bluuebunny/crossref_metadata_2025 huggingface dataset:
Cognitive household labor: gender disparities and consequences for maternal mental health and wellbeing
Authors: Aviv Elizabeth , Waizman Yael , Kim Elizabeth , Liu Jasmine , Rodsky Eve , Saxbe Darby
Published: July 2024

Purpose

Although the division of unpaid household labor has been studied as a driver of global gender inequity, the cognitive dimension of household labor—planning, anticipating, and delegating household tasks—has received less empirical investigation. Cognitive household labor represents a form of invisible and often unacknowledged domestic work that has been challenging to measure.

Methods

Within 322 mothers of young children, we assessed the division of both cognitive (“planning”) and physical (“execution”) household labor within 30 common household tasks using a self-report measure.

Results

We found that while mothers did more of the overall domestic labor than their partners, the division of cognitive labor was particularly gendered, such that women’s share of cognitive labor was more disproportionate than physical household labor. We found that cognitive labor was associated with women’s depression, stress, burnout, overall mental health, and relationship functioning.

Conclusions

This study is one of the first to investigate cognitive labor quantitatively, and the first to investigate cognitive and physical dimensions within the same household tasks. Understanding how cognitive labor affects mothers’ mental wellbeing has important implications for both practice and policy.
Instability in Computational Models of Vascular Smooth Muscle Cell Contraction
Authors: Giudici Alessandro , Szafron Jason M. , Ramachandra Abhay B. , Spronck Bart
Published: July 2024

Purpose

Through their contractile and synthetic capacity, vascular smooth muscle cells (VSMCs) can regulate the stiffness and resistance of the circulation. To model the contraction of blood vessels, an active stress component can be added to the (passive) Cauchy stress tensor. Different constitutive formulations have been proposed to describe this active stress component. Notably, however, measuring biomechanical behaviour of contracted blood vessels ex vivo presents several experimental challenges, which complicate the acquisition of comprehensive datasets to inform complex active stress models. In this work, we examine formulations for use with limited experimental contraction data as well as those developed to capture more comprehensive datasets.

Methods

First, we prove analytically that a subset of constitutive active stress formulations exhibits unstable behaviours (i.e., a non-unique diameter solution for a given pressure) in certain parameter ranges, particularly for large contractile deformations. Second, using experimental literature data, we present two case studies where these formulations are used to capture the contractile response of VSMCs in the presence of (1) limited and (2) extensive contraction data.

Results

We show how limited contraction data complicates selecting an appropriate active stress model for vascular applications, potentially resulting in unrealistic modelled behaviours.

Conclusion

Our data provide a useful reference for selecting an active stress model which balances the trade-off between accuracy and available biomechanical information. Whilst complex physiologically motivated models’ superior accuracy is recommended whenever active biomechanics can be extensively characterised experimentally, a constant 2nd Piola-Kirchhoff active stress model balances well accuracy and applicability with sparse contractile data.
Verification and Validation for Large Eddy Simulation of Cavitating Flow Around a Projectile Near the Free Surface
Authors: Deng Linfeng , Long Yun , Cheng Huaiyu , Ji Bin
Published: July 2024

Verification and validation (V&V) is a helpful tool for evaluating simulation errors, but its application in unsteady cavitating flow remains a challenging issue due to the difficulty in meeting the requirement of an asymptotic range. Hence, a new V&V approach for large eddy simulation (LES) is proposed. This approach offers a viable solution for the error estimation of simulation data that are unable to satisfy the asymptotic range. The simulation errors of cavitating flow around a projectile near the free surface are assessed using the new V&V method. The evident error values are primarily dispersed around the cavity region and free surface. The increasingly intense cavitating flow increases the error magnitudes. In addition, the modeling error magnitudes of the Dynamic Smagorinsky–Lilly model are substantially smaller than that of the Smagorinsky–Lilly model. The present V&V method can capture the decrease in the modeling errors due to model enhancements, further exhibiting its applicability in cavitating flow simulations. Moreover, the monitoring points where the simulation data are beyond the asymptotic range are primarily dispersed near the cavity region, and the number of such points grows as the cavitating flow intensifies. The simulation outcomes also suggest that the re-entrant jet and shedding cavity collapse are the chief sources of vorticity motions, which remarkably affect the simulation accuracy. The results of this study provide a valuable reference for V&V research.
Examining the influencing factors of elementary and high school STEM teachers’ self-efficacy: a meta-analysis
Authors: Wu Xin-Ning , Liao Hui-Yan , Guan Lin-Xue
Published: July 2024

