bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the costs of GHG emissions of multi-product agricultural systems in Vietnam
Authors: Yamamoto Aito , Huynh Thi Kim Uyen , Saito Yoko , Matsuishi Takashi Fritz 
Published: October 2022

AbstractBesides a vital sector of the economy, agriculture is a primary source of greenhouse gas (GHG) emissions. The present paper investigates the impact of carbon tax policy on Vietnamese agriculture by focusing on multi-product systems such as rice, livestock, and aquaculture, traditionally called the Vuon (Garden)–Ao (Pond)–Chuong (livestock pen) system (VAC). In it, farmers use garden, pond, and pen by-products as fertilizer and feed. We use shadow prices and Morishima substitution elasticities as greenhouse gas emissions indicators, estimated with directional output distance function. Farmers in the Mekong Delta region are found to be technologically less efficient than in other regions of Vietnam, though the shadow prices of GHG emissions are lower there too. This indicates that farmers in the Mekong Delta, generally concentrating either on livestock or aquaculture, have greater potential for reducing GHG emissions by way of improvements in technical efficiency than do those in other regions. However, Morishima elasticity estimates show that policy impacts diminish more quickly in the Mekong than elswhere. We suggest the Vietnamese government encourage Mekong Delta farmers to employ technologically more efficient methods or shift to more balanced farming to reduce the shadow price of GHG emissions, encouraging more efficient emissions reduction.
Influence of energy loss function to the Monte Carlo simulated electron backscattering coefficient
Authors: Chen Haotian , Zou Yanbo , Mao Shifeng , Khan M. S. S. , Tőkési Károly , Ding Z. J. 
Published: October 2022

AbstractWe report an improved calculation of the electron backscattering coefficients (BSCs) for beryllium, molybdenum and tungsten at electron energies of 0.1–100 keV based on an up-to-date Monte Carlo simulation method with different input of energy loss function (ELF) data. The electron inelastic cross-section is derived from the relativistic dielectric functional formalism, where the full Penn’s algorithm is applied for the extension of the ELF from the optical limit of $$q \to 0$$

q
→
0

into the $$\left( {q,\omega } \right)$$

q
,
ω

-plane. We have found that the accuracy of energy loss function may affect largely the calculated BSC. We also show that this has close relationship with the f- and ps-sum rules.
Effects of oligo-fucoidan on the immune response, inflammatory status and pulmonary function in patients with asthma: a randomized, double-blind, placebo-controlled trial
Authors: Yeh Chia-wei , Shih Chia-Ju , Liu Tu-Chen , Chiou Ya-ling 
Published: October 2022

AbstractAsthma is a common disease occurring worldwide. The clinical treatment of asthma is constantly revised and updated; however, it is associated with side effects. Our previous in vitro and ex vivo studies found that oligo-fucoidan can improve allergic immune responses and reduce airway inflammation. The purpose of this clinical trial was to investigate the effects of oligo-fucoidan on the immune status, inflammatory response, and pulmonary function of patients with asthma. Twenty asthmatic patients were randomly divided into two groups: (1) control group: receiving regular asthma treatment and supplementation with placebo; (2) fucoidan group: receiving regular asthma treatment and supplementation with oligo-fucoidan. Pulmonary function tests, the "Asthma Control Questionnaire" survey, biochemical data, inflammatory factors, and immune cell subtypes were detected. During treatment, the levels of WBC (p = 0.038) and creatinine (p = 0.012 and p = 0.008 at 12th and 24th weeks) were significantly decreased in the fucoidan group. Lung function (FEV1/FVC pr) significantly increased in the fucoidan group (p = 0.046). Regarding the proportion of immune cells, the level of IFN+ and CD4+IFN+cells in the fucoidan group was significantly increased during the treatment period (P < 0.05), while the proportions of CD3+CD4+ cells (p = 0.048) and CD3+CD8+ cells (p = 0.009) in the fucoidan group were significantly decreased during the treatment period. Regarding cytokines, the level of interleukin-8 (IL-8) was also significantly reduced in the fucoidan group during the treatment period.
RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images
Authors: Tiwari Ravi Shekhar , D Lakshmi , Das Tapan Kumar , Srinivasan Kathiravan , Chang Chuan-Yu 
Published: October 2022

