bluuebunny/crossref_metadata_2025 huggingface dataset:
Young Researchers School 2024 Maynooth: Lectures on CFT, BCFT and DCFT
Authors: Northe Christian
Published: December 2024

These notes were presented at the Young Researchers School (YRS) in Maynooth in April 2024 and provide an introduction to Conformal Field Theory CFT, Boundary Conformal Field Theory (BCFT) and Defect Conformal Field Theory (DCFT). This class is mostly self-contained and includes exercises with solutions. The first part of these notes is concerned with the basics of CFT, and was taught by the author during the pre-school for the YRS 2024. Here the aim is to convey the notion of conformal families, their fusion and the construction of partition functions. The second part of these notes is dedicated to boundaries and defects in CFT and was presented by the author at the main school. As far as boundaries are concerned, emphasis is placed on boundary operators and their state spaces, as well as the boundary state formalism with the Cardy constraint. Topological defects are discussed in analogy, i.e. defect state spaces and the relevant consistency constraint are derived. Verlinde lines are constructed as their simplest solution and their properties are inspected.
Exploring forest disturbance detection using Sentinel-2 imagery: A case study at FRIM campus
Authors: Muhamad Afizzul M. , Hamdan O. , Tan S.A.
Published: December 2024

Monitoring forest disturbances is crucial for the preservation of biodiversity, climate change mitigation, and overall ecosystem health. Remote sensing offers a powerful tool for monitoring forest disturbances as it can cover large areas, detect changes in near real-time, and cost effective as compared to survey method. Thus, this study tries to utilize the use of Sentinel-2 imagery to detect forest disturbances in Forest Research Institute Malaysia (FRIM) campus. Continuous change detection and classification (CCDC) algorithm was employed to identify disturbances that occurred between 2019 and 2023. NDVI, NDMI, and NBR were also used to improve the accuracy of disturbance detection. The study returned a promising result, achieving a precision of 85 percent in detecting forest disturbances. The effective utilization of Sentinel-2 imagery and the CCDC method in this study highlights not only the efficiency of remote sensing in precisely detecting forest disturbances but also underscores its potential to contribute significantly to sustainable conservation initiatives.
Automatic extraction of Kuala Terengganu coastline using google earth engine (GEE) by automated water extraction index (AWEI)
Authors: Khurram Saima , Pour Amin Beiranvand , Bagheri Milad , Ariffin Effi Helmy , Akhir Mohd Fadzil
Published: December 2024

The study aimed to evaluate changes in Kuala Terengganu’s coastline area from 2014 to 2023 using Google Earth Engine (GEE) and satellite-based images. The approach used automated satellite image searches and JavaScript to analyze specific regions, avoiding the need for downloading whole images. The GEE API code, which incorporates the Automated Water Extraction Index (AWEI) and Otsu Thresholding algorithms, was used to capture small shifts in the shoreline over time. The study demonstrated the global applicability of GEE for automating coastline extraction and object identification using satellite imagery. The research demonstrated the effectiveness of using satellite imagery and GEE to monitor the entire shoreline, providing crucial data for coastal adaptation and management. The study underscores the role of GEE and its API services in advancing automated shoreline extraction technologies, providing valuable insights for environmental research and coastal management.
Nickel ore export prohibition and mapping the business performance of nickel mining companies in Indonesia
Authors: Hati S N A P , Hapsari M A , Ika S R , Widagdo A K
Published: December 2024

As part of its efforts towards establishing the green energy value chain internationally, Indonesia has banned the export of raw nickel since 2020. The export ban is also aimed at adding value to the nickel supply chain for the sake of nickel mining sustainability in the country. The nickel ore export prohibition has caused the European Union to file a lawsuit against the Indonesian government in violation of the World Trade Organization agreement. Instead of discussing the dispute, this study will focus on whether the export ban will impact the business performance of the nickel mining industry. Given that the country is the biggest nickel reserve on earth, this study aims to accomplish three distinct objectives: to create a thematic map of (1) nickel mining locations in Indonesia by province; (2) mining locations for the top ten nickel companies and nickel industry hubs in Indonesia; and (3) the business performance of leading nickel issuers in the country. Thematic maps are created based on Geographic Information Systems (GIS). The results show that nickel reserves are available in seven provinces; two nickel industrial hubs and ten big mining companies operate in these provinces; and these companies have sound business performance following the ore nickel export ban. Our analysis offers a new perspective on the effect of the national nickel policy on the business viability of selected mining companies in Indonesia by applying the company’s financial healthiness measurement in GIS.
Estimation of Sound Speed Profile at Southbound of South China Sea For Bathymetric Survey Using Machine Learning
Authors: Abdullah Muhamad Khairul bin , Yuhaoa Ang , Mohd Shafri Helmi Zulhaidi bin , Mohd Akhir Mohd Fadzil bin , Mohd Azmi Nuur Hakeem bin , Al Habshi Mohammed Mustafa Abdulrahman , Mat Dani Mohd Ikmal Fama bin
Published: December 2024

