bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting spatial patterns of peatland greenhouse gas sinks and sources with geospatial environmental and remote sensing data
Authors: Christiani Priscillia , Rana Parvez , Räsänen Aleksi , Pitkänen Timo P. , Tolvanen Anne 
Published: January 2024

Abstract
Peatlands play a key role in the circulation of the main greenhouse gases (GHG) – methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Therefore, detecting the spatial pattern of GHG sinks and sources in peatlands is pivotal for guiding effective climate change mitigation in the land use sector. While geospatial environmental data, which provide detailed spatial information on ecosystems and land use, offer valuable insights into GHG sinks and sources, the potential of directly using remote sensing data from satellites remains largely unexplored. We assessed predicted the spatial distribution of three major GHGs (CH4, CO2, and N2O) sinks and sources across Finland. Utilizing 143 field measurements, we compared the predictive capacity of three different data sets with MaxEnt machine-learning modelling: (1) geospatial environmental data including climate, topography and habitat variables, (2) remote sensing data (Sentinel-1 and Sentinel-2), and (3) a combination of both. The combined dataset yielded the highest accuracy with an average test area under the receiver operating characteristic curve (AUC) of 0.771 and AUC stability of 0.859. A slightly lower accuracy was achieved using only geospatial environmental data (test AUC 0.745, stability AUC 0.857). In contrast, using only remote sensing data resulted in reduced predictive accuracy (test AUC 0.667, stability AUC 0.865). Our results suggest that (1) reliable estimates of GHG sinks and sources cannot be produced with remote sensing data only and (2) integrating multiple data sources is recommended to achieve accurate and realistic predictions of GHG spatial patterns.
Cuproptosis-related genes affect the prognosis of prostate cancer by potentially regulating the immune microenvironment
Authors: Qin Xin , Li Xilei , Jiang Wei , Zhao Yan , Li Haopeng , Zi Tong , Zhu Yaru , Wang Qinghua , Xu Chengdang , Yang Tao , Wang Xinan , Yao Yicong , Wang Licheng , Chen Xi , Zhou Juan , Wu Gang 
Published: January 2024

Abstract
Purpose
As a novel form of cell death, cuproptosis has been found to have an effect on the occurrence, development and prognosis of many other carcinomas, such as renal clear cell carcinoma, hepatocellular carcinoma and breast cancer. However, the role of cuproptosis in prostate cancer is poorly understood.
Methods
We retrieved and downloaded gene expression profiles and clinical information from public databases. We used unsupervised clustering to find differences in the immune microenvironment between subgroups and then obtained the immune differential genes between subgroups. Based on the random forest machine learning algorithm, we constructed a model to distinguish patients’ outcomes and verified their predictive efficacy. Gene with potential regulatory effects was further selected and validated experimentally.
Results
From our findings, most of the cuproptosis-related genes were differentially expressed between prostate cancer and tumor-adjacent tissues. Two clusters based on consensus cluster analysis of cuproptosis-related genes expression showed different clinical information and immune microenvironment. We used random forest as the core algorithm to screen prognostic relevant cuproptosis immune-related genes and constructed a risk stratification model. Different risk groups predicted by the model presented different clinicopathological stages and prognosis. Subsequent experiments revealed that inhibition of PROK1 expression promoted prostate cancer cell proliferation and invasion, and overexpression of PROK1 gave the opposite result, validating the anticancer effect of PROK1 and its potential to become a novel therapeutic target.
Conclusion
Our study reveals a landscape of cuproptosis-related genes in prostate cancer that may influence prostate cancer progression by modulating immunity. One of the key genes, PROK1, was found to be an oncogene. Our study provides new ideas about the immune environment of prostate cancer.
Prioritizing Forest Landscape Restoration Areas by Integrating Farmers' and Experts' Preference Factors in a GIS-Based Multicriteria Decision Analysis
Authors: Pedrollo Camilo Tomazini , Pedercini Fabio , Martins Minella 
Published: January 2024