The meta-analysis study aimed to investigate the impact of Science, Technology, Engineering, and Mathematics (STEM) education on the elementary and high school teachers’ self-efficacy. By analyzing 20 studies (N = 1,964) published between 2007 and 2022, we explored the effect size of STEM education on teachers’ self-efficacy and examined the potential moderating variables. The results of the study indicate that compared to non-STEM education and other educational models, STEM education has a significantly positive impact on the self-efficacy of elementary and high school teachers. This means that through receiving STEM education, teachers become more confident and capable in teaching STEM subjects. This finding is of great importance in improving teachers’ instructional effectiveness and students’ learning outcomes. Additionally, we examined several moderating variables, including sample size, control treatments, subject, educational levels, countries, and questionnaire measurements. The analysis of these moderating variables revealed differences in their impact on the relationship between STEM education and teachers’ self-efficacy. This provides valuable information for future research and offers guidance for designing and implementing STEM education programs and interventions. Therefore, the findings of this meta-analysis study emphasize the positive impact of STEM education on the elementary and high school teachers’ self-efficacy. It provides important guidance for educational policymakers and practitioners to promote the development of STEM education and enhance teachers’ instructional abilities and students’ learning outcomes.
Lattice Boltzmann simulation of cross-linked polymer gel injection in porous media
Authors: Kamel Targhi Elahe , Emami Niri Mohammad , Rasaei Mohammad Reza , Zitha Pacelli L. J.
Published: July 2024

This study addresses the critical challenge of excessive water production in mature oil and gas reservoirs. It focuses on the effectiveness of polymer gel injection into porous media as a solution, with an emphasis on understanding its impact at the pore scale. A step-wise Lattice Boltzmann Method (LBM) is employed to simulate polymer gel injection into a 2D Berea sample, representing a realistic porous media. The non-Newtonian, time-dependent characteristics of polymer gel fluid necessitate this detailed pore-scale analysis. Validation of the simulation results is conducted at each procedural step. The study reveals that the methodology is successful in predicting the effect of polymer gel on reducing permeability as the gel was mainly formed in relatively larger pores, as it is desirable for controlling water cut. Mathematical model presented in this study accurately predicts permeability reductions up to 100% (complete blockage). In addition, simulations conducted over a wide range of gelation parameters, TD_factor from 1 to 1.14 and Threshold between 0.55 and 0.95, revealed a quadratic relationship between permeability reduction and these parameters. The result of this research indicates LBM can be considered as promising tool for investigating time-dependant fluids on porous media.
Cellulose, graphene and graphene-cellulose composite aerogels and their application in water treatment: a review
Authors: Sekwele Karabo G. , Tichapondwa Shepherd M. , Mhike Washington
Published: July 2024

Due to their unique properties such as outstanding specific surface areas, aerogels can potentially offer a cost-effective and efficient water and wastewater treatment solution thereby solving the worldwide ever-growing water pollution conundrum. This review explores the developments and progress in the application of cellulose, graphene, and cellulose-graphene composite aerogels in addressing water treatment challenges. The objective of this study was to critically review the state-of-the-art with respect to synthesis methods and properties of cellulose, graphene, and cellulose-graphene composite aerogels and their application, effectiveness and performance in water and wastewater treatment. It is evident from the review that cost-effective aerogel production methods that can be scaled up easily still need to be developed to enable commercial viability of aerogels in water treatment. Furthermore, the mechanical properties of aerogels, particularly stiffness and strength, are a hinderance to expanding their utility. The potential for functionalization of the aerogels through fabrication of aerogel nanocomposites or incorporation of functional groups was recognized as a viable method to enhance their properties and effectiveness in water treatment and other advanced industrial-scale applications. Graphene-cellulose composite aerogels have enhanced properties, removal capacities and efficiencies for pollutants from water compared to aerogels prepared from the individual materials. The regeneration ability and reusability of the composite aerogels make them an attractive option for practical applications. Although the potential of cellulose, graphene, and cellulose-graphene composite aerogels in water treatment applications is apparent, the impact of their production, utilization and subsequent disposal on sustainability and the environmental is yet to be fully studied.
The continuing trend of air warming and humidifying in Kowloon, Hong Kong—is there an effective intervention?
Authors: Li Yuguo , Fan Yifan , Yin Shi , Little John C.
Published: July 2024