Bathymetric survey is the method done to acquire the depth of sea and understand the shape and layout of the underwater seabed by hydrographic surveyor for safety of navigation. To conduct bathymetric survey one of the main components are sound speed profile. The current practice faces many difficulties like the sensor covered by biofouling over time, sensor defects occurrence and laborious efforts. Thus, conducting post processing are the suggestion with creating a new model using Machine Learning (ML). The model is created using historical In-Situ data collected by National Hydrographic Centre as responsible agency updating nautical products for safety of navigation in Malaysian water. In this study, the area of study was done in Southbound of South China Sea and propose new method of estimating Sound Speed Profile using ML. The study describes the preprocessing steps, followed with spatial distribution of Sound Speed Profile Values using Inverse Distance Weightage (IDW). Then, based on the historical data, the data are divided between training and test dataset. Training dataset were experimented with 4 ML algorithm which is Decision Tree, Random Forest, Linear Regression and Gradient Boosting. With Random Forest showing the best result with R2 value of 0.998. After fine tune the Training Dataset, the prediction is done using test dataset which bring a final result of 0.905. Although the finding not intended to replace the real time data, the model provide hydrographic surveyors with valuable tool for conducting post-processing with more accurate estimation of sound speed profile using historical in situ data.
Optimization of in-car-abandoned children detection model via deep learning approaches
Authors: Norman Masayu , Ahmad Shukri Ainaa Ilyana , Mohammad Hafiz Ismail , Mohamad Zuraihan , Rahim Ashnita , Fazly Amri Mohd , Mohd Shafri Helmi Zulhaidi
Published: December 2024

Children being trapped in vehicles is a critical issue that has led to many deaths. The number has increased dramatically regardless of the precautionary measures taken by their parents and guardians, which not only become a local but also a global concern. Though several technologies and systems have been developed to identify the in-car-abandoned children (iCAC), there are still reported cases occurring every day due to several factors. Deficiencies in the existing device need to be identified and improved to detect the children precisely. Over the past few years, the use of deep learning (DL) algorithms for remote sensing image analysis has risen, and several intelligent and efficient detection algorithms have been proposed. Yet, the improvement strategies DL-based object detection has never been summarized in detail. This paper will evaluate the accuracy of iCAC’s images taken from the ground platform using DL algorithms. Besides, the hyperparameter is tuned to optimize detection accuracy. Convolutional Neural Networks (CNNs) and MobileNet V2 are two types of DL image classification models utilized in this study. Both models were used to extract features and train the three categories of child, no-child, and adult. Next, the classification outcomes are compared, and the model’s effectiveness is evaluated. Learning rate, epoch and batch size were applied and tested to examine the change in the model’s accuracy rate for the model’s optimization. The results obtained demonstrated that the accuracy of children’s image detection could be improved by optimizing the DL algorithms. This research contributes valuable insights into real-time image detection because the DL models have shown promising results in advancing the capabilities of image detection. The findings are significant to the ongoing optimization of remote sensing techniques for accurate and efficient image recognition.
Multi-Sensor Satellite Data Analysis for Urban Land Use Pattern Recognition
Authors: Gautam Anita , Aithal Bharath H.
Published: December 2024

A global phenomenon, urbanization is characterized by the expansion of cities and rapid growth of urban areas. As cities continue to grow, assessing the distribution and trends in land use is increasingly essential to identify, connect, quantify, validate, and predict the negative consequences of urban areas on the environment. In recent decades, the availability of multi-sensor data has revolutionized and emerged as a promising approach for improving efficiency and precision, and each sensor provides distinct capabilities as well as characteristics that make it possible for an in-depth assessment of urban land use. In this context, this research aims to understand the optical data for urban land use mapping using high-resolution remote sensing satellite imagery of different periods. Traditional and machine learning techniques are used to classify urban land use patterns. The results revealed a major land use change with more built-up regions. Urban land area increased from 36.52 % (2012) to 60.23 % (2023), indicating significant urbanization. Additionally, random forest outperformed methods in comparison analysis. Moreover, the significance of this present study is that it offers insightful information into structural and spectral features of urban areas and emphasizes how precise land use classification is crucial for efficient land management, urban planning, and sustainable urban development. The study also exhibits different sources of data from different types of sensors, indicating the possible usage under any condition for emergency monitoring and immediate response.
Climate Change Impact Assessment Based on Perception and Participation in Telagah Village
Authors: Rahmawaty  , Ismail Mohd Hasmadi , Rauf Abdul , Gandaseca Seca , Karuniasa Mahawan , Kim Yekyoum
Published: December 2024

