bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deforestation in the Brazilian Amazon: Threats and opportunities
Authors: Ziccardi Leonardo , yanai Aurora , Fearnside Philip , Stark Scott 
Published: July 2024

Abstract

Brazil holds the largest portion of the Amazon rainforest, which, in addition to its enormous biodiversity and vital role regulating local and global climate, is home to a great diversity of traditional communities and Indigenous peoples. Between August 2020 and July 2021, deforestation in the Brazilian Amazon reached its highest rate in a decade, and record numbers of forest fires were detected. Considering the 2009–2022 period, an upward trend in deforestation was observed both inside and outside of conservation units (protected areas for biodiversity). One type of conservation unit, Environmental Protection Areas (APAs), had little or no effect in slowing deforestation. We show that deforestation rates during the last decade were partially associated with profits to soy growers, increases in cattle ranching and agricultural areas, and government policies. The recent increases in deforestation and forest degradation in Amazonian forests have led to international proposals that could drastically affect Brazil’s economy, which is the largest in Latin America. At the same time, these proposals also open new avenues for sustainable economic development that have been successful in reducing deforestation in developing countries. The search for more sustainable forms of income and development that protect ecosystem services provided by forests is essential for the Amazonian population and for climate change mitigation in Brazil.
TGF-β1 inhibits autophagy and collagen degradation in rat bronchial fibroblasts
Authors: Zhang Lanying , Yu Siyuan , Zhang Fuan , Chen Jie , Liao Shixia , Liu Yuting , Zhou Jian , An Xuan , Zhang Wei , Ouyang Yao 
Published: July 2024

Abstract

Chronic obstructive pulmonary disease (COPD) is a major global health concern with an increasing prevalence. Notably, autophagy plays a crucial role in the pathophysiology of airway remodeling. However, further research is required to determine the precise mechanism of autophagy in rat bronchial fibroblasts (RBFs). In this study, we investigated the role of transforming growth factor-β1 (TGF-β1) on RBF-related collagen degradation following autophagy. We established rapamycin- and starvation-induced autophagy models of RBFs and then analyzed the expression of autophagy-related proteins (LC3-II, Beclin-1, and P62) in RBFs treated with TGF-β1. We also measured the levels of matrix metalloproteinase-1 (MMP-1) and its inhibitor, matrix metalloproteinase inhibitor-1 (TIMP-1) in the RBF supernatant using an enzyme-linked immunosorbent assay. Our results showed that TGF-β1 promoted RBF growth in a concentration-dependent manner, while rapamycin lowered RBF survival rates. TGF-β1 downregulated LC3-II and Beclin-1 expression but increased P62 expression levels after rapamycin and starvation-induced autophagy in RBFs. Adding TGF-β1 elevated TIMP-1 protein levels and reduced MMP-1 protein levels. The present study showed for the first time that TGF-β1 reduces airway remodeling in RBFs by inhibiting autophagy and collagen degradation, suggesting the potential therapeutic value of TGF-β1 for the prevention and treatment of COPD.
Signal timing Coordination optimization for intersections adjacent to roundabout
Authors: Zhang He , Zhang Xinyue , Fan Jihong , Zhang Yuanzheng , Guo Jinna 
Published: July 2024

Abstract

Aiming at the problem of traffic congestion or even locking in the roundabout, this paper takes the traffic flow of the roundabout as the research object, and researches the coordinated control method of the intersection near the roundabout. Firstly, the traffic flow theory is applied to the roundabout and its upstream intersection, and the correlation on the regional relationship and geometric condition between the inlet area and the weaving area of the roundabout and the upstream intersection is analyzed. The traffic state change between the regions is used to represent the traffic flow operation. and the upstream entrance of the intersection is used as the control source to apply the theory of coordinated control of signals at arterial intersections, combining regional relationship and geometric condition.The roundabout of Huanan Square in Dalian is used as a case study to solve the problem of internal locking of the roundabout.The results show that this method can play a good role in solving the serious congestion of the roundabout.
Re-staging of Meige's syndrome Based on Clinical Radiofrequency Treatment Options
Authors: Huang Bing , Huang Hao , Lin Huidan , Zhao Xian , Li Xue , Li Yang , Qian Tao , Zhou Qinghe 
Published: July 2024

