bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing Groundwater Vulnerability Potential using Modified DRASTIC in Ajabshir Plain, NW of Iran
Authors: Moghaddam Asghar Ashari , sangarab Sorayya Nouri , Ilkhchi Ali Kadkhodaie 
Published: November 2021

Abstract
The vulnerability of groundwater, as the primary source of water for human survival, should be assessed for the purpose of pollution management. The Ajabshir plain, one of the major agricultural areas in the northwest of Iran, is always prone to pollution. Therefore, to prevent the increase in pollution, it is necessary to determine the polluting factors and areas prone to groundwater pollution. In this study, by modifying the DRASTIC method using the land-use layer, called DRASTICL, vulnerable areas and pollution index were mapped. To ensure dealing with the uncertainty of the parameters, the DRASTICL model was optimized utilizing the Sugeno-type fuzzy inference system. The models were validated based on nitrate pollution. The correlation of DRASTICL and its optimized model with the nitrate pollution are 0.31 and 0.80, respectively. The results of this study show that integrating the DRASTIC model and fuzzy knowledge is an instrumental way for assessment of vulnerability potential.
Passive and Active Slab Waveguide Mode Analysis Using Transfer Matrix Method
Authors: Khorrami Yaser , Fathi DAVOOD , Khavasi Amin , Rumpf Raymond C. 
Published: November 2021

Abstract
We present a general approach for numerical mode analysis of the multilayer slab waveguides using the Transfer Matrix Method (TMM) instead of the Finite Difference Frequency Domain (FDFD) method. TMM consists of working through the device one layer at a time and calculating an overall transfer matrix. Using the scattering matrix technique, we develop the proposed method for multilayer structures. We find waveguide modes for both passive and active slabs upon determinant analysis of the scattering matrix of the slab. To do this, we enhance the formulation of spatial scattering matrix to reach spatiotemporal scattering matrix. Our proposed technique is more efficient and faster than other numerical methods. Simulation results show either the spatial modes of inactive and hybrid spacetime modes of active planar waveguide. Also, spacetime wave packets can be seen using plane wave injection into the time-dependent slab waveguide instead of previously reported diffraction-free wave packets which have been obtained using the multifrequency input injection into the un-patterned inactive slab waveguides.
Perirenal White Adipose Tissue Browning and Epithelial-Mesenchymal Transition, Contributes To Tumor Development in Kidney Cancer
Authors: Ferrando Matias , Bruna Flavia , Romeo Leonardo , Contador David , Moya-Morales Daiana , Santiano Flavia , Zyla Leila , Gomez Silvina , Lopez-Fontana Constanza Matilde , Calvo Juan Carlos , Carón Rubén Walter , Toneatto Judith , Pistone-Creydt Virginia 
Published: November 2021

Abstract
Tumor cells can interact with neighboring adipose tissue and adipocyte dedifferentiation appears to be an important aspect of tumorigenesis. We evaluated the size of adipocytes in human adipose explants from normal (hRAN) and kidney cancer (hRAT); changes in the expression of WAT and BAT/beige markers in hRAN and hRAT; and changes in the expression of epithelial-mesenchymal transition (EMT) cell markers; in human kidney tumor (786-O, ACHN and Caki-1) and non-tumor (HK-2) epithelial cell lines incubated with the conditioned media (CMs) of hRAN and hRAT. We observed that hRAT adipocytes showed a significantly minor size compared to hRAN adipocytes. Also, we observed that both Prdm16 and Tbx1 mRNA and the expression of UCP1, TBX1, PPARγ, PCG1α, c/EBPα LAP and c/EBPα LIP was significantly higher in hRAT than hRAN. Finally, we found an increase in vimentin and N-cadherin expression in HK-2 cell incubated for 24 h with hRAT-CMs compared to hRAN- and control-CMs. Furthermore, desmin and N-cadherin expression also increased significantly in 786-O when these cells were incubated with hRAT-CMs compared to the value observed with hRAN- and control-CMs. The results obtained, together with the results previously published by our group, allow us to conclude that perirenal white adipose tissue browning and epithelial-mesenchymal transition, contributes to tumor development in kidney cancer.
Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks
Authors: Orooji Azam , Shanbehzadeh Mostafa , Kazemi-Arpanahi Hadi , Shafiee Mohsen 
Published: November 2021

Abstract
BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. Conclusion﻿The developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.
Does More Voluntary “Green” Information Disclosure Cut Down the Cost of Equity: Heavy Pollution Industries in China
Authors: Wendai Lv , Jing Feng , Bin Li 
Published: November 2021

