bluuebunny/crossref_metadata_2025_split huggingface dataset:
A causal relationship between the lipidome and central nervous system tumors
Authors: Li yongxue , Lin Lihao , Zhang Wenhui , Wang Yan , GUAN YI 
Published: May 2024

Abstract

Background
The incidence of central nervous system (CNS) tumors is increasing. However, despite advances in treatment, the etiological factors and mechanisms remain poorly understood. Recent studies have increasingly linked the lipidome to the development of CNS tumors. However, the actual association between liposomes and nervous system tumors remains speculative and lacks definitive conclusions.
Methods
To address this gap, we used a Mendelian randomization approach to systematically evaluate the association between the lipidome and five common types of CNS tumors.
Results
Our findings revealed a causal association between 16 lipids and glioblastoma, six lipids and benign meningiomas, two lipids and pituitary tumors and craniopharyngiomas, three lipids and benign cranial nerve tumors, and two lipids and benign spinal cord tumors.
Conclusions
This study represents the first comprehensive examination of the association between lipid groups and common CNS tumors, offering crucial insights for further fundamental research into the etiology and clinical management of these conditions.
Experience of implementing metagenomic next-generation sequencing in patients with suspected pulmonary infection in clinical practice
Authors: Lai Yuting , Chen Binqi , Chen Sida , Shen Yan 
Published: May 2024

Abstract

Background Pulmonary infection remains one of the leading infectious diseases of hospitalization. Metagenomic next-generation sequencing (mNGS) has been proven to be a promising diagnostic technology in etiological identification for pulmonary infection. But when applying mNGS to clinical practice, physicians still face many challenges. Methods We retrospectively analyzed the data of 97 patients admitted to our hospital with suspected pulmonary infection prescribed to mNGS during the past 3 years. The clinical application of mNGS in the diagnosis and management of pulmonary infection and also challenges were investigated. Results Causative or possibly causative pathogens were detected in 63.9% of patients by mNGS, performing consistently well for Mycobacterium tuberculosis, non-tuberculous mycobacteria, fungus and rare pathogens. In 43.3% of patients, 65 microbes reported as causative or possibly causative pathogens by laboratory were reclassified as colonization after fully interpretation by physicians. Antibiotics were adjusted for 34.0% of patients mainly based on positive mNGS results and not adjusted for 41.2% with pathogens identified already covered by empirical therapy or negative mNGS results. Conclusions mNGS is a promising tool in etiological diagnosis of pulmonary infection. However, physicians should go beyond the reported pathogens by laboratory and investigate fully in clinical practice. The effect on clincal treatment deserves further investigation from aspect of cost effectiveness and also application scenarios should be illustrated.
A bio-economy model with reforestation value chain framework needed for sustainability and perpetuity in rehabilitated sites in IHR region
Authors: Ghosh Paromita 
Published: May 2024

Abstract

This study investigated the current status of five sites where rehabilitation of degraded land were carried out. The interventions were executed in most scientific manner which took into account the socio-economic and behavioural pattern of stakeholders involved in rehabilitation. Basic social and ecological parameters were also considered to evaluate the sustainability of the rehabilitated sites. Out of the five rehabilitated sites analysed, four sites that were handed over to stakeholders were not sustainable anymore while the site that is still under the institute management has reached an ideal state. All stake holders were engaged during the rehabilitation process and vegetation was established successfully. However the stakeholders were not able to reap the economic benefit post establishment of the sites which led to discontent and degradation of the rehabilitated sites. To make the sites sustainable beyond the intervention period, a site specific bio-economy model needs to be created along with reforestation value chain frame work (RVC) aimed at augmenting livelihood opportunities and economic growth from the rehabilitated site, to attain perpetuity. It is also envisaged that a minimum threshold area for rehabilitation needs to be calculated based on various productive agri-environmental schemes as enabling policy mechanism for sustainable restoration.
Removal of Mn2+ from Polluted Groundwater Using a Natural Ghanaian Brown coal: Batch experiments and geochemical modelling
Authors: Obiri-Nyarko Franklin , Darko Daniel Amoako , Quansah Jude Ofei , Asare Sandra Vincentia , Karikari Anthony Yaw 
Published: May 2024