The Kowloon Peninsula, an urban area of Hong Kong, might be one of the most studied cities in terms of urban climate in the world. Both annual mean urban air temperature and humidity have been on the rise following the continual increase in the building volume and continual reduction in the average wind speed in the urban canopy layer. The observed urban warming and humidifying phenomena are a direct result of the city form (i.e., the city shape and size), including the building height and density. We call for a system-of-systems approach to be adopted in urban climate studies.
First report of Mugilogobius rambaiae (Smith, 1945) (Family: Gobiidae) from India along with geographical expansion of two goby fishes from Kerala
Authors: Sreeraj C. R. , Hussna N. , Sen Arya , Limna Mol V. P.
Published: July 2024

The discovery of three previously undocumented gobioid fish species in Kerala's mangrove ecosystems, namely Eugnathogobius mindora, Mugilogobius mertoni, and Mugilogobius rambaiae, marks a significant advancement in our understanding of the region's aquatic biodiversity. These findings highlight the importance of targeted sampling initiatives and underscore the need for continued exploration and conservation efforts to protect Kerala's fragile mangrove habitats. The adaptability of gobioid fish species from the genera Eugnathogobius and Mugilogobius to fluctuating salinity levels makes them particularly well-suited to thrive in the dynamic environments of shallow intertidal and subtidal estuaries, as well as mangrove ecosystems. Their presence is crucial in maintaining the ecological balance of coastal habitats in the Indo-Pacific region, including Kerala. The discovery of these gobioid fish species not only enhances the ecological richness of Kerala but also offers a significant understanding of the intricate relationships between these fish and their habitats. By enhancing the understanding of the range of gobioid fish species found in the mangroves of Kerala, more effective conservation and management approaches to safeguard these vital coastal ecosystems can be developed. Continuing study and conservation efforts are crucial for the preservation of Kerala’s mangrove ecosystems and the species diversity. By comprehending the enigmas of gobioid fish diversity and other fundamental facets of mangrove ecosystems, substantial advancements can be made in guaranteeing enduring sustainability for future generations.
Deportation and human rights: the right to respect for private life in MK (Albania) v Minister for Justice and Equality
Authors: Murphy Clíodhna
Published: July 2024

The sovereign power to control the entry and residence of persons in the state, and the corollary power to deport, has long been considered to be a defining feature of statehood. State discretion as to who may remain within the national border is, however, tempered by international and regional human rights obligations, as well as domestic constitutional principles. In this context, it is well established that a deportation will violate Article 8 of the European Convention on Human Rights (ECHR) if it constitutes a disproportionate interference with family and/or private life in the host country.
The Role of Climate, Mineralogy and Stable Aggregates for Soil Organic Carbon Dynamics Along a Geoclimatic Gradient
Authors: Wasner Daniel , Abramoff Rose , Griepentrog Marco , Venegas Erick Zagal , Boeckx Pascal , Doetterl Sebastian
Published: July 2024

Organic matter accumulation in soil is understood as the result of the dynamics between mineral‐associated (more decomposed, microbial derived) organic matter and free particulate (less decomposed, plant derived) organic matter. However, from regional to global scales, patterns and drivers behind main soil organic carbon (SOC) fractions are not well understood and remain poorly linked to the pedogenetic variation across soil types. Here, we separated SOC associated with silt‐ and clay‐sized particles (S + C), stable aggregates (>63 μm, SA) and particulate organic matter (POM) from a diverse range of grassland topsoils sampled along a geoclimatic gradient. The relative contribution of the two mineral‐associated fractions (S + C & SA) to SOC differed significantly across the gradient, while POM was never the dominant SOC fraction. Stable aggregates (>63 μm) emerged as the major SOC fraction in carbon‐rich soils. The degree of decomposition of carbon in stable aggregates (>63 μm) was consistently between that of the S + C and POM fractions and did not change along the investigated gradient. In contrast, carbon associated with the S + C fraction was less microbially decomposed in carbon‐rich soils than in carbon‐poor soils. The amount of SOC in the S + C fraction was positively correlated to pedogenic oxide contents and texture, whereas the amount of SOC associated with stable aggregates (>63 μm) was positively correlated to pedogenic oxide contents and negatively to temperature. We present a conceptual summary of our findings, which integrates the role of stable aggregates (>63 μm) with other major SOC fractions and illustrates their changing importance across (soil‐)environmental gradients.
Stabilization of EREG via STT3B-mediated N-glycosylation is critical for PDL1 upregulation and immune evasion in head and neck squamous cell carcinoma
Authors: Xu Shengming , Wang Haifeng , Zhu Yu , Han Yong , Liu Liu , Zhang Xiangkai , Hu Jingzhou , Zhang Wuchang , Duan Shengzhong , Deng Jiong , Zhang Zhiyuan , Liu Shuli
Published: July 2024