Rural communities like Telagah Village often have economies and ways of life deeply intertwined with their natural environment. They were particularly vulnerable to the impacts of climate change due to their dependence on agriculture and forestry, which are directly affected by changes in weather patterns, temperature, and precipitation. This study aimed to assess community perceptions and participation regarding the impacts of climate change in Telagah Village, Sei Bingei Sub-district, Langkat Regency, North Sumatra Province, Indonesia. In-depth interviews were conducted with eight key informants who had lived in this village for a long time. Data analysis was carried out descriptively. The results of interviews with key informants in Telagah Village show that the most influential impacts of climate change are drought, uncertain weather conditions, and high rainfall. Forest land clearing is the main cause of climate change in this area. Community participation and perception in efforts to prevent the impacts of climate Community participation and perception in efforts to address climate change impacts are generally positive. Perceptions are very good (37.5%) and good (62.5%). Reforestation and forest conservation efforts have been carried out. Through its focus on Telagah Village, this research contributes valuable insights into the localized effects of global climate change, offering a basis for informed decision-making and sustainable development strategies in similar rural settings. Understanding the specific impacts of climate change on Telagah Village enables the development of tailored adaptation and mitigation strategies. It allows for targeted interventions that address the unique vulnerabilities and capabilities of the community, enhancing resilience to climate-related stresses.
Design and Simulation of a Sustainable Green DC-DC Multi-Outlet Electric Vehicle Charger
Authors: Somefun T. E. , Longe O. M.
Published: December 2024

This paper presents the design and simulation of a DC-DC charger with four outlets, utilising both outer and inner loop control strategies to efficiently charge lithium-ion batteries of varying nominal voltages and capacities. To manage the charging process of electric vehicles (EVs), the authors investigated single converter switching mode (SCSM) and double converter switching mode (DCSM). SCSM uses one insulated-gate bipolar transistor (IGBT) for buck converter switching, while DCSM uses two IGBTs. The proposed system is designed to handle four different battery configurations (12 V, 1000 Ah; 24 V, 1500 Ah; 48 V, 2000 Ah; 96 V, 2500 Ah) and operate from a 400 V DC source. Simulation results validate the effectiveness of the control strategies and indicate that SCSM achieves a steady charging and discharging operation process over all the switching stages compared to the DCSM. This work further presents the comparative analysis of SCSM and DCSM in a multi-outlet EV charger, and the result shows an average percentage increase in charging rate of 0.002% for both SCSM and DCSM. This study provides a foundation for developing advanced EV charger capable of handling diverse battery configurations with improved performance. The charger is modelled using 2024a Matlab/Simulink software, and simulation results ascertain a good operation performance of the EV charger.
Rainfall variability: implication for flood occurrence in Ibadan and Ikeja, south-west Nigeria
Authors: Ayinla Titilope Olubunmi , Arijaje Theophilus Emuobor
Published: December 2024

Rainfall in Nigeria varies in distribution and amount at temporal and spatial scales. One of the risks associated with rainfall variability is flood. Flood is a natural phenomenon that causes havoc to lives, properties, economic, cultural heritage and environment in a flood-prone region. Literature has revealed that the major determinant of this natural phenomenon is rainfall. Reoccurrence of floods in some parts of southwest (SW) Nigeria has demanded investigation into the distribution of rainfall for proper monitoring of its increment. Rainfall distribution data covering Ikeja and Ibadan from year 2005 to 2011 were obtained from the Nigerian Meteorological Agency, Nigeria. The obtained data were used to compute monthly and annual average rainfall occurrences in the two locations using a semi-automated technique. Basic statistics were performed on the data to know the trend in the rainfall pattern, which could be used for flood prediction and management in the study area. The maximum annual rainfall recorded in Ikeja during the period of investigation ranged from 327.6 to 476.9 mm, while that of Ibadan ranged from 234.1 to 373.4 mm. The driest years in Ibadan and Ikeja were 2007 and 2009, while the wettest years in the two locations were 2008, respectively. The correlation between the monthly variations in each year across the two locations showed positive correlation, which are significant at 1% level. The trend of rainfall occurrence in SW Nigeria revealed that for every dry year, there is always subsequent wet year, which could generate flood. Proper data acquisition, management and analysis of rainfall data would assist to mitigate the occurrence of flood disaster in our environment, which is directed towards actualizing sustainable cities and communities.
Unveiling the therapeutic benefits and potentials of Water Hyacinth in promoting a sustainable healthy living: an overview
Authors: Uamai I. J. , Ayanda I. O. , Omohinmin C. A.
Published: December 2024

Water hyacinth (Eicchornia crassipes) is a notorious aquatic weed with an extraordinary proliferation rate globally, it can grow in different aquatic ecosystem types and varying climatic conditions, outcompete other indigenous plants, and establish to endanger biodiversity, shelter pests, clog fresh waterways, increase evapotranspiration, and hinder waterways traffic; but a little is known about the importance of its wide range of metabolites. The Nigerian health sector is faced with setbacks in meeting its increasing population’s basic health (medicine) needs. This situation has led to a call for the exploration of herbal plants as alternatives to synthetic drugs, primarily due to their availability and affordability. Despite the negative impacts of the invasive hydrophyte, water hyacinth, on the economy, ecology, and society, it remains an untapped bioactive resource. Given to the United Nations Development Goal of Good Health and Wellbeing (SDG 3), this review aims to highlight the bioactive components of water hyacinth and their potential for various therapeutic applications in improving human health, instilling a sense of hope for the future. Literatureswere sourced from databases including; Scopus, ScienceDirect, Google, and Google Scholar. Water hyacinth is a natural, easily accessible reservoir of various bioactive compounds comprising those that have been used for therapeutic purposes which include: the treatment of specific fungi and bacteria-related infections, malaria, cancer, ulceration, and malnutrition in various parts of the world. This plant therefore offers a promising sustainable supplementary solution in meeting the health sector’s needs.
Path Loss Prediction on Earth-Space Link Using Statistical and Time Series Approach at Ka-Band in Abuja, North Central Nigeria
Authors: Arijaje T. E. , Omotosho T. V. , Aizebeokhai A. P.
Published: December 2024