Abstract
The success of Forest Landscape Restoration (FLR) in the tropics hinges on factors like local community needs, ecological conditions, and conservation goals. In the Amazon basin, resource scarcity and lack of special credit lines hinder FLR implementation, pushing initiatives to build robust spatial planning in order to obtain the most cost efficient strategies. Establishing a validated set of criteria is crucial for determining priority areas, requiring stakeholder’s inputs. This study employed Multicriteria Decision Analysis (MCDA) to integrate spatially explicit factors with stakeholders' preferences. The focus was on identifying priority FLR areas in the Northeastern mesoregion (NE) of Pará state, Brazil, populated by small household farmers. Twenty farmers and 23 experts ranked 21 socioecological factors segregated in two clusters (economic/infrastructure and biophysical/land use restrictions), derived from a regression model study that evaluated secondary vegetation dynamics. Key infrastructural factors ('distance to roads,' 'distance to markets,' and 'distance to urban centers') were mostly consistent for both groups. Variations emerged in biophysical/restriction factors, with 'conservation unit of sustainable use' prioritized by farmers but not deemed relevant by experts. Synergies and disparities also surfaced for factors like 'deficit of legal reserve' and ‘high agricultural suitability’. Stakeholders' ranking scores were employed as weights to create prioritization maps. Experts identified 51.5% of NE Pará area as nonpriority and 5.4% as high priority, while farmers indicated 48.3% and 5.2%, respectively. Combining both groups' perceptions yielded consensus maps designating 9.1% of NE Pará as high priority. Bragantina microregion stood out in consensus maps, concentrating 32.7% of high priority areas. This approach provides a cost-effective method for stakeholder’s engagement. Policy recommendations emphasize focusing on western Bragantina and southwestern Salgado microregions to promote large-scale FLR effectively.
DNA as a perfect quantum computer based on the quantum physics principle
Authors: Aroche Raul Riera , García Yveth Marlene Ortiz , Arellano Meylín Aymeé Martínez , Leal Annie Riera 
Published: January 2024

Abstract
DNA is a complex multi-resolution molecule whose theoretical study is a challenge. Its intrinsic multiscale nature requires chemistry and quantum physics to understand the structure and quantum informatics to explain its operation as a perfect quantum computer. Here, we present theoretical results of DNA that allow a better description of its structure and the operation process in the transmission, coding, and decoding of genetic information. Aromaticity is explained by the oscillatory resonant quantum state of correlated electron and hole pairs due to the quantized molecular vibrational energy acting as an attractive force. The correlated pairs form a supercurrent in the nitrogenous bases in a single band π-molecular orbital (π-MO). The MO wave function (Φ) is assumed to be the linear combination of the n constituent atomic orbitals. The central Hydrogen bond between Adenine (A) and Thymine (T) or Guanine (G) and Cytosine (C) functions like an ideal Josephson Junction. The approach of a Josephson Effect between two superconductors is correctly described, as well as the condensation of the nitrogenous bases to obtain the two entangled quantum states that form the qubit. Combining the quantum state of the composite system with the classical information, RNA polymerase teleports one of the four Bell states. DNA is a perfect quantum computer.
Small Meckel’s Cave Volume and Localization of the Foramen Ovale and Rotundum Are Associated with No-Neurovascular Compression Trigeminal Neuralgia
Authors: Lv Wenming , Liu Xiaoliang , Zhang Liangwen 
Published: January 2024