Abstract

The study investigated the efficacy of natural Ghanaian brown coal (BC) in treating water contaminated with harmful levels of manganese (Mn2+). Batch adsorption experiments were performed to assess various factors, including Mn2+ concentration, BC dosage, temperature, and pH, to determine optimal conditions for Mn2+ removal. The thermodynamic and equilibrium studies indicated that the adsorption was spontaneous, favourable and endothermic. The highest adsorption occurred at pH 6. The adsorption kinetics was accurately described by the pseudo-second-order kinetic model, while the Langmuir isotherm excellently fit the experimental data, with a maximum adsorption capacity of 1.19 mg/g. The BC proved to be environmentally friendly and recyclable. Adsorption efficiency remained high (>80%) after 4 cycles of adsorption/desorption. However, the presence of competing cations (e.g., Cu2+) could impact its effectiveness. The adsorption mechanisms involved ion exchange with Ca2+, Mn2+, Na+, K+, and complexation with surface functional groups. The study in general suggests that the Ghanaian BC possesses favourable environmental characteristics and holds promise for Mn2+ removal in industrial applications.
Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability
Authors: Gómez-Martínez Vanesa , Chushig-Muzo David , Veierød Marit B. , Granja Conceição , Soguero-Ruiz Cristina 
Published: May 2024

Abstract

Background: Cutaneous melanoma, the most serious skin cancer, constitutes a considerable health burden in fair-skinned populations. Its increasing incidence highlights the need to develop automated computer-aided approaches that support dermatologists in melanoma detection. Recent advances in artificial intelligence (AI), jointly with the availability of public dermoscopy image datasets, have led to create data-driven models with high predictive performance, thus contributing to dermatology research. Despite these advantages, most image datasets present the class imbalance problem, where a few classes have numerous samples, whereas others are under-represented, impacting the performance of AI-based models.
Methods: In this paper, we propose to combine ensemble feature selection (FS) methods and data augmentation with the conditional tabular generative adversarial networks (CTGAN) to enhance melanoma identification in imbalanced datasets. We employed dermoscopy images from two public datasets, PH2 and Derm7pt, which contain melanoma and not-melanoma lesions. To capture intrinsic information from skin lesions, we conduct two feature extraction (FE) approaches, including handcrafted and embedding features. For the former, color, geometric and first-, second-, and higher-order texture features were extracted, while for the latter, embeddings were obtained using ResNet-based models. To alleviate the high-dimensionality in the FE, ensemble FS with filter methods were used and evaluated. For data augmentation, we conducted a progressive analysis of the imbalance ratio (IR), related to the amount of synthetic samples created, and evaluated the impact on the predictive results. To gain interpretability on predictive models, we used SHAP, bootstrap resampling statistical tests and UMAP visualizations.
Results: Experimental results showed that the combination of CTGAN and linear models achieved the best predictive results, achieving AUCROC values of 86% (with LASSO and IR=0.8) and 71% (with support vector machine and IR=0.9) for the PH2 and Derm7pt, respectively. We also identified that ‘melanoma’ lesions were mainly characterized by features related to color, while ‘not melanoma’ lesions were characterized by texture features.
Conclusions: Our results demonstrated the effectiveness of synthetic data to build generalizable models for melanoma prediction. Our work contributes to skin lesion research, helping in melanoma identification and the interpretation of main characteristics associated with melanoma.
The novel combination of mocetinostat and capecitabine has a greater anti-tumoral effect on induced breast cancer in BALB/c mice
Authors: KAYA Hacer , EROĞLU Onur 
Published: May 2024

