bluuebunny/crossref_metadata_2025_split huggingface dataset:
Community-based environment and natural resources management: case of Parangtritis disaster resilient village
Authors: Yusrinawati Chafda , Damayanti Maya 
Published: September 2024

Abstract
Management of the environment and natural resources is a strategic key to reducing the risk of disasters as environmental problems and natural disasters are related to each other. This strategic key is implemented through active community participation in line with the Disaster Resilient Village program. Hence, this study aims to identify the community efforts to manage the environment and natural resources in the case of Parangtritis as one of the Main Resilient Villages (the highest level) in Bantul Regency. This research uses a quantitative descriptive approach by collecting data through document review, interviews, and questionnaires. The population studied was 97 people who were members of the Paris Resilience using census sampling techniques. The results of this study show that community efforts are realized in coastal area reforestation activities, drainage construction, dams and river embankments, river normalization, and waste bank management. The community actively participates in the form of manpower, opinions, and skills/expertise. It is expected that the results of this research can be a lesson for other disaster-resilient villages that are in the early stages of managing natural resources and the environment to reduce the risk of disasters for the community.
Photodegradation of RB5 dye with modified zeolites: influence of temperature and UV irradiation
Authors: Sánchez-Viveros José Manuel , Petranovskii Vitalii , Tlacuatl Gregorio Zacahua , Lara Hernández Minerva , Chávez–Rivas Fernando , Falcony–Guajardo Ciro , Espinoza–Tapia Julio César 
Published: September 2024

Abstract
This study explores the photocatalytic degradation of Reactive Black 5 (RB5) dye using thermally modified natural zeolites, aiming to improve water purification methods. Zeolites were calcined at 250 °C, 350 °C, and 500 °C, and characterized through x–ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier Transform Infrared Spectroscopy (FTIR) to analyze their structural and morphological transformations. The results reveal that calcination significantly enhances the photocatalytic performance, particularly for ZNM500, which exhibited the highest efficiency, reaching a 60% removal rate of RB5. The degradation process follows a pseudo-first-order kinetic model at lower dye concentrations but adheres more closely to the Langmuir–Hinshelwood equation at higher concentrations, emphasizing the role of surface adsorption in catalysis. UV irradiation was a key factor in boosting reaction rates, with shorter wavelengths (254 nm) providing greater energy, leading to more effective dye breakdown by facilitating the generation of reactive hydroxyl radicals (·OH). These findings suggest that thermally modified zeolites, especially ZNM500, represent a promising solution for wastewater treatment, offering an efficient, cost–effective, and environmentally friendly approach to removing synthetic dyes from contaminated water sources.
Energy-dependent barren plateau in bosonic variational quantum circuits
Authors: Zhang Bingzhi , Zhuang Quntao 
Published: September 2024

Abstract
Bosonic variational quantum circuits (VQCs) are crucial for information processing in microwave cavities, trapped ions, and optical systems, widely applicable in quantum communication, sensing and error correction. The trainability of such VQCs is less understood, hindered by the lack of theoretical tools such as t-design due to the infinite dimension of the continuous-variable systems involved. We overcome this difficulty to reveal an energy-dependent barren plateau in such VQCs. The variance of the gradient decays as

1

/

E

M
ν

, exponential in the number of modes M but polynomial in the (per-mode) circuit energy E. The exponent ν = 1 for shallow circuits and ν = 2 for deep circuits. We prove these results for state preparation of general Gaussian states and number states. We also provide numerical evidence demonstrating that the results extend to general state preparation tasks. As circuit energy is a controllable parameter, we provide a strategy to mitigate the barren plateau in bosonic continuous-variable VQCs.
The variability and predictability of summer southwest monsoon intensity measurement index across mainland indochina: from local synoptic to large scale perspectives
Authors: Oo Kyaw Than , Dong Yinshuo , Jonah Kazora 
Published: September 2024