Abstract
Objective
To investigate the new pathogenesis of no-neurovascular compression (N-NVC) trigeminal neuralgia (TN).
Methods
Twenty-two patients (mean age 57 years; male-to-female 4:18) with N-NVC TN who were diagnosed by trigeminal nerve MRI (TN-MRI), then another 22 sex-matched healthy controls (mean age 56 years)with skull base computerized tomography (CT) and TN-MRI was selected to investigate the pathogenesis.The volume of Meckel’s Cave (MC) was measured by the Brainlab navigation imaging system in MRI, and the areas of foramen ovale (FO) and foramen rotundum (FR) were evaluated by the three-dimensional reconstruction of skull base CT using the Radiant DICOM software. The sagittal (y) axis was passing through the middle of the dorsum sellae, which connected the anterior-most point and the rearmost point of the skull. The coordinates perpendicular to the y-axis and transverse (x) axis passing through the middle of the sellar dorsum were established to determine the positions of FO and FR in the x-axis (FO-x, FR-x), and y-axis (FO-y, FR-y), and the region of the area of interest enclosed within the x and y axes (ROI-FO, ROI-FR).
Results
No difference was found in the volume of bilateral Meckel’s Cave, area and location of the FO and FR, and area of ROI in HCs.The volume of MC on the affected side of patients with N-NVC TN was (0.402 ± 0.116) mm3, FO-x (2.109 ± 0.216) cm, FO-y (0.892 ± 0.103) cm, ROI-FO (1.889 ± 0.333) cm2, FR-x (2.090 ± 0.117) cm, and ROI-FR (4.353 ± 0.272) cm2. The volume of MC on the healthy side was (0.473 ± 0.158) mm3, FO-x (2.379 ± 0.171) cm, FO-y (1.142 ± 0.154) cm, ROI-FO (2.719 ± 0.432) cm2, FR-x (2.294 ± 0.103) cm, and ROI-FR (4.828 ± 0.319) cm2.U test showed that the above parameters on the affected side of patients with TN were significantly less than those on the healthy side (P˂0.01).
Conclusion
In patients who classify as idiopathic TN based on ICHD-3, a statistically smaller volume of MC, area of ROI, and the distances of FO and FR to the sagittal or transverse axis suggested that they could be the causes of N-NVC TN.
Research on human-robot collaboration method for parallel robots oriented to segment docking
Authors: Sun Deyuan , Wang Junyi , Xu Zhigang , Bao Jianwen , Lu Han 
Published: January 2024

Abstract
In the field of aerospace, large and heavy cabin segments present a significant challenge in assembling space engines. The considerable inertial force of cabin segments’ mass causes unexpected motion during docking, leading to segment collisions and difficulty in ensuring precise engine segment docking. Traditional manual docking utilizes workers' expertise, yet the labor-intensive nature and low productivity are unsuitable for practical applications. Human-robot collaboration can effectively integrate the advantages of humans and robots. Additionally, parallel robots, known for their precision and load-bearing ability, are widely employed in precise assembly tasks under heavy loads. Thus, human-parallel-robot collaboration serves as an excellent solution for these challenges. This paper proposes an easily implementable framework, employing human-parallel-robot collaboration technology for cabin segment docking in production. A fractional-order variable damping admittance control and an inverse dynamics robust controller are suggested to improve the robot's compliance, responsiveness, and trajectory tracking accuracy in collaboration. This allows operators to dynamically adjust the robot's motion in real-time, counterbalancing inertial forces and preventing collisions. Segment docking assembly experiments are conducted utilizing the Stewart platform in this study. The results show that the proposed method enables the robot to quickly respond to interaction forces, ensuring compliance and stable motion accuracy, even under unknown interaction forces.
A nomogram for predicting survival in patients with upper limb melanoma: A study based on the Surveillance, Epidemiology, and End Results database
Authors: Dong Zhiqin , Zhang Jinrong , Li Zehua 
Published: January 2024

Abstract
Introduction:
In order to predict cancer-specific survival (CSS) in patients with upper limb melanoma (ULM) at three-, five-, and eight-year intervals following diagnosis, a nomogram was developed and validated in this study.
Methods:
Patient data about the upper limb melanoma (ULM) cases were collected from the comprehensive Surveillance, Epidemiology, and End Results (SEER) database. A training cohort consisting of 70% of the patient population and a validation cohort comprising 30% of the patients were established through a random selection process. A stepwise backward Cox regression model was employed to identify independent prognostic variables. The nomogram was then updated to integrate these factors, making it possible to estimate the rates of cancer-specific survival (CSS) after 3, 5 and 8 years after diagnosis. A number of metrics were used to assess the nomogram's performance, including the calibration curves, decision-curve analysis (DCA), net reclassification improvement (NRI), concordance index (C-index), integrated discrimination improvement (IDI), and the area under the time-dependent receiver operating characteristic curve (AUC).
Results:
This study involved a comprehensive cohort comprising 36,621 patients diagnosed with upper limb melanoma (ULM). Through an analysis of the Cox regression model within the training cohort, a total of 13 prognostic factors were identified, namely age, RNP (regional node positive), sex, race, marriage, AJCC (American Joint Committee on Cancer) stage, surgical status, radiation status, chemotherapy status, income status, survival time and current status. An extensive set of internal and external validation processes were then applied to the development of a nomogram. The nomogram demonstrated excellent discriminatory abilities, as reflected by significantly high C-index and AUC values. Calibration curves provided further confirmation of the nomogram's reliability. Notably, the nomogram exhibited superior performance compared to the AJCC model, as evidenced by improved NRI and IDI values. The decision-curve analysis (DCA) curves further validated the clinical utility of the nomogram, underscoring its practical relevance in prognostication for patients with ULM.
Conclusions:
The current investigation has effectively developed and validated an initial nomogram for prognosticating outcomes in patients with upper limb melanoma (ULM). The nomogram's impressive performance and practical applicability highlight its potential usefulness within clinical settings. However, it is important to note that additional external validation is necessary to further substantiate its reliability and generalizability.
Revealing Distinctive Insights: Machine Learning-Enhanced Ensembled MRI Sequences for Pediatric Posterior Fossa Tumor Classification
Authors: Ünalan Serhat , Onat Fatih Ekrem , Şahin Feyza , Keserci Bilgin 
Published: January 2024