Predictive path loss modelling is essential in designing wireless communication systems. However, the empirical methods of path loss prediction are inaccurate as the empirical models cannot be implemented outside the area or region where they are developed. This study focuses on improving the prediction of path loss using statistical approaches such as multiple linear regression (MLR) and time series models using eight (2014 – 2021) data retrieved from the global precipitation measuring mission (GPM) at Ka-band. The step-wise selected method was adopted for the multiple linear regression (MLR). In the exponential smoothing method, weighting is carried out exponentially, decreasing in the direction of the older values. The MLR analysis revealed that the MLR model performs well, with an accuracy of 99.52%. The R2 value of 99.52% indicated a strong correlation between the estimated and predicted path loss in the model. PL = 18.706-0.98459Pr
+1.60027Pt
+0.99808Gr
The P-value of the regression model is 0.000, indicating that the model estimated by the MLR procedure is statistically significant at a level of 0.05. Also, the results showed that the transmitted antenna gain is the most contributed predictor in the path loss with a value of 1.60027 dB. Likewise, the results from the exponential smoothing models revealed that the single exponential smoothing (SES) model performs better than the double exponential smoothing model with a mean absolute percentage error (MAPE) of 14.47%, indicating that the model’s performance is good because the MAPE value falls within 10 – 20%. The mean absolute deviation (MAD), mean square deviation (MSD), and smoothing constant are 0.3215, 0.2313, and 0.9685, respectively. Striking a balance, the application of MLR and the SES models will improve wireless communication systems in Abuja, the North Central region of Nigeria, at Ka-band. Therefore, engineers and policymakers in the Nigerian telecommunication industry should assess the impacts of path loss within the study locations towards sustainable telecommunications and infrastructure (SDG goal 9) for planning and setting radiowave propagation technologies in the study locations and Nigeria at large.
Habit-inspired methods for embodied intelligence
Authors: Kuske Nicolas , Roehrbein Florian
Published: December 2024

Algorithms that employ habitual processing may be characterized as context-contingent input-output pairings. They learn to relate recurring patterns with swift and effective responses, relying on minimal computational resources. While habits have been extensively researched in the fields of psychology, cognition, and neuroscience, they have yet to be systematically investigated in computer science. Data volumes are expanding at an exponential rate, and there is often a requirement for this data to be processed in real-time. Against this backdrop, principles of habitual cognition present a promising alternative to resource-intensive planning algorithms. We propose that future research focus on algorithmic and neuronal models of the neurocognitive foundations of habits to develop new perspectives on artificial intelligence and robotics.
Embodied Intelligence in Additive Manufacturing
Authors: Xu Jie , Xie Yue , Iida Fumiya
Published: December 2024

Embodied intelligence reflects the ability of an agent to interact with its environment through its body to gain intelligence and autonomy. Such a concept has become popular in recent years for the design of robotic systems. This paper generalises the embodied intelligence concept to Additive Manufacturing (AM) to provide a new understanding of the technology with inspirations from biological production processes including organism growth and animal construction. Morphology-based embodied intelligence is analysed on the design and manufacturing aspects for delivering intelligent products (including robots) while embodied artificial intelligence (AI) (foundation models) is discussed for the printer itself. The paper opens a new perspective for AM research.
Evaluation of The Stochasticity in The Collective Bernoulli-ball System as A Physical Reservoir Computer
Authors: Ye Fan , Abdulali Arsen , Iida Fumiya
Published: December 2024

Reservoir computing is a type of machine learning approach that uses a fixed, randomly generated dynamic system called a reservoir to process input data for prediction and classification tasks. The performance of a reservoir computer is highly dependent on the properties of its physical reservoir, particularly its stochasticity. Currently, there is no practical guide for selecting an appropriate reservoir to accomplish desired tasks. In this paper, we utilized the collective Bernoulli-ball system as the reservoir and investigated the effect of stochasticity on its performance in a non-linear prediction task. Additionally, we examined other properties of the reservoir computer, such as the input delay, input gain, n-samples average, and prediction target delay. This paper reveals how stochasticity affects the prediction performance of a reservoir and provides some possible methods of adjusting the parameters mentioned above to mitigate the drawbacks of stochasticity. Our findings show that in low-noise cases the reservoir output follows the trend of the target, but sometimes jumps to a significant offset. For high-level noise, the system remains stable, but the trend information in the output is compressed since noises cover the effect of input, which finally makes the reservoir computer useless for the task.
Variable Value Alignment by Design; averting risks with robot religion
Authors: White Jeffrey
Published: December 2024