Dysregulated Epiregulin (EREG) can activate epidermal growth factor receptor (EGFR) and promote tumor progression in head and neck squamous cell carcinoma (HNSCC). However, the mechanisms underlying EREG dysregulation remain largely unknown. Here, we showed that dysregulated EREG was highly associated with enhanced PDL1 in HNSCC tissues. Treatment of HNSCC cells with EREG resulted in upregulated PDL1 via the c-myc pathway. Of note, we found that N-glycosylation of EREG was essential for its stability, membrane location, biological function, and upregulation of its downstream target PDL1 in HNSCC. EREG was glycosylated at N47 via STT3B glycosyltransferases, whereas mutations at N47 site abrogated N-glycosylation and destabilized EREG. Consistently, knockdown of STT3B suppressed glycosylated EREG and inhibited PDL1 in HNSCC cells. Moreover, treatment of HNSCC cells with NGI-1, an inhibitor of STT3B, blocked STT3B-mediated glycosylation of EREG, leading to its degradation and suppression of PDL1. Finally, combination of NGI-1 treatment with anti-PDLl therapy synergistically enhanced the efficacy of immunotherapy of HNSCC in vivo. Taken together, STT3B-mediated N-glycosylation is essential for stabilization of EREG, which mediates PDL1 upregulation and immune evasion in HNSCC.
VAMP2 regulates phase separation of α-synuclein
Authors: Agarwal Aishwarya , Chandran Aswathy , Raza Farheen , Ungureanu Irina-Maria , Hilcenko Christine , Stott Katherine , Bright Nicholas A. , Morone Nobuhiro , Warren Alan J. , Lautenschläger Janin
Published: July 2024

α-Synuclein (αSYN), a pivotal synaptic protein implicated in synucleinopathies such as Parkinson’s disease and Lewy body dementia, undergoes protein phase separation. We reveal that vesicle-associated membrane protein 2 (VAMP2) orchestrates αSYN phase separation both in vitro and in cells. Electrostatic interactions, specifically mediated by VAMP2 via its juxtamembrane domain and the αSYN C-terminal region, drive phase separation. Condensate formation is specific for R-SNARE VAMP2 and dependent on αSYN lipid membrane binding. Our results delineate a regulatory mechanism for αSYN phase separation in cells. Furthermore, we show that αSYN condensates sequester vesicles and attract complexin-1 and -2, thus supporting a role in synaptic physiology and pathophysiology.
Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021
Authors: Maze Mona , Taqi Mohamed Omar , Tolba Rania , Abdel-Wareth Ahmed A. A. , Lohakare Jayant
Published: July 2024

This study investigates methane emissions from the livestock sector, representing by enteric fermentation and manure management, in Egypt from 1989 to 2021, focusing on spatial and temporal variations at the governorate level. Utilizing IPCC guidelines and emission factors, methane emissions were estimated for dairy and non-dairy cattle, buffalo, sheep and goat, poultry, and other livestock categories. Results reveal fluctuating emission patterns over the study period, with notable declines in certain governorates such as Kafr El-Sheikh and Red Sea, attributed to reductions in livestock populations. However, increasing trends were observed overall, driven by population growth in other regions. Hotspots of methane emissions were identified in delta governorates like Behera and Sharkia, as well as agriculturally rich regions including Menia and Suhag. While livestock populations varied between regions, factors such as water availability, climatic conditions, and farming practices influenced distribution. Notably, cluster analysis did not reveal regional clustering among governorates, suggesting emissions changes were not dependent on specific geographic or climatic boundaries. Manure management accounted for only 5–6% of total emissions, with emissions at their lowest in the last three years due to population declines. Despite the highest livestock populations being sheep and goats, emissions from enteric fermentation and manure management were highest from buffalo and cattle. This study underscores the importance of accurate data collection and adherence to IPCC recommendations for estimating GHG emissions, enabling the development of targeted mitigation strategies to address climate change challenges in the livestock sector.
Analysis of banana plant health using machine learning techniques
Authors: Thiagarajan Joshva Devadas , Kulkarni Siddharaj Vitthal , Jadhav Shreyas Anil , Waghe Ayush Ashish , Raja S. P. , Rajagopal Sivakumar , Poddar Harshit , Subramaniam Shamala
Published: July 2024

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.
Automated detection, labelling and radiological grading of clinical spinal MRIs
Authors: Windsor Rhydian , Jamaludin Amir , Kadir Timor , Zisserman Andrew
Published: July 2024

Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model’s grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.
Scattering integral equation formulation for intravascular inclusion biosensing
Authors: Valagiannopoulos Constantinos , Tosi Daniele
Published: July 2024