Abstract
Pediatric posterior fossa (PF) tumors pose a significant concern in pediatric oncology because of their high prevalence in the pediatric population. Magnetic Resonance Imaging (MRI) has emerged as a practical and non-invasive imaging modality for detecting and classifying PF malignancies. For the differentiation of MB, the LGBM model, using the feature combination of T2, FLAIR, DWI, and ADC, demonstrated the highest performance. It achieved an AUROC of 0.938, accuracy of 0.778, specificity of 0.916, sensitivity of 0.645, and F1 score of 0.664. In the classification of EP, the XGB model, which utilizes the feature combination of T2 and DWI, demonstrated the highest performance with a sensitivity of 1. With respect to the distinction of PA, the RF model, using the feature combination of T2, T1CE, DWI, and ADC, exhibited the highest performance. For the classification of BG, the RF model, using the feature combination of T2, DWI, and ADC, exhibited the highest performance. This comprehensive approach has notably enhanced our comprehension of pediatric PF tumor classification and carries substantial potential for the advancement of diagnostic tools and the refinement of ML models.
A Modal Fusion Network with Dual Attention Mechanism for 6D Pose Estimation
Authors: Wei Liangrui , Xie Feifei , Sun Lin , Chen Jinpeng , Zhang Zhipeng 
Published: January 2024

Abstract

The 6D pose estimation based on RGB-D data holds significant application value in computer vision and related fields. Currently, deep learning methods commonly employ convolutional networks for feature extraction, which are sensitive to keypoints at close distances but overlook information related to keypoints at longer distances. Moreover, in subsequent stages, there is a failure to effectively fuse spatial features (depth channel features) and color-texture features (RGB channel features). Consequently, this limitation results in compromised accuracy in existing 6D pose networks based on RGB-D data. In addressing this issue, this paper proposes a novel end-to-end network. Specifically, in the depth value extraction branch, a mask vector attention mechanism is used to establish global spatial weights, achieving robust extraction of depth values. In the fusion stage, a symmetrical fusion module is introduced, which utilizes a cross-attention mechanism to integrate spatial features and color-texture features, enhancing the efficiency of target representation and achieving self-correlated fusion between modalities, thereby effectively improving the accuracy of 6D pose estimation. Experimental evaluations were conducted on the LINEMOD and LINEMOD-OCLUSION datasets, and the results indicate that the proposed method achieved ADD(-S) scores of 95.84% and 47.89%, respectively. Compared to state-of-the-art methods, our approach demonstrates superior performance in pose estimation for objects with complex shapes. Moreover, in the presence of occlusion, the accuracy of pose estimation for asymmetric objects has been effectively improved.
Association of dietary patterns with anxiety and depressive symptoms during pregnancy: a cross-sectional study
Authors: Yang Rusi , Yang Panzi , Luo Yangzhenlin , Zhang Yixin , Xie Zhaolong , Hu Ming , Yang Guilian 
Published: January 2024