One approach to alignment with human values in AI and robotics is to engineer artificial systems isomorphic with human beings. The idea is that robots so designed may autonomously align with human values through similar developmental processes, to realize project ideal conditions through iterative interaction with social and object environments just as humans do, such as are expressed in narratives and life stories. One persistent problem with human value orientation is that different human beings champion different values as ideal, meaning that the values to which an AI should be aligned are ambiguous. Prior work considered human development of purpose and source of meaning in life in terms of project ideal conditions, in effect establishing lifelong value orientations according to which intermediate situations are evaluated. The present work compares views on motivating values from St. Augustine and from popular cognitive science. These accounts are described as divergent error theories which present to their proponents as mutually exclusive yet accurate accounts of personal experience due to differential development of variable innate potentials. Specifically, the hypothesis proposed is that differential development of spindle neural projections establishes enduring connections between prior established relatively immediate routine processes entrained during childhood and prioritized in popular cognitive science, and later developing higher-level social and self-processes entrained during adolescence and emphasized in Augustine’s account, with these projections hard-wiring lifelong motivating value orientations more or less inaccessible to modification through material interaction. Robot experiments informed by this study may evaluate variable value orientation by design, with for example autonomous robots developing motivating associations with temporally distal project ideal conditions through love for humanity, as described by Augustine, and others pursuing adaptive fit to passing norms consistent with popular contemporary accounts.
Exploring the dynamic and thermo-mechanical behavior of Banana/Kenaf/Jute epoxy hybrid biocomposites: impact of stacking sequences
Authors: Jeyaguru Sangilimuthukumar , Thiagamani Senthil Muthu Kumar , Pulikkalparambil Harikrishnan , Siengchin Suchart , Hashem Mohamed , Fouad Hassan
Published: December 2024

This study investigates the thermal behavior and thermo-mechanical properties of banana, jute, and kenaf fiber-reinforced epoxy composites, focusing on the impact of different layering sequences and hybrid configurations. The novelty of the work lies in the tailored stacking of natural fibers to optimize composite performance, a topic of growing significance in sustainable engineering. Thermal analysis revealed the highest endothermic peak at 72 °C in BJKKJBE hybrid composites, while jute fiber-reinforced composites exhibited a marginally higher peak at 73 °C. Dynamic mechanical analysis highlighted kenaf fiber-reinforced composites as having superior storage modulus values, reaching 152 MPa at 10 Hz, followed by the BJKKJBE hybrid, which achieved 137 MPa. Additionally, banana fiber-reinforced composites and neat epoxy recorded the highest loss modulus values (52 MPa and 51 MPa), indicating excellent energy dissipation. Neat epoxy and kenaf fiber reinforced composites displayed the highest tan delta values, with BJKKJBE hybrids also showing notable damping behavior, suggesting effective vibration attenuation. On the other hand, jute-based composites demonstrated the lowest tan delta, reflecting increased stiffness. A significant outcome is the thermal expansion behavior, where BJKKJBE composites exhibited the highest shrinkage (0.3%), while the KBJJBKE reinforcement, with kenaf as a skin layer, recorded the highest coefficient of thermal expansion (257 ppm °C−1). These findings present new opportunities for optimizing fiber-reinforced epoxy composites in applications requiring tailored thermal and thermo-mechanical performance, contributing to advancements in sustainable materials design for engineering applications.
Closed form expressions for CTEs of composite materials by the thermo-elastic micromechanics approach
Authors: M V Peereswara Rao , Harursampath Dineshkumar , M V V S Murthy
Published: December 2024

An analytical thermo-elastic micromechanics model is developed to estimate the homogenized Coefficients of Thermal Expansion (CTEs) properties of a composite material. This model is based on the Concentric Cylinder Model (CCM), using Variational Asymptotic Method (VAM). Closed form expressions for both axial and transverse CTEs of composite materials are derived, satisfying the displacement continuity and the transverse stress equilibria conditions at the interface between the reinforcement and the matrix material. The resulting expressions are functions of fiber material properties, matrix material properties and their volume fractions. The results obtained through these derived closed form expressions of CTEs are compared with those available in literature and the experimentally measured CTEs. Further, closed form expressions for the calculation of 3D displacement, strains and stresses of both the reinforcement and matrix are derived. The results obtained by the use of the developed expressions are validated with the results obtained through 3D Finite Element analysis of a composite beam like structure for a given temperature load.
Central vs. Brachial Blood Pressure and Pulse Pressure Amplification for Mortality Risk Prediction in Patients Undergoing Coronary Angiography
Authors: Daschner Clara , Kleber Marcus E , Ayasse Niklas , Stach Ksenija , Yücel Gökhan , Husain-Syed Faeq , Niessner Alexander , Krüger Bernd , März Winfried , Krämer Bernhard K , Yazdani Babak
Published: December 2024

BACKGROUND

Arterial hypertension is a significant risk factor for cardiovascular (CV) morbidity and mortality. Although central blood pressure (BP) evaluation is considered the gold standard, the reliability of non-invasive measurements remains unclear. Therefore, we compared the predictive value of invasively measured central BP with non-invasively measured brachial BP and analyzed pulse pressure (PP) amplification (delta-PP; the difference between central and peripheral PP) as an independent predictor of mortality.