Abstract
The mainland Indochina Southwest Monsoon (MSwM) is a crucial climatic phenomenon in Southeast Asia, significantly influencing the climate of countries such as Myanmar, Bangladesh, Thailand, Laos, and Southwest China. Traditionally, the intensity of the MSwM has been challenging to quantify due to its complex nature. This study introduces a new Southwest Monsoon Intensity Index (SMII), based on sea level pressure (SLP), which offers a more comprehensive understanding of the monsoon’s intraseasonal to interannual variability and its associated climate anomalies. The SMII is designed to overtake the limitations of local indices, which often rely on data from only two costal stations and may lack the resolution necessary for seasonal outlooks or long-term forecast modeling. By considering both south-north and west-east annual dominant pressure field gradients, the SMII provides a more robust measure of monsoon intensity. This large-scale index is influenced by key pressure systems, such as the Tibet Low and the Mascarenes High, and is characterized by the movement of a concentrated north-south rain belt and a quasi-stationary low-level jet. Our analysis reveals strong correlations between the SMII and regional rainfall patterns, particularly during the peak intensity of the summer months. Additionally, significant associations with El Niño Southern Oscillation (ENSO) indices and the eastern Indian Ocean suggest potential teleconnections, which can enhance predictive capabilities. This improved index is crucial for operational climate centers to monitor and predict monsoon behavior effectively, ultimately aiding in the forecasting and managing the MSwM’s socio-economic impacts.
Novel plant extract-chitosan nanocomposite (SCE/ChNPs) biosensor for trace level arsenic sensing in water samples using quartz crystal microbalance with dissipation monitoring
Authors: Siddiq Miad Ali 
Published: September 2024

Abstract
This study introduces a novel green biosensor for ultra-low arsenic detection in water, combining Saussurea costus ethanolic extract (SCE) with chitosan nanoparticles (ChNPs) in a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) system. The SCE/ChNPs were characterized using AFM, SEM, TEM, DLS, and zeta potential measurements, revealing a mean particle size of 37.0 ± 2.0 nm, spherical morphology, and a stable colloidal system with −28.3 mV surface charge. The QCM-D-based biosensor demonstrated exceptional performance with a detection limit of 0.05 ppb, significantly below the WHO guideline for arsenic in drinking water. It exhibited a linear response from 0.1 to 10 ppb (R2 = 0.99) and high selectivity against potential interferents. The sensor showed a rapid 10 s response time and maintained stability over five adsorption–desorption cycles. Validation in spiked tap water samples yielded results comparable to ICP spectroscopy, confirming the sensor’s applicability in complex matrices. This eco-friendly approach offers advantages over conventional methods, including high sensitivity, rapid response, and potential for on-site analysis. The synergistic combination of plant extract and biodegradable nanoparticles presents a sustainable solution for heavy metal sensing. Further research on long-term stability and field testing could lead to practical tools for monitoring arsenic contamination in water resources, contributing to public health and environmental safety.
Evaluating the levelized costs and life cycle greenhouse gas emissions of electricity generation from rooftop solar photovoltaics: a Swiss case study
Authors: Zhang Xiaojin , Walch Alina , Rüdisüli Martin , Bauer Christian , Burgherr Peter , McKenna Russell , Habert Guillaume 
Published: September 2024

