bluuebunny/crossref_metadata_2025_split huggingface dataset:
Natural floating Fe films: microbial interactions and function within Fe cycling bacteria in wetlands
Authors: Dong Leheng , Chen Manjia , Liu Chengshuai , Lv Yahui , Wang Xugang , Lei Qinkai , Fang Yujuan , Tong Hui 
Published: August 2022

Abstract
Floating iron (Fe) films are widely found in numerous environments that can form oxic-anoxic boundaries under circumneutral conditions, including wetlands, estuaries, and small lakes. These films play a crucial role in the redox transformations and bioavailability of nutrients, trace metals, and heavy metals. Current studies mainly focus on chemical oxidation during Fe film formation and the characterization of these films. The functional microorganisms and associated microbial processes involved in Fe film formation have yet to be investigated in detail. Here, we investigated the microbial communities and involved microbial processes of floating Fe films in lake wetlands. Ferrihydrite was the dominant Fe(III) phase in films, accompanied by moderate levels of carbon and silicon. The Fe species and microbial analysis indicates that Fe films contain mixed-valent Fe and can form biotically. The analysis of microbial community of these films showed that the dominant genera were Fe-oxidizing and reducing bacteria and methanotrophs, including Leptothrix, Ferriphasclus, Gallionella, Geobacter, and Methylococcales. Leptothrix, Ferriphasclus, and Gallionella, as classical Fe(II)-oxidizing bacteria (FeOB), can oxidize Fe(II) with limited oxygen and form special structures that are closely associated with Fe film formation and are consistent with Fe film morphology. Geobacter can provide a source of Fe(II) for FeOB growth, and Methylococcales can perform methane oxidation to provide energy for Fe cycling. The diversity of floating film community suggest Fe(II) oxidation can trigger carbon fixation, while Fe(III) reduction accelerates Fe and carbon cycling through anaerobic respiration and lithoautotrophic chemosynthesis. These results highlight the contribution of these multiple microbial processes to floating film formation in wetlands. However, further studies are required to fully elucidate the interaction of functional microorganisms involved in floating film formation and their biogeochemical role in wetlands.
Abnormal regional homogeneity (ReHo) in Congenital Amusia: a resting-state functional MRI study
Authors: Yang Ru , Hou Feng , Sun Jun-Jie , Liao Xuan , Jin Zhi-Shuai , Wu Daxing , Liu Jun 
Published: July 2022

Abstract
Congenital amusia (CA) is a disorder of pitch perception, a core function of the human auditory system. A total of seventeen amusics, fitting stringent criteria in Montreal Battery of Evaluation of Amusia (MBEA), and fourteen healthy controls participated in our study. Here, we aimed to characterize a local resting state functional measure, regional homogeneity (ReHo) to evaluate abnormal local consistency of the neurons spontaneous activity in CA group compared with the healthy control group. We focused on potential brain areas with significant differences between the two groups. The partial correlation analyses applied to ReHo values as well as scores of MBEA subtests. The CA group showed a significant decrease ReHo values in the left postcentral gyrus and left middle cingulum cortex when compared to controls. The ReHo values of those two significant regions were both positively correlated with MBEA scores. All in all, this study identified brain regions which were not discussed previously that may partly contribute to or be modulated by CA.
Comparative mitochondrial genome analysis of Spirometra tapeworms from different hosts
Authors: He Jun-Lin , Xie Shi-Chen , Gong Teng-Fang , Chen Shu-Yu , Xie Xin-Rui , Li Wen-Chao , Liu Guo-Hua , Liu Wei 
Published: July 2022

