bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of nanostructured anti reflection coating for various perovskite layer thicknesses in perovskite silicon tandem solar cells
Authors: Kumar B Kranthi , Gupta Nikhil Deep 
Published: December 2023

Abstract
The anti-reflection coating (ARC) plays an important role in the design of every kind of solar cell. The suitable optimization of the ARC layer can make a lot of difference in the final output of the cell, by reducing the reflections at the surface. In this regard, the present paper highlights and analyses numerically the effect of nanostructured ARC for different top perovskite layer thicknesses in perovskite-silicon tandem solar cells. In the present case, the nanostructures for ARC are considered to be made up of SiO2 nanoparticles (NP) embedded in ITO. To evaluate the effect of nanostructure for this proposed cell, the nanostructured tandem cell is compared with its planar ARC-based reference cell. The top perovskite active thickness is varied from 100 nm to 800 nm. It has been found that the effect of nanostructured ARC is more pronounced for thinner perovskite layer-based cells than for thicker layers. To reduce reflections at the front surface, the SiO2 NP diameter and inter-particle spacing are optimized. With the nanostructured ARC at the top, the cell achieved the current density rise of 11.3% as compared to the reference cell for a 100 nm thick perovskite-based tandem cell design. As both the sub-cells are in series in tandem design, the top cell current is matched to the bottom silicon layer current by optimizing the bottom cell too. The proposed ARC design has the added advantage that it can simply be done with sol–gel processes.
Multi-layered micro-patterns co-printed with Ag@CuO nano-ink for flexible devices
Authors: Yu Zhiheng , Zhang Tiancheng , Zhou Shipeng , Pan Taiyao , Yao Haoyang , Huang Fengli 
Published: December 2023

Abstract
Silver (Ag) nano-ink is widely used to fabricate the micro-patterns of flexible electronic devices owing to its excellent conductivity and stability. However, the cost of micro-patterns prepared with silver nano-ink is high. Here, multi-layered, multi-inked (silver@copper oxide) micro-patterns were co-printed layer by layer using an in-house silver nano-ink and commercial copper oxide (CuO) nano-ink. The prepared micro-patterns were solidified by laser sintering. Among the co-printed micro-patterns, the micro-pattern with a top layer of copper (1CuO@2Ag) had the lowest resistance, which was 13.1 Ω cm–1. Additionally, 1CuO@2Ag had the smoothest topography and lowest porosity, which was attributed to effective sintering at the optimal laser power (3 W) for all co-printed micropatterns owing to the high absorptivity and low reflectivity of copper. Moreover, after 500, 1000, and 1500 cycles of fatigue testing, the resistance of 1CuO@2Ag increased by 1.5%, 8.4%, and 13.7%, respectively, indicating good reliability. The proposed method lays the foundation for further studies on Ag@CuO composites for micro-pattern preparation.
Physics-enhanced neural networks for equation-of-state calculations
Authors: Callow Timothy J , Nikl Jan , Kraisler Eli , Cangi Attila 
Published: December 2023

Abstract
Rapid access to accurate equation-of-state (EOS) data is crucial in the warm-dense matter (WDM) regime, as it is employed in various applications, such as providing input for hydrodynamic codes to model inertial confinement fusion processes. In this study, we develop neural network models for predicting the EOS based on first-principles data. The first model utilises basic physical properties, while the second model incorporates more sophisticated physical information, using output from average-atom (AA) calculations as features. AA models are often noted for providing a reasonable balance of accuracy and speed; however, our comparison of AA models and higher-fidelity calculations shows that more accurate models are required in the WDM regime. Both the neural network models we propose, particularly the physics-enhanced one, demonstrate significant potential as accurate and efficient methods for computing EOS data in WDM.
Optimizing building solutions in a changing climate: parameter-based analysis of embodied and operational environmental impacts
Authors: Ramon Delphine , Allacker Karen 
Published: December 2023

Abstract
Buildings contribute significantly to global energy consumption and carbon dioxide emissions. Climate change affects building performance, particularly heating and cooling demands. While current policies focus on improving energy performance and reducing operational emissions, the embodied emissions from building materials become more significant in energy-efficient buildings. This study aims to investigate optimal building solutions considering both operational and embodied environmental impact in the context of climate change in the Belgian context. The research questions address the influence of building characteristics on environmental impact and the contribution of embodied and operational emissions to optimal design. The study employs parametric life cycle assessment and dynamic building energy simulation to explore design strategies for a medium-sized office building. The results reveal the trade-offs between operational and embodied impacts. Buildings with better energy performance exhibit higher embodied emissions, highlighting the importance of considering both aspects. Pareto optimal buildings are identified, minimizing total life cycle environmental cost and operational environmental cost. Insulation levels, solar shading, and orientation are key factors in achieving optimal design. HVAC systems and electricity mixes also significantly influence optimal solutions. Lightweight and heavyweight buildings have distinct characteristics affecting heating and cooling demands. Variations in electricity mixes impact energy consumption and environmental costs of different HVAC system scenarios. The study emphasizes the need for a holistic life cycle approach and considering both operational and embodied impacts in building design. It underscores the importance of optimizing building characteristics while addressing climate change challenges. Further research should explore additional factors such as night cooling, HVAC system performance under climate change, and the inclusion of financial costs and visual comfort in the analysis.
Bibliometric analysis of neuroscience publications quantifies the impact of data sharing
Authors: Emissah Herve , Ljungquist Bengt , Ascoli Giorgio A 
Published: December 2023

