bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust fossil evidence for Proboscidean frugivory and its lasting impact on South American ecosystems
Authors: González Erwin , Loayza Andrea , Segovia Ricardo , Rivals Florent , Petermann Alia , Pedraza Iván Ramírez , Asevedo Lidiane , Tornero Carlos , Labarca Rafael , Latorre Claudio 
Published: March 2024

Abstract
Most megaherbivores in the Americas went extinct around 10,000 years ago, presumably disrupting the long-distance seed dispersal of large, fleshy-fruited plant species1. The neotropical anachronism hypothesis of Janzen and Martin2 is often used to explain many key adaptations of these "megafaunal fruit" plants, yet it lacks robust paleoecological evidence. Here, we use a multiproxy approach that provides unequivocal evidence of widespread frugivory by the extinct South American proboscidean Notiomastodon platensis. Using a model that reveals the extinction effects of these megafaunal dispersers in modern ecosystems, we show how losing these interactions has increased the extinction risk of megafaunal fruit plants in regions across South America lacking smaller surrogate dispersers. We further discuss the extent of functional changes that current ecosystems may undergo with continuing anthropogenic pressures.
Machine Learning-Based Yield Prediction for First-Row Transition Metal Catalyzed Cross-Coupling Reactions
Authors: C Rajalakshmi , Vijay Vivek , Vijayakumar Abhirami , Santhoshkumar Parvathi , Kottooran John B , Abraham Ann Miriam , G Krishnaveni , S Anjanakutty C , Varghese Binuja , Thomas Vibin Ipe 
Published: March 2024

Abstract
The advent of first-row transition metal-catalyzed cross-coupling reactions has marked a significant milestone in the field of organic chemistry, primarily due to their pivotal role in facilitating the construction of carbon-carbon and carbon-heteroatom bonds. Traditionally, the determination of reaction yields has relied on experimental methods, but in recent times, the integration of efficient machine learning techniques has revolutionized this process. Developing a highly accurate predictive model for reaction yields applicable to diverse categories of cross-coupling reactions, however, remains a formidable challenge. In our study, we curated an extendable dataset encompassing a wide range of yields of cross-coupling reactions catalyzed by first-row transition metals through rigorous literature mining efforts. Using this dataset, we have developed an automated and open-access reaction model, employing both regression and classification methodologies. Our ML model could be used even by non-expert users, who can solely input the reaction components as datasets to predict the yields. We have achieved a correlation of 0.46 using the Random Forest regression approach and an accuracy of 0.54 using the K-Nearest Neighbours (KNN) classification which employs hyperparameter tuning. Considering the vast chemical space of our small dataset encompassing various transition metals catalysts and different categories of reactions, the above results are commendable. By releasing an open-access dataset comprising cross-coupling reactions catalyzed by 3d-transition metal, our study is anticipated to make a substantial contribution to the progression of predictive modeling for sustainable transition metal catalysis, thereby shaping the future landscape of synthetic chemistry.
Investigation of Fattening Performance, Slaughter, Carcass and Some Meat Quality Characteristics of Imported Simmental, Charolaise and Belgian Blue x Holstein F1 Bullocks
Authors: Ünal Necmettin , Polat Mustafa , Yakan Akin 
Published: March 2024

