bluuebunny/crossref_metadata_2025_split huggingface dataset:
Water’s path from moss to soil Vol. 2: How soil-moss combinations affect the soil water balance and soil erosion
Authors: Gall Corinna , Nebel Martin , Scholten Thomas , Thielen Sonja M. , Seitz Steffen 
Published: November 2023

Abstract
Mosses are key components of many ecosystems and particularly related to the soil water balance. In principle, the importance of mosses for water-related processes is known; however, their influence is rarely quantified in scientific studies. To fill this research gap, this study concentrates on the influence of mosses of different species on surface runoff, the amount of percolated water, soil erosion, and the temporal dynamics of soil water content. For this purpose, an experimental approach consisting of an ex situ rainfall simulation with infiltration boxes equipped with biocrust wetness probes was applied. On average, mosses significantly reduced surface runoff by 91% and soil erosion by almost 100%, while the amount of percolated water was increased by 85% compared with bare soils. Similarly, the moss treatments tended to have lower water contents in 3 cm soil depth during 30 minutes of rainfall simulations compared to the bare treatments. However, specific effects of moss treatments were superimposed by desiccation cracks as well as soil hydrophobicity, two major factors that clearly override individual species effects. Therefore, it is imperative that further experiments will be conducted to elucidate the apparently underestimated effects of mosses and their specific traits on the soil water balance and sediment transport.
Multi-omics Based Chemical Ecology of Philippine Forest Honey highlights endangered Pterocarpus indicus Willd. tree and important community-driven conservation efforts
Authors: Molino Ralph John Emerson , Weerd Merlijn , Torreno Vicenzo Paolo , Rellin Klidel Fae , Mondragon Miguel , Parungao Louie , Manila-Fajardo Analinda , Santos Daisy May , Junio Hiyas 
Published: November 2023

Abstract
Cooperation among Filipino scientists, conservationists, and forest-based communities paved the way for the chemical ecology investigation of Apis breviligula Maa., Apis cerana Fabr., and Tetragonula biroi (Friese) in priority forest landscapes of the Sierra Madre Mountain Range. The Philippine National Tree and unfortunately endangered Pterocarpus indicus Willd (loc. Narra) proved to be important, especially in Palaui Island where honey from A. cerana is close to being monofloral. Aside from P. indicus and its small molecule marker hypaphorine, caffeine was detected in Palaui honey beautifully reflecting the way of life of native Agtas who manage a small coffee plantation. The abundance of caffeine, however, is higher in stingless honey samples from Tanay, Rizal where Coffea trees have been extensively included in restoration activities over the past few decades. Our results imply the possibility of using honey as an ecological monitoring tool while generating baseline chemical information that reflects the state of Philippine forests. Furthermore, the identification of unique chemical components in forest honey can be further used in programs that assist indigenous communities in safeguarding the ownership and origin of forest honey sources.
Development of simple sequence repeat markers and genetic diversity of Lutraria sieboldii along the northern Beibu Gulf
Authors: Yang Guohao , Han Chunli , Huang Biling , Qin Wenxia , Zhu Peng , Peng Huijing , Xu Youhou , Yang Jialin , Zou Jie , Wang Pengliang 
Published: November 2023

Abstract
Background Lutraria sieboldii is an economically important shellfish in the Beibu Gulf of China. However, genetic diversity has not been systematically reported, and the number of simple sequence repeat (SSR) markers is still limited. These issues seriously hinder germplasm utilization and genetic study.Methods and results SSR markers were identified and developed in the transcriptome, and then the genetic diversity was revealed along the northern Beibu Gulf. We identified a total of 438 SSR loci and designed 329 primers. Of them, 100 SSRs were synthesized and used to reveal genetic diversity. We detected Na of 19.583 with the range from 10 to 36, Ne of 9.139 with the range from 4.075 to 20.629, PIC of 0.861 with the range from 0.723 to 0.949, Ho of 0.516 with the range from 0.000 to 0.964 and He of 0.873 with the range from 0.755 to 0.952 in the population of 117 individuals; the genetic distances between populations ranged from 0.186 to 0.276; Fst ranged from 0.126 to 0.198, and Nm ranged from 1.014 to 1.737; the genetic structure was detected and the population was divided into five groups.Conclusion A total of 438 SSR loci were identified in the transcriptome, and 100 SSR primers of the 329 designed primers were synthesized. The genetic diversity and structure of L. sieboldii along the northern Beibu Gulf were revealed by 36 highly polymorphic SSR markers. These findings will provide useful tools and a solid foundation for the exploration and effective utilization of genetic resources in L. sieboldii.
Using Sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage biomass, crude protein, and fiber content
Authors: Fernandes Marcia Helena Machado da Rocha , Junior Jalme de Souza Fernandes , Adams Jordan Melissa , Lee Mingyung , Reis Ricardo Andrade , Tedeschi Luis Orlindo 
Published: November 2023