Abstract

Background
The pathogenesis of Meige's syndrome (MS) remains unknown, posing a challenge for clinicians. The current classification system for MS causes confusion and difficulty in selecting target nerves for cranial nerve radiofrequency therapy. Therefore, to simplify the process of choosing radiofrequency therapy for MS, we suggest reorganizing the syndrome based on the affected cranial nerves.
Methods
The new classification of MS, which is based on the cranial nerves responsible, is more aligned with the requirements of radiofrequency therapy. Start by identifying the initial frequency of spasms from the patient's clinical symptoms, including muscles such as the frontal, orbicularis oculi, buccinator, orbicularis oris, latissimus, temporal, masseter, medial and lateral pterygoid, pharyngeal, lingual, sternocleidomastoid, and trapezius. Then, determine the involved cranial nerve based on the muscle's nerve innervation.
Results
The 117 MS patients were categorized into different groups based on their cranial nerve involvement: 73.50% were classified as the facial nerve type, 5.13% as the trigeminal nerve type, 2.56% as the sublingual nerve type, 0.85% as the glossopharyngeal nerve type, 4.27% as the accessory nerve type, and 13.67% as multiple groups of the cranial nerve complex.
Conclusions
The current classification of MS, which is based on clinical symptoms, does not make it easy to choose the target nerves for MS radiofrequency treatment. However, reclassifying MS according to the affected cranial nerves could simplify the process of radiofrequency treatment for MS.
Exploring Alu-Driven DNA Transductions in the Primate Genomes
Authors: Halabian Reza , Storer Jessica M. , Hoyt Savannah J. , Hartley Gabrielle A. , Brosius Jürgen , O’Neill Rachel J. , Makalowski Wojciech 
Published: July 2024

Abstract

Long terminal repeats (LTRs) and non-LTRs retrotransposons, aka retroelements, collectively occupy a substantial part of the human genome. Certain non-LTR retroelements, such as L1 and SVA, have the potential for DNA transduction, which involves the concurrent mobilization of flanking non-transposon DNA during retrotransposition. These events can be detected by computational approaches. Despite being the most abundant short interspersed sequences (SINEs) that are still active within the genomes of humans and other primates, the transduction rate caused by Alu sequences remains unexplored. Therefore, we conducted an analysis to address this research gap and utilized an in-house program to probe for the presence of Alu-related transductions in the human genome. We analyzed 118,489 full-length AluY subfamilies annotated within the first complete human reference genome, T2T-CHM13. For comparative insights, we extended our exploration to two non-human primate genomes, the chimpanzee and the rhesus monkey. After manual curation, our findings did not confirm any Alu-mediated transductions, whose source genes are, unlike L1 or SVA, transcribed by RNA polymerase III, implying that they are infrequent or possibly absent not only in the human but also in chimpanzee and rhesus monkey genomes. Although we identified loci in which the 3’ Target Site Duplication (TSD) was located distantly from the retrotransposed AluYs, a transduction hallmark, our study could not find further support for such events. The observation of these instances can be explained by the incorporation of other nucleotides into the poly(A) tails in conjunction with polymerase slippage.
Behavioral Risk Factors Clusters and their Associations with Self-Reported Burdens Among University Students in Finland
Authors: Ansari Walid El , El-Ansari Kareem , Sebena Rene 
Published: July 2024