Abstract

This research was carried out to examine the fattening performance, slaughter, carcass and some meat quality characteristics under intensive fattening conditions of Simmental, Charolaise and Belgian Blue x Holstein F1 bullocks imported from Europe to Türkiye. The animal material of the study was consisted of 13 Simmental, 14 Charolaise and 15 Belgian Blue x Holstein F1 (BBxH F1) bullocks, in totally 42 heads. Data for fattening performance were taken for 9 months of fattening period between the ages of 11–19 months. The bullocks were fed ad libitum with total mixed ration (TMR) (17% crude protein and 2700 kcal/kg DM metabolic energy). The fattening period of the Simmental, Charolaise and BBxH F1 bullocks was started at the 11 month of age with the live weight of 415.63 ± 10.72, 434.78 ± 15.97 kg and 405.60 ± 16.83 kg (P > 0.05), and finished at 19 month of age with the live weight of 648.04 ± 17.54, 742.91 ± 17.50 and 680.51 ± 15.02 kg (P < 0.001), respectively. In the genotypes, the means during whole fattening period were determined as 0.969 ± 0.053, 1.313 ± 0.076, and, 1.146 ± 0.055 kg (P < 0.001) for average daily weight gains (ADWG); 12.349, 9.192 and 10.368 kg for feed conversion rate (FCR); 62 ± 1.11, 60.12 ± 0.77, and 58.33 ± 0.83% for chilled dressing percentages, respectively. In the genotypes, while the ratios of total meat in the whole carcass were obtained as 79.20 ± 0.69, 74.96 ± 0.52 and 76.52 ± 0.98% (P < 0.01), the ratios of total bone were 17.57 ± 0.13, 15.49 ± 0.72 and 16.17 ± 0.49%, respectively. The means for the meat quality traits in the genotypes ascertained at 24th hours after slaughter were obtained as 5.54 ± 0.03, 5.65 ± 0.16, and 5.59 ± 0.06 for pH of longissimus thoracic muscle; 41.71 ± 1.14, 37.73 ± 2.22, and 43.22 ± 1.17 for L* (P < 0.05); 19.39 ± 0.64, 17.91 ± 1.76, and 19.40 ± 0.29 for a*; 11.93 ± 0.33, 10.66 ± 1.38, and 11.35 ± 0.39 for b*; 16.45 ± 0.40, 13.72 ± 1.08, and 15.85 ± 0.95% for water holding capacity; 23.16 ± 1.85, 26.28 ± 1.16, and 18.37 ± 0.94% for cooking loss (P < 0.01), respectively. In conclusion, it was determined that the fattening performances of imported Simmental, Charolaise, and BBxH F1 bullocks under intensive fattening conditions were acceptable, and the best in Charolaise bullocks compared to the other genotypes. While the ratios of the valuable cuts in the whole carcass, and some meat quality characteristics was the lowest in Charolaise, it was determined that the ratios of various bones in the whole carcass was higher in the Simmental bullock compared to the other genotypes.
Sourcing twin transitions: How the ICT sector influences green technology development in European cities
Authors: Barbieri Nicolò , Montanaro Alessandro , Gerli Paolo , Mora Luca 
Published: March 2024

Abstract

The growing interest and prominent position of urban environments in the European policy agenda contrast sharply with the limited attention they have received in existing research regarding their role in twin transitions. This article examines whether and to what extent the expansion of the Information and Technology (ICT) sector in urban settings influences their advancement in green technology domains. Leveraging a unique dataset focused on city-level developments, we find that cities with a larger ICT sector are more likely to experience the emergence of green technologies. Moreover, our analysis reveals that examining the heterogeneity of green technologies uncovers significant policy insights. Specifically, a more dynamic ICT sector contributes to notable advancements in cutting-edge technological fields like carbon capture and storage, energy production, and transportation.
The presence of a significant endophytic fungus in mycobiome of rice seed compartments
Authors: Jeong Eunji , Abdellaoui Najib , Lim Jae Yun , Seo Jeong-Ah 
Published: March 2024

Abstract

Seed microbial communities have been known to have a crucial role in the life cycle of a plant. In this study, we examined the distribution of the fungal communities in three compartments (husk, brown rice, and milled rice) of the fourteen rice seed samples using Illumina MiSeq sequencing. A total of 894 fungal operational taxonomic units were found at 97% sequence identity, and ten fungal genera distributed throughout the three compartments of the rice seeds were identified as the core mycobiome of the rice seed. Based on the diversity analysis, the distribution of the fungal community in milled rice was found to be more diversified, evenly distributed, and differently clustered from the other two compartments. Among core mycobiome, Moesziomyces dominated almost 80% of the fungal communities in the outer compartments of rice seeds, whereas the abundances of other endophytic pathogenic fungi declined. Our results provide that antagonistic yeast Moesziomyces may be able to control the endogenous pathogenic fungal communities in rice seeds, hence maintaining the quality of rice seeds. In addition, the distribution of fungal communities differs depending on the rice seed’s compartment, indicating that the compartment can affect the distribution of the seed microbial community.
Exploring multiple bioprocess monitoring modalities for Large-scale 3D Bioprinted Tissue Cultivation
Authors: CHASTAGNIER Laura , Pragnere Sarah , Gimènez Yilbert , Loubière Céline , el-Kholti Naima , Mazarakis Kleanthis , Schmidberger Timo , Olmos Eric , Lambert Simon Auguste , Marquette Christophe , Petiot Emma 
Published: March 2024