Abstract
Grasslands cover approximately 24% of the Earth’s surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and quality of pastures. This study demonstrates the potential of estimating pasture leaf biomass, crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate biomass were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.
A Comprehensive Study on MWCNTs and SDS Integration in Anionic Crude Oil for Enhanced EOR Processes
Authors: Bagheri Hamid , Mousavi Seyedborhan , Nami Shamin Hosseini , Heris Saeed Zeinali 
Published: November 2023

Abstract
This research delved into the effects of introducing multi-walled carbon nanotubes (MWCNTs) and sodium dodecyl sulfate (SDS) into crude oil with an anionic base, aiming to enhance its thermophysical attributes in the EOR process. The investigation encompassed a range of properties, including interfacial tension, viscosity, density, and electrical conductivity, measured using an LV rheometer with spindle 18. Various ratios of MWCNTs and SDS were mixed with the crude oil to evaluate their impacts. The study uncovered that the 1:1 ratio of MWCNTs to SDS achieved a noteworthy 10% reduction in interfacial tension while minimally affecting viscosity, showing promise for practical applications. Temperature had diverse effects on viscosity and interfacial tension, displaying fluctuating trends. The highest observed result was a substantial 190% viscosity increase at 40°C with a 2:1 ratio, whereas the lowest was a slight − 0.81% density change at 60°C for the 1:1 ratio. Additionally, interfacial tension consistently decreased over time for all ratios at various temperatures, with the most significant reductions observed at 40°C and 60°C, particularly for the 1:2 and 1:1 ratios. Furthermore, adding MWCNTs and SDS led to a remarkable drop in electrical conductivity, indicating a substantial decrease in salt content within the crude oil. However, it was noted that higher MWCNT concentrations might not be cost-effective, emphasizing the importance of optimizing additive ratios for a balance between performance and economic viability. This study highlights the potential of tailored nanofluid formulations to improve the thermophysical properties of crude oil, potentially enhancing extraction and refining processes.
The use of cell and larval assays to identify target genes for RNA interference mediate control of the Australian sheep blowfly (Lucilia cuprina)
Authors: Yang Yunjia , Zhang Bing , Yong Jiaxi , James Peter , Xu Zhi Ping , Mitter Neena , Mahony Timothy. J , Mody Karishma. T 
Published: November 2023

Abstract
Flystrike is primarily caused by Lucilia cuprina, which is a major health and welfare issue for the sheep in wool industries. Current chemical-based controls can have limited effectiveness due to emerging resistance in the parasite. RNA interference (RNAi), which uses double-stranded RNA (dsRNA) as a trigger molecule, has been successfully investigated for the development of innovative pest control strategies. Although RNAi offers great potential, the efficient identification, selection of target genes and delivery of dsRNA represent challenges to be overcome for the successful application of RNAi for control of L. cuprina. In this study, the application of RNAi was explored using a novel L. cuprina embryonic cell line as well as in vitro larval bioassays. A primary L. cuprina (blowfly) embryo cell line (BFEC) was established and confirmed as being derived from L. cuprina eggs by PCR amplicon sequencing. The BFECs were successfully transfected with plasmids and messenger RNA (mRNA) expressing fluorescent reporter proteins and dsRNA using lipid-based transfection reagents. The transfection of dsRNA into BEFC in this study demonstrated RNAi-mediated knockdown of target gene expression. Three of the dsRNAs identified in this study, resulted in significant reductions in target gene expression in BFEC and loss of biological fitness in a L. cuprina larvae feeding bioassay. This study confirms that the novel BFEC cell line can be used to improve the efficacy of dsRNA-mediated screening to accelerate the identification of potential target genes in the development of RNAi mediated control approaches for L. cuprina. These results contribute to the development of sustainable control strategies for this important agricultural pest.
Research on preprocessing algorithm of indoor map partitioning and global path planning based on FAST
Authors: Yang Jifan , Pan Xunding , Liu Xiaoyang , Wang Lei 
Published: November 2023