Abstract

Introduction: No research among Finnish universities grouped students into clusters, based on their lifestyle behavioral risk factors (BRFs), and appraised relationships of the clusters with self-reported burdens, adjusting for confounders. The current study undertook this task.
Methods: Students (n=1169) at Turku University completed online questionnaire comprising sociodemographic variables (age, sex, income, social support), 18 burdens, and 5 BRFs (smoking, alcohol, drug use, food habits). Factor analysis reduced burdens into factors; cluster analysis of BRFs categorized students into clusters. Regression models appraised associations between sociodemographics and clusters with burdens.
Results: Mean age was ≈23 years, with ≈70% females, 23.4% smokers, 28.8% problematic drinkers, 21% illicit drug/s users, and mean dietary guideline adherence=4.84±1.57. Factor analysis of burdens generated four factors: ‘Studies’=3 items; ‘Future’=3 items; ‘Relationships’=7 items; and ‘Needs’=5 items. Cluster analysis produced four BRFs clusters with significantly different BRFs and sociodemographics. Cluster 1 exhibited less risk-taking behaviors, Cluster 4 comprised more risk-taking, and the other two clusters fell in-between. Regression showed that females were more likely to report ‘Studies’+‘Relationships’ burdens; higher social support was associated with less burdens generally; older age was associated with less ‘Studies’+‘Future’+‘Relationships’ burdens; and sufficient income was associated with less ‘Studies’+‘Future’+‘Needs’ burdens. Compared to Cluster 1, Cluster 4 membership was more likely to feel ‘Needs’ burdens; Cluster 3 more likely to report ‘Relationships’+‘Needs’ burdens (p range: <0.05 to <0.01 for all).
Conclusion: Controlling for sociodemographics, cluster membership was more influenced by students’ perceptions of ‘Relationships’+‘Needs’, rather than academic difficulties of ‘Studies’ or unsecure ‘Future’. Risk taking was more likely with relationship difficulties, isolation, and day-to-day problems (housing, financial situation, health) rather than academic load or concerns for future prospects. Preventive and intervention efforts tackling students’ lifestyle behaviours need to consider programs aimed at better relationship building/maintenance to prevent isolation, while mitigating ‘on-the-ground’ everyday challenges that students face.
PV-GNN:Point-Voxel 3D Object Detection based on Graph Neural Network
Authors: Fei Hansheng , Zhao Jiale , Zhang ZhiPeng , Wang Honggeng , Huang Xiangdang 
Published: July 2024

Abstract

Recently, two-stage detectors have been widely used in 3D object detection.Most of them perform ROI feature extraction using keypoint sampling or re-voxelization in the second stage.However, these methods do not make full use of the point correlation and ignore the surface texture information hidden in the point cloud.In this paper, we propose a novel 3D object detection framework, PointVoxel-GNN (PV-GNN), to accurately detect 3D objects from point cloud data.The framework uses both voxel neural network and PointNet-based set abstraction to extract more diverse point cloud features, and uses graph neural network to fully exploit the correlation of point cloud data.Specifically, the framework firstly converts the sparse point cloud data into a regular voxel network, and extracts effective features through the neural network. Then, all the voxels of the whole scene feature volume are summarized into a small number of feature keypoints by using the voxel-to-keypoint scene coding module.In order to further generate high-quality proposals, a GNN-RoI pooling layer is proposed to explicitly extract the texture information of the object surface through the graph neural network.Compared with the traditional pooling operation, GNN-RoI can encode richer context information, which is used to accurately estimate the confidence and location of the object.The framework has conducted a large number of experiments on KITTI dataset and Waymo Open dataset, and our proposed PV-GNN redetection accuracy has achieved significant progress.
Characterization of Changes in Torrefied Cellulose and Their Relationship with Glucose Yield: A Mechanistic Study
Authors: Tripathi Jaya , Ciolkosz Daniel 
Published: July 2024

Abstract

Various physical, chemical, and structural changes were assessed that occur during torrefaction of cellulose were assessed, including water uptake, tensile strength, FTIR, TG-MS & H-NMR spectra, and glucose yield. The water uptake capacity is positively correlated to glucose yield which suggests that water-substrate interaction may be a primary driver of the hydrolysis process. The wet-tensile strength of fibrous cellulose increases with torrefaction and is negatively correlated to glucose yield. The hydrogen bonding capacity of torrefied samples is likely to be impacted by the dehydration reaction induced by torrefaction, as indicated by discernible changes in both the FTIR spectra and solid-state H-NMR spectra. The emergence of a peak at 1724 cm-1 in the spectrum of torrefied cellulose is also suggestive of an inhibitory compound containing a carbonyl group. FTIR spectra show evidence of the removal of the suspected inhibitor by alkaline pretreatment. Additionally, with an increase in torrefaction severity, the specific surface area decreases. These results, taken together, suggest that decreased hydrolysis efficiency from torrefaction is a function of the combined effects on surface area, surface composition and chemistry, and H-bonding network.
Temporal changes in the role of species sorting and evolution determine community dynamics
Authors: Hoffmann Julius , Hogle Shane , Hiltunen Teppo , Becks Lutz 
Published: July 2024