Abstract

In tissue engineering (TE) and regenerative medicine (RM), challenges persist in achieving optimal tissue maturation due to uncontrolled physicochemical environments and the necessity for a dynamic nutrient supply. Real-time monitoring tools are crucial to address these challenges effectively. Our study evaluates nondestructive qualification tools for pre-implantation tissue assessment, aiming to enhance their quality assessment capabilities and broaden their biomedical applications. These tools target internal tissue structure, nutritive medium flow paths, and tissue metabolic state. We extend the capabilities of tissue culture monitoring by integrating advanced bioprocess technologies like Raman spectroscopy or in-vivo imaging tools like magnetic resonance imaging (MRI). Through comparative analysis with Computational Fluid Dynamics (CFD) simulations and MRI velocity mapping, we highlight the synergistic relationship between simulation-based and experimental approaches in optimising tissue feeding and oxygenation. MRI emerges as a precious tool for longitudinal tissue development monitoring, surpassing traditional destructive methods. Our findings underscore the importance of dynamic regulation in tissue culture protocols, facilitated by continuous monitoring and adjustment of the physicochemical tissue environment. Based on evidence from industrial cell-culture processes, Raman spectroscopy emerges as a standard tool for monitoring metabolic tissue. These advancements significantly propel RM and TE, paving the way for comprehensive studies and quantitative analyses essential for developing functional engineered tissues across diverse biomedical applications.
Highly efficient removal of heavy metals from wastewater by MnO2-NP-CPC and γ-Fe2O3-NP-CPC nanomaterials: modeling and optimization with machine learning (Artificial Neural Network).
Authors: Belcaid Ayoub , Beakou Buscotin Horax , Bouhsina Saad , Anouar Abdellah 
Published: March 2024

Abstract

In this study, two nanomaterials with excellent adsorption capacities were developed to remove chromium (VI) and cobalt (II) heavy metals efficiently from wastewater. MnO2-NP-CPC and γ-Fe2O3-NP-CPC nanomaterials were successfully synthesized using an agricultural waste which is cassava peels, and characterized by different techniques namely FTIR, XRD, BET, SEM, and EDX analysis. The experimental tests for the adsorption process were done in a batch system, and the influence of various parameters such as temperature, initial concentration, pH, and contact time on the biosorption of cobalt (II) and chromium (VI) were fully investigated. Furthermore, the Qmax were 546,32 mg/g and 349,59 mg/g for chromium (VI) and cobalt (II) respectively. The results fitted well the monolayer Langmuir with the pseudo-second-order model, revealing that chemisorption controls heavy metals removal, while the thermodynamic sorption was an endothermic and spontaneous reaction. Artificial Neural Network (ANN) model was developed to predict as well as to simulate the experimental results, for this purpose, the proposed model was based on five independent inputs or variables and one output or response which is the predicted adsorbed amount of Cr (VI) and Co (II), the predicted results were in good agreement with the experimental values, indeed the proposed ANN model showed an appreciable prediction accuracy with high optimization ability for chromium (VI) and cobalt (II) removal. Hence the present work has great potential for the industrial and environmental applications of biochar and nanomaterials especially in wastewater treatment and green chemistry.
Air and surface temperatures differently drive terrestrial carbon and water cycles in the high latitudes
Authors: TANG Jing , Chen Shouzhi , Belda David , Rinnan Riikka , Körner Christian , Fu Yongshuo 
Published: March 2024

