSL in the northern hemisphere is opposite to that in the southern hemisphere. The PWC results show that after removing the influence of Niño 3.4, the significant coherent regions of the Pacific Decadal Oscillation (PDO), Dipole Mode Index (DMI), Atlantic Multidecadal Oscillation (AMO), and Southern Oscillation Index (SOI) decrease to varying degrees on different time scales in different regions, demonstrating the influence of Niño 3.4. Our work emphasizes the independent relationship between MMSL and its influencing factors on various time scales and the use of PWC and WC to describe this relationship. The study has important reference significance for selecting the best predictors of sea level change or climate systems.
Classifying the Unknown: Identification of Insects by Deep Zero-shot Bayesian Learning
Authors: Badirli Sarkhan , Picard Christine J. , Mohler George , Akata Zeynep , Dundar Murat 
Published: December 2021

Abstract
Insects represent a large majority of biodiversity on Earth, yet so few species are described. Describing new species typicallyrequires specific taxonomic expertise to identify morphological characters that distinguish it from other known species andDNA-based methods have aided in providing additional evidence of separate species. Machine learning (ML) provides apowerful method in identifying new species given its analytical processing is more sensitive to subtle physical differencesin images humans may not process. Existing ML algorithms are limited by image repositories that only contain describedspecies, leaving out the possibility of identifying new species. We develop a Bayesian deep learning method for zero-shotclassification of species. The proposed approach forms a Bayesian hierarchy of species around corresponding genera anduses deep embeddings of images and DNA barcodes to identify insects to the lowest taxonomic level possible. To demonstratethis proof of concept, we use a database of 32,848 insect images from 1,040 described species split into training and test datawherein the test data includes 243 species not present in the training data. Our results demonstrate that using DNA sequencesand images together, known insects can be classified with 96.66% accuracy while unknown (to the database) insects have anaccuracy of 81.39% in identifying the correct genus. The proposed deep zero-shot Bayesian model demonstrates a powerfulnew approach that can be used for the gargantuan task of identifying new insect species.
The Effect of the Classical TSPO Ligand PK 11195 On in Vitro Cobalt Chloride Model of Hypoxia in Non-Neuronal and Brain Cell Lines
Authors: Amara Rula , Zeineh Nidal , Monga Sheelu , Weisman Abraham , Gavish Moshe 
Published: December 2021

Abstract
The mitochondrial translocator protein (TSPO) is a modulator of the apoptotic pathway involving reactive oxygen species (ROS) generation, mitochondrial membrane potential (Δψm) collapse, activation of caspases and eventually initiation of the apoptotic process. In this in vitro study, H1299 lung cells and BV-2 microglial cells were exposed to the hypoxic effect of CoCl2 with or without PK 11195. Exposing the H1299 cells to 0.5 mM CoCl2 for 24 hours resulted in decreases in cell viability (63%, p<0.05), elevation of cardiolipin peroxidation levels (38%, p<0.05), mitochondrial membrane potential depolarization (13%, p<0.001), and apoptotic cell death (117%, p<0.05). Pretreatment with PK 11195 (25 µM) exhibited significant protective capacity on CoCl2-induced alterations in the mentioned processes. Exposure of BV-2 cells to increasing concentrations of CoCl2 (0.3, 0.5, 0.7 mM) for 4 hours resulted in alterations in the same cellular processes. These alterations were obtained in a dose-dependent manner, except the changes in caspases 3 and 9. The novel ligands as well as PK 1195 attenuated the in vitro hypoxic effects of CoCl2.It appears that the TSPO ligand PK 11195 can prevent CoCl2-induced cellular damage in both non-neuronal and brain cell lines, and they may offer new therapeutic options in hypoxia-related lung and brain diseases which fail to respond to conventional therapies.
Increased HIF-1α Expression in T Cells and Associated with Enhanced Th17 Pathway in Systemic Lupus Erythematosus
Authors: Wu Chien-Sheng , Liao Hsiu-Jung , Chyuan I-Tsu , Wang Szu-Chieh , Lee Hua-Yi , Hsu Ping-Ning 
Published: December 2021

Abstract
Recent emerging evidences indicate that dysfunction of metabolic remodeling underlies aberrant T cell immune responses in systemic lupus erythematosus (SLE). However, how these aberrant immune activation and metabolic dysfunction interact in lupus patients are not fully understood. This study was undertaken to investigate the expression of HIF-1α, a regulator of metabolic reprogramming, in T cells from SLE. Our results demonstrated that HIF-1α expression is increased in CD4 T cells from SLE patients both in intracellular staining and quantitative real-time PCR analysis. In addition, there is enhanced HIF-1α expression in Th17-skewing murine T cells, and lentivirus-mediated HIF-1α overexpression promotes Th17 differentiation. Moreover, HIF-1α gene expression is positively correlated with the expression of glycolysis- and IL-17-associated genes in SLE patients. These results indicate that HIF-1α expression is increased in T cells from SLE patients, and is associated with enhanced Th17 pathway, implicating HIF-1α contributes to the activation of Th17 cells in SLE, and represents a potential novel therapeutic target.