Abstract

The understanding of when and how ecological and evolutionary processes structure communities is a central goal in ecology. Both evolutionary changes within community members and shifts in species composition via species sorting can contribute to the dynamics of community traits and composition over time. However, we currently lack an understanding of when and how species sorting and evolution contribute to community dynamics. Here, we estimated contributions of species sorting and evolution over time (60 days) in bacterial communities consisting of 24 species under selection by a protozoan predator Tetrahymena thermophila. We found that species sorting contributed to the communities' carrying capacity, while evolution contributed to its anti-predator defences, and that relative roles of both processes changed over time. In contrast to previous observations from single bacterial populations, bacterial defences declined rapidly. We also found that manipulating the evolutionary history of predator and prey (as a phenotypic match-mismatch combination) affected species sorting and evolution. Community composition, population densities and genomic evolution differed contingent on predator and prey history. Overall, our analysis indicates that the relative roles of ecology and evolution depended on the initial trait variation present in the community. If variation in the direction of selection was present species sorting prevailed, otherwise evolution drove phenotypic change.
Optimal pipe-sizing design of water distribution networks using modified Rao-II algorithm
Authors: Gangwani Laxmi , Palod Nikita , Dongre Shilpa , Gupta Rajesh 
Published: July 2024

ABSTRACT

Several evolutionary algorithms (EAs) have been suggested in the last three decades for the least-cost design of water distribution networks (WDNs). EAs generally worked well to identify the global/near-global optimal solutions for small- to moderate-size networks in a reasonable time and computational effort. However, their applications to large-size networks are still challenging due to large computational effort. Recent developments in EAs are towards parameter-less techniques that avoid fine-tuning of case-specific parameters to reduce the computational effort. Further, several self-adaptive penalties and search-space reduction methodologies have been suggested to reduce the computational effort. A fast, efficient, and parameter-less Rao-II algorithm has been used earlier with penalty-based approaches for the optimal design of WDNs. In this study, the application of a Rao-II algorithm is further explored with three self-adaptive penalty approaches to compare the convergence characteristics. The Rao-II algorithm is observed to converge at an infeasible solution in cases that the applied penalty to an infeasible solution is not so substantial to make it inferior to the feasible solutions. Modifications are suggested to improve the Rao-II algorithm, named the modified Rao-II algorithm. The modified Rao-II algorithm with the self-adaptive penalty methods resulted in better solutions than those obtained earlier.
Fabrication and characterization of magnetic graphene oxide-g-poly(acrylamide)/gelatin hydrogel nanocomposites for effective adsorption of copper ions from aqueous solutions
Authors: Kheiry Parinaz , Hosseinzadeh Hossein , Saraei Mahnaz , Masoumi Bakhshali 
Published: July 2024

ABSTRACT

In this work, a magnetic nanocomposite adsorbent was synthesized by crosslinking graft copolymerization of gelatin and acrylamide onto graphene oxide nanosheets in the presence of Fe3O4 nanoparticles. The structure of nanocomposites was characterized by using FTIR, SEM, TEM, XRD, VSM and point of zero charge (pHpzc). The prepared nanocomposites were employed as bioadsorbents for adsorption of copper ions. The effects of adsorption parameters under different conditions were studied in detail through batch experiments. The maximum ion adsorption capacity of 259 g. g−1 was achieved under the optimum conditions that were found to be: pH=8.0, copper concentration=80 mg L−1, adsorbent dosage=0.5 mg mg. L−1, contact time=2 hours, and temperature=60 °C. The kinetics and isotherms of adsorption fitted well at various ion concentrations (40, 80 and 160 mg L−1) using second-order and Langmuir models with R2=0.9991 and 0.9928, respectively. Moreover, the thermodynamic parameters show that the adsorption process was spontaneous and endothermic. In addition, the adsorbents depicted excellent regeneration ability after six adsorption-desorption cycles and maintained 89% of its initial adsorption capacity. In conclusion, the obtained results confirmed that the prepared environmentally friendly nanocomposites with high adsorption capacities could be effective adsorbents for the elimination of different ions from wastewaters.
Diagnostic Stewardship in Clinical Microbiology: An Indispensable Component of Patient Care
Authors: Singhal Lipika , Gupta Parakriti , Gupta Varsha 
Published: July 2024