Abstract
Background: Spirometra erinaceieuropaei, the causative agent of food/water-borne sparganosis, has been widely reported worldwide. However, the taxonomy of the genus Spirometra has always been complicated. The main objectives of this study were to assemble 7 complete mitochondrial genomes of Spirometra erinaceieuropaei, collected from different hosts in the Hunan province of China, and to analyze the phylogenetic relationship and genetic diversity of cestode species.Methods: In this study, seven Spirometra erinaceieuropaei (three spargana and 4 adults) were collected from different hosts in Hunan province, China. The long-PCR was performed to amplify the four large fragments of the Spirometra mitochondria (mt) genome by using specific primers reported in a previous study. Then, the mt genome of each S. erinaceieuropaei was assembled and annotated after overlapping four large fragments. Sliding window analysis was carried out to explore the nucleotide variation of the mt genome between 7 isolates obtained in this study and 8 reported Spirometra. The genetic diversity of cestode species was also investigated by Bayesian analysis based on 12 protein-coding genes.Results: Seven mt genomes of S. erinaceieuropaei obtained in this study were successfully assembled and annotated. The genome features of S. erinaceieuropaei are similar to other reported cestode species, containing 12 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and two ribosomal RNA genes (rRNA), large non-coding regions (NC), and small non-coding regions (NR). Sequencing analysis revealed 97.40%-99.90% sequence similarity among seven mt genomes identified in this study. Sliding window analysis indicated that the Korea isolate (KJ599680) might be the differential specie of genus Spirometra, and nad4L, cox3, and nad6 were the top three genes with the lowest variation rates in mt DNA. Phylogenetic analysis based on 12 PCGs further demonstrated that S. mansoni (AB374543) might be the misnamed S. erinaceieuropaei.Conclusion: The results of the current study supports the previously reported conclusion that multiple genotypes exist within S. erinaceieuropaei, and the Korean isolate (KJ599680) be a novel genotype or even a novel species of the genus Spirometra, and we strongly suggests that S. decipines may be a misnamed of S. erinaceieuropaei like S. mansoni is. nad4L, cox3 and nad6 are more suitable molecular genetic markers than cox1 for S. erinaceieuropaei identification. However, the authentic relationships among S. erinaceieuropaei isolates from different hosts and geographical sites are still unknown. More samples must be collected in different host and geographical positions to help us understand the genus Spirometra.
Off-axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification
Authors: Lam H. H.S. , Zhu Yanmin , Buranasiri Prathan 
Published: July 2022

Abstract
This paper proposes a method with an off-axis interferometer and an ensemble deep learning (I-EDL) hologram-classifier to interpret noisy digital holograms captured from the tissues of flawed biological specimens. The holograms are captured by an interferometer which serves as a digital holographic scanner to scan the tissue with 3D information. A red laser beam penetrates the tissue of the specimen. It scans across the x-y directions to capture thousands of off-axis hologram samples for the training, testing, and recognizing the microstructure of the tissue and, hence, identifying the specimens' identity. The method achieves a high success rate of 99.60% in identifying the specimens through the tissue holograms. It is found that the ensemble deep learning hologram-classifier can effectively adapt to optical aberration coming from dust on mirrors and optical lenses aberration such as the Airy-plaque-like rings out-turn from the lenses in the interferometer. The deep learning network effectively adapts to these irregularities during the training stage and performs well in the later recognition stage without prior optical background compensations. The method does not require an intact sample with a full outline shape of the specimens or the organs to understand the objects' identities. It demonstrates a new paradigm in object identification by ensemble deep learning through a direct wavefront recognition technique.
Determination of concentration of heavy metals and metalloids in grapes grown in Gonabad vineyards and assessment of associated health risks
Authors: Peirovi-Minaee Roya , Alami Ali , Moghaddam Alireza , Zarei Ahmad 
Published: July 2022

Abstract
Metals and metalloids are considered as major public health hazards, they are known to accumulate in fruits, which are heavily consumed by humans because of their unique sweet taste and potential health benefits. Therefore, the aim of this study was to measure the concentration of ten heavy metals and metalloids including arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), Iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in grapes samples grown in Gonabad vineyards and to estimate the associated health risks of metals in terms of chronic daily intake (CDI), and carcinogenic and non-carcinogenic risks by hazard quotient (HQ), hazard index (HI) and cancer risk (CR). The overall concentration of in red grapes were in range 0.07–0.5 (mean 0.14), 0.08–0.13 (mean 0.10), 0.07–0.13 (mean 0.09), 0.06–1.49 (mean 0.29), 0.52–4.12 (mean 1.65), 6.43–42.17 (mean 19.01), 0.89–4.04 (mean 1.89), 0.07–9.23 (mean 0.71), 0.07–0.37 (mean 0.18), 0.40–4.13 (mean 1.05) mg/kg dry weight for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively. Based on the results, cadmium for all samples and Pb in 64.7 % and As in 35.3% of the samples exceeded the FAO/WHO permissible limits. The estimated non-carcinogenic and carcinogenic risk indices showed that the results were lower than the critical value (1) and in acceptable range, respectively, therefore red grape is safe for consumption with no impact on the human health. The obtained data can be used in remediation techniques, as well as in implementing control measures of metals contamination in grapes.
Efficacy of Insect Endogeneous Cellulases for Ligno-cellulosic Biofuels Deciphered Using Molecular Docking Studies
Authors: Nivetha Ramanathan , Bhuvaragavan Sreeramulu , Meenakumari Mani , Janarthanan Sundaram 
Published: August 2022