Abstract

Background
Combining drugs has been suggested as a potential therapeutic approach to combat drug resistance and boost the effectiveness of cancer monotherapy regimens. The present study is intended to investigate the efficacy of the combination of histone deacetylase inhibitor (HDACi) mocetinostat and the antimetabolite drug capecitabine in breast cancer treatment in a pre-clinical mouse model.
Methods and Results
Female mice were assorted into the control group, capecitabine group, mocetinostat group, and capecitabine plus mocetinostat group. At the end of the 21-day experimental period, the body weight, tumor weight, tumor tissue recorded, and lung tissue of the animals were examined histologically. The body weight of the mice in the drug-treated groups decreased by approximately 18%. Tumor weights decreased by 21% in the mocetinostat group, 27.5% in the capecitabine group, and 45% in the combined group. In lung tissue, it was observed that nodules decreased, tissue integrity was preserved with dual drug administration, and it was effective on metastases.
Conclusion
In summary, mocetinostat combined with capecitabine produced a synergistic effect on the inhibition of breast cancer and was more effective in reducing tumor size than single use.
Removing carbon, restoring trust: public perceptions of industry and community roles in U.S. carbon dioxide removal policy
Authors: Scott-Buechler Celina 
Published: May 2024

Abstract

In a nationally representative survey (N = 1,488), I examine public perceptions of carbon dioxide removal (CDR) governance, justice, moral hazard, and the role of the fossil fuel industry in transitioning towards net-negative emissions. Qualitative coding reveals cost and misplaced financial responsibility as the most common concern, followed by doubts about CDR’s effectiveness and distrust towards industry profit motives. The most frequently cited benefit was direct job creation, followed by improved air quality, and long-term climate change benefits. I find that public support for CDR policies is significantly influenced by partisan and sectoral endorsements, with environmental NGOs bolstering support overall but sometimes reducing support among Republicans. The public shows no evidence of identifying the risk of moral hazard among policymakers supportive of CDR. Strong preferences emerge for policies that hold polluting industries accountable for their CO2 emissions, public over private ownership, and significant community engagement in CDR projects, reflecting a demand for transparent and equitable CDR governance.
Effect of Land Use Dynamics on the Distribution of Chromolaena Odorata (Asteraceae) and Mesosphaerum Suaveolens (Lamiaceae), Two Invasive Alien Species in Benin, West Africa
Authors: AÏKPON Gorgias , GANGLO Jean Cossi 
Published: May 2024

Abstract

Invasive plants are now a major threat to biodiversity, agriculture and natural habitats around the world. It is now increasingly recognized that land uses are linked to invasion processes. Despite the various mutations and land cover changes observed in Benin, the expansion of invasive alien species in relation to land uses is poorly documented. This study, through systemic analysis and remote sensing, aims to investigate the link between land uses and the spread of Chromolaena odorata and Mesosphaerum suaveolens, two dominant invasive alien species in Benin, West Africa. This study sheds light on the structure of the different land cover classes and establishes the link between the distribution of invasive alien plants and the dynamics of land use in the study environment. A mapping of land use by object-oriented approach, a typology of landscape elements by a systemic analysis and verification on the ground were carried out. We noted that agricultural practices and the exploitation of natural resources are the main drivers of land use. Several land use units have been identified. It appeared that mosaics of crops and fallow land are the most dominated by invasive plants, followed by open forest, wooded and shrubby savannahs. The study also revealed that the degree of invasion is linked to antropic disturbance. This suggests that managed land should be reforested, and that heavy thinning in forests and savannahs should be avoided.
An attempt to promote intelligent petroleum processing: a look at how different programming languages works with deep learning models for gasoline quality detection
Authors: Alzahawi Abdullah H. , Issa Hayder M. 
Published: May 2024

