bluuebunny/crossref_metadata_2025_split huggingface dataset:
Solvent-mediated synthesis of multi-color narrow-bandwidth emissive carbon quantum dots and their potential in white light- emitting diodes
Authors: Sami Mohamed , El-Khouly Mohamed E. , Ghali Mohsen 
Published: August 2024

Abstract

The development of efficient and environmentally sustainable materials for white light-emitting diodes (WLEDs) is of paramount importance in the field of lighting technology. In this study, we present a solvent-modulated synthesis approach for the fabrication of multicolor narrow-bandwidth emissive carbon quantum dots (CQDs) as a promising solution for constructing WLEDs. The synthesis method involves the controlled reaction of organic precursors in different solvent environments, leading to the formation of CQDs with distinct emission wavelengths with a relatively small full width at half maximum, ranging from 28 to 42 nm. Moreover, these synthesized multicolor CQDs demonstrate a remarkably high fluorescence quantum yield of up to 65%, indicating their potential for constructing efficient WLED when incorporated in polymer matrix and coated on the surface of blue light-emitting diode (LED).
Proteomic and phosphoproteomic profilings reveal distinct cellular responses during Tilapinevirus tilapiae entry and replication
Authors: Lertwanakarn Tuchakorn , Khemthong Matepiya , Setthawong Piyathip , Phaonakrop Narumon , Roytrakul Sittiruk , Ploypetch Sekkarin , Surachetpong Win 
Published: August 2024

Abstract

Tilapia lake virus (TiLV) poses a significant threat to global tilapia aquaculture, as it causes high mortality rates and severe economic losses. Nevertheless, the molecular mechanisms of TiLV–host interactions remain poorly understood. We investigated the proteomic and phosphoproteomic changes in E-11 and RHTiB cell lines following TiLV infection. Using mass spectrometry-based analyses, we identified substantial alterations in protein expression and phosphorylation states, highlighted distinct cell line responses. In E-11 cells, TiLV infection suppressed the proteins involved in the Janus kinase–signal transducer and activators of transcription and Fas-associated death domain protein–tumor necrosis factor receptor associated factor pathways, leading to the activation of nucleotide oligomerization domain signaling and cellular apoptosis. The TiLV-infected RHTiB cells showed suppression of the host cellular metabolism through a reduction in protein phosphatase to facilitate early viral entry, while later stages of infection involved increased activity of myosin heavy chain 9 and enhanced host immune responses via the phosphorylation of ribosomal protein L17 and GTPase immunity-associated protein family member 7. These findings suggest that TiLV employs different strategies to manipulate host cellular pathways depending on the cell type. Further studies are essential to validate these findings and ultimately facilitate the development of effective antiviral strategies.
Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry
Authors: Navarro Alejandro Leonardo García , Koneva Nataliia , Sánchez-Macián Alfonso , Hernández José Alberto , de Dios Oscar Gonzalez , Rivas-Moscoso José Manuel 
Published: August 2024

Abstract

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.
Mathematics Subject Classification (2020) MSC code1 · MSC code2 · more
GPT-4o and the Quest for Machine Learning Interpretability in ICU Mortality Prediction
Authors: Samadi Moein Einollahzadeh , Nikulina Kateryna , Fritsch Sebastian Johannes , Schuppert Andreas 
Published: August 2024

Abstract

Background
Clinical utilization of machine learning is hampered by the lack of interpretability inherent in most non-linear black box modeling approaches, reducing trust among clinicians and regulators. Advanced large language models, such as GPT-4o, offer a potential framework for integrating medical knowledge into these models, potentially enhancing their interpretability.
Methods
Our study utilizes GPT-4o to generate detailed medical feature descriptions, which are aggregated into a comprehensive corpus and processed using TF-IDF vectorization. We then apply fuzzy C-means clustering to these vectorized features to identify significant mortality cause-specific feature clusters. A physician reviews the resulting clusters, validating their relevance to specific mortality causes in mechanically ventilated ICU patients. Subsequently, the resulting clusters inform the creation of weak mortality classifiers, which are combined into a strong classifier using boosting techniques, ultimately producing a GPT-enhanced boosting model for ICU mortality prediction.
Results
This study enrolled 16,018 mechanically ventilated ICU patients, divided into derivation (12,758) and validation (3,260) cohorts, to develop and evaluate a GPT-enhanced boosting model for predicting in-ICU mortality. Leveraging GPT-4o, we implemented an automated process for clustering mortality cause-specific features, resulting in six feature clusters: Liver Failure, Infection, Renal Failure, Hypoxia, Cardiac Failure, and Mechanical Ventilation. This approach significantly improved upon previous manual methods, automating the reconstruction of structured boosting models. While the GPT-enhanced model showed similar predictive accuracy to an XGBoost model, it demonstrated superior interpretability and clinical relevance by incorporating a wider array of features and providing a hierarchical structure of feature importance aligned with medical knowledge.
Conclusion
We introduce a novel approach to predicting in-ICU mortality for mechanically ventilated patients using a GPT-enhanced boosting model. Our methodology demonstrates the potential of integrating large language models with traditional machine learning techniques to create interpretable and clinically relevant predictive models.
Novel CRISPR-Cas12a Clades Discovery Using Large Language Model
Authors: Huang Xingxu , Feng Yuanyuan , Shi Junchao , Yang Jiaxi , Li Zhanwei , Li Yongqian , Tang Jin , Yang Yao , Zheng Jinfang , Han Wei , Huang Shisheng , Zhang Jun , Qiao Yunbo , Ma Peixiang , Liu Qi , Wu Lina , Hu Chunyi , Zhang Xiaokang 
Published: August 2024

