bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of snow absence on N pools and enzyme activities within soil aggregates in a spruce forest on the eastern Tibetan Plateau
Authors: Li Zhijie , Li Zimin , Reichel Rüdiger , Yang Kaijun , Zhang Li , Tan Bo , Yin Rui , Xu Zhenfeng 
Published: February 2021

Abstract
Background

Snow cover change has a great potential to impact soil nitrogen (N) pools and enzyme activities in boreal forests. Yet, the nature of this biochemical processes within soil aggregates is still limited. We conducted a snow manipulation experiment to investigate the effects of snow absence on N pools and enzyme activities within soil aggregates in a subalpine spruce forest on the eastern Tibetan Plateau of China.
Results

Snow absence increased extractable inorganic N pools (ammonium and nitrate) and enzyme activities, accompanying with the improvement of N mineralization rate. Regardless of snow manipulations, both soil extractable inorganic N and net N mineralization was higher in macroaggregates than that in the other two aggregates. In contrast, microaggregates had higher urease and nitrite reductase activities compared to macroaggregates and large macroaggregates. Compared with small macroaggregates and large macroaggregates, N pools and enzymes within microaggregates is more sensitive to snow absence.
Conclusions

Our results indicated that the impacts of snow cover change on soil N dynamic depend on aggregate sizes and winter conditions (e.g., snow cover and temperature). Such findings have important implication for soil N cycling in snow-covered subalpine forests experiencing pronounced winter climate change.
Protective Effect of Occupational Complexity on Working Memory in Patients with Frontal Lobe Tumors
Authors: Ebina Kota , Matsui Mie , Kinoshita Masashi , Saito Daisuke , Nakada Mitsutoshi 
Published: February 2021

Abstract
Cognitive reserve (CR) is the capacity to cope with cognitive impairments due to brain damage by neurological disease. CR is increased by intellectually enriching activities, such as education, occupation, and leisure. After brain tumor resection, patients show working memory impairment because of damage to fronto-parietal networks, such as the superior longitudinal fascicle (SLF). To date, whether occupational experience represented as CR impacts postoperative working memory impairment in patients with frontal lobe tumors remains unknown. We hypothesized that occupational experience predicted postoperative working memory and that higher damage in the SLF was associated with poorer working memory. We enrolled 27 patients who had undergone tumor resection. Patient's occupational experience was estimated using an occupational complexity index based on a dictionary of occupational titles. Working memory was measured using verbal and spatial working memory tasks. Our results showed that patients who had engaged in more complex occupations showed higher performance of postoperative working memory, which supported the previous CR hypothesis. In conclusion, CR has protective effects against working memory impairment in patients with frontal lobe tumors. CR measures, such as occupational experience, will help more accurately predict the severity of working memory deficits and the likelihood of recovery in the postoperative period.
Evaluating the Impact of Covariate Lookback Times on Performance of Patient-Level Prediction Models
Authors: Hardin Jill , Reps Jenna M. 
Published: February 2021

Abstract
Background: The goal of our study is to provide guidance for deciding which length of lookback to implement when engineering features to use when developing predictive models using observational healthcare data. Using a longer lookback for feature engineering gives more insight about patients but increases the issue of left-censoring. Methods: We used five US observational databases to develop patient-level prediction models. A target cohort of subjects with hypertensive drug exposures and outcome cohorts of subjects with acute (stroke and gastrointestinal bleeding) and chronic outcomes (diabetes and chronic kidney disease) were developed. Candidate predictors that exist on or prior to the target index date were derived within the following lookback periods: 14, 30, 90, 180, 365, 730, and all days prior to index were evaluated. We predicted the risk of outcomes occurring 1 day until 365 days after index. Ten lasso logistic models for each lookback period were generated to create a distribution of area under the curve (AUC) metrics to evaluate the discriminative performance of the models. Impact on external validation performance was investigated across five databases. Results: Our results show that a shorter lookback time for the acute outcomes, stroke and gastrointestinal bleeding results in equivalent performance as using longer lookback times. A lookback time of at least 365 days for the chronic outcomes, diabetes and renal impairment, results in equivalent performance as using lookback times greater than 365 days.Conclusions: Our results suggest the optimal model performance and choice of length of lookback is dependent on the outcome type (acute or chronic). Our study illustrates that use of at least 365 days results in equivalent performance as using longer lookback periods. Researchers should evaluate lookback in the context of the prediction question to determine optimal model performance.
Research and Application of 3D Laser Scanning in Transparent Longwall
Authors: Sen LI , Yao RONG , Qiong CAO 
Published: February 2021