:
Emerging infectious diseases and increasing resistance to available antimicrobials
are mapping the evolution of clinical microbiology and escalating the nature of undertakings
required. Rapid diagnosis has become the need of the hour, which can affect diagnostic algorithms and therapeutic decisions simultaneously. Subsequently, the concept of ‘diagnostic
stewardship’ was introduced into clinical practice for coherent implementation of available
diagnostic modalities to ensure that these new rapid diagnostic technologies are conserved,
rather than consumed as part of health care resources, with a view to improve the patient care
and reduce Turnaround Time (TAT) and treatment expense. The present study highlights the
requisite of diagnostic stewardship and outlines the infectious disease diagnostic modalities
that can assist in its successful implementation. Diagnostic stewardship promotes precise,
timely diagnostics, from the initial specimen collection and identification to reporting with
appropriate TAT, so as to enable timely management of the patient. The main aim of diagnostic stewardship is to optimize the right choice of diagnostic test for the right patient to
attain clinically significant reports with the least possible TAT for timely management and
the least expected adverse effects for the patient, community, and the healthcare system. This
underlines the requisite of a multifaceted approach to make technological advancements effective and successful for implementation as a part of diagnostic stewardship for the best
patient care.
Evaluation of Land Subsidence Susceptibility in Kunming Basin Based on Remote Sensing Interpretation and Convolutional Neural Network
Authors: Wang Fa-long , Fa-you A. , Zhu Chuan-bing , Zhang Hua , He Rao-sheng , Wang Rui , Liu Zhang-zhen 
Published: July 2024

Objective:
This study aims to utilize the Machine Learning (ML) model to produce highprecision
maps of urban ground subsidence susceptibility, providing a scientific basis for disaster
prevention and mitigation efforts in the Kunming Basin.

Methods:
In this patent study, remote sensing interpretation of Kunming City was conducted using
SBAS-InSAR technology to acquire subsidence data. Based on the frequency ratio method, ten evaluative
factors with strong correlations were selected to establish an evaluation index system for the
subsidence susceptibility of the Kunming Basin. Five models, including CNN, Back Propagation
Neural Network (BPNN), Genetic Algorithm optimized BPNN (GA-BPNN), Particle Swarm Optimization
optimized BPNN (PSO-BPNN), and Radial Basis Function Neural Network (RBFNN),
were employed. The frequency ratio method and the ROC curve were used to compare the effectiveness
and precision of these models.

Results:
The frequency ratio method indicated that the CNN model had the highest values in the very
high and high susceptibility areas, reaching 4.10, which was the highest among all models; in the
very low and low susceptibility areas, its value was 0.34, which was the lowest among the models.
The ROC curve demonstrated that the CNN model, based on deep learning (AUC = 0.952), was
more precise than the machine learning-based models such as BPNN (AUC = 0.896), RBFNN (AUC
= 0.917), GA-BPNN (AUC = 0.890), and PSO-BPNN (AUC = 0.906).

Conclusion:
The CNN model has predicted that 81.06% of the ground subsidence grid cells fall into
the very high and high susceptibility categories, demonstrating good predictive performance. According
to the established evaluation index system for ground subsidence susceptibility, the fundamental
causes of ground subsidence in the Kunming Basin are identified as poor soil mechanical
properties and low bearing capacity, while construction activities have exacerbated the development
of ground subsidence.