bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular Docking of Nigella Sativa L. with PXR Receptors and the Effect of Thymoquinone on PXR Expression in HepG2 Cells
Authors: Megawati Annik , Nurrochmad Arief , Nugroho Agung Endro Endro , Lukitaningsih Endang 
Published: August 2024

ABSTRACT. The interaction between herbal remedies and drugs is a fascinating phenomenon that might cause therapeutic complications in patients. Warfarin is an anticoagulant drug that provides anti-blood-clotting effects by inhibiting the formation of vitamin K-dependent coagulation factors, and warfarin interacts with the nuclear receptor PXR, subsequently modulating cytochrome P450 during the metabolism process. St. John's wort, an herbal medicine with antidepressant effects, can alter the pharmacokinetics of warfarin. Thymoquinone is one of the active compounds in N. sativa and has pharmacological activities such as anticoagulant, antidiabetic, diuretic, antidepressant, and anti-inflammatory effects. In this study, we investigated the compounds in Nigella sativa (N. sativa) against pregnane X receptor (PXR) and evaluated the impact of thymoquinone (TQ) administration on PXR expression in HepG2 cells. The molecular docking analysis was conducted utilizing the Molecular Operating Environment (MOE) with the PXR (PDB ID: 7AXJ). At the same time, the PXR gene expression was measured using RT-PCR instruments. The RMSD value in docking represents the deviation criteria between the native ligand position and the redocking position result, indicating the capability of MOE and PDB qualification for performing molecular docking. The docking analysis showed that warfarin had the strongest binding energy (7AXJ -6.0507) by forming hydrogen binding type on Arg410. Despite TQ being the major component, it also displayed a high affinity for the two PDB IDs (7AXJ -4.5962). Furthermore, the concurrent administration of warfarin and TQ (19.27 μM) in HepG2 cells showed a significant reduction in the relative mRNA expression of the PXR gene. Given the above-mentioned findings, our study adequately enables us to predict the mechanism behind herb-drug interactions (HDIs) implicating N. sativa, specifically the TQ compound to warfarin metabolism via the activation of the PXR receptor.

Keywords: Thymoquinone, expression mRNA, PXR, molecular docking, HepG2 cells.
Evaluation on the Application of Pseudommonas fluorescens and Chitosan Against Soybean Mosaic Virus
Authors: Andayanie Wuye Ria , Dinurani Praptiningsih Gamawati , Lukito Martin 
Published: August 2024

ABSTRACT. Soybean mosaic virus (SMV) causes a decrease in soybean [Glycine max (Leguminosae) Merril] production. Activity of the Pseudomonas fluorescens (Pseudomonadaceae ) and chitosan can increase soybean plant resistance to SMV and stimulate plant growth. Therefore, The study aimed to evaluate the effect of increasing salicylic acid and total phenol contents on the biological control activity of P. fluorescens and chitosan against SMV. This study used two soybean varieties (Wilis and Anjasmoro) and three lines (GK/Mlg 3288-7-11, W/PI 200.485-7-14, and GK/PI200.485-7-2). Treatment using seed encapsulation with P. fluorescens combined with chitosan on the Wilis variety was the highest percentage of the total phenolic and salicylic acid content. In addition, Applying P. fluorescens with chitosan suppresses disease incidence on the Wilis variety and GK/Mlg 3288-7-11 line. Moreover, the ELISA absorbance value was low (0.182−0.224) and reacted negatively with no symptoms. Therefore, applying P. fluorescens combined with chitosan on the GK/Mlg 3288-7-11 line could be developed as the candidate variety for SMV control in the endemic soybean plant area.