Abstract
With the rapid development of science and technology, the development of coal mining in China is stepping into intelligent mining stage from the mechanized automatic mining stage. And the research of intelligent mining is also upgrading to the self-adaptive automation mining from visual remote intervention. In 2019, the first practice of self-adaptive digital mining technology, which is based on transparent longwall theory, was performed in #43101 longwall of Yujialiang coal mine and made notable gains. The 3D laser scanning technology, which played an important role in technology architecture of Yujialiang coal mine’s transparent longwall practice, transformed the mined longwall space information into digital format and then provided reliable basic data for cutting template calculation. This paper introduces application of 3D laser scanning in Yujialiang coal mine in detail, including principle of 3D laser scanning, detection of intersecting curve between longwall’s coal wall and roof, neighbor point-clouds splicing, transformation for longwall pointcloud from local space coordinate system to 3D geological model’s global space coordinate system. The experiment result in #43101 longwall of Yujialiang coal mine demonstrated that 3D laser scanning technology, which is able to quickly and precisely capture mined longwall space information, is a important sensing technology involved by self-adaptive automation mining.
Smoking Cessation Therapy is a Cost-Effective&nbsp;Intervention to Avoid Tooth Loss in Subjects With Periodontitis – An Economic Evaluation&nbsp;
Authors: Souto Maria Luisa Silveira , Carrer Fernanda Campos Almeida , Braga Mariana Minatel , Pannuti Cláudio Mendes 
Published: February 2021

Abstract
Background: Smokers present a higher prevalence and severity of periodontitis and, consequently, higher prevalence of tooth loss. Smoking cessation improves the response to periodontal treatment and reduces tooth loss. So, the aim of this study was evaluated the efficiency in resources allocation when implementing smoking cessation therapy vs. its non-implementation in smokers with periodontitis. Methods: We adopted the Brazilian public system perspective to determine the incremental cost-effectiveness (cost per tooth loss avoided) and cost-utility (cost per oral-related quality-adjusted life-year ([QALY] gained) of implementing smoking cessation therapy. Base-case was defined as a 48 years-old male subject and horizon of 30 years. Effects and costs were combined in a decision analytic modeling framework to permit a quantitative approach aiming to estimate the value of the consequences of smoking cessation therapy adjusted for their probability of occurrence. Markov models were carried over annual cycles. Sensitivity analysis tested methodological assumptions. Results: Implementation of smoking cessation therapy had an average incremental cost of U$60.58 per tooth loss avoided and U$4.55 per oral related-QALY gained. Considering uncertainties, the therapy could be cost-effective in the most part of simulated cases, even being cheaper and more effective in 53% of cases in which the oral-health related outcome is used as effect. Considering a willingness-to-pay of US$100 per health effect, smoking cessation therapy was cost-effective, respectively, in 81% and 100% of cases in cost-utility and cost-effectiveness analyses. Conclusions: Implementation of smoking cessation therapy may be cost-effective, considering the avoidance of tooth loss and oral health-related consequences to patients.
A Mixed Methods Analysis of Environmental and Household Chaos: Implications for Childhood Obesity Prevention Through Toddlerhood
Authors: Krupsky Kathryn L. , Parrott Andria , Andridge Rebecca , Zvara Bharathi J. , Keim Sarah A. , Anderson Sarah E. 
Published: February 2021