Abstract
Cellulose, the substance that makes up most of a plant’s cell wall, is pondered to be one of the most abundant natural organic polymers on earth made up of glucose units linked by β-1, 4 glycosidic bonds. Insects possess cellulolytic system capable of producing variegate enzymes with multifarious specificities to break down complex lignocellulosic products. Astonishingly, endoglucanases, exoglucanase, and β-glycosidases act sequentially in a synergistic system to facilitate the breakdown of cellulose to utilizable energy source glucose. These extremely versatile enzymes are a better source in terms of environmental performance and overall energy efficiency. Pertaining to four main glycosyl hydrolase families (GHF), insect cellulases are distributed in all the insect orders explored up until now. In silico docking studies of endo-β-1,4-glucanase from 19 different insects belonging to six different orders identified that it possesses high affinity for all the six substrates, including CMC, cellulose, cellotriose, cellotetraose, cellopentose, and cellohexaose. Additionally, β-glucosidase from nearly all the reported insect sources also showed considerable affinity towards cellobiose. Van der Waals, conventional hydrogen bonds, and carbon-hydrogen bonds stabilize the interaction between the enzyme and different substrates. Molecular dynamics simulations also held up the stability of various complexes. With lignocelluloses-based biofuels becoming a major focus of industrial and academic communities worldwide, this study can perhaps complement the propensity of insect cellulases for prospected applications.
Effect of discharge teaching quality on readiness for hospital discharge in total knee arthroplasty patients: the role of depression and self-efficacy for rehabilitation
Authors: LI Na , GUO Manjie , YOU Simeng , JI Hong 
Published: August 2022

Abstract
Background: As the number of total knee arthroplasty (TKA) increases and the length of stay decreases, the readiness of patients for discharge needs attention. The purpose of this study is to clarify the relationship between the quality of discharge teaching, depression, self-efficacy for rehabilitation, and readiness for hospital discharge (RHD), thereby providing evidence support for improving readiness for discharge in patients with TKA.Methods: A total of 319 TKA patients participated in this cross-sectional study. Bivariate Pearson’s correlation was used to test the correlation between the four variables. Multivariate regression models and Bootstrap tests were used to examine the mediating role of depression and self-efficacy for rehabilitation. In addition, we used a mediation model 6 to explore the relationship between the quality of discharge teaching, depression, self-efficacy for rehabilitation, and RHD. All regression models were adjusted for age, sex, residential status, education level, number of joint replacements, and number of co-morbidities.Results: Depression was negatively associated with RHD (r=-0.281, P＜0.01) ,and self-efficacy for rehabilitation was positively associated with RHD (r=0.396, P＜0.01), with both playing a partially mediating role between the quality of discharge teaching and RHD, the effect value being 62.7%. The effect of discharge teaching quality on RHD in patients with TKA was weakened when depression and recovery self-efficacy were added.Conclusions: The results of this study emphasized the importance of depression and self-efficacy for rehabilitation on the RHD of patients with TKA. When providing discharge teaching, physicians and nurses could use relevant psychological intervention methods to improve the patient's physical and mental condition, with the final goal of improving their RHD.
Economic Forecasting with Big Data: A Literature Review
Authors: Lin Wencan , Wang Shouyang , Wei Yunjie 
Published: July 2022