Abstract
Background
Dietary intake plays a significant role in mental health. Our objective was to examine the association of dietary patterns (DPs) with anxiety and depressive symptoms in pregnant women.
Methods
This study was a cross-sectional study conducted in Hunan Province. Food frequency questionnaire, Self-rating Anxiety Scale and the Edinburgh Postnatal Depression Scale were used. Dietary patterns were identified through the utilization of factor analysis. The correlation analysis employed logistic regression.
Results
The detection rates of anxiety and depression symptoms during pregnancy were 16.8% and 51.3%, respectively. We have identified five DPs in this study. After accounting for possible confounding factors, a positive correlation was found between DP3 and the risk of anxiety (T3:T1, OR = 1.637, 95%CI:1.064–2.519). However, no significant correlations were observed between DP1, DP2, DP4, or DP5 and anxiety symptoms. Additionally, a positive correlation was identified between DP1 and the risk of depression (T3:T1, OR = 1.483, 95%CI: 1.046–2.104). While no significant associations were found between DP2, DP3, DP4 or DP5 and depressive symptoms.
Conclusions
This study indicates that the dietary pattern high in processed meats, fried foods, sweets, desserts, instant noodles, snacks, and pickled vegetables may potentially elevate the risk of anxiety during pregnancy. The dietary pattern dominated by milk and its products, eggs and nuts may increase the risk of depression during pregnancy.
Usefulness and social impact of Minimum Income Schemes against poverty in Spain
Authors: Albarracín Daniel 
Published: January 2024

Abstract
Social assistance policies, such as minimum incomes, occupy a marginal place in welfare policies, as they are not linked to real wage contributions. These social protection schemes were developed as solutions of last resort, providing minimum social protection below the poverty line. They have served in several cases the public image of governments and have been used to achieve some political differentiation with other administrations, regional or central, seen as adversaries.
In this article, from a public policy evaluation approach, we analyse the evolution, usefulness, coverage, and social impact of the Minimum Insertion Income in Andalusia (RMISA), for the years 2018–2021. We examine its characteristics and impact and compare it with other analogous European social benefits and, more specifically, with other significant Spanish autonomous regional incomes. From the outset, the benefit was designed with a complex application and award procedure, and the regional government provided the programme with a meagre budget. In this period, from 2020 onwards, the implementation of the Minimum Vital Income, by the central government, became the ideal pretext to reduce this budget even further, avoiding a significant mutual reinforcement of both incomes and widening the gap between their application and their theoretical objectives.
JEL Clasification: I38 Government Policy; Provision and Effects of Welfare Programs, A14 Sociology of Economics, I320 Measurement and Analysis of Poverty
Declining global sensitivity of stomatal conductance to photosynthesis
Authors: Leng Jiye , Chen Jing M. , Li Wenyu , Luo Xiangzhong , Xu Mingzhu , Rogers Cheryl , Yan Yulin 
Published: January 2024

Abstract
Terrestrial ecosystems play a pivotal role in mitigating climate change through photosynthesis and transpiration regulated by plant stomata. Terrestrial biosphere models (TBMs) commonly couple a biochemical photosynthesis model, i.e., the Farquhar model, and a semi-empirical stomatal conductance model, i.e., the Ball-Berry model, to estimate gross primary productivity (GPP) and evapotranspiration (ET). However, the key ecosystem parameters (i.e., maximum carboxylation rate Vcmax and the Ball-Berry slope m) are commonly prescribed as constant in TBMs, hindering accurate quantification of global GPP and ET. m depicts ‘compromise between the costs and benefits of stomatal conductance relative to the photosynthetic activity of the leaf’, namely the sensitivity of stomatal conductance to photosynthesis. However, although recent work has retrieved continuous seasonal V_cmax over the globe, there is a lack of an effective method to derive spatially and temporally explicit m in TBMs. In this study, we estimated monthly m through a Bayesian parameter optimization approach at 136 eddy covariance flux sites (809 site years) and upscaled site-level m to global gridded m during 2001-2020 via a machine learning model. We found a significant declining trend of global m (R2 = 0.72, P < 0.001) in 2001-2020. We found the largest decline of m in grassland (0.04 per year), moderate declines in deciduous broadleaf forests, mixed forests, and cropland (0.02 per year), the smallest decline in evergreen needleleaf forests and savannas (0.01 per year). We found the air temperature plays the predominant role in driving the declining trend of m. This study generates and analyzes a global timeseries of m by ‘handshaking’ the measured fluxes, terrestrial biosphere models, and remote sensing. Our results highlight the changing relationship between photosynthesis and stomatal conductance under climate change and identify the importance of incorporating m as a dynamic parameter in future GPP and ET simulations with terrestrial biosphere models.