Abstract
Background: Chaos has implications for child health that may extend to childhood obesity. Yet, results from studies describing associations between chaos and childhood obesity are mixed. Challenges to studying chaos-obesity relationships may include inconsistencies in how chaos is operationalized and reliance on caregiver perceptions. Furthermore, multiple pathways may link chaos to obesity, though few have been empirically examined.Methods: We conducted a concurrent mixed methods analysis of quantitative and qualitative data describing home and neighborhood chaos among a diverse cohort of 283 caregiver-toddlers dyads from Ohio. We examined the underlying structure of environmental and household chaos using exploratory factor analysis then sought to validate the structure using qualitative field notes. We generated total scores for factors of chaos and described their distributions overall and according to cohort characteristics. Additionally, we conducted a thematic content analysis of brief ethnographies to identify potential pathways linking chaos to childhood obesity with the intention to direct future research efforts.Results: Dyads varied according to household composition, income, education, and race/ethnicity. We found evidence for a multi-factor structure for chaos, which included disorganization and neighborhood noise. Household disorganization scores ranged from 8-18 and were on average 11.37 (SD = 2.58). Neighborhood noise scores ranged from 4-12 and were on average 6.93 (SD = 1.89). Both disorganization and neighborhood noise were associated with indicators of socioeconomic disadvantage, such as food insecurity and lower income-to-poverty ratio, though only disorganization was associated with additional social factors within homes, such as caregiver mental health and overall health. Finally, we identified unique themes from brief ethnographies which future contextualize the social and material environments in which chaos was observed, including child behavior and caregiver-child interactions.Conclusions: Chaos is a complex construct composed of multiple factors and the mechanisms linking chaos to childhood obesity may be equally complex. Future studies of chaos-obesity relationships may require greater specificity when operationalizing chaos and empirical study of pathways, like child behavior and caregiver-child interactions, may inform future obesity prevention strategies.
rLH/rFSH Combination Improved Cumulative Live Birth Rate in Patients With LH&nbsp;Over-Suppression Following GnRH&nbsp;Agonist Pituitary Down-Regulation Compared to rFSH or hMG Alone: A Retrospective Cohort Study With Propensity Score Matching
Authors: Tu Ansu , Geng Lihong , Zhong Ying 
Published: February 2021

Abstract
PurposeThe suppression of luteinizing hormone (LH) in patients undergoing gonadotropin-releasing hormone agonist (GnRH-a) pituitary down-regulation may cause LH deficiency which may impact follicular development. However, little is known about effect of LH adding in patients with LH over-suppression. This study to investigate the effects of different gonadotropins on the cumulative live birth rate (CLBR) in the patients with LH over-suppression after GnRH-a pituitary down-regulation. MethodsThis retrospective study used propensity score-matching methodology to compare CLBR, as the primary endpoint, in the patients with LH over-suppression after different GnRH-a pituitary down-regulation regimens, including recombinant follicle stimulating hormone (rFSH) combined with recombinant LH (rLH), or using rFSH alone, or human menopausal gonadotropin (hMG) alone. The secondary endpoints included biochemical pregnancy rate, clinical pregnancy rate, and live birth rate in fresh embryo transfer cycles. ResultsA total of 88 patients were enrolled after matching: 22 patients in the rFSH+rLH group, 44 in the rFSH group, and 22 in the hMG group. The CLBR of the rFSH+rLH group was significantly higher than that of the rFSH and hMG groups (19/22, 86.4% vs. 25/44, 56.8%, P = 0.014; vs. 7/22, 31.8%, P < 0.001). Moreover, the rFSH group had a higher CLBR than the hMG group (P = 0.048). There were no significant differences in any of the secondary endpoints (all P > 0.05). Conclusion﻿Exogenous rLH supplementation achieved a higher CLBR than rFSH or hMG alone among patients with LH over-suppression; furthermore, rFSH alone was superior to hMG alone for CLBR.
Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design
Authors: Tchagang Alain Beaudelaire , Tewfik Ahmed H. , Valdés Julio J. 
Published: February 2021