Abstract
Path planning is a critical factor in the successful performance of navigation tasks. This paper proposes a novel approach for indoor map partitioning and global path-planning preprocessing. The proposed algorithm aims to enhance the efficiency of path planning tasks by eliminating irrelevant areas. In view of the deformation problem encountered in the original indoor map partitioning method, initially, the contour detection algorithm is employed to identify and eliminate obstacles. Subsequently, the FAST algorithm is utilized to detect key points. These key points are then subjected to filtering and clustering using the K-means algorithm. Based on the 8-neighborhood characteristics, door points and inflection points within the room are selected. A clustering algorithm is employed to retain the door points, which are subsequently connected to form door line segments through averaging and filtering procedures. This process ensures the closure of the sub-room. Finally, the connected domain function is employed to extract the sub-room map, thereby completing the map partitioning process. Based on the sub-room map centroid coordinate point data obtained from the partitioning, two combinations are used as the starting point and the end point, respectively, and the A* algorithm is employed to calculate and store all path information from the starting point to the end point. Based on the sub-room map obtained through partitioning and the stored path information, the path is traversed to eliminate irrelevant areas, thereby achieving the preprocessing of global path planning. The simulation results showed that the A*, Bi-A*, JPS, Dijkstra, PRM, and RRT algorithms increased their rates by 18.2%, 43.6%, 20.5%, 31.9%, 29.1%, and 29.7%, respectively.
Symmetry breaking and spiral similarity of Oseen-like vortex in confined compressible fluid
Authors: Yang Chen 
Published: November 2023

Abstract
Vortex flow has been widely observed in fluid systems in confined regions. In this study, an Oseen-like vortex in a confined compressible fluid was numerically investigated to understand its distinct patterns and associated symmetries. The vortex was studied within multiple two-dimensional polygon confined fluid domains (with different orders) with a rotational solid core in the center. It was demonstrated that the vortex in a compressible fluid is governed by two coexisting mechanisms. Dynamic viscosity provides a circular pattern that is diffusive from the core to the boundary. Shock dynamics provides the mechanism of starry patterns with distinguished arm-like features. The presence of arm-like features alters the symmetries of the original Lamb-Oseen vortex and manifests sensitivity to the initial rotational conditions. In summary, the combined influence of the two coexisting mechanisms generates distinct features from the original Lamb-Oseen vortex. The understanding of the vortex development and features is useful to develop levitation device in microgravity science.
Induction of microbe-mediated responsive proteomes by Methylobacterium oryzae CBMB20 on the salt-tolerant and salt-sensitive rice genotypes towards enhanced salt tolerance
Authors: Walitang Denver I. , Kim Kiyoon , Lee Yi , Choudhury Aritra Roy , Sa Tongmin 
Published: November 2023

Abstract
Background
Salt stress creates combinatorial stress conditions in the plants encompassing ion toxicity, physiological drought, nutritional imbalance, and oxidative stress. Severe salinity critically impacts both sensitive and tolerant rice genotypes. In addition, plants also recruit microbes establishing plant-microbe interactions leading to a complex array of microbe-mediated plant responses resulting to a cumulative overall enhancement of tolerance to salinity. The utilization of proteomics in the study of rice-microbe interactions expands our understanding of microbe-mediated responsive proteomes towards salt stress tolerance.
Results
Under severe salt stress, the proteomes of rice in terms of abundance and identity are mainly influenced by salt stress, rice genotypes, and Methylobacterium oryzae CBMB20 inoculation. Interestingly, the endophytic plant growth-promoting (PGP) M. oryzae CBMB20 mediated changes in the salt-stressed salt-sensitive IR29 resulting to similar proteomes to that of the salt-tolerant FL478. There are common upregulated and downregulated DAPs in both IR29 and FL478 due to salt stress indicating similar mechanisms of salt stress tolerance and similar biological and molecular processes severely affected by salt stress conditions. However, inoculation with M. oryzae CBMB20 resulted to shared significantly increased DAPs between the inoculated IR29 and FL478 indicating common mechanisms of microbe-mediated salt stress tolerance. In addition, there are genotype-specific DAPs with restored functions as a result of the application of M. oryzae CBMB20 in IR29 and FL478 indicating other key proteins essential for microbe-mediated salt stress tolerance.
Conclusions
Under severe salt stress conditions, there are common and genotype-specific proteomic changes in the salt-sensitive and the moderately salt-tolerant rice genotypes. However, the moderately salt-tolerant FL478 upregulated more proteins with higher fold change values indicating more efficient responses to salt stress. The bioinoculation with the versatile PGP M. oryzae CBMB20 onto the two contrasting rice genotypes mediated proteomic changes under salt stress conditions further enhancing stress responses in both rice genotypes.
Role of CDK4 as prognostic biomarker and synergistic effect of its inhibition in dedifferentiated liposarcoma sequential treatment
Authors: Vanni Silvia , Miserocchi Giacomo , Gallo Graziana , Fausti Valentina , Gabellone Sofia , Liverani Chiara , Spadazzi Chiara , Cocchi Claudia , Calabrese Chiara , De Luca Giovanni , Bassi Massimo , Gessaroli Manlio , Tomasetti Nicola , Campobassi Angelo , Pieri Federica , Ercolani Giorgio , Cavaliere Davide , Gurrieri Lorena , Riva Nada , Recine Federica , Ibrahim Toni , Mercatali Laura , Jones Robin , De Vita Alessandro 
Published: November 2023