Abstract
Big data technology has changed the research paradigm of economic forecasting regardless of the data source, forecasting method or forecasting result. In this paper, by utilizing bibliometric methods, we analyze the existing literature on big data economic forecasting with 821 articles, collected from the ‘‘Web of Science Core Collection’’ database. Using cooccurrence analysis, co-citation analysis, and structural variation analysis, this work not only presents a comprehensive review of big data in economic forecasting research but also captures articles with transformative potential in this field. This study provides meaningful guidance for researchers to enhance the understanding of crucial research, emerging trends, and new developments in big data economic forecasting.
Comparative Analysis of Segmentation Models to Detect Leaf Diseases in Tomato Plant
Authors: Kaur Prabhjot , Harnal Shilpi , Gautam Vinay , Singh Mukund Pratap , Singh Santar Pal 
Published: August 2022

Abstract
In agriculture around 22% of crop yield loss is due to living and non-living organisms such as biotic and abiotic stress/disease. The early-stage diagnosis of these stresses is an important issue for farmers through naked eyes. Using computer vision technologies can detect the pattern and clustering of diseases at an early stage. However, inthe last few years, computer vision-based deep learning technology is helpful for the diagnosis of biotic stress (single biotic and multi biotic) in tomato plant leaves.In this work, the PlantVillage dataset is gathered for the segmentation of object detection. The labeled, enhanced and augmented data has been used for training the model. The proposed hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN)model has been segmenting the diseased objects in the tomato plant. This Hybrid-DSCNN is assembled usingU-Net and Seg-Net pre-trained models with instance segmentation for better detection of objects. The result of the proposed Hybrid DSCNN model is compared with other modified U-Net, M-SegNet, and modified U-SegNet in terms of Accuracy, Precision, Recall, Intersection over Union (IoU), and mean Intersection over Union (mIoU). The proposed model processed 1004 images in 30ns,whichis better than other compared models. The accuracy achieved using the proposed model is 98.24%,which is far better than other modified segmentation models. In this paper, the semantic segmented data has been recognized for the single and multiple leaf diseases for identification and classification.
Neuroepidemiology study of headache in the region of Jammu of north Indian population
Authors: Sudershan Amrit , Younis Mohd , Pushap Aghar Chander , Kumar Hardeep , Sudershan Srishty , Kumar Parvinder 
Published: August 2022

Abstract
Background
Headache disorders represent a major public health problem globally. It is more in developing countries with the rising trends in young adults affecting negatively their quality of life. There has been very little information on the epidemiology of headache disorder in the Jammu division of the north Indian population.
Aim
The present study is aimed to find out the prevalence of headache and its two major types i.e., migraine and TTH (Tension-Type Headache) in population of Jammu division.
Methods
The study was conducted in two phases: (Phase I: Face-face interview and Phase II: E-based sampling) and sufferers of headaches were incorporated into the study based on the ICHD-3 criteria for a representative sample. Frequency distribution and mean ± SD were used in descriptive statistics to describe the data sets, while a t-test, chi-square test, and a logistic regression model using odds ratio were used in inferential statistics.
Results
A total of 3,148 subjects were recruited and found the overall prevalence of headache was 53.84% with a female preponderance (38.18%) in comparison to male 15.66%. Regarding the type, migraine was found to be highly prevalent (33.25%) type than TTH (20.58%). Female suffering from migraine shows the highest prevalence (25.28%). Regarding the environmental factors, bright light OR: 5.81, 95% CIs [4.96–6.81] loud sound OR: 5.17, 95% CIs [4.44–6.03], stress OR: 3.58, 95% CIs [3.07–4.17], empty stomach OR: 3.42, 95% CIs [2.93–3.98] increases the likely hood of the diseases. Also, high comorbidity association was found in between PCOS OR: 8.63, 95% CIs [3.95–18.85], panic disorder OR: 5.04, 95% CIs [1.94–3.05], anxiety OR: 4.53, 95% CIs [3.90–5.27], IBS OR: 3.80, 95% [1.76–8.23] with headache.
Conclusion
The prevalence of headache is high in the Jammu division of J&K India with migraine being the high prevalent type. Light and sound with high intensity, empty stomach, stress, and less water intake are some highly associated environmental risk factor that increases the severity diseases.
A new paradigm in tracking the dynamics of glucose and cortisol: An observational study from human sweat enabled by a skin sensor
Authors: Sardesai Abha Umesh , Greyling Cornelia Felicia , Lin Kai-Chun , Muthukumar Sriram , Prasad Shalini 
Published: August 2022