Abstract

The current study explores how several programming languages affect deep learning models for gasoline quality detection, highlighting the significance of choosing the best appropriate language according to project needs. Featuring several scientific computations using an integrated development environment of the Visual Studio Code (VS Code) tool. This work has reached multiple outcomes: Python is known for its rapid development and adaptability, which are well-suited for exploring quality control techniques in petroleum products. Java is recognized for its stability and flexibility to work across many platforms, making it ideal for large-scale deployments that require dependable performance in refinery equipment. C/C++ is lauded for its speed and ability to manage hardware resources effectively, which is essential in real-time production settings, allowing for quick analysis and little delay in quality control processes. The study utilizes a deep learning architecture in Java, C, and Python, with frameworks such as Visual Studio Code to aid in model construction, with the goal of creating flexible tools for monitoring the dynamic quality of petroleum products.
In silico analysis of the antisickling activity of curcuminoids and curcumin metabolites and their ADMET properties
Authors: Mbadiko Clément , Ngbolua Koto-te-Nyiwa , Bongo Gédéon , Matondo Aristote , Kilembe Jason , Tshibangu Damien , Tshilanda Dorothée , Mbemba Théophile , Mpiana Pius 
Published: May 2024

Abstract

The aim of this study was to demonstrate the in silico the potential of curcuminoids and curcumin metabolites to inhibit the polymerization of HbS. Ten molecules were analyzed, including curcuminoids (curcumin, bisdemethoxycurcumin, demethoxycurcumin and cyclocurcumin) and curcumin metabolites (dihydrocurcumin, glucuronocurcumin, hexahydrocurcumin, hexahydrocurcumin, curcumin sulphate and tetrahydrocurcumin). In silico screenings were carried out by molecular docking to determine the free enthalpies of complexation and the interactions between different ligands studied and HbS. Moreover, in silico screening using the ADMET test was carried out to determine the pharmacokinetic profile of various compounds studied. The findings revealed that all the ligands can form stable complexes with HbS. The binding energies of the complexes formed between the ligands and Hb S ranged from -5.8 Kcal/moL to -8.1 Kcal/moL. These bindings between curcumin glucuronide and cyclocurcumin with HbS were stable compared with the other compounds (-8.1 Kcal/moL and -8.0 Kcal/moL). The in silico toxicity screening showed that the majority of ligands fulfilled the Lipinski and Veber rules. Thus, we believe that curcuminoids can individually or synergistically inhibit HbS polymerization and sickle cell sickling.
Stochastic modeling for energy generation in Sub Saharan Africa (Case Study of NAWEC Power Plants-The Gambia)
Authors: Appiah Simon Kojo , Opoku Richard , Frempong Nana Kena , Cham Amadou 
Published: May 2024

Abstract

This study aims to enhance the operational efficiency of NAWEC thermal power stations through a comprehensive approach involving stochastic modeling, analysis, and forecasting of energy generation (EG) and fuel consumption (FC). The historical power plant monitoring data underwent rigorous examination using both the bivariate autoregressive moving average model BARMA and artificial neural networks (ANNs). The investigation revealed that KT1ENG8 demonstrated exceptional performance in univariate analysis, as evidenced by the autoregressive moving average model, which provides minimized Akaike information criterion (AIC) and Kulback information criterion (KIC) values. Consequently, the effectiveness of the bivariate ARMA (p,q) model extends the top prediction of fuel consumption and energy generation from thermal power plants, providing valuable insights for thermal health and maintenance scheduling. To help the reader grasp the inherent dynamic of each of these strategies and their performances, a comparative synopsis of the outcomes is provided. The ANN results further enrich the study by presenting R2 values for different engines, offering ranked perspectives on their respective predictive capabilities. BK1ENG1 and KT1ENG8 have higher r2 values of 99.6% and 97.3%, respectively, than do the other power plant engines.
Going beyond the Black-Scholes option pricing model: financial rogue waves in quantum mechanics
Authors: Li Lingfei , Zhu Minting , Wu Jingyu , Wang Mancang , Xie Yingying 
Published: May 2024