Abstract

Summary
Neural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions.

Availability and implementation
The application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.
Drug repositioning with adaptive graph convolutional networks
Authors: Sun Xinliang , Jia Xiao , Lu Zhangli , Tang Jing , Li Min 
Published: December 2023

Abstract

Motivation
Drug repositioning is an effective strategy to identify new indications for existing drugs, providing the quickest possible transition from bench to bedside. With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs based methods exist limitations in deeply integrating node features and topological structures, which may hinder the capability of GCNs.

Results
In this study, we propose an adaptive GCNs approach, termed AdaDR, for drug repositioning by deeply integrating node features and topological structures. Distinct from conventional graph convolution networks, AdaDR models interactive information between them with adaptive graph convolution operation, which enhances the expression of model. Concretely, AdaDR simultaneously extracts embeddings from node features and topological structures and then uses the attention mechanism to learn adaptive importance weights of the embeddings. Experimental results show that AdaDR achieves better performance than multiple baselines for drug repositioning. Moreover, in the case study, exploratory analyses are offered for finding novel drug–disease associations.

Availability and implementation
The soure code of AdaDR is available at: https://github.com/xinliangSun/AdaDR.
Nephrology Partnership for Advancing Technology in Healthcare (N-PATH) program: the teachers’ perspective
Authors: Lomonte Carlo , Rossini Michele , Ibeas Jose , Forcella Mauro , Ponikvar Jadranka Buturovic , Gallieni Maurizio , Russo Roberto , Goumenos Dimitrios , Tesar Vladimir , Hruskova Zdenka , Roelofs Joris , Florquin Sandrine , Snoeijs Maarten , Giusto Antonio , Shemesh David , Rotmans Joris , Torra Roser , Wanner Christoph , Gesualdo Loreto 
Published: December 2023

ABSTRACT
The N-PATH (Nephrology Partnership for Advancing Technology in Healthcare) program concluded with the 60th European Renal Association 2023 Congress in Milan, Italy. This collaborative initiative aimed to provide advanced training in interventional nephrology to young European nephrologists. Funded by Erasmus+ Knowledge Alliance, N-PATH addressed the global burden of chronic kidney disease (CKD) and the shortage of nephrologists. CKD affects &gt;850 million people worldwide, yet nephrology struggles to attract medical talent, leading to unfilled positions in residency programs. To address this, N-PATH focused on enhancing nephrology education through four specialized modules: renal expert in renal pathology (ReMAP), renal expert in vascular access (ReVAC), renal expert in medical ultrasound (ReMUS) and renal expert in peritoneal dialysis (RePED). ReMAP emphasized the importance of kidney biopsy in nephrology diagnosis and treatment, providing theoretical knowledge and hands-on training. ReVAC centred on vascular access in haemodialysis, teaching trainees about different access types, placement techniques and managing complications. ReMUS recognized the significance of ultrasound in nephrology, promoting interdisciplinary collaboration and preparing nephrologists for comprehensive patient care. RePED addressed chronic peritoneal dialysis, offering comprehensive training in patient selection, prescription, monitoring, complications and surgical techniques for catheter insertion. Overall, N-PATH's strategy involved collaborative networks, hands-on training, mentorship, an interdisciplinary approach and the integration of emerging technologies. By bridging the gap between theoretical knowledge and practical skills, N-PATH aimed to revitalize interest in nephrology and prepare proficient nephrologists to tackle the challenges of kidney diseases. In conclusion, the N-PATH program aimed to address the shortage of nephrologists and improve the quality of nephrology care in Europe. By providing specialized training, fostering collaboration and promoting patient-centred care, N-PATH aimed to inspire future nephrology professionals to meet the growing healthcare demands related to kidney diseases and elevate the specialty's status within the medical community.
A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning
Authors: Baker Dirk Norbert , Bauer Felix Maximilian , Giraud Mona , Schnepf Andrea , Göbbert Jens Henrik , Scharr Hanno , Hvannberg Ebba Þora , Riedel Morris 
Published: December 2023

Abstract
In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.
Simultaneous Quantification of Two Neonicotinoids Using QuEChERS–LC–MS/MS in Moroccan Spearmint (Mentha Spicata.L): Qualimetry of the Method by Uncertainty Estimation Using Generalized Pivotal Quantities Approach and Monte Carlo Simulation
Authors: Aaziz Hicham , Saffaj Taoufiq , Benchekroun Yassine Hameda , Ihssane Bouchaib 
Published: December 2023

Abstract

Background
Neonicotinoids (NEOs) are used for the phytosanitary treatment of Mentha Spicata.L crops, and this practice requires precise control of these harmful substances at very low concentrations.