Abstract
Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML.
A Prognostic Nomogram and Risk Stratification in Pediatric With Hemisphere Lower-Grade Gliomas: a SEER-Based Study 1998-2016
Authors: Che Wenqiang , Xu Fengshuo , Xu Tong , Lyu Jun , Wang Xiangyu 
Published: February 2021

Abstract
BackgroundPediatric lower-grade gliomas (pLGGs) are a rare intracranial tumor that lacks a prognostic prediction model for clinical application. The present study aimed to construct a novel nomogram to predict overall survival for pLGGs.MethodsBased on data from the Surveillance, Epidemiology, and End Results (SEER) database, 746 pediatrics diagnosed with cerebral hemispheres lower-grade gliomas from 1998 to 2016 were enrolled for the research. All patients were randomly divided into training and validation datasets at a ratio of 7:3. The Cox and stepwise regression analysis was used to screen the independent prognosticators for developing the nomogram. The discriminating abilities and calibration of the nomogram were assessed by concordance index (C-index), receiver operating characteristic curves (ROCs), area under the ROCs (AUCs), and calibration curves. The accuracy and net benefits of the nomogram were evaluated by comparing it to the traditional prediction method by the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and the decision curve analysis (DCA). Finally, we employed risk stratifications for pLGGs. ResultsFive independent predicted indicators were associated with OS rates. The constructed nomogram showed reliable discrimination by the C-indexes of the novel nomogram for OS, which were 0.830 and 0.871, much higher than that in the traditional prediction method (0.749 and 0.728, respectively). The plotted calibration curves showed good consistency between the prediction survival rates and actual observed survival rates in both the training and validation dataset. IDI, NRI, and DCA showed the nomogram had a comparable clinical application to the traditional prediction method. Kaplan-Meier survival curves showed a significant difference among the three risk classifications.ConclusionIn conclusion, we developed a novel prognostic nomogram with improved accuracy, better clinical utility, and a more precise prediction of OS rates for hemisphere pLGGs than the traditional prediction method.
Classification of Colorectal Carcinoma Based on Ferroptosis-related Gene Signature
Authors: Yue QingFang , Zhang Yuan , Wang Fei , Cao Fei , Duan Xianglong , Bai Jun 
Published: February 2021

Abstract
Background: Ferroptosis is an iron-dependent form of programmed cell death, involves in the development of many cancers. However, systematic analysis of ferroptosis related-genes in colorectal carcinoma (CRC) remains to be clarified. We herein analyzed the public databases to identify the molecular features of CRC by the development of a classification based on the gene expression profile of ferroptosis-related genes.Methods: We collected the gene expression data and clinical information from The Cancer Atlas and Gene Expression Omnibus to explore the correlation of ferroptosis-related gene expression of CRC. Consensus clustering was performed to determine the clusters of colorectal cancer patients, then we analyzed the prognostic value, transcriptome features, immune microenvironment, drug sensitivity, gene mutations differences of the subclasses. Results: Four subclasses of CRC (C1, C2, C3 and C4) were identified. There were significant differences in the prognosis of patients between the four subtypes. Functional enrichment suggested that these ferroptosis related-genes were associated with biological processes such as metabolic processes and oxidative stress, and the KEGG pathway suggested that it was closely related to ferroptosis and glutathione metabolic pathway. There were significant differences in immune cell infiltrations, immune score, stromal score and tumor purity among four subclasses. The expression of PD-L1 was differentially expressed in four subclasses and PD-L1 was significantly correlated with the expression of several ferroptosis-related genes in the differentially expressed subtypes. There were significant differences in stemness indices and drug sensitivity in four subclasses, and gene mutations analysis showed that ferroptosis-related genes such as TP53 had high mutations in different subtypes, and there were significant differences in mutation frequencies in the subclasses.Conclusion: This study established a new CRC classification based on ferroptosis-related genes expression profiles, and different subgroups may have their unique gene expression patterns, indicating that the heterogeneity within CRC and the classification might provide valuable stratification for the design of future treatment.
Distributions of total mercury and methylmercury in dragonflies from a large abandoned mercury mining region in China
Authors: Wu Gaoen , Tang Shunlin , Han Jialiang , Li Chan , Liu Lin , Xu Xiaohang , Xu Zhidong , Chen Zhuo , Wang Yajie , Qiu Guangle 
Published: February 2021