Abstract

This paper proposes a nonlinear alternative for the Black-Scholes option pricing model that is defined as the Gross-Pitaevskii equation. It models the controlled Brownian motion of the financial market and describes the option price wave function in terms of the compound price and time. Here, we analytically predict the existence of a family of financial rogue wave solutions of the Black-Scholes equation in the form of complex rational functions by introducing small perturbation and using symbolic computation. The obtained solutions compose of a single symmetric soliton or several solitary peaks. The existence of non-zero offset parameters provides nontrivial alterations to higher order solutions as they would decompose into first order ones that can be used to simulate the evolution of financial risks. Financial rogue wave demonstrates a condition that investors may face a huge risk or return in the financial market, which might be an actual theoretical basis for the existence of it. Our study is the first to give solid proof of the connection between option pricing and quantum mechanics. This finding opens a novel path for the excitation and control of financial waveforms of quantum mechanic footprint and extreme financial crisis.
Out of Pocket and Catastrophic Health Expenditure in Tanzania: Recent Evidence on the Incidence, Intensity and Distribution
Authors: Massito John Geofrey , Hinju Gabriel John 
Published: May 2024

Abstract

Background Health care in most of the low-income countries (LICs) is financed mainly by out-of-pocket (OOP) expenditures. However, it is contentious that this payment mechanism tends to cause a massive burden on poor households. This study investigates the catastrophic impact of out-of-pocket health expenditures in Tanzania. Specifically, the study estimates catastrophic health expenditure levels, intensity and distribution.Methods Using the panel dataset of 2020/2021, the study applied descriptive analytical methods such as frequencies, means, proportions and concentration indices to investigate the incidence, intensity and distribution of catastrophic health expenditure in Tanzania.Results The study found that 21.9% (19.1% from rural and 24.6% from urban) of the respondents reported visiting a healthcare facility within four weeks before the survey. Over 50% (53.5% from rural and 57.4% from urban) reported an incidence of illness or injury within the same period. The study also found that among those who utilized health care, about 7.1% (8.4% from rural and 5.7% from urban areas) experienced catastrophic health expenditures. The results of the concentration index indicate that poor households have a higher probability of incurring catastrophic health expenditures than rich households, which is evident in the context of Tanzania, where health insurance is in the embryo stage of development.Conclusion We conclude that out-of-pocket health expenditures tend to lead to financial catastrophe for poor households, thereby exposing them to more poverty and forcing them to resort to coping mechanisms that compromise their welfare. This calls for a need to explore new and strengthened existing mechanisms for shielding poor households against out-of-pocket and catastrophic health expenditures.
Potassium ferrate's disinfecting ability: a study on human adenovirus, Giardia duodenalis, and microbial indicators under varying pH and water temperature conditions
Authors: Boczek Laura A. , Ware Michael W. , Rodgers Mark R. , Ryu Hodon 
Published: May 2024

ABSTRACT
Ferrate (Fe(VI): HFeO4− /FeO42−), a potent oxidant, has been investigated as an alternative chemical disinfectant in water treatment due to its reduced production of disinfection by-products. In this study, we assessed the disinfecting ability of potassium ferrate against a variety of microorganisms, including waterborne pathogens, under varying pH and water temperature conditions. We presented CT values, a metric of ferrate concentrations (C) and contact time (T), to quantify microbial inactivation rates. Among the tested microorganisms, human adenovirus was the least resistant to ferrate, followed by waterborne bacteria such as Escherichia coli and Vibrio cholerae, and finally, the protozoan parasite Giardia duodenalis. We further investigated the impact of two pH values (7 and 8) and two temperatures (5 and 25 °C) on microbial inactivation rates, observing that inactivation rates increased with lower pH and higher temperature. In addition to showcasing ferrate's capacity to effectively inactivate a range of the tested microorganisms, we offer a ferrate CT table to facilitate the comparison of the effectiveness of various disinfection methods.