A dielectric waveguide, inserted into blood vessels, supports its basic mode that is being scattered by a near-field intravascular inclusion. A rigorous integral equation formulation is performed and the electromagnetic response from that inhomogeneity is semi-analytically evaluated. The detectability of the formation, based on spatial distribution of the recorded signal, is estimated by considering various inclusion sizes, locations and textural contrasts. The proposed technique, with its variants and generalizations, provides a generic versatile toolbox to efficiently model biosensor layouts involved in healthcare monitoring and disease screening.
Comparison of digital and analog [68Ga]Ga-PSMA-11 PET/CT for detecting post-prostatectomy biochemical recurrence in prostate cancer patients: a prospective study
Authors: Kim Yong-il , Lee Dong Yun , Sung Changhwan , Lee Sang Ju , Oh Seung Jun , Oh Jungsu S. , Yoon Shinkyo , Lee Jae Lyun , Lim Bumjin , Suh Jungyo , Park Juhyun , You Dalsan , Jeong In Gab , Hong Jun Hyuk , Ahn Hanjong , Kim Choung-Soo , Ryu Jin-Sook
Published: July 2024

Digital positron emission tomography/computed tomography (PET/CT) has shown enhanced sensitivity and spatial resolution compared with analog PET/CT. The present study compared the diagnostic performance of digital and analog PET/CT with [68Ga]Ga-PSMA-11 in prostate cancer patients who experienced biochemical recurrence (BCR) after prostatectomy. Forty prostate cancer patients who experienced BCR, defined as serum prostate-specific antigen (PSA) concentrations exceeding 0.2 ng/mL after prostatectomy, were prospectively recruited. These patients were stratified into three groups based on their serum PSA levels. [68Ga]Ga-PSMA-11 was injected into each patient, and images were acquired using both analog and digital PET/CT scanners. Analog and digital PET/CT showed comparable lesion detection rate (71.8% vs. 74.4%), sensitivity (85.0% vs. 90.0%), and positive predictive value (PPV, 100.0% vs. 100.0%). However, digital PET/CT detected more lesions (139 vs. 111) and had higher maximum standardized uptake values (SUVmax, 14.3 vs. 10.3) and higher kappa index (0.657 vs. 0.502) than analog PET/CT, regardless of serum PSA levels. On both analog and digital PET/CT, lesion detection rates and interrater agreement increased with increasing serum PSA levels. Compared with analog PET/CT, digital PET/CT detected more lesions with a higher SUVmax and better interrater agreement in prostate cancer patients who experienced BCR after prostatectomy.
AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype
Authors: Ding Jin-Ting , Peng Yong-Yu , Huang Min , Zhou Sheng-Jun
Published: July 2024

Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.
Complex artificial intelligence models for energy sustainability in educational buildings
Authors: Tariq Rasikh , Mohammed Awsan , Alshibani Adel , Ramírez-Montoya Maria Soledad
Published: July 2024

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.
Base preference for inosine 3′-riboendonuclease activity of human endonuclease V: implications for cleavage of poly-A tails containing inosine
Authors: Mitsuoka Kazuma , Kim Jung In , Yoshida Aya , Matsumoto Akane , Aoki-Shioi Narumi , Iwai Shigenori , Kuraoka Isao
Published: July 2024

Deamination of bases is a form of DNA damage that occurs spontaneously via the hydrolysis and nitrosation of living cells, generating hypoxanthine from adenine. E. coli endonuclease V (eEndoV) cleaves hypoxanthine-containing double-stranded DNA, whereas human endonuclease V (hEndoV) cleaves hypoxanthine-containing RNA; however, hEndoV in vivo function remains unclear. To date, hEndoV has only been examined using hypoxanthine, because it binds closely to the base located at the cleavage site. Here, we examined whether hEndoV cleaves other lesions (e.g., AP site, 6-methyladenine, xanthine) to reveal its function and whether 2′-nucleoside modification affects its cleavage activity. We observed that hEndoV is hypoxanthine-specific; its activity was the highest with 2′-OH modification in ribose. The cleavage activity of hEndoV was compared based on its base sequence. We observed that it has specificity for adenine located on the 3′-end of hypoxanthine at the cleavage site, both before and after cleavage. These data suggest that hEndoV recognizes and cleaves the inosine generated on the poly A tail to maintain RNA quality. Our results provide mechanistic insight into the role of hEndoV in vivo.
Viable eco-efficiency targets for waste collection communities
Authors: Daraio Cinzia , Di Leo Simone , Simar Léopold
Published: July 2024