Keywords: Disease incidence, ELISA, induce systemic resistance, secondary metabolites, soybean.
Unveiling the inverted N-shaped environmental Kuznets curve in OECD nations: an empirical analysis
Authors: BULUT ABDULKADİR , TEKDEMIR Nazmiye 
Published: August 2024

Abstract

In the contemporary global context, economic growth has engendered various factors such as increased trade, industrialization and technological advancements, giving rise to negative externalities on a worldwide scale. Among these, environmental destruction stands as a significant global public bad resulting from these adverse effects. Balancing economic growth objectives while addressing environmental challenges has become a paramount issue for modern society. This study investigates the correlation between economic growth and environmental degradation across 33 OECD countries during the period 1995–2015, employing fixed effect with Driscoll-Kraay standard error estimation approach. The findings surprisingly revealed an inverted N-shaped relationship between economic growth and air pollution levels, contradicting the conventional Environmental Kuznets Curve theory. Our analysis also shows that rising renewable energy consumption helps reduce CO2 emissions, while non-renewable energy consumption has the opposite effect.
Hunting activity modulates wolves’ activity patterns during pup caring
Authors: López Ilad Gabriel Vivas , Zafra Alicia , Barja Isabel 
Published: August 2024

Abstract

Hunting any wildlife species is an anthropogenic pressure that modulates target species populations, affecting phenotype expressions like behavior. Such behavioral adaptations occur in the Iberian wolf (Canis lupus signatus), by adjusting its activity pattern and activity level to satisfy living needs. This response is mediated by temporal avoidance mechanisms that can induce physiological alterations in individuals. We have estimated activity levels and activity patterns from 640 camera trapping records of Iberian wolf and 829 of hunting disturbance, for 3 hunting allowed areas and 1 hunting banned area in the Macizo Central Ourensano, along the pup raring season. Wolves were active 10.83 h/day in hunting banned areas, and between 12 and 17.9 h/day in hunting permitted areas. The activity pattern of wolves was cathemeral in all the sampled areas, except for Baldriz, which is a hunting permitted area, where it was mostly nocturnal. Wolves were active up to 7 hours longer in hunting permitted areas than hunting banned areas. Wolf reproductive groups differed in their daily activity pattern due to differential hunting intensity, having activity peaks before noon in protected areas, and at twilight in non-protected areas. There was no change in wolf activity observed in the presence of off-road trucks, compared to the presence of hunting dogs and hunters, which are directly linked to hunting. This revealed that wolves adjust their activity pattern depending on conditions of the habitat such as presence of anthropic disturbances, like hunting, even when the wolf is not a target species.
Explainable AI for the Classification of Brain MRIs
Authors: Blake Nathan , Kelly David , Peña Santiago , Chanchal Akchunya , Chockler Hana 
Published: August 2024

Abstract

Background: Machine learning applied to medical imaging labor under a lack of trust due to the inscrutability of AI models and the lack of explanations for their outcomes. Explainable AI has therefore become an essential research topic. Unfortunately, many AI models, both research and commercial, are unavailable for white-box examination, in which access to a model’s internals is required. There is therefore a need for black-box explainability tools in the medical domain. Several such tools for general images exist, but their relative strengths and weak- nesses when applied to medical images have not been explored.
Methods: We use a publicly available dataset of brain MRI images and a model trained to classify cancerous and non-cancerous slices to assess a number of black-box explainability tools (LIME, RISE, IG, SHAP and ReX) and one white-box tool (Grad-CAM) as a baseline comparator. We use several common measures to assess the con- condance of the explanations with clinician provided annotations, including the Dice Coefficient, Hausdorff Distance, Jaccard Index and propose a Penalised Dice Coefficient which combines the strengths of these measures.
Results: ReX (Dice Coefficient = 0.42±0.20) consistently performs relatively well across all measures with comparable performance to Grad-CAM (Dice Coef- ficient = 0.33±0.22). A panel of images is presented for qualitative inspection, showing a number of failure modes.
Conclusion: In contrast to general images, we find evidence that most black-box explainability tools do not perform well for medical image classifications when used with default settings.
Development of the Crisis and Emergency Intervention Skills Scale (CEISS) and an analysis of its psychometric properties
Authors: Muñoz Fernando , Alarcón Rafael , Trillo Javier , Miranda Jesús , Polo Cristina 
Published: August 2024