Objective
The objective of this study is to apply an approach allowing simultaneously validation and evaluation of measurement uncertainty based on total error methodology, in order to accurately quantify the presence of two NEOs in Mentha Spicata.L utilizing a Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS)–LC–MS/MS methodology.

Methods
The quantification of imidacloprid and acetamiprid employing a QuEChERS extraction method, coupled with LC–MS/MS, ensuring the accuracy of the analytical method and managing the risks associated with its routine use. A complete and exhaustive validation approach based on the “β-content, γ-confidence” tolerance interval was used for the uncertainty assessment, using the generalized pivot quantity (GPQ) concept and Monte Carlo simulation, which avoids the need for additional data while achieving intermediate precision for each concentration level within predetermined acceptable limits.

Results
The validation procedure is based on the choice of a quadratic model for the two NEOs, allowing the validation of acetamiprid and imidacloprid by LC–MS/MS assay within the range of working concentration. The flexibility of the uncertainty profile intervals was demonstrated with a variation in β-content values (66.7, 80, and 90%) and risk values (10 and 5%), which remained within the acceptability limits of 20%, and the relative expanded uncertainty did not exceed 15 and 11%.

Conclusion
A QuEChERS–LC–MS/MS method for the analysis of two NEOs has been successfully fully validated using the uncertainty profile strategy.

Highlights
Implementation of an overall validation strategy, which involves both the validation and uncertainty assessment known as the uncertainty profile, for the quantification of two important NEOs in Mentha Spicata.L using QuEChERS–LC–MS/MS. This qualimetric approach has been conducted by computing the measurement uncertainty of the method utilizing data from analytical validation under conditions of intermediate precision at each level of concentration without additional effort. After that we have demonstrated the flexibility of this strategy for the LC–MS/MS quantification of acetamiprid and imidacloprid, using a decision tool that enables the choice and modification of β-content and γ-confidence values.
Enfortumab vedotin following platinum-based chemotherapy and immune checkpoint inhibitors for advanced urothelial carcinoma: response, survival and safety analysis from a multicentre real-world Japanese cohort
Authors: Miyake Makito , Nishimura Nobutaka , Oda Yuki , Miyamoto Tatsuki , Ohmori Chihiro , Takamatsu Norimi , Itami Yoshitaka , Tachibana Akira , Matsumoto Yoshihiro , Kiba Keisuke , Tomioka Atsushi , Yamamoto Hiroaki , Okajima Eijiro , Masaomi Kuwata , Sakamoto Keichi , Tomizawa Mitsuru , Shimizu Takuto , Ohnishi Kenta , Hori Shunta , Morizawa Yosuke , Gotoh Daisuke , Nakai Yasushi , Torimoto Kazumasa , Tanaka Nobumichi , Fujimoto Kiyohide ,   the Nara Urological Research and Treatment Group
Published: December 2023

Abstract

Objective
Real-world evidence regarding enfortumab vedotin for unresectable or metastatic urothelial carcinoma is scarce, particularly in Japan. We investigated real-world data focusing on patient background, previous treatments, response, survival and adverse events in patients receiving enfortumab vedotin.

Methods
A multicentre database was used to register 556 patients diagnosed with metastatic urothelial carcinoma from 2008 to 2023; 34 patients (6.1%) treated with enfortumab vedotin were included. Best radiographic objective responses were evaluated using the Response Evaluation Criteria in Solid Tumors (v1.1) during treatments. Overall survival and progression-free survival were estimated (Kaplan–Meier method). Toxicities were reported according to the Common Terminology Criteria for Adverse Events, version 5.0. The relative dose intensity, which could impact oncological outcomes, was calculated.

Results
The median number of enfortumab vedotin therapy cycles was 5. The best objective response to enfortumab vedotin was partial response, stable disease and progressive disease in 19 (56%), 5 (15%) and 10 (29%) patients, respectively. The median overall survival and progression-free survival after the first enfortumab vedotin dose were 16 and 9 months, respectively. No significant relationship was observed between survival outcomes after enfortumab vedotin initiation and the enfortumab vedotin relative dose intensity. The median overall survival from first-line platinum-based chemotherapy initiation was 42 months. Twenty-six (76%) patients experienced any grade of enfortumab vedotin-related toxicities; eight (24%) experienced Grades 3–4 toxicities, the most common being skin toxicity (any grade, 47%; Grades 3–4, 12%).