Waste management is crucial for advancing the circular economy, and Italy has begun to address this issue by organizing municipalities into collaborative communities of municipalities, named ATOs. In this paper, we propose a quantitative approach based on conditional efficiency analysis to estimate viable eco-efficiency targets for these waste collection communities. The proposed targets are both eco-efficient, because they reflect optimal resource allocation within the eco-efficiency framework, and viable, because they consider the unique specificities of each waste community. The methodology determines a pathway or direction for municipalities to reach the eco-efficiency frontier based on specific external factors, ensuring that each municipality is benchmarked against others with similar contexts within the same community. Our analysis focuses on 89 Italian municipalities within the ATO “Città metropolitana di Roma Capitale” in 2021, revealing that size and economic development significantly contributed to viable eco-efficiency within the community during this period. The proposed approach is general and flexible and can be applied to other municipalities in Italy or across Europe. It can also be extended to meso (regional) or macro (country) levels of analysis.
Continent-wide genomic analysis of the African buffalo (Syncerus caffer)
Authors: Talenti Andrea , Wilkinson Toby , Cook Elizabeth A. , Hemmink Johanneke D. , Paxton Edith , Mutinda Matthew , Ngulu Stephen D. , Jayaraman Siddharth , Bishop Richard P. , Obara Isaiah , Hourlier Thibaut , Garcia Giron Carlos , Martin Fergal J. , Labuschagne Michel , Atimnedi Patrick , Nanteza Anne , Keyyu Julius D. , Mramba Furaha , Caron Alexandre , Cornelis Daniel , Chardonnet Philippe , Fyumagwa Robert , Lembo Tiziana , Auty Harriet K. , Michaux Johan , Smitz Nathalie , Toye Philip , Robert Christelle , Prendergast James G. D. , Morrison Liam J.
Published: July 2024

The African buffalo (Syncerus caffer) is a wild bovid with a historical distribution across much of sub-Saharan Africa. Genomic analysis can provide insights into the evolutionary history of the species, and the key selective pressures shaping populations, including assessment of population level differentiation, population fragmentation, and population genetic structure. In this study we generated the highest quality de novo genome assembly (2.65 Gb, scaffold N50 69.17 Mb) of African buffalo to date, and sequenced a further 195 genomes from across the species distribution. Principal component and admixture analyses provided little support for the currently described four subspecies. Estimating Effective Migration Surfaces analysis suggested that geographical barriers have played a significant role in shaping gene flow and the population structure. Estimated effective population sizes indicated a substantial drop occurring in all populations 5-10,000 years ago, coinciding with the increase in human populations. Finally, signatures of selection were enriched for key genes associated with the immune response, suggesting infectious disease exert a substantial selective pressure upon the African buffalo. These findings have important implications for understanding bovid evolution, buffalo conservation and population management.
Tracing nerve fibers with volume electron microscopy to quantitatively analyze brain connectivity
Authors: Turegano-Lopez Marta , de las Pozas Felix , Santuy Andrea , Rodriguez Jose-Rodrigo , DeFelipe Javier , Merchan-Perez Angel
Published: July 2024

The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified “skeleton” of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.
[18F]F-AraG imaging reveals association between neuroinflammation and brown- and bone marrow adipose tissue
Authors: Levi Jelena , Guglielmetti Caroline , Henrich Timothy J. , Yoon John C. , Gokhale Prafulla C. , Reardon David A. , Packiasamy Juliet , Huynh Lyna , Cabrera Hilda , Ruzevich Marisa , Blecha Joseph , Peluso Michael J. , Huynh Tony L. , An Sung-Min , Dornan Mark , Belanger Anthony P. , Nguyen Quang-Dé , Seo Youngho , Song Hong , Chaumeil Myriam M. , VanBrocklin Henry F. , Chae Hee-Don
Published: July 2024

Brown and brown-like adipose tissues have attracted significant attention for their role in metabolism and therapeutic potential in diabetes and obesity. Despite compelling evidence of an interplay between adipocytes and lymphocytes, the involvement of these tissues in immune responses remains largely unexplored. This study explicates a newfound connection between neuroinflammation and brown- and bone marrow adipose tissue. Leveraging the use of [18F]F-AraG, a mitochondrial metabolic tracer capable of tracking activated lymphocytes and adipocytes simultaneously, we demonstrate, in models of glioblastoma and multiple sclerosis, the correlation between intracerebral immune infiltration and changes in brown- and bone marrow adipose tissue. Significantly, we show initial evidence that a neuroinflammation-adipose tissue link may also exist in humans. This study proposes the concept of an intricate immuno-neuro-adipose circuit, and highlights brown- and bone marrow adipose tissue as an intermediary in the communication between the immune and nervous systems. Understanding the interconnectedness within this circuitry may lead to advancements in the treatment and management of various conditions, including cancer, neurodegenerative diseases and metabolic disorders.
Development of Nurr1 agonists from amodiaquine by scaffold hopping and fragment growing
Authors: Sai Minh , Hank Emily C. , Tai Hin-Man , Kasch Till , Lewandowski Max , Vincendeau Michelle , Marschner Julian A. , Merk Daniel
Published: July 2024