Abstract

Emergency psychology is a branch of psychology that, although it began several years ago due to dramatic events around the world, is increasingly being established in the day-to-day work of the emergency department. Due to the very specific nature of this type of intervention, it is essential that, in addition to proper training and experience, emergency psychologists have a series of competencies. In this study, we developed a questionnaire, the Crisis and Emergency Intervention Skills Scale (CEISS), ad hoc for this research, and we determined the relationship of the CEISS score with the most relevant competencies. These capacities to intervene and provide assistance in crisis situations were found to be the sum of several skills. Positive correlations of CEISS scores with resilience, attention, clarity and regulation, which are related to emotional intelligence, were found. Knowing what psychological competencies are necessary for the correct performance of this type of task, as well as for professionals to have good psychological self-protection, is of great help when managing situations that require this type of intervention.
Analysis of Corn Leaf Area Index and Dry Matter Simulation Under Different Fertilization Treatments Based on APSIM Model
Authors: Aierken Gulaimubaier , Wei Renjuan , Liang Yi , Zhao Xin 
Published: August 2024

Abstract

The study simulated the effects of three fertilization schemes on maize leaf area index and dry matter accumulation using the APSIM model, and also investigated the adaptability of the APSIM model in simulating leaf area index and dry matter accumulation of summer maize in the region. Results indicated that the maize leaf area index was highest during the silking stage and lowest at the 9-leaf stage throughout the growth period. The maize leaf area index of the fertilization treatments was significantly higher than that of the non-fertilization treatment. As fertilization rates increased, the maize leaf area index gradually increased. Maize dry matter accumulation also increased with higher fertilization rates, with the fertilization treatments showing significantly higher accumulation compared to the control (CK). The APSIM model demonstrated high precision in simulating maize leaf area index and dry matter accumulation under different fertilization schemes, with root mean square error (RMSE) for leaf area index ranging from 0.093 to 0.250, coefficient of determination (R2) ranging from 0.956 to 0.992, and efficiency index (Me) ranging from 0.936 to 0.990. The RMSE for maize dry matter accumulation ranged from 0.03 to 0.332, R2 ranged from 0.961 to 0.998, and Me ranged from 0.880 to 0.980. Among the fertilization schemes, N3 exhibited the highest RMSE, R2, and Me values for both maize leaf area index and dry matter accumulation.
A process evaluation of the Walking In ScHools (WISH) Study using the RE-AIM framework
Authors: Carlin Angela , Doherty Leanne C. , O'Kane S. Maria , Jago Russ , Lahart Ian M. , McDermott Gary , Faulkner Maria , Gallagher Alison M. , Murphy Marie H. 
Published: August 2024

Abstract

Background
Physical inactivity in adolescents is a major public health issue. Schools are important settings for physical activity interventions. The WISH Study was a peer-led, school-based walking intervention that aimed to increase physical activity levels of adolescent girls. This study uses the RE-AIM Framework to present the process evaluation of the WISH Study with a focus on factors that affected participation, motivation, and enjoyment of the intervention.
Methods
Pupils (n589 12–14 years) were recruited from 18 schools in the border region of Ireland and Northern Ireland. In intervention schools (n9), older pupils (15–18 years) were trained as walk leaders and led pupils (12–14 years) in brisk walks (10–15 minutes) during the school day, across the school year (2021-22). Questionnaires were administered throughout the intervention, and at the end of the intervention focus groups were held with pupils, and interviews conducted with teachers and walk leaders. Attendance at walks was monitored by walk leaders. Quantitative data were analysed descriptively, and qualitative analysis was undertaken using thematic analysis. Themes were reported under RE-AIM indicators (reach, effectiveness, adoption, implementation, and maintenance).
Results
Participating pupils (n286) were representative of adolescent girls in Northern Ireland and Ireland, with 15% of pupils, achieving recommended physical activity guidelines (60 mins moderate-vigorous physical activity per day) at baseline. The total number of walks reported per school ranged from 2-104 across intervention schools. One quarter of pupils (n74, 26%) never attended a walk and 15% (n23) of walk leaders did not lead a walk. Pupils and walk leaders self-reported improvements in their physical health, mental well-being, and social relationships, and walk leaders reported gaining transferrable skills. Implementation was aided by school support in some schools, although barriers such as COVID-19, lack of time, harsh weather, inadequate facilities for indoor walks, and walk leader availability, were identified. Staff engagement was a key driver for the maintenance of walks.
Conclusions
Despite a promising feasibility study and a comprehensive walk leader training programme, the extent to which the intervention was adopted and implemented varied, suggesting future school-based physical activity interventions need to consider individual school contexts to ensure intervention fidelity.
Trial registration
: ISRCTN 12847782
Factors associated with adherence or hesitation to protective measures that influenced the control of COVID-19 in Brazil in the pre-vaccine era
Authors: Araújo Juliana Soares Tenório , Delpino Felipe Mendes , Moura Heriederson Sávio Dias , Nascimento Murilo César , Berra Thaís Zamboni , Ramos Antônio Carlos Vieira , Soares Débora Almeida , Arcêncio Ricardo Alexandre 
Published: August 2024