Abstract
Background
Soft tissue sarcomas represent a heterogeneous group of rare mesenchymal tumors comprising 1% of all solid malignancies. Among them liposarcoma is one of the most common histotypes, with atypical lipomatous tumor/well differentiated liposarcoma and dedifferentiated liposarcoma (ALT/WDLPS and DDLPS) as the major sub entities. The unavailability of predictive, prognostic and druggable biomarkers make the management of these lesions challenging. In recent years, CDK4 and its inhibitors have emerged as potential weapons for these lesions especially for ALT/WDLPS and DDLPS but the results are not conclusive and need to be elucidated.
Methods
This study involved 21 ALT/WDLPS and DDLPS patients. Histological analyses of MDM2 and CDK4 were carried out. Moreover, a DDLPS patient-derived cancer model was established in vitro and in vivo assessing the efficacy of palbociclib in combination and sequential treatment. Finally, in silico analyses on CDK4 expression were carried out.
Results
The results showed a higher expression of CDK4 and MDM2 in DDLPS compared to ALT/WDLPS. Moreover, no correlation between MDM2 expression and CDK4 was observed. In addition, CDK4 expression seemed to correlate with specific anatomic regions. Next, in vitro analysis of CDK4 inhibitor palbociclib showed an antagonistic effect when combined to other chemotherapeutics, while it exhibited a significant synergy when administered in sequential schedule in both 2D and 3D culture models with ifosfamide, ifosfamide plus epirubicin, trabectedin, dacarbazine, eribulin and lenvatinib. The data were further corroborated by scratch wound healing assay and live-dead analysis. Next, in vivo analysis on DDLPS xenotransplanted embryos assessing the efficacy and safety profile of the in vitro tested schedules confirmed the observed data. Finally, in silico analysis supported the role of CDK4 in sarcoma management, especially DDLPS.
Conclusion
This proof-of-concept study sheds light on the natural history of ALT/WDLPS and DDLPS and provides the rationale for the clinical applicability of sequential treatment with palbociclib in the management of DDLPS.
Google Trends Forecasting: Model re-specification of time-series for enhancing forecasts of youth unemployment
Authors: Wijnhoven Fons , De Bruijn Nathan , Effing Robin 
Published: November 2023

Abstract
In the last decades, the forecasting field has been using the surge in big data and advanced computational capabilities. Despite these developments, forecasters continue using traditional forecasting procedures that assume static relationships between phenomena. To address the reality of dynamic relations among phenomena, this study discusses time-variant re-specification methods as part of time-series based forecasts and compares the outcomes with the traditional procedures. This method-comparison is applied to a real-world exercise, the forecasting of Dutch youth unemployment with big data based on Google Trends. For youth unemployment forecasts, our results show 44% more forecasting accuracy by time-varying forecasting models than the traditional static forecasting models. Additionally, this study makes labour market forecasting an accessible endeavour to all organizations by sharing the algorithm for forecasting youth unemployment rates with publicly available data such as Google Trends. Moreover, our study stresses a reconsideration of forecasting methodologies towards model re-specification instead of model recalibration.
Intelligent Integration of Assessment Tools for Specialized Prognosis in Spinal Cord Injuries: A scoping review
Authors: Chrysanthakopoulou Dionysia , Koutsojannis Constantinos , Matzaroglou Charalampos , Trachani Eftychia 
Published: November 2023

Abstract
Spinal cord injury is a life-threatening condition resulting from spinal cord trauma, leading to paralysis, loss of sensation, bowel and bladder control. Accurate assessment tools are crucial for diagnosing and treating spinal cord injuries, and various scales have been developed for this purpose. Additionally, electrophysiological measures, including somatosensory evoked potentials, motor evoked potentials, and nerve conduction studies, can aid in patient stratification. Recent developments in spinal cord injury assessment have shown promise, particularly with the use of advanced imaging techniques and artificial intelligence. Neuroimaging and molecular biomarkers combined with electrophysiological measures, promise to predict outcomes and guide treatment decisions. Machine learning and Artificial intelligence have revolutionized the healthcare industry, including the field of spinal cord injuries, as they can facilitate personalized medicine by accurately predicting. Challenges remain in validating machine learning models and ensuring they are safe and effective for clinical use. Quality data and expertise are crucial for accurately interpreting and applying machine learning results in spinal cord injury management. Moreover, due to artificial intelligence entering healthcare to assist in processing data, electrophysiology can eventually meet the high-quality information it can provide, as it is easier to analyze data recordings from somatosensory evoked potentials and other electrophysiologic measures. Summing up, the integration of advanced imaging techniques, biomarkers, and machine learning leading to maximizing the use and importance of electrophysiology as far as the information it can reveal, has the potential to revolutionize the diagnosis, prognosis, and treatment of spinal cord injuries, leading to improved patient outcomes and personalized care.