Abstract

High-latitude vegetation can experience much higher surface temperatures (Tsurf, at radiative equilibrium) than the ambient air temperature (Tair). In snow-free seasons, Tsurf is closely linked to plant physiological and biochemical processes. We drove the dynamic ecosystem model LPJ-GUESS with reanalysis data ERA5-land Tsurf and 2-m Tair to understand regional responses to these two temperatures. We show that plant growth in the tundra is stimulated by warmer Tsurf in the summer months, but in the boreal forests, colder Tsurf in the non-summer months constrains leaf development and enzyme activity for the growing season. Tsurf drives higher primary productivity of tundra plant individuals, but in the boreal forests, productivity is co-influenced by less productive individuals and compensatory changes in vegetation structure (coverage and density). This study demonstrates the importance of forcing temperature in simulating high-latitude ecosystem processes and calls for using correct plant-experienced temperatures to reduce uncertainties in estimating responses and feedback to the climate.
Nomogram prediction for the risk of medical adhesive-related skin injury at the peripherally inserted central catheter insertion site in patients with cancer
Authors: hu mengdie , wang xiaoyu , sun wenyuan , li yang , li xin , zheng qian , gao guanghui 
Published: March 2024

Abstract
Objective To establish a nomogram graph model to accurately predict the risk of medical adhesive-related skin injury(MARSI) at the peripherally inserted central catheter(PICC) insertion site in patients with cancer. Methods Based on data from patients with cancer in Hospital of China, the independent risk factors of MARSI at the PICC insertion site were identified by the logistic univariable and multivariable analysis and were integrated to construct a nomogram, which was validated externally.The predictive effectiveness of the nomogram was evaluated by the receiver operating characteristic curve (ROC) and calibration curve. Results A total of 352 cancer patients were included for analysis.The nomogram incorporated independent MARSI risk factors at the PICC insertion site including delayed cycle of catheter maintenance, moist skin, history of skin allergy, activated partial thromboplastin time(APTT). The C-index of the nomogram model was 0.917 and 0.864 in the training and validation cohort, respectively, demonstrating good discriminative power. The calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities. Conclusions We established and validated a novel nomogram for predicting the risk of MARSI at the PICC insertion site in patients with cancer. The nomogram model could precisely estimate the MARSI risk at the PICC insertion siteof cancer patients and identify high-risk patients who are in need of a specific treatment strategy.
Machine Learning Models for Predicting Heart Failure: Unveiling Patterns and Enhancing Precision in Cardiac Risk Assessment
Authors: Navaei Mahdi , Doogchi Zohreh 
Published: March 2024

Abstract
Introduction:
In the modern era, significant efforts are being made to forecast heart diseases using medical data and advancements in data science. Cardiovascular diseases (CVDs) stand as the foremost cause of global mortality, annually claiming millions of lives and contributing to about 31% of total deaths. Heart failure, a prevalent complication of CVDs, necessitates early diagnosis and management for individuals with a history of cardiovascular ailments or those at high risk. Machine learning models offer promising prospects in this regard.
Methodology:
This study employs five distinct machine learning models: logistic regression, support vector classifier, decision tree classifier, random forest classifier, and K-nearest neighbors. These models are applied to analyze medical data accurately. The dataset comprises information from over 900 individuals sourced from reputable medical institutions. It includes various features such as age, sex, chest pain severity, blood pressure, cholesterol levels, blood sugar levels, electrocardiogram results, and other pertinent medical characteristics.
Results:
Upon evaluation using different metrics, the performance of the machine learning models varied. Logistic regression demonstrated the highest accuracy in predicting heart failure at 88%, while the support vector classifier also achieved 88%. The decision tree classifier fell below 85%, the random forest classifier attained 84%, and the K-nearest neighbors classifier showed an accuracy of 82%. The analysis of the dataset revealed a balanced distribution and highlighted gender-based disparities in heart failure. Significant correlations with heart disease were observed for factors such as age, chest pain severity, blood glucose levels, and indicators of physical activity.
Conclusion:
Combining multiple machine learning models facilitates early detection of heart failure. However, selecting the most appropriate model requires careful consideration of project requirements, considering the observed variations in accuracy across different models.
Inbreeding depression affects the growth of seedlings of an African timber species with a mixed mating reproductive system, Pericopsis elata (Harms) Meeuwen
Authors: Angbonda Dieu Merci , Ilunga-Mulala Crispin , Bourland Nils , Beeckman Hans , Boyemba Faustin , Hatakiwe Hulda , Ngongo Jean Pierre , Hardy Olivier 
Published: March 2024