METHODS

We analyzed systolic BP (SBP), diastolic BP (DBP), mean arterial BP (MAP), PP, and delta-PP as predictors of CV and all-cause mortality in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, involving 3,316 patients referred for coronary angiography.

RESULTS

All brachial BP parameters, except DBP, were significantly linked to all-cause and CV mortality in a univariate analysis. A 10 mm Hg increase in SBP, MAP, and PP corresponded to increased risks of all-cause (11%, 10%, and 19%) and CV mortality (11%, 11%, and 18%). Central SBP and PP showed similar, but numerically weaker, associations with increased risks of all-cause (5% and 10%) and CV mortality (4% and 8%). After adjusting for age, sex, body mass index, diabetes mellitus, and eGFR, only delta-PP independently predicted mortality with a 10 mm Hg increase linked to a 4% reduction in all causes and a 6% reduction in CV mortality.

CONCLUSIONS

Neither brachial nor centrally measured BP parameters were independent mortality predictors in contrast to PP amplification, which remained an independent predictor of mortality in multivariate analysis, in a cohort with a medium to high CV risk profile. As PP amplification decreased, mortality increased.
Evaluation of the validity of a perfluorooctane sulfonic acid exposure reconstruction using a measured serum concentration among workers with a wide range of exposure
Authors: Campbell Jerry L , Linakis Matthew W , Porter Anna K , Rosen Emma M , Logan Perry W , Kleinschmidt Sarah E , Andres Kara L , Chang Sue , Taiwo Oyebode A , Olsen Geary W , Clewell Harvey J , Longnecker Matthew P
Published: December 2024

Background

Studies among workers with a wide range of exposure to perfluoroalkyl substances inform risk assessments. Perfluorooctane sulfonate (PFOS), a ubiquitous environmental contaminant, was recently examined in relation to mortality and cancer incidence in an occupationally exposed population by Alexander et al. in 2024. In that study, cumulative occupational exposure (mg/m3 PFOS-equivalents in air) was reconstructed using a job-exposure matrix and individual work history. While the exposure reconstruction had good face validity, an assessment of its performance in relation to serum PFOS levels would allow improved interpretation of the occupational epidemiology findings.

Objective

The objective of this study was to assess the validity of the exposure reconstruction used by Alexander et al. (2024).

Methods

A previous study by Olsen et al. (2003) measured serum PFOS levels in 1998 for 260 workers and because these workers were included in the epidemiologic study by Alexander et al. (2024), the study reported herein compared serum PFOS levels to those predicted using a simple compartmental pharmacokinetic model.

Results

The Pearson correlation coefficient between the observed and pharmacokinetic model-predicted serum PFOS concentration was 0.80 (95% confidence interval, 0.75 to 0.84). The median ratio of predicted to observed serum concentrations was 12 (i.e. actual exposure was significantly less than predicted). The predicted serum PFOS concentrations were not sensitive to the parameters used in the pharmacokinetic model other than exposure concentration or absorption.

Conclusions

The model did not predict absolute exposure well, probably because of personal protective equipment use not being accounted for and absorption of PFOS or precursors being lower than modeled. On the other hand, the model did a reasonably good job of ranking the workers’ exposure, thus classification of workers according to relative amount of cumulative PFOS-equivalents was reasonably accurate in the study by Alexander et al. (2024) when validated using the measured serum PFOS data.
Preserved social facilitation and preferential processing of partner-relevant information in Huntington’s disease
Authors: Van den Stock Jan , Cypers Gert , De Winter François-Laurent , Vandenbulcke Mathieu
Published: December 2024

This scientific commentary refers to ‘The joint memory effect: challenging the selfish stigma in Huntington’s disease?’, by Dalléry et al. (https://doi.org/10.1093/braincomms/fcae440).
Acute kidney injury with hypernatremia and major adverse kidney events
Authors: Zaragoza Jose J , Gómez-Fregoso Juan A , Hernández-Barajas Eduardo M , Andrade-Jorge Zarahi , de Leon Juarez Correa- , Granado Rolando Claure-Del , Padilla-Armas Jorge L , Ornelas-Ruvalcaba R Lizzete , Cabrera-Aguilar J Said , Chávez-Alonso Gael , Villalvazo-Maciel Estefania , Orozco-Chan Carlos E , Cárdenas-Mercado Carlos B , Rodríguez-García Gonzalo , Navarro-Blackaller Guillermo , Medina-González Ramón , Gallardo-González Alejandro Martínez , Alcantar-Vallin Luz , Abundis-Mora Gabriela J , García-García Guillermo , Chávez-Iñiguez Jonathan S
Published: December 2024

Introduction

Consequences of hypernatremia in akute kidney injury (AKI-hyperNa) is poorly understood. We analyzed the risk of major adverse kidney events (MAKE) in comparison with AKI and normal serum sodium (AKI-normalNa). Such data could help in understanding this complex interaction.