Abstract
Focusing on the unique background of the coexistence of mandatory and voluntary disclosure of environmental information by domestic companies in heavy pollution industries for which is lost sight of in the existing literature. The purpose of this paper is to identify, under the premise of compulsory disclosure of environmental information in the financial report and separate environmental report, whether the further voluntary environmental information disclosure in the corporate social responsibility (CSR_E) captures the discount from investors during equity financing. Employing the sample of 4390 China’s A-share listed companies in the heavy pollution industries between 2010 and 2018, we adopt Python to conduct texture analysis and image recognition, applying the fixed effect regression model to text hypothesizes, within the robust analysis, our empirical results show that the CSR disclosure, higher quality of CSR reports, greater extent of CSR_E disclosure including accurate environmental investment information as well as the amount of graphs and texts all have the positive impact on the cost reduction of equity financing. Moreover, the degree of CSR_E disclosure in reducing cost of equity is 30 times that of CSR disclosure, which indicates that voluntary disclosure of environmental information is better to get extra discount of equity financing by satisfying favor of investors instead of keep silent on the basis of compulsory disclosure of environmental information. In addition, the charts have specific positive effects that’s not available for the text, the accurate quantitative environmental information creates more values for those enterprises disclosed. This study offers guidelines for regulatory authorities to explore the coordination effect of mandatory and voluntary disclosure policies, and achieve environmental governance and sustainable development of enterprises by improving their corporate governance.
Selection of Moment Vectors in Protein Sequence Comparison Under Binary Representation
Authors: Pal Jayanta , Ghosh Soumen , Maji Bansibadan , Bhattacharya Dilip Kumar 
Published: November 2021

Abstract
Similarity/dissimilarity study of protein and genome sequences remains a challenging task and selection of techniques and descriptors to be adopted, plays an important role in computational biology. Again, genome sequence comparison is always preferred to protein sequence comparison due the presence of 20 amino acids in protein sequence compared to only 4 nucleotides in genome sequence. So it is important to consider suitable representation that is both time and space efficient and also equally applicable to protein sequences of equal and unequal lengths. In the binary form of representation, Fourier transform of a protein sequence reduces to the transformation of 20 simple binary sequences in Fourier domain, where in each such sequence, Perseval’s Identity gives a very simple computable form of power spectrum. This gives rise to readily acceptable forms of moments of different degrees. Again such moments, when properly normalized, show a monotonically descending trend with the increase in the degrees of the moments. So it is better to stick to moments of smaller degrees only. In this paper, descriptors are taken as 20 component vectors, where each component corresponds to a general second order moment of one of the 20 simple binary sequences. Then distance matrices are obtained by using Euclidean distance as the distance measure between each pair of sequence. Phylogenetic trees are obtained from the distance matrices using UPGMA algorithm. In the present paper, the datasets used for similarity/dissimilarity study are 9 ND4, 16 ND5, 9 ND6, 24 TF proteins and 12 Baculovirus proteins. It is found that the phylogenetic trees produced by the present method are at par with those produced by the earlier methods adopted by other authors and also their known biological references. Further it takes less computational time and also it is equally applicable to sequences of equal and unequal lengths.
The classification of neurodegenerative disease from acoustic speech data
Authors: Schultz Benjamin , Joukhadar Zaher , Quiroga Maria del Mar , Nattala Usha , Noffs Gustavo , Rojas Sandra , Reece Hannah , Walt Anneke van der , Vogel Adam 
Published: November 2021

Abstract
Neurodegenerative diseases often affect speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to differentiate healthy speech and speech from people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We extracted 74 spectral and temporal prosodic features from the speech recordings, which were subjected to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.
Dynamic Nomogram Predicts Overall Survival in Patients With Occult Breast Cancer: a Population-based Analysis
Authors: Yang Xiaomin , Li Kunlong , Song Zhangjun , Wang Huxia , He Sai , Ding Yanni , Ma Dongmei , He Jianjun , Zhou Can 
Published: November 2021