The neuroprotective transcription factor nuclear receptor-related 1 (Nurr1) has shown great promise as a therapeutic target in Parkinson’s and Alzheimer’s disease as well as multiple sclerosis but high-quality chemical tools for pharmacological target validation of Nurr1 are rare. We have employed the weak Nurr1 modulator amodiaquine (AQ) and AQ-derived fragments as templates to design a new Nurr1 agonist chemotype by scaffold hopping and fragment growing strategies. Systematic structural optimization of this scaffold yielded Nurr1 agonists with nanomolar potency and binding affinity. Comprehensive in vitro profiling revealed efficient cellular target engagement and compliance with the highest probe criteria. In human midbrain organoids bearing a Parkinson-driving LRRK2 mutation, a novel Nurr1 agonist rescued tyrosine hydroxylase expression highlighting the potential of the new Nurr1 modulator chemotype as lead and as a chemical tool for biological studies.
Targeting senescence induced by age or chemotherapy with a polyphenol-rich natural extract improves longevity and healthspan in mice
Authors: Zumerle Sara , Sarill Miles , Saponaro Miriam , Colucci Manuel , Contu Liliana , Lazzarini Edoardo , Sartori Roberta , Pezzini Camilla , Rinaldi Anna , Scanu Anna , Sgrignani Jacopo , Locatelli Patrizia , Sabbadin Marianna , Valdata Aurora , Brina Daniela , Giacomini Isabella , Rizzo Beatrice , Pierantoni Alessandra , Sharifi Saman , Bressan Silvia , Altomare Claudia , Goshovska Yulia , Giraudo Chiara , Luisetto Roberto , Iaccarino Luca , Torcasio Cristina , Mosole Simone , Pasquini Emiliano , Rinaldi Andrea , Pellegrini Laura , Peron Gregorio , Fassan Matteo , Masiero Stefano , Giori Andrea Maria , Dall’Acqua Stefano , Auwerx Johan , Cippà Pietro , Cavalli Andrea , Bolis Marco , Sandri Marco , Barile Lucio , Montopoli Monica , Alimonti Andrea
Published: July 2024

Accumulating senescent cells within tissues contribute to the progression of aging and age-related diseases. Botanical extracts, rich in phytoconstituents, present a useful resource for discovering therapies that could target senescence and thus improve healthspan. Here, we show that daily oral administration of a standardized extract of Salvia haenkei (Haenkenium (HK)) extended lifespan and healthspan of naturally aged mice. HK treatment inhibited age-induced inflammation, fibrosis and senescence markers across several tissues, as well as increased muscle strength and fur thickness compared with age-matched controls. We also found that HK treatment reduced acutely induced senescence by the chemotherapeutic agent doxorubicin, using p16LUC reporter mice. We profiled the constituent components of HK by mass spectrometry, and identified luteolin—the most concentrated flavonoid in HK—as a senomorphic compound. Mechanistically, by performing surface plasmon resonance and in situ proximity ligation assay, we found that luteolin disrupted the p16–CDK6 interaction. This work demonstrates that administration of HK promotes longevity in mice, possibly by modulating cellular senescence and by disrupting the p16–CDK6 interaction.
Ru nanoclusters supported on a bimodal N,S-doped mesoporous carbon: an efficient and reusable catalyst for selective aerobic oxidation of alcohols and amines
Authors: Heydari Mohsen , Ganji Nasim , Veisi Hamzeh Hassanaki , Karimi Babak , Vali Hojatollah
Published: July 2024

Ru nanoparticles supported on a bimodal N, S-doped mesoporous carbon catalyzing the oxidation of various alcohols and amines, giving the respective aldehydes/ketones and nitriles/imines in high yields and selectivity.
Machine learning of stability scores from kinetic data
Authors: Singla Veerupaksh , Zhao Qiyuan , Savoie Brett M.
Published: July 2024