Methods

In this retrospective cohort we compared the AKI-hyperNa with the AKI-normalNa regarding the risk of MAKE, which include death, new dialysis requirement, and worsening kidney function (≥25% decrease in estimated glomerular filtration rate), at 10 (MAKE10) and at 30–90 days (MAKE30–90) using multivariate logistic regression and area under the curve (AUC) analysis. The association between serum sodium levels (per 1 mEq/l increase) and the number of days with hypernatremia was also compared.

Results

A total of 357 patients were included (78 with AKI-hyperNa and 279 with AKI-normalNa). Compared to the AKI-normalNa, AKI-hyperNa were predominantly male (73% versus 59%), experienced hypernatremia for a longer duration (3 days versus 0 days), and took 5 days to normalize serum sodium levels. After multivariate regression analysis, the AKI-hyperNa group had higher risk of MAKE10 [odds ratio (OR) 5.7, confidence interval (CI) 2.5 to 12.89, p < 0.001] with an AUC of 0.79. Also its components such as mortality and decreased estimated glomerular filtration rate (OR 2.13, CI 1.11 to 4.07, p = 0.021 and OR 20.14, CI 7.69 to 10.03, p = 0.001, respectively). A similar trend was found for MAKE30–90 (OR 4.17, CI 1.73 to 10.03, p ≤ 0.001). A gradual increase in serum sodium was associated with a higher risk of MAKE (OR 1.07, CI 1.04 to 1.11, p ≤ 0.001), as was the number of days spend with hypernatremia (OR 1.51, CI 1.22 to 1.87, p = 0.001).

Conclusions

In this cohort, AKI-hyperNa compared with AKI-normalNa had a fivefold risk of short- and long-term MAKE. This event was more frequently observed as serum sodium increased and it was closely related to the number of days that hypernatremia persisted.
JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps
Authors: Li Jiru , Pan Dinghao , Yang Zhihao , Sun Yuanyuan , Lin Hongfei , Wang Jian
Published: December 2024

Biomedical Relation Extraction (RE) is central to Biomedical Natural Language Processing and is crucial for various downstream applications. Existing RE challenges in the field of biology have primarily focused on intra-sentential analysis. However, with the rapid increase in the volume of literature and the complexity of relationships between biomedical entities, it often becomes necessary to consider multiple sentences to fully extract the relationship between a pair of entities. Current methods often fail to fully capture the complex semantic structures of information in documents, thereby affecting extraction accuracy. Therefore, unlike traditional RE methods that rely on sentence-level analysis and heuristic rules, our method focuses on extracting entity relationships from biomedical literature titles and abstracts and classifying relations that are novel findings. In our method, a multitask training approach is employed for fine-tuning a Pre-trained Language Model in the field of biology. Based on a broad spectrum of carefully designed tasks, our multitask method not only extracts relations of better quality due to more effective supervision but also achieves a more accurate classification of whether the entity pairs are novel findings. Moreover, by applying a model ensemble method, we further enhance our model’s performance. The extensive experiments demonstrate that our method achieves significant performance improvements, i.e. surpassing the existing baseline by 3.94% in RE and 3.27% in Triplet Novel Typing in F1 score on BioRED, confirming its effectiveness in handling complex biomedical literature RE tasks.

Database URL: https://codalab.lisn.upsaclay.fr/competitions/13377#learn_the_details-dataset
Exploring pectinolytic yeast diversity: toward effective polygalacturonase producers for applications in wine-making
Authors: Gazaloğlu Mehmet , Camarasa Carole , Nevoigt Elke
Published: December 2024

Pectinolytic enzymes secreted by yeasts have an untapped potential in industry, particularly in wine-making. This study addresses the limitations of the current screening methods in reliably predicting the capacity of pectinolytic yeast strains to secrete polygalacturonase (PGase) under industrial conditions, suggesting a novel screening approach. Using the context of wine-making as an example, a diverse collection of 512 yeast strains from 17 species was analysed for PGase secretion, a key enzyme in pectinolysis. The traditional halo assay on solid yeast–pepton–dextrose (YPD) medium revealed 118 strains from nine genera being PGase positive. Screening these strains by incubating them at 20°C on a solid synthetic grape juice medium containing polygalacturonic acid (PG) significantly reduced the number of promising strains to 35. They belong to five genera: Kluyveromyces sp., Cryptococcus, Pichia, Torulaspora, and Rhodotorula. Afterward, a newly developed pectin–iodine assay was used to precisely quantify the PGase activity of the best-performing strains in a liquid medium. Strains from Kluyveromyces and Cryptococcus sp. stood out regarding high pectinolytic activity. Our methodological advancements tailored to identify highly promising pectinolytic yeasts for industrial use open new avenues for wine-making and other industrial processes encompassing media rich in pectin and sugars.
Senolysis by GLS1 Inhibition Ameliorates Kidney Aging by Inducing Excessive mPTP Opening through MFN1
Authors: Chen Yuting , Zhao Nan , Zhang Yu , Chen Xueqi , Chen Yi , Wang Yifan , Wu Jianqing , Zhao Weihong
Published: December 2024