Abstract
Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs’ CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust architecture that will classify the COVID positive patients from COVID negative patients with less training. We have developed a neural network based on the number of channels present in the images. The CNN architecture is developed in accordance with the number of the channels present in the dataset and are extracting the features separately from the channels present in the CT-Scan dataset. In the tower architecture, the first tower is dedicated for only the first channel present in the image; the second CNN tower is dedicated to the first and second channel feature maps, and finally the third channel takes account of all the feature maps from all three channels. We have used two datasets viz. one from Tongji Hospital, Wuhan, China and another SARS-CoV-2 dataset to train and evaluate our CNN architecture. The proposed model brought about an average accuracy of 99.4%, F1 score 0.988, and AUC 0.99.
Transcriptome analysis of mulberry (Morus alba L.) leaves to identify differentially expressed genes associated with post-harvest shelf-life elongation
Authors: Das Dipayan , Bhattacharyya Subires , Bhattacharyya Monidipa , Sashankar Puja , Ghosh Arindam , Mandal Palash 
Published: October 2022

AbstractPresent study deals with molecular expression patterns responsible for post-harvest shelf-life extension of mulberry leaves. Quantitative profiling showed retention of primary metabolite and accumulation of stress markers in NS7 and CO7 respectively. The leaf mRNA profiles was sequenced using the Illumina platform to identify DEGs. A total of 3413 DEGs were identified between the treatments. Annotation with Arabidopsis database has identified 1022 DEGs unigenes. STRING generated protein–protein interaction, identified 1013 DEGs nodes with p < 1.0e−16. KEGG classifier has identified genes and their participating biological processes. MCODE and BiNGO detected sub-networking and ontological enrichment, respectively at p ≤ 0.05. Genes associated with chloroplast architecture, photosynthesis, detoxifying ROS and RCS, and innate-immune response were significantly up-regulated, responsible for extending shelf-life in NS7. Loss of storage sucrose, enhanced activity of senescence-related hormones, accumulation of xenobiotics, and development of osmotic stress inside tissue system was the probable reason for tissue deterioration in CO7. qPCR validation of DEGs was in good agreement with RNA sequencing results, indicating the reliability of the sequencing platform. Present outcome provides a molecular insight regarding involvement of genes in self-life extension, which might help the sericulture industry to overcome their pre-existing problems related to landless farmers and larval feeding during monsoon.
A study of nitrogen dioxide (NO2) periodicity over the United Arab Emirates using wavelet analysis
Authors: Al Yammahi Aishah , Aung Zeyar 
Published: October 2022

AbstractNO2and nitric oxide (NO) are the most reactive gases in the atmosphere. The interaction of NOxmolecules with oxygen, water and other chemicals leads to the formation of acid rain. The presence of NO2in the air affects human health and forms a photochemical smog. In this study, we utilize wavelet analysis, namely, the Morlet wavelet, which is a type of continuous wavelet transform, to conduct a spectral analysis of the periodicity of nitrogen dioxide (NO2). The study is conducted using data from 14 weather stations located in diverse geographic areas of the United Arab Emirates (UAE) over a period of two years (2019 and 2020). We explain and relate the significance of human activities to the concentration level of NO2, particularly considering the effect of the COVID-19 lockdown to the periodicity of NO2. The results show that NO2concentrations in desert areas such as Liwa and Al Quaa were unaffected by the lockdown period (April–July 2020) resulting from the COVID-19 pandemic. The other stations in the urban areas of Abu Dhabi city, Al Dhafra and Al Ain, showed a reduction in NO2during the lockdown. NO2is more highly concentrated during winter seasons than during other seasons. The periodicity of NO2lasted from a few days up to 16 days in most regions. However, some stations located in the Al Dhafra region, such as Al Ruwais and the Gayathi School stations, exhibited a longer period of more than 32 days with a 0.05 significance test. In the Abu Dhabi region, NO2lasted between 64 and 128 days at the Al Mafraq station. The correlation between the NO2concentration across several ground stations was studied using wavelet coherence.
Design considerations for photovoltaic energy harvesting in wearable devices
Authors: Kim Katherine A. , Bagci F. Selin , Dorsey Kristen L. 
Published: October 2022