Abstract

Preventive behavior is one of the main strategies to contain the spread of the coronavirus, understand the factors that influence adherence or hesitation to protective measures and the way the population behaves during a health crisis is of great importance.
Aim: To analyze the factors associated with adherence to protection measures against Covid-19 in Brazil.
Method: cross-sectional study, survey type online, between the period of August 2020 and February 2021. The population included in the study were Brazilians, aged 18 years or older. Non-probabilistic sampling was used to obtain the sample. The data was stored on the "Redcap" platform and analyzed in a descriptive and inferential approach.
Results: The sample consisted of 1,516 people, women adopted 10% more protective measures than men, people with higher education level and higher income, within the age group between 40-59 years, were those who most adhered to the measures imposed by health agencies. Carrier of Asthma, Diabetes Mellitus, Systemic Arterial Hypertension, Obesity and smoking were factors that increased the adherence of protective measures in the fight against COVID-19.
Conclusion: Being female, aged between 40 and 59 years, higher education, smoking, not having a religion, having health insurance, and being a carrier of chronic diseases were associated with greater adherence to protective measures against COVID-19.
Mixed Monitoring Model of Concrete Arch Dam on Residual Sequence Correction
Authors: Wang Zixuan , Fu Shuyan , Chen Dehui , Chi Haoquan , Bin Ou , Yang Meng 
Published: August 2024

Abstract

Under the dual influence of internal factors and the external environment, dam deformation shows a high degree of nonlinear characteristics. In the traditional dam deformation prediction model, an abnormal value of the residual sequence reflects an abnormal situation or potential fault in the dam deformation prediction. Therefore, it is particularly important to fully mine the effective information in the residual sequence. Considering the nonlinear, time-varying and chaotic characteristics of dam time series, a hybrid monitoring model of concrete arch dams based on residual sequence correction is proposed in this paper. Firstly, combined with the finite element method, the statistical model was used to establish the initial mixed prediction model based on the monitoring data. In view of the specific characteristics and periodicity of the prediction residuals of the hybrid model, this paper uses symplectic geometric mode decomposition (SGMD) to decompose the residual sequence. Then, the convolutional neural network (CNN) is optimized by the improved sparrow search algorithm (ISSA), and the decomposed modal components are predicted and reconstructed by the bidirectional gated recurrent unit (BiGRU) of the attention mechanism. Subsequently, the differential autoregressive moving average model (ARIMA) is used to correct the reconstructed residual sequence. Finally, the modified residual sequence is combined with the initial hybrid model to construct a hybrid monitoring model of a concrete arch dam based on residual sequence correction. Through the refinement of the residuals, the accuracy and reliability of the monitoring data are further improved. This provides a new and effective method for safety monitoring and early warning of concrete arch dams.
Skin mucus extract derived from Channa punctatus, Channa striatus, and Heteropneustes fossilis induces apoptosis and suppresses proliferation in human adenocarcinoma cells via ROS mediated pathway and cell cycle arrest
Authors: Jameel Farheen , Agarwal Priyanka , Ahmad Rumana , Siddiqui Sahabjada , Serajuddin M. 
Published: August 2024