Abstract
Dragonflies (Order Odonata) are often considered to be biosentinels of environmental contamination. Dragonflies (n = 439) belonging to 15 species of eight genera were collected from an abandoned mercury (Hg) mining region in China to investigate the bioaccumulation of total Hg (THg) and methylmercury (MeHg). The THg and MeHg concentrations in dragonflies varied widely within ranges of 0.06–19 mg/kg and 0.02–5.7 mg/kg, respectively. THg and MeHg were positively correlated with bodyweight (THg: r2 = 0.10, P = 0.000; MeHg: r2 = 0.09, P = 0.000). Significant variations were observed among species, with the highest MeHg value (in Orthetrum triangulare) being 5-fold higher than the lowest (in Pantala flavescens). These variations were consistent with those of nitrogen isotope (δ15N) values. A health risk assessment found hazard quotients for specialist dragonfly-consuming birds of up to 7.2, which is 2.4 times greater than the permissible limit of 3, suggesting a potential health risk of exposure.
Molecular and Serological Characterization of Pathogenic Leptospira Spp. Isolated From Symptomatic Dogs in a Highly Endemic Area, Brazil
Authors: Santos Cassia Moreira , Dias Gabrielle Cristini Del Rigo Santos , Saldanha Alexya Victória Pinheiro , Esteves Stephanie Bergmann , Cortez Adriana , Guedes Israel Barbosa , Heinemann Marcos Bryan , Gonçales Amane Paldês , Miotto Bruno Alonso 
Published: February 2021

Abstract
Background: Leptospirosis is an endemic zoonosis in Brazil, with great impact in human and animal health. Although dogs are frequently infected by pathogenic Leptospira, the current epidemiological understanding of canine leptospirosis is mainly based on serological tests that predict the infecting serogroup/serovar. Thus, the present study aimed at identifying the causative agent for severe cases of canine leptospirosis in a highly endemic area through the isolation and characterization of the isolated strains. Results: Urine, serum and blood samples were collected from 31 dogs with suspected acute leptospirosis treated at the Veterinary Hospital Service of Santo Amaro University between 2018 and 2019. Acute infection was confirmed in 17 dogs (54.8%) by the associated use of Polymerase Chain Reaction (PCR), Microscopic Agglutination (MAT) and bacteriological culture. Eleven dogs (35.5%) had titers ≥800, with the most frequent serogroups being Autumnalis and Icterohaemorrhagiae (n = 4 each) and Canicola (n = 2). Leptospires were recovered from four dogs, and Multilocus Sequence Analysis (MLSA) revealed infection caused by L. interrogans, which were further characterized as serogroups Canicola (n=1) and Icterohaemorrhagiae (n=3). Conclusion: The identification of circulating strains in poverty-stricken locations with humans living in rural-urban interfaces may assist local authorities to implement effective prevention policies. The identity of the isolates and serological pattern of MAT titers found among the suspected dogs suggest that dogs are highly exposed to the serogroup Icterohaemorrhagiae, also indicating possible circulation of serogroups not yet isolated in Brazil, notably serogroup Autumnalis.