Conclusions
Here, we report real-world evidence for enfortumab vedotin therapy in Japan. Tumour responses and safety profiles were comparable with those of clinical trials on this novel treatment.
Phytonutrients and outcomes following breast cancer: a systematic review and meta-analysis of observational studies
Authors: van Die M Diana , Bone Kerry M , Visvanathan Kala , Kyrø Cecile , Aune Dagfinn , Ee Carolyn , Paller Channing J 
Published: December 2023

Abstract

Background
Phytonutrient intakes may improve outcomes following breast cancer, but the impact of postdiagnosis introduction vs established prediagnostic exposure as well as optimum doses has not been established. Evidence from observational studies for key exposures was evaluated, including dosage and intake time frames.

Methods
MEDLINE, EMBASE, CINAHL, Cochrane Library, ClinicalTrials.gov, and the ISRCTN registry were searched for prospective and retrospective observational studies investigating the impact of soybean, lignans, cruciferous (cabbage-family) vegetables, green tea, or their phytonutrients on breast cancer survival outcomes. A random-effects model was used to calculate summary hazard ratios (HRs) and 95% confidence intervals (CIs). Nonlinear dose-response analyses were conducted using restricted cubic splines.

Results
Thirty-two articles were included. Soy isoflavones were associated with a 26% reduced risk of recurrence (HR = 0.74, 95% CI = 0.60 to 0.92), particularly among postmenopausal (HR = 0.72, 95% CI = 0.55 to 0.94) and estrogen receptor–positive survivors (HR = 0.82, 95% CI = 0.70 to 0.97), with the greatest risk reduction at 60 mg/day. In mortality outcomes, the reduction was mostly at 20 to 40 mg/day. Soy protein and products were inversely associated with cancer-specific mortality for estrogen receptor–positive disease (HR = 0.75, 95% CI = 0.60 to 0.92). An inverse association was observed for serum or plasma enterolactone, measured prediagnosis and early postdiagnosis, with cancer-specific mortality (HR = 0.72, 95% CI = 0.58 to 0.90) and all-cause mortality (HR = 0.69, 95% CI = 0.57 to 0.83). No effects were observed for cruciferous vegetables. There was a 44% reduced risk of recurrence with prediagnostic green tea for stage I and II breast cancer (HR = 0.56, 95% CI = 0.38 to 0.83).

Conclusions
Soy, enterolactone, and green tea demonstrated significant risk reductions in outcomes following breast cancer. Evidence is needed regarding the impact of postdiagnostic introduction or substantial increase of these exposures.
Predictive Value of Methicillin-Resistant Staphylococcus aureus Nasal Swab PCR Assay for MRSA Infection in Critically Ill Pediatric Patients
Authors: Crawford Lexi , Pertsovskaya Vera , Shanklin Alice , Zhang Anqing , Hamdy Rana F 
Published: December 2023

Abstract

Background
Critically ill pediatric patients are frequently initiated methicillin-resistant Staphylococcus aureus (MRSA) active antibiotics during infection evaluation even though MRSA infections are rare in many patient populations. The MRSA nasal swab polymerase chain reaction assay (MRSA-NS-PCR) is a test that has been shown to have a high negative predictive value (NPV) for MRSA infection in adults. This study evaluated the diagnostic test characteristics of the MRSA-NS-PCR in predicting the presence of MRSA infection in critically ill pediatric patients.

Study Design
A retrospective cohort study was performed in a 44-bed pediatric intensive care unit (PICU) between 2013 and 2017. 3860 pediatric patients (54% male, median age 4 years [IQR 1–11 years]) admitted to the PICU who met pediatric systemic inflammatory response syndrome (pSIRS) criteria, were screened with a MRSA-NS-PCR, and had cultures obtained within seven days of MRSA-NS-PCR collection were included. Predictive values and post-test probabilities of the MRSA-NS-PCR for MRSA infection were calculated.

Results
MRSA-NS-PCR was positive in 8.6% of patients. MRSA infection was identified in 40 patients, equaling an incidence rate of 2 per 1000 patient days. The MRSA-NS-PCR demonstrated a positive predictive value (PPV) of 9.7%, a NPV of 99.8%, and a post-test probability for a negative test of 0.2% for MRSA infection.

Conclusions
The MRSA-NS-PCR has a poor PPV but a high NPV for MRSA infection in PICU patients when the incidence of MRSA infection is low. Creation of protocols to guide antimicrobial selection based on MRSA-NS-PCR results may lead to improved antimicrobial stewardship and significant risk reduction.