Abstract

Purpose
Skin mucus of freshwater fish species living in muddy habitats possesses the ability to inhibit the growth of human pathogens, and may also have the potential to inhibit the growth of cancer cells. This study first explored the anti-cancer activity of skin mucus acetic extracts of Channa punctatus (CPMA), Channa striatus (CSMA) and Heteropneustes fossilis (HFMA) against lung adenocarcinoma.
Methods
The cytotoxicity of the extract(s) on A549 and NRK-52E cells were evaluated using MTT assay and morpholological analysis of cells. Nuclear condensation, ROS generation, apoptosis and cell cycle analysis was performed on A549 cells. The protein profiles of extract(s) were evaluated via. SDS-PAGE.
Results
SDS-PAGE showed 15.2–69.1, 14.3–70.2 and 14- 97.5 kDa proteins in CPMA, CSMA and HFMA respectively. The extracts inhibited cellular proliferation in A549 cells in a dose-dependent manner, with IC50 values of 619 (CPMA), 591 (CSMA), and 538 µg/mL (HFMA). Morphological changes in the cells, presence of condensed nuclei and intracellular production of ROS increased dose dependently in the order HFMA > CSMA > CSMA. Early and late apoptotic cells were observed at low and high doses respectively. Moreover, G2/M arrest was found at low dose and apoptosis at high dose of CPMA and CSMA, while both G2/M arrest and apoptosis was found in a dose dependent manner in HFMA treated cells. Interestingly, no toxicity was observed against healthy NRK-52E cells.
Conclusion
HFMA possess the highest cytotoxic effect against A549 cells, followed by CSMA and then CPMA, which directly corresponds to the range of MW of proteins present therein.
Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions
Authors: Ma Jia , Ma Xinru , Li Chulian , Li Tongyan 
Published: August 2024

Abstract

Intelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. However, how to improve the distribution efficiency with low operating costs, and mitigate environmental pollution with high service quality is still a significant challenge in the practical industry applications. To address the above challenge, in this paper, we take into account both the economic cost and environmental cost, and propose a joint distribution path planning model based on neural architecture search (NAS) for electric vehicles with double-decked drones. More specifically, in our design, the factors such as energy consumption and carbon emissions of vehicles and drones during different distribution stages are considered. Then, a mixed integer linear programming model is established under the constraints of customer time window, vehicle capacity and vehicle battery capacity. Based on this model, a hybrid genetic algorithm is proposed to solve the optimization problem, where the carbon emission cost is estimated by the convolution neural network model, which is optimized by the neural architecture search technique. We conduct extensive experiments to validate the effectiveness of the proposed method. The experimental results show that, compared with CPLEX, the proposed method excelled in both solution quality and speed, which verify the effectives of our hybrid algorithm in dealing with the 2E-VRPD problem, where delivery routes can be well optimized, the efficiency of vehicle-machine collaborative delivery can be improved, and delivery costs are reduced.
Comparative study of lignin biocellulose and bacterial cellulose
Authors: Othman Jofry Bin , Rahman Norliza Abd , Ali Jarinah Mohd , Kamarudin Siti Kartom 
Published: August 2024

Abstract

The abundant oil palm empty fruit bunches possess attractive properties of lignin and biocellulose that present significant potential for conversion into a range of products. Nonetheless, this requires an appropriate pretreatment strategy that achieves a favorable equilibrium between effective lignin removal and mild cellulose extraction, to fully exploit these biopolymer constituents. This study demonstrated the effective utilization of OPEFB lignin and β-cellulose in developing an innovative biofilm product, underscoring the efficient utilization of lignin biocellulose. An encouraging technique involving bacterial cellulose, which is increasingly being explored for new applications in the food and medical fields, is evaluated for its suitability in OPEFB pretreatment. Nevertheless, it was observed that the current enzymatic approach is not ideal for overcoming the lignin protective barrier during OPEFB pretreatment. Areas for enhancement in this method are pinpointed, particularly in the production of ligninolytic enzymes that can rival emerging alkaline pretreatment methods.