The absence of computational methods to predict stressor-specific degradation susceptibilities represents a significant and costly challenge to the introduction of new materials into applications.
On-resin synthesis of Lanreotide epimers and studies of their structure–activity relationships
Authors: Chowdhury Arnab , Tripathi Nitesh Mani , Jadav Rohit , Gour Vinod , Purohit Parva , Bandyopadhyay Anupam
Published: July 2024

This report demonstrates the rapid and clean on-resin assembly of disulfide, which enabled impurity and biological profiling of eight possible epimers of a somatostatin analog, lanreotide.
The impact of macrophage phenotype and heterogeneity on the total internalized gold nanoparticle counts
Authors: Lee Henry , Vanhecke Dimitri , Balog Sandor , Taladriz-Blanco Patricia , Petri-Fink Alke , Rothen-Rutishauser Barbara
Published: July 2024

Multimodal analysis and quantification of gold nanoparticles at the single-cell level using FIB-SEM tomography in human monocyte-derived macrophages revealed phenotype-dependent behaviors influencing distinct kinetics and heterogeneity in uptake.
Simulated HRTEM images of nanoparticles to train a neural network to classify nanoparticles for crystallinity
Authors: Gumbiowski Nina , Barthel Juri , Loza Kateryna , Heggen Marc , Epple Matthias
Published: July 2024

Machine learning approaches for image analysis require extensive training datasets for an accurate analysis.
Dynamic macromolecular material design: the versatility of cucurbituril over cyclodextrin in host–guest chemistry
Authors: Pajoubpong Jinnipha , Mayhan Collin M. , Dar Ajaz Ahmad , Greenwood Alexander I. , Klebba Karoline C. , Cremer Matthew L. , Kumari Harshita
Published: July 2024

CB[7] shows high selectivity and affinity to the keto form, while β-CD binds non-specifically to the keto form in low ethanol and both forms in high ethanol. Increased ethanol concentration weakens the interaction of AVO with CB[7] or β-CD.
Non-derivatizing solvent assisted waste-derived cellulose/ MOF composite porous matrix for efficient metal ion removal: comprehensive analysis of batch and continuous packed-bed column sorption studies
Authors: K. Anil Kumar , Jujaru Mohan , Panwar Jitendra , Gupta Suresh
Published: July 2024

The synthesis of highly efficient calcium-based metal–organic framework (Ca-MOF) immobilized cellulose beads is crucial for the removal of hazardous heavy metal ions in both batch and continuous packed-bed column sorption studies.
Impacts of polyethylene glycol (PEG) dispersity on protein adsorption, pharmacokinetics, and biodistribution of PEGylated gold nanoparticles
Authors: Tian Xinsheng , Yuan Yumin
Published: July 2024

Monodisperse PEG-AuNPs in blood vessels can avoid being rapidly cleared by the reticuloendothelial system and thus accumulate in tumor tissue.
An optimized impedance matching construction strategy: carbon nanofibers inlaid with Ni nanocrystals by electrospinning for high-performance microwave absorber
Authors: Zhang Danfeng , Gao Heng , Han Congai , Zeng Guoxun , Wu Qibai
Published: July 2024

With the widespread use of electronic goods, solving electromagnetic pollution has become one of the new challenges. This paper fabricated Ni/C nanofibers and results indicate that it is promising for MAM application with excellent MA performance.
Direct synthesis of carbamates, thiocarbamates, and ureas from Boc-protected amines: a sustainable and efficient approach
Authors: Li Wanyong , Lv Mengting , Luo Xiaolin , Wang Zhouyu , Song Qiao , Yu Xiaoqi
Published: July 2024

This investigation unveils a novel methodology for the straightforward transformation of Boc-protected amines into carbamates, thiocarbamates, and ureas, utilizing tert-butoxide lithium as the sole base.
Pressure-dependent CO2 thermolysis on barium titanate nanocatalysts
Authors: Takawane Smita , Miyamoto Masatoshi , Watanabe Takumi , Ohba Tomonori
Published: July 2024

BaTiO3 nanocatalysts chemisorb and reduce CO2 efficiently at 700 K and 0.1–1.0 MPa.
Unrevealing the potential of multicomponent metal-ion incorporation and sulfide modification in cobalt oxide for efficient water oxidation
Authors: Masood Muzzayab , Aamir Muhammad , Khan Muhammad Ejaz , Sher Muhammad , Akram Khush Bakhat , Shafi Hafiz Zahid , Almohamadi Hamad , Akhtaruzzaman M. d. , Shahiduzzaman M. d.
Published: July 2024

The design and development of highly efficient electrocatalysts from transition metals have shown a great potential for substituting precious metal-based electrocatalysts in water-splitting processes.