Abstract
A sedentary lifestyle is one of the leading risk factors to develop obesity. Abdominal obesity is one of the metabolic disorders that lead to an increased risk of developing cardiovascular disease. In this work, we developed a novel approach of tracking glucose and cortisol. With the aim of observing the dynamic relationship between the target biomarkers as a result of physical activity, nutrition, and the circadian cycle, with the help of the machine learning-assisted wearable sweat-based sensor designed in our previous work. The machine learning approach helps to provide real-time biomarker value and a basis for actionable insight. Through this observational study, we illustrate the real-world performance of our sweat-based sensor by examining the glucose and cortisol levels of 4 healthy participants over multiple days. This novel glucose and cortisol tracking paradigm establishes the foundation for future research on the effect of lifestyle choices on target biomarkers for metabolic disorders.
METTL3 promotes glycolysis and cholangiocarcinoma progression by mediating m6A modification of AKR1B10
Authors: Cai Jingli , Cui Zheng , Zhou Jingyi , Zhang Bosen , Lu Ruiqi , Ding Youcheng , Hu Hai 
Published: August 2022

Abstract
Objective
N6-methyladenosine (m6A) RNA methylation involve in governing mechanism of tumor progression. We aimed to excavate the biological role and mechanism of m6A methyltransferase METTL3 in Cholangiocarcinoma (CCA).
Methods
METTL3 expression was determined by database and tissue microarray. The role of METTL3 in CCA was explored by loss- and gain-of-function experiments. m6A target of METTL3 was detected by RNA sequencing. The role of AKR1B10 in CCA was explored and the association between METTL3 and AKR1B10 was confirmed by rescue experiments.
Result
METTL3 expression was upregulated in CCA tissue and higher METTL3 expression was implicated a poor prognosis in CCA patients. Overexpression of METTL3 facilitated proliferation, migration, invasion, glucose uptake, and lactate production of CCA cells, whereas knockdown of METTL3 had the opposite effect. We further found that METTL3 deficiency inhibited tumor growth of CCA in vivo. RNA sequencing and MeRIP-qPCR confirmed that METTL3 enhanced AKR1B10 expression and m6A modification level. Further, METTL3 directly bind with AKR1B10 at an m6A modification site also confirmed. CCA tissue microarray showed that AKR1B10 expression was upregulated in CCA tissue and silencing of AKR1B10 suppressed the malignant phenotype mentioned-above in CCA. Notably, knockdown of AKR1B10 rescued the tumor-promoting effects induced by METTL3 overexpression.
Conclusion
Elevated METTL3 expression promotes tumor growth and glycolysis in CCA through m6A modification of AKR1B10, indicating that METTL3 is a potential target for blocking glycolysis to apply in CCA therapy.
Changes in soil fungal communities after onset of wheat yellow mosaic virus disease
Authors: Lu Qi-sen , Cai Lin-na , Wu Chuan-fa , Zhang Hao-qing , Wei Liang , Zhang Tian-ye , Hu Hai-chao , Liu Shuang , Lei Jia-jia , Ge Ti-da , Dai Liang-ying , Yang Jian , Chen Jian-ping 
Published: August 2022

Abstract
Rhizosphere-associated microbes have important implications for plant health, but knowledge of the association between the pathological conditions of soil-borne virus-infected wheat and soil microbial communities, especially changes in fungal communities, remains limited. We investigated the succession of fungal communities from bulk soil to wheat rhizosphere soil in both infected and healthy plants using amplicon sequencing methods, and attempted to assess their potential role in plant health. Fungal diversity and richness decreased significantly from the bulk soil to the wheat rhizosphere soil, and had fewer fungi in the bulk soil of infected plant than healthy plants. The structure differences in fungal community at the two wheat health statuses or two compartment niches were obvious, soil physicochemical properties (i.e., NH4+ and soil organic carbon) contribute to differences in fungal community structure and alpha diversity. Comparison analysis showed Mortierellomycetes and Dothideomycetes are found as dominant communities in healthy wheat soils at class level. The genus Cystofilobasidium, Cladosporium, Mortierella and Stephanonectria are significantly enriched in bulk soil of healthy plant. Furthermore, co-occurrence network analysis showed that the fungi in healthy wheat soil has higher mutual benefit and connectivity compared with diseased wheat. In conclusion, we demonstrate that the occurrence of soil-borne viral diseases causes changes in the fungal community that depend on soil abiotic factors.