AbstractWearable technology is emerging as a solution for various bio-mechanic and health-metric applications. Solar photovoltaic energy is a viable supplemental power source that can reduce battery size requirements in wearables. This study outlines the considerations for a wearable sleeve device and its associated power converter system using commercially-available flexible photovoltaic panels located on the forearm. Investigation of the effects of curvature shows that while curvature of the panel around a forearm does reduce output power, the angle relative to the light source has a more pronounced effect on both output power and voltage characteristics. Among various panel arrangements on the forearm, that with five individual panels of smaller width provided the highest output power after the boost converter power stage. Testing under various static positions, the PV sleeve provided up to 94 mW outdoors, which can effectively reduce the battery size while maintaining user safety.
Real-time COVID-19 detection via graphite oxide-based field-effect transistor biosensors decorated with Pt/Pd nanoparticles
Authors: Wasfi Asma , Awwad Falah , Qamhieh Naser , Al Murshidi Badria , Palakkott Abdul Rasheed , Gelovani Juri George 
Published: October 2022

AbstractCoronavirus 2019 (COVID-19) spreads an extremely infectious disease where there is no specific treatment. COVID-19 virus had a rapid and unexpected spread rate which resulted in critical difficulties for public health and unprecedented daily life disruption. Thus, accurate, rapid, and early diagnosis of COVID-19 virus is critical to maintain public health safety. A graphite oxide-based field-effect transistor (GO-FET) was fabricated and functionalized with COVID-19 antibody for the purpose of real-time detection of COVID-19 spike protein antigen. Thermal evaporation process was used to deposit the gold electrodes on the surface of the sensor substrate. Graphite oxide channel was placed between the gold electrodes. Bimetallic nanoparticles of platinum and palladium were generated via an ultra-high vacuum (UHV) compatible system by sputtering and inert-gas condensation technique. The biosensor graphite oxide channel was immobilized with specific antibodies against the COVID-19 spike protein to achieve selectivity and specificity. This technique uses the attractive semiconductor characteristics of the graphite oxide-based materials resulting in highly specific and sensitive detection of COVID-19 spike protein. The GO-FET biosensor was decorated with bimetallic nanoparticles of platinum and palladium to investigate the improvement in the sensor sensitivity. The in-house developed biosensor limit of detection (LOD) is 1 fg/mL of COVID-19 spike antigen in phosphate-buffered saline (PBS). Moreover, magnetic labelled SARS-CoV-2 spike antibody were studied to investigate any enhancement in the sensor performance. The results indicate the successful fabrication of a promising field effect transistor biosensor for COVID-19 diagnosis.
Serum biomarkers, including nitric oxide metabolites (NOx), for prognosis of cardiovascular death and acute myocardial infarction in an ESSE-RF case–control cohort with 6.5-year follow up
Authors: Gumanova Nadezhda G. , Bogdanova Natalya L. , Metelskaya Victoria A. , Tarasov Vladimir I. , Kots Alexander Ya. , Kutsenko Vladimir A. , Kontsevaya Anna V. , Drapkina Oksana M. 
Published: October 2022

AbstractThe present case–control study aimed to assess associations of routine and experimental biomarkers with risk for cardiovascular death and acute myocardial infarction (AMI) in a cohort recruited from the multicenter study “Cardiovascular Epidemiology in Russian Federation” (ESSE-RF) to identify experimental biomarkers potentially suitable for expanded evaluation. A total of 222 subjects included cardiovascular death (N = 48) and AMI cases (N = 63) during 6.5-year follow up and matched healthy controls. Seven routine and eight experimental biomarkers were assayed to analyze associations with outcomes using logistic and Cox proportional hazard regressions. Elevated levels of cardiac troponin I (cTnI), C-reactive protein (CRP), and nitric oxide metabolites (NOx) were independently associated (P < 0.001) with higher risk of cardiovascular death (estimated hazard ratio (eHR) = 1.83–3.74). Elevated levels of NOx and cTnI were independently (P < 0.001) associated with higher risk of nonfatal AMI (eHRs = 1.78–2.67). Elevated levels of angiopoietin-like protein 3 (ANGPTL3) were independently associated (P < 0.001) with lower risk of cardiovascular death (eHRs 0.09–0.16) and higher risk of nonfatal AMI (eHR = 2.07; P = 0.01). These results indicated that subsequent expanded validation should focus on predictive impact of cTnI, NOx, CRP, and ANGPTL3 to develop nationwide recommendations for individual stratification of patients with cardiovascular risks.
The clinical and pathological features of low-grade epilepsy-associated glioneuronal tumors
Authors: Xie Mingguo , Wang Xiongfei , Qiao Jiao , Zhou Jian , Guan Yuguang , Li Tianfu , Qi Xueling , Luan Guoming 
Published: October 2022