Abstract
The transition to renewable energy sources is pivotal in addressing global climate change challenges, with rooftop solar photovoltaic (PV) systems playing a crucial role. For informed decision-making in energy policy, it is important to have a comprehensive understanding of both the economic and environmental performance of rooftop solar PV. This study provides a high-resolution analysis of existing rooftop solar PV systems in Switzerland by assessing the robustness of the potential estimation to properly derive the amount of electricity generated by individual systems, and subsequently quantify the levelized cost of electricity and life cycle greenhouse gas (GHG) emissions of electricity generation from PV and compare them with those of grid electricity supplies. Our results indicate substantial geographical variations between potential estimations and real-world installations, with notable underestimations of approximately 1.3 Gigawatt-peak, primarily for systems around 10 kWp in size, mainly due to the quality of input data and conservative estimation. The study finds that in many regions and for most of the installed capacity, electricity generated from rooftop PV systems is more economical than the grid electricity supply, mainly driven by factors including high electricity prices, larger installations and abundant solar irradiance. The GHG emissions assessment further emphasizes the importance of methodological choice, with stark contrasts between electricity certificate-based approaches and others that are based on the consumption mix. This study suggests the need for more accurate geographical potential estimations, enhanced support for small-scale rooftop PV systems, and more incentives to maximize the potential of their roof area for PV deployment. As Switzerland progresses towards its renewable energy goals, our research underscores the importance of informed policymaking based on a retrospective analysis of existing installations, essential for maximizing the potential and benefits of rooftop solar PV systems.
ASHP and ASHP Foundation Pharmacy Forecast 2025: Strategic Planning Guidance for Pharmacy Departments in Hospitals and Health Systems
Authors: DiPiro Joseph T , Hoffman, James M , Tichy Eric , Shea Suzanne , Sanborn Michael , Hung Anna , Fox Erin R , Watanabe Jonathan H , Torrise Virginia , Abourjaily Paul , Cunningham Francesca E , Schweitzer Pamela , Nelson Scott D , Stump Lisa S , Castro Harvey , Nesbit Todd W , Scott Christopher M 
Published: September 2024

Abstract

Purpose
The 2025 ASHP Pharmacy Forecast report is written to identify and contextualize emerging trends that will influence healthcare, health systems, and the pharmacy profession. It provides recommendations to inform strategic planning that should prompt action by pharmacists and health-system leaders.

Methods
Drawing on the “wisdom of crowds” concept, a survey was constructed by an advisory committee with 6 themes, each with 7 focused statements and a seventh theme on preparedness (54 survey items in total). A survey panel of 321 pharmacy leaders was purposefully designed to capture opinions from a wide range of pharmacists across the United States. Panelists were asked to consider the likelihood of the statements occurring in the next 5 years as being likely, somewhat likely, somewhat unlikely, or very unlikely to occur. Forecast panelists also assessed health systems’ preparedness (from very unprepared to very prepared) for 12 of the statements.

Results
The 6 survey themes identified were The Business of Healthcare: Challenges in Health-System Pharmacy, Managing Ultra-High-Cost Drugs, Addressing the Gap in Primary Care, Whole Person Health, Navigating Generative Artificial Intelligence: Opportunity and Risk, and Stabilizing the Pharmacy Workforce. The survey was completed by 269 respondents, yielding an 84% response rate. Descriptive statistics of the survey results were provided to chapter authors to inform commentary and strategic recommendations for each theme.

Conclusion
The Pharmacy Forecast provides insights on large-scale, long-term trends that will influence healthcare and the pharmacy profession over the years ahead. Insight from the report can stimulate strategic thinking and provides a starting point to proactively position leaders, their teams, and departments for potential future trends.
DGCL: dual-graph neural networks contrastive learning for molecular property prediction
Authors: Jiang Xiuyu , Tan Liqin , Zou Qingsong 
Published: September 2024

Abstract
In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraining stage, we utilize two different GNNs as encoders to construct CL, rather than using the method of generating enhanced graphs as before. Precisely, DGCL aggregates and enhances features of the same molecule by the Graph Isomorphism Network and the Graph Attention Network, with representations extracted from the same molecule serving as positive samples, and others marked as negative ones. In the downstream tasks training stage, features extracted from the two above pretrained graph networks and the meticulously selected MFPs are concated together to predict molecular properties. Our experiments show that DGCL enhances the performance of existing GNNs by achieving or surpassing the state-of-the-art self-supervised learning models on multiple benchmark datasets. Specifically, DGCL increases the average performance of classification tasks by 3.73$\%$ and improves the performance of regression task Lipo by 0.126. Through ablation studies, we validate the impact of network fusion strategies and MFPs on model performance. In addition, DGCL’s predictive performance is further enhanced by weighting different molecular features based on the Extended Connectivity Fingerprint. The code and datasets of DGCL will be made publicly available.
CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization
Authors: Chen Jing , Tao Ran , Qiu Yi , Yuan Qun 
Published: September 2024