Abstract
Background: Due the rarity of occult breast cancer (OBC), no precise prognostic instruments were available to assess the overall survival (OS) in patients with OBC. The aim of this study is to construct a nomogram for predicting the OS probability in patients with OBC. Methods: Patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We constructed a dynamic nomogram that can predict prognosis in patients with OBC based on crucial independent factors by using univariate and multivariate Cox regression analyses. C-index and calibration plots were chosen for validation. Net reclassification index (NRI), integrated discrimination improvement (IDI) and DCA (Ddecision Curve Analysis) were used to evaluate the nomogram’s clinical pragmatism. Results: Totally, 693 patients with OBC were included in this study. The nomogram integrated six independent prognostic factors through multivariate Cox regression analysis, such as surgical method, radiotherapy status, chemotherapy status, ER status, AJCC-stage and age. The prediction model exhibited robustness with the C-index 0.75 (95%CI: 0.72-0.77) in training cohort and 0.79 (95%CI: 0.76-0.82) in validation group. Moreover, the calibration curves presented favorably. The NRI values of 0.61 (95%CI: 0.28-0.99) for 5-year,0.53 (95%CI: 0.23-0.77) for 8-year OS prediction in the training cohort,0.75 (95%CI: 0.36-1.23) for 5-year and 0.6 (95%CI: 0.15-1.2) for 8-year OS prediction in the validation cohort,and the IDI values of 0.1 (95%CI: 0.04-0.17) for 5-year and 0.11 (95%CI: 0.03-0.19) for 8-year OS prediction in the training cohort, 0.21 (95%CI: 0.09-0.3) for 5-year and 0.22 (95%CI: 0.08-0.32) for 8-year OS prediction in the validation cohort,, indicated that the established nomogram performed significantly better than the AJCC stage system alone. Furthermore, DCA showed that the nomogram in our study was clinically useful and had better discriminative ability than the AJCC stage system. Conclusions: A nomogram was developed and validated to accurately predict the individualized probability OS for patients with occult breast cancer (OBC) and is expected to offer guidance for strategic decision.
Hydrochemistry Characteristics of Groundwater with the Influence of Spatial Variability and Water Flow in Hetao Irrigation District, China
Authors: Yuan Hongying , Yang Shuqing , Wang Bo 
Published: November 2021

Abstract
Groundwater is an important resource of water in arid and semi-arid agricultural regions. This study considered the spatial differentiation of geographical features and the concentration of groundwater flow. The upstream of the Hetao Irrigation District Shenwu Irrigation Area (SWIA) and the downstream Wulate Irrigation Area (WLTIA) were selected as the study area, and a total of 85 groundwater samples (42 from SW and 43 from WLTIA) were collected. The aims of the study were to analyze the chemical composition and main control mechanisms of groundwater, and to evaluate the suitability of groundwater irrigation in the study area from the perspective of salt and alkali damage. Geological and environmental factors increase the spatial variability of groundwater chemical characteristics in the Hetao Irrigation District. In addition the groundwater of the study area is weakly alkaline, with the flow of groundwater; the solute content of downstream (WLTIA) is higher than that of upstream (SWIA); SWIA is mainly fresh water (47.62%); and WLTIA is mainly brackish water (65.12%). The main water chemistry types are Cl-Na type, Cl·SO-Ca· Mg type, Na+ and Cl− have obvious advantages in WLTIA, and they are the main contribution indicators of groundwater TDS in the study area. Rock weathering, ions exchange and evaporate crystallization are the main controlling factors for groundwater in the Hetao Irrigation District. Na+ mainly originates from the dissolution of evaporate salt rock and silicate rock, and Ca2+ from the dissolution of gypsum and carbonate. The order of contribution of different rocks is evaporation rock > silicate rock > carbonate rock, and the contribution rates of human activities and atmospheric input are small. The groundwater quality of the upstream SW is better than that of the downstream WLTIA. However, due to the high chemical ion concentration of the groundwater, most of the groundwater cannot be directly used for irrigation, which may cause salt and alkali damage. Therefore, when using groundwater irrigation, either drip irrigation or irrigation water aeration pretreatment can be used to avoid damages such as reduced soil permeability and compaction.
m6 A Regulator-Mediated Methylation Modification Patterns and Tumor Microenvironment Infiltration Characterization in Early Cervical Cancer
Authors: Sun Dan , Zhao Xingping , Zhang Aiqian , Zou Lingxiao , Huang Huan , Fan Jiangtao , Xu Dabao 
Published: November 2021

Abstract
Background
Cervical cancer (CC) is the malignancy of female and almost cases of cervical cancer were caused by high-risk human papillomavirus (HPV) infection. Understanding the pathogenesis and characteristics of the postinfection microenvironment (PIM) in early-stage cervical cancer is needed. The mechanism of N6-methyladenosine (m6A) in the regulation of immune microenvironment in cervical cancer is unclear.
Methods
Messenger RNA (mRNA) expression profiles and clinical information of cervical cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset GSE44001. We comprehensively evaluated the m6A modification patterns in early cervical cancer and systematically correlated these modification patterns with tumor microenvironment (TME) cell-infiltrating characteristics. Analysis of tumor mutational signatures and biological enrichment analysis were also conducted.
Results
LRPPRC had the highest mutation frequency. Writers METTL14 and ZC3H13, as well as reader YTHDF3, were prognostic risk-related genes. DEGs were significantly enriched in the C-type lectin receptor signaling pathway. The m6A cluster A showed a higher level of infiltration immunocytes, and the activity of most immune cells increased. The low-m6Sig score group was poor in prognosis compared with the high-m6Sig score group. Further, we found that the 23 immunocytes, excluding plasmacytoid dendritic cells, negatively correlated with the m6Sig score.
Conclusions
Dysregulation of m6A lays a critical foundation for understanding the regulation of early CC immunity. What’s more, evaluating the m6A modification pattern of early CC contributes to enhancing our knowledge of the characteristics of PIM and provides an important insight into the efficacy of HPV treatment.
Working capacity of deep networks with topology attacks
Authors: Zhang Longjie , Chen Yong , Ali Ikram 
Published: November 2021