Cellular senescence is a pivotal contributor to aging and age-related diseases. The targeted elimination of senescent cells, known as senolysis, has emerged as a promising therapeutic strategy for mitigating these conditions. Glutaminase 1 (GLS1), a key enzyme in the glutaminolysis pathway, has been implicated in various cellular senescence processes. However, its specific role in senescent renal tubular epithelial cells (TECs) remains unclear. This study investigates the role and underlying mechanisms of GLS1 in senescent TECs. Using D-galactose (D-gal)-induced senescence of HK-2 cells, we found that GLS1 inhibition eliminated senescent TECs by promoting excessive mitochondrial permeability transition pore (mPTP) opening. Mechanistically, the excessive mPTP opening is associated with upregulation of mitofusin 1 (MFN1). Inhibition of GLS1 in D-gal-treated HK-2 cells induced a shift in mitochondrial dynamics from fission to fusion, accompanied by a significant increase in MFN1 expression. Knocking down MFN1 reduced the mPTP opening and the expression of mPTP-related genes (PPIF, VDAC and BAX) in cells co-treated with D-gal and the GLS1 inhibitor BPTES. Moreover, treatment of aged mice with BPTES specifically eliminated senescent TECs and ameliorated age-associated kidney disease. These findings reveal that GLS1 inhibition eliminate senescent TECs by promoting excessive mPTP opening, suggesting that targeting GLS1 may be a novel senolytic strategy for alleviating aging-related kidney diseases.
Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics
Authors: Gibson Greg , Rioux John D , Cho Judy H , Haritunians Talin , Thoutam Akshaya , Abreu Maria T , Brant Steven R , Kugathasan Subra , McCauley Jacob L , Silverberg Mark , McGovern Dermot
Published: December 2024

The past 2 decades have witnessed extraordinary advances in our understanding of the genetic factors influencing inflammatory bowel disease (IBD), providing a foundation for the approaching era of genomic medicine. On behalf of the NIDDK IBD Genetics Consortium, we herein survey 11 grand challenges for the field as it embarks on the next 2 decades of research utilizing integrative genomic and systems biology approaches. These involve elucidation of the genetic architecture of IBD (how it compares across populations, the role of rare variants, and prospects of polygenic risk scores), in-depth cellular and molecular characterization (fine-mapping causal variants, cellular contributions to pathology, molecular pathways, interactions with environmental exposures, and advanced organoid models), and applications in personalized medicine (unmet medical needs, working toward molecular nosology, and precision therapeutics). We review recent advances in each of the 11 areas and pose challenges for the genetics and genomics communities of IBD researchers.
The need for a cancer exposome atlas: a scoping review
Authors: Young Anna S , Mullins Catherine E , Sehgal Neha , Vermeulen Roel C H , Kolijn P Martijn , Vlaanderen Jelle , Rahman Mohammad L , Birmann Brenda M , Barupal Dinesh , Lan Qing , Rothman Nathaniel , Walker Douglas I
Published: December 2024

Background

Despite advances in understanding genetic susceptibility to cancer, much of cancer heritability remains unidentified. At the same time, the makeup of industrial chemicals in our environment only grows more complex. This gap in knowledge on cancer risk has prompted calls to expand cancer research to the comprehensive, discovery-based study of nongenetic environmental influences, conceptualized as the “exposome.”

Methods

Our scoping review aimed to describe the exposome and its application to cancer epidemiology and to study design limitations, challenges in analytical methods, and major unmet opportunities in advanced exposome profiling methods that allow the quantification of complex chemical exposure profiles in biological matrices. To evaluate progress on incorporating measurements of the exposome into cancer research, we performed a review of such “cancer exposome” studies published through August 2023.

Results

We found that only 1 study leveraged untargeted chemical profiling of the exposome as a method to measure tens of thousands of environmental chemicals and identify prospective associations with future cancer risk. The other 13 studies used hypothesis-driven exposome approaches that targeted a set of preselected lifestyle, occupational, air quality, social determinant, or other external risk factors. Many of the included studies could only leverage sample sizes with less than 400 cancer cases (67% of nonecologic studies) and exposures experienced after diagnosis (29% of studies). Six cancer types were covered, most commonly blood (43%), lung (21%), or breast (14%) cancer.

Conclusion

The exposome is underutilized in cancer research, despite its potential to unravel complex relationships between environmental exposures and cancer and to inform primary prevention.
Shifting national interest in petroleum-producing countries during the energy transition: the Omani experience
Authors: Al Yahyai Ahad , Soliman-Hunter Tina
Published: December 2024

Petroleum-producing countries are at particular economic and social risk during the energy transition, primarily due to the shift from petroleum revenues that can be used to stabilize the state to the development of renewable energy sources, which do not bring revenue to the state. Exploring this conundrum within the context of the national interest, this article examines petroleum-producing countries, focusing on Oman as an example of a non-democratic rentier state. Examining the national interest as both a concept and a practice within the energy sector, the study concludes that the energy transition will place certain challenges on the Omani government, but with a focus on establishing investment opportunities and energy justice, there are many positive experiences ahead for Oman.