Abstract
Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe–disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe–disease association matrix. Our CMFHMDA model is subsequently developed by integrating the network data of both microbes and diseases to predict potential associations. The key innovations of this method include using the WKNKN algorithm to preprocess missing values in the association matrix and incorporating cross-domain information from microbes and diseases into the CMFHMDA model. To validate CMFHMDA, we employed three different cross-validation techniques to evaluate the model’s accuracy. The results indicate that the CMFHMDA model achieved Area Under the Receiver Operating Characteristic Curve scores of 0.9172, 0.8551, and 0.9351$\pm $0.0052 in global Leave-One-Out Cross-Validation (LOOCV), local LOOCV, and five-fold CV, respectively. Furthermore, many predicted associations have been confirmed by published experimental studies, establishing CMFHMDA as an effective tool for predicting potential disease-associated microbes.
How pairs of insertion mutations impact protein structure: an exhaustive computational study
Authors: Li Changrui , Zheng Yang , Jagodzinski Filip 
Published: September 2024

Abstract

Summary
Understanding how amino acid insertion mutations affect protein structure can inform pharmaceutical efforts targeting diseases that are caused by protein mutants. In silico simulation of mutations complements experiments performed on physical proteins which are time and cost prohibitive. We have computationally generated the exhaustive sets of two amino acid insertion mutations for five protein structures in the Protein Data Bank. To probe and identify how pairs of insertions affect structural stability and flexibility, we tally the count of hydrogen bonds and analyze a variety of metrics of each mutant. We identify hotspots where pairs of insertions have a pronounced effect, and study how amino acid properties such as size and type, and insertion into alpha helices, affect a protein’s structure. The findings show that although there are some residues, Proline and Tryptophan specifically, which if inserted have a significant impact on the protein’s structure, there is also a great deal of variance in the effects of the exhaustive insertions both for any single protein, and across the five proteins. That suggests that computational or otherwise quantitative efforts should consider large representative sample sizes especially when training models to make predictions about the effects of insertions.

Availability and implementation
The data underlying this article is available at https://multimute.cs.wwu.edu.
Chronogram: an R package for data curation and analysis of infection and vaccination cohort studies
Authors: Greenwood David , Shawe-Taylor Marianne , Townsley Hermaleigh , Gahir Joshua , Sahadeo Nikita , Alhassan Yakubu , Chaloner Charlotte , Galgut Oliver , Kelly Gavin , Bauer David L V , Wall Emma C , Wu Mary Y , Carr Edward J 
Published: September 2024

Abstract

Motivation
Observational cohort studies that track vaccine and infection responses offer real-world data to inform pandemic policy. Translating biological hypotheses, such as whether different patterns of accumulated antigenic exposures confer differing antibody responses, into analysis code can be onerous, particularly when source data is dis-aggregated.

Results
The R package chronogram introduces the class chronogram, where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data. Each experimental modality is added sequentially, allowing the incorporation of new data, such as specialized time-consuming research assays, or their downstream analyses. Source data can be any rectangular data format, including database tables (such as structured query language databases). This supports annotations that aggregate data types/sources, for example, combining symptoms, molecular testing, and sequencing of one or more infectious episodes in a pathogen-agnostic manner. Chronogram arranges observational data to allow the translation of biological hypotheses into their corresponding code via a shared vocabulary.