Abstract
Deep learning plays an important role in the development of artificial intelligence (AI) technology. The security of deep networks has become the crucial thing to be considered. When the deep learning algorithms are implemented in the hardware platform, the interference for topology structure will appear because of cyber-attacks. We analyze the working capacity of acyclic deep networks under the topology attacks and injection attacks. Considering the topology structure of the deep network, the maximum working capacity is studied under the topology attacks and injection attacks. Furthermore, the robustness of the random networks is researched and the structural robustness index (SRI) is proposed to measure the toleration for the topology attacks. This work supplies some suggestions for building a robust deep network and improving the endogenous safety and security (ESS) of the deep networks.
New Chronostratigraphic Constraints on the Lower Jurassic Pliensbachian–Toarcian Boundary at Chacay Melehue (Neuquén Basin, Argentina)
Authors: Al-Suwaidi Aisha H. , Ruhl Micha , Jenkyns Hugh C. , Damborenea Susana E. , Manceñido Miguel O. , Condon Daniel J. , Angelozzi Gladys N. , Kamo Sandra L. , Storm Marisa , Riccardi Alberto C. , Hesselbo Stephen 
Published: November 2021

Abstract
The Pliensbachian–Toarcian boundary interval is characterized by a ~3‰ negative carbon-isotope excursion (CIE) in organic and inorganic marine and terrestrial archives from sections in Europe, such as Peniche (Portugal) and Hawsker Bottoms, Yorkshire (UK). A new high-resolution organic-carbon isotope record, illustrating the same chemostratigraphic feature, is presented from the Southern Hemisphere Arroyo Chacay Melehue section, Chos Malal, Argentina, corroborating the global significance of this disturbance to the carbon cycle. The negative carbon-isotope excursion, mercury and organic-matter enrichment is accompanied by high-resolution ammonite and nannofossil biostratigraphy together with U-Pb CA-ID-TIMS geochronology derived from intercalated volcanic ash beds. A new age of ~183.71 ± 0.40/-0.51 Ma for the Pliensbachian–Toarcian boundary, and 182.77 +0.11/-0.21 for the tenuicostatum–serpentinum zonal boundary, is assigned based on high-precision U-Pb zircon geochronology and a Bayesian Markov chain Monte Carlo (MCMC) stratigraphic age model.
Tracking SARS-COV-2 Variants Using Nanopore Sequencing in Ukraine in Summer 2021
Authors: Yakovleva Anna , Kovalenko Ganna , Redlinger Matthew , Liulchuk Mariia G. , Bortz Eric , Zadorozhna Viktoria I. , Scherbinska Alla M. , Wertheim Joel O. , Goodfellow Ian , Meredith Luke , Vasylyeva Tetyana I. 
Published: November 2021

Abstract
Since spring 2020, Ukraine has experienced at least two COVID-19 waves and has just entered a third wave in autumn 2021. The use of real-time genomic epidemiology has enabled the tracking of SARS-CoV-2 circulation patterns worldwide, thus informing evidence-based public health decision making, including implementation of travel restrictions and vaccine rollout strategies. However, insufficient capacity for local genetic sequencing in Ukraine and other Lower and Middle-Income countries limit opportunities for similar analyses. Herein, we report local sequencing of 24 SARS-CoV-2 genomes from patient samples collected in Kyiv in July 2021 using Oxford Nanopore MinION technology. Together with other published Ukrainian SARS-COV-2 genomes sequenced mostly abroad, our data suggest that the second wave of the epidemic in Ukraine (February-April 2021) was dominated by the Alpha variant of concern (VOC), while the beginning of the third wave has been dominated by the Delta VOC. Furthermore, our phylogeographic analysis revealed that the Delta variant was introduced into Ukraine in summer 2021 from multiple locations worldwide, with most introductions coming from Central and Eastern European countries. This study highlights the need to urgently integrate affordable and easily-scaled pathogen sequencing technologies in locations with less developed genomic infrastructure, in order to support local public health decision making.