Abstract

Selfing or mating between related individuals can lead to inbreeding depression (ID), which can influence the survival, growth and evolution of populations of tree species. As selective logging involves a decrease in the density of congeneric partners, it could lead to increasing biparental inbreeding or self-fertilization, exposing the population to higher ID. We assessed the influence of inbreeding on the growth of a commercial timber species, Pericopsis elata (Fabaceae), which produced about 54% of self-fertilized seedlings in a natural population of the Congo basin. We followed the survival and growth of 540 plants raised in a plantation along a gradient of plant density (0.07 to 15.9 plants per m2). Parentage analysis allowed us distinguishing selfed and outcrossed seedlings. The annual growth was higher for outcrossed than selfed plants, on average by 10.8% for diameter and 12.9% for height growth. Based on the above ground biomass after 41 months, we estimated ID at δ = 0.32, while a life-time estimate of ID based on the proportions of selfed plants at seedling and adult stages led to δ = 0.7. The level of ID on growth rate did not change significantly with age but tended to vanish under high competition. P. elata is a particularly interesting model because inbreeding depression is partial, with about 26% of reproducing adults resulting from selfing, contrary to most tropical tree species where selfed individuals usually die before reaching adulthood. Hence, the risks of ID must be considered in the management and conservation of the species.
Paenibacillus spp. may facilitate anaerobic soil disinfestation under low temperature condition
Authors: SHISHIDO Masahiro , Pitti Eduardo , Momma Noriaki , Kobara Yuso , Usami Toshiyuki 
Published: March 2024

Abstract

We screened bacteria that may promote the suppressive effect on Ralstonia solanacearum in anaerobic soil disinfestation (ASD) using 1% ethanol at low temperatures (< 30℃). Among 22 isolates grown under anaerobic condition, we selected three isolates that their inoculation significantly suppressed the R. solanacearum population after ASD at 25℃. Based on the 16S rRNA sequences, two isolates were inferred as Paenibacillus polymyxa and the other as Paenibacillus sp. Using qPCR primers designed for these strains, we found that all strains increased in the soil after ASD at 25℃. When ASD was performed using 1% ethanol inoculated with or without these three strains in a concrete frame field site for the mean temperature of 26.3℃, the R. solanacearum population was significantly decreased in the inoculation treatment at the depth of 10 cm. These results suggest that these Paenibacillus strains have the potential to promote the ASD effect using 1% ethanol at low temperatures.
A machine learning approach to predict gene expression levels based on stochastic simulation
Authors: taweraqi Nada Al , King Ross 
Published: March 2024

Abstract

Purpose: Genes are key players in cellular systems as they control different aspects of cell behaviour and progression. Abnormalities in gene activities are indicators of cancer development and pathogenesis. However, despite the many advances in prediction tools and algorithms, predicting gene expression levels is considered challenging due to the noisy measurements of genes and the high complexity of the cellar networks. This study highlights the role of literature knowledge represented as signalling pathways in improving machine learning models' performance as they predict changes in gene expression levels.
Methods: In this work, we introduce simulation-based machine learning models, where we combine stochastic simulation models with multi-target machine learning models to predict gene expression levels.Prior knowledge of gene activities is used to run simulations to infer gene similarities based on their activities.Then these similarities are used to build the machine learning models.
Results: The results show that our method significantly outperforms the baseline, which does not include the literature knowledge.Moreover, It turns out that models of multi-target regression are sensitive to the number of included targets.Also, these models manage to capture the relationship between different targets even if their performance varies in the level of individual genes.