Availability and implementation
Chronogram is implemented R and available under an MIT licence at: https://www.github.com/FrancisCrickInstitute/chronogram; a user manual is available at: https://franciscrickinstitute.github.io/chronogram/
Neoadjuvant therapy versus upfront surgery in resectable pancreatic cancer: reconstructed patient-level meta-analysis of randomized clinical trials
Authors: Aliseda Daniel , Martí-Cruchaga Pablo , Zozaya Gabriel , Blanco Nuria , Ponz Mariano , Chopitea Ana , Rodríguez Javier , Castañón Eduardo , Pardo Fernando , Rotellar Fernando 
Published: September 2024

Abstract

Background
Neoadjuvant treatment has shown promising results in patients with borderline resectable pancreatic ductal adenocarcinoma. The potential benefits of neoadjuvant treatment on long-term overall survival in patients with resectable pancreatic ductal adenocarcinoma have not yet been established. The aim of this study was to compare long-term overall survival of patients with resectable pancreatic ductal adenocarcinoma based on whether they received neoadjuvant treatment or underwent upfront surgery.

Methods
A systematic review including randomized clinical trials on the overall survival outcomes between neoadjuvant treatment and upfront surgery in patients with resectable pancreatic ductal adenocarcinoma was conducted up to 1 August 2023 from PubMed, MEDLINE and Web of Science databases. Patient-level survival data was extracted and reconstructed from available Kaplan–Meier curves. A frequentist one-stage meta-analysis was employed, using Cox-based models and a non-parametric method (restricted mean survival time), to assess the difference in overall survival between groups. A Bayesian meta-analysis was also conducted.

Results
Five randomized clinical trials comprising 625 patients were included. Among patients with resectable pancreatic ductal adenocarcinoma, neoadjuvant treatment was not significantly associated with a reduction in the hazard of death compared with upfront surgery (shared frailty HR 0.88, 95% c.i. 0.72 to 1.08, P = 0.223); this result was consistent in the non-parametric restricted mean survival time model (+2.41 months, 95% c.i. −1.22 to 6.04, P &lt; 0.194), in the sensitivity analysis that excluded randomized clinical trials with a high risk of bias (shared frailty HR 0.91 (95% c.i. 0.72 to 1.15; P = 0.424)) and in the Bayesian analysis with a posterior shared frailty HR of 0.86 (95% c.i. 0.70 to 1.05).

Conclusion
Neoadjuvant treatment does not demonstrate a survival advantage over upfront surgery for patients with resectable pancreatic ductal adenocarcinoma.
Intergenerational Care and Subjective Well-being: A Study of the Particularities of Ageing Amongst the Indigenous Peoples of Chile
Authors: Valencia-Gálvez Lorena , Gallardo-Peralta Lorena , Andrade-Guzmán Carlos , Rain-Rain Alicia , Rodriguez-Venegas Viviana 
Published: September 2024

Abstract
This empirical study explores intergenerational caregiving and subjective well-being amongst older adults in Indigenous communities in rural Chile. Facing substantial social and economic challenges, these communities depend heavily on cultural traditions and family support systems. Data were collected from 1,348 older adults, aged sixty and above, using surveys to assess caregiving practices, intergenerational support and subjective well-being, measured by the Personal Wellbeing Index and the Perceived Social Support Questionnaire. Findings reveal that caregiving is deeply rooted in cultural and historical contexts, with positive aspects such as increased satisfaction and social connectedness, and negative aspects including stress and social isolation. Women predominantly assume caregiving roles, maintaining cultural traditions and supporting family well-being. Notable differences in intergenerational support and subjective well-being were observed based on ethnic group and gender. The implications for social work practice are insightful. The study highlights the need for culturally sensitive social work policies and inclusive strategies that address the well-being of older adults in these communities. Social workers and policymakers must consider cultural nuances and the unique needs of Indigenous populations, taking into account cultural identity, economic conditions and acculturation to develop effective support programmes.