AbstractThe aim of the study was to evaluate the clinicopathological features, as well as the surgical prognosis, of epilepsy-associated glioneuronal tumors (GNT) with CD34 expression and BRAF mutation. Clinical data of patients who underwent epilepsy surgery for GNT were retrospectively studied. Univariate and multivariate analyses were performed to evaluate the correlations of clinical and pathological factors with molecular markers of CD34 expression and BRAFV600E mutation in GNT. A total of 247 patients with GNT had immunohistochemical detection of CD34 expression (CD34 positive vs. negative: 198/49), and among them, 102 patients had immunohistochemical detection of BRAFV600E mutation (BRAF positive vs. negative: 59/43). Univariate analysis found that tumor types (P < 0.001), patient population (P = 0.015), seizure aura (P = 0.007), drug-resistant epilepsy (P = 0.036), concordance of ictal electroencephalogram (EEG) findings (P = 0.032), surgical resection extent (P = 0.045), tumor location (P = 0.007) and duration of epilepsy (P = 0.027) were related to CD34 expression, and that concordance of ictal EEG findings (P = 0.031) and age at surgery (P = 0.015) were related to BRAFV600E mutation. In addition, history of generalized tonic–clonic seizure (HR 0.12; P = 0.035), drug-resistant epilepsy (HR 0.13; P = 0.030) and concordance of interictal EEG findings (HR 8.01; P = 0.039) were associated with tumor progression-free survival (PFS). However, CD34 expression or BRAFV600E mutation in GNT was not associated with surgical outcomes of seizure control and tumor PFS. The CD34 expression or BRAFV600E mutation in GNT may partly influence the distribution of clinicopathological features of patients with epilepsy, but they may be not able to predict the surgical prognosis of seizure outcome and tumor recurrence.
A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study
Authors: Cisterna-García Alejandro , Guillén-Teruel Antonio , Caracena Marcos , Pérez Enrique , Jiménez Fernando , Francisco-Verdú Francisco J. , Reina Gabriel , González-Billalabeitia Enrique , Palma José , Sánchez-Ferrer Álvaro , Botía Juan A. 
Published: October 2022

AbstractThe development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90–93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71–73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed (https://alejandrocisterna.shinyapps.io/PROVIA).
Anthropogenic pollution gradient along a mountain river affects bacterial community composition and genera with potential pathogenic species
Authors: Lenart-Boroń Anna , Boroń Piotr , Kulik Klaudia , Prajsnar Justyna , Żelazny Mirosław , Chmiel Maria J. 
Published: October 2022

AbstractMountain regions in Poland are among the most frequently visited tourist destinations, causing a significant anthropogenic pressure put on the local rivers. In this study, based on numbers of 9 microorganisms, content of 17 antibiotics and 17 physicochemical parameters, we determined a pollution gradient in six sites along Białka, a typical mountain river in southern Poland. The E.coli/Staphylococcus ratio varied evidently between polluted and non-polluted sites, indicating that the possible utility of this parameter in assessing the anthropogenic impact on river ecosystems is worth further investigation. Then, using next generation sequencing, we assessed the changes in bacterial community structure and diversity as a response to the pollution gradient. Proteobacteria and Bacteroidetes were the most abundant phyla in the majority of samples. Actinobacteria were the most abundant in the most pristine (groundwater) sample, while Firmicutes and Verrucomicrobia were more prevalent in polluted sites. Bacterial diversity at various levels increased with water pollution. Eleven bacterial genera potentially containing pathogenic species were detected in the examined samples, among which Acinetobacter, Rhodococcus, and Mycobacterium were the most frequent. At the species level, Acinetobacter johnsonii was most prevalent potential pathogen, detected in all surface water samples, including the pristine ones. Two bacterial taxa—genus Flectobacillus and order Clostridiales showed very distinct variation in the relative abundance between the polluted and non-polluted sites, indicating their possible potential as biomarkers of anthropogenic impact on mountain river waters.
Skin lesion classification of dermoscopic images using machine learning and convolutional neural network
Authors: Shetty Bhuvaneshwari , Fernandes Roshan , Rodrigues Anisha P. , Chengoden Rajeswari , Bhattacharya Sweta , Lakshmanna Kuruva 
Published: October 2022

AbstractDetecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.
Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation
Authors: Yadav Yasharth , Subbaroyan Ajay , Martin Olivier C. , Samal Areejit 
Published: October 2022

AbstractBoolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively,bipartiteBoolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise tocomposition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding tocis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.