Abstract

The identification and engineering of CRISPR-Cas systems revolutionized life science. Metagenome contains millions of unknown Cas proteins, which require precise prediction and characterization. Traditional protein mining mainly depends on protein sequence alignments. In this work, we harnessed the capability of the evolutionary scale language model (ESM) to learn the information beyond the sequence. After training with the CRISPR-Cas sequences and their functional annotation, the ESM model can identify the CRISPR-Cas proteins from the annotated genome sequences accurately and robustly without sequence alignment. However, due to the lack of experimental data, the feature prediction is limited by the small sample size. Integrated with machine learning on small size experimental data, the model is able to predict the trans-cleavage activity of novel Cas12a. Furthermore, we discovered 7 novel subtypes of Cas12a proteins with unique organization of CRISPR loci and protein sequences. Notably, structural alignments revealed that Cas1, Cas2, and Cas4 also exhibit 8 subtypes, with the absence of integrase proteins correlating with a reduction in spacer numbers within CRISPR loci. In addition, the Cas12a subtypes displayed distinct 3D foldings, a finding further corroborated by CryoEM analyses that unveiled unique interaction patterns with RNA. Accordingly, these proteins show distinct double-strand and single-strand DNA cleavage preferences and broad PAM recognition. Finally, we established a specific detection strategy for the oncogene SNP without traditional Cas12a PAM. This study shows the great potential of the language model in the novel Cas protein function exploration via gene cluster classification.
The Access to Credit in the Context of the ESG Framework at Global Level
Authors: Arnone Massimo , Laureti Lucio , Costantiello Alberto , Anobile Fabio , Leogrande Angelo 
Published: August 2024

Abstract

This paper explores the critical role of the banking sector in facilitating the ecological transition through the integration of ESG (Environmental, Social, and Governance) factors. By analyzing the intersection of ESG considerations and access to credit globally, the study highlights how banks can catalyze sustainable investments while balancing financial risks. Using a systematic literature review and k-Means clustering analysis, we assess the global landscape of credit access, emphasizing the implications of ESG adoption on financial stability and economic growth. The findings suggest that while ESG integration presents challenges, it offers significant opportunities for banks to enhance their competitiveness and foster resilient financial systems. The paper concludes by proposing policy recommendations aimed at improving the incorporation of ESG factors within credit risk management and promoting sustainable finance.
Spatio-temporal fish gill microbiota analysis as indicators in estuarine fish health monitoring
Authors: Koll Raphael , Hauten Elena , Theilen Jesse , Bang Corinna , Bouchard Michelle , Thiel Ralf , Möllmann Christian , Woodhouse Jason Nicholas , Fabrizius Andrej 
Published: August 2024

Abstract

Coastal marine and estuarine systems are subject to enormous endogenous and exogenous pressures, particularly climate change, while at the same time being highly productive sources and nurseries for fish populations. Interactions between host and microbiome are increasingly recognized for their importance for fish health, with growing evidence indicating that the abundance and virulence of pathogenic bacteria are rapidly increasing. The microbial composition of the gill mucus reflects environmental conditions and represents a major entry route for pathogens into the fish body. High-throughput sequencing of prokaryotic populations from 250 samples of two fish species with highly different habitat preferences, as well as seasonal and spatial distributions in the Elbe estuary system, allowed us to describe the variation of the microbiota along a salinity gradient and under fluctuating environmental conditions. The analysis of estuarine fish core microbiota in relation to variable bacterial components indicated dysbiotic states under sustained hypoxia and high nutrient loads largely driven by the takeover of opportunistic pathogens (Acinetobacter, Shewanella, Aeromonas). By correlating bacterial abundances with environmental and physiological parameters in a co-occurrence network approach, we describe plasticity in microbiota composition, identify biomarkers of fish health and reconstruct movement patterns of the fish. Our results will help to shape future non-invasive and cost-effective monitoring programs, and identify factors that might be controlled in the estuary to promote fish and stock health.
High-coverage immunopeptidomics using timsTOF mass spectrometers with Thunder-DDA-PASEF boosted by MS2Rescore
Authors: Gomez-Zepeda David , Beyrle Julian , Preikschat Annica , Declercq Arthur , Chen Yannic , Gabriels Ralf , Martens Lennart , Distler Ute , Tenzer Stefan 
Published: August 2024

Abstract

Major histocompatibility complex (MHC, or Human leukocyte antigen, HLA) peptide ligands can be exploited to develop immunotherapies targeting immunogenic disease-specific immunopeptides, such as virus- or cancer mutation-derived peptides. Liquid chromatography-coupled with mass spectrometry (LC-MS)-based immunopeptidomics is the gold standard for identifying MHC ligands. We previously optimized a workflow enabling the identification of more than 10,000 MHC class I ligands per cell line. This process comprises three major steps: (I) a high-recovery immunopeptidome enrichment, (II) an optimized MS acquisition in the timsTOF Pro called Thunder-Data-Dependent Acquisition with Parallel Accumulation-SErial Fragmentation (Thunder-DDA-PASEF), (III) and peptide identification using PEAKS XPro boosted by MS2Rescore data-driven rescoring. Here, we describe our workflow for deep-coverage immunopeptidomics step-by-step, from sample preparation to data analysis and validation.
Investigation on the effect of operation of solar dryer technology according to the drying characteristic of dried product
Authors: Rahman Reza Abdu , Sulistyo Sulistyo , Utomo Mohamad Said Kartono Tony Suryo , Gunawan Kaprian Rizki , Ismail Ismail 
Published: August 2024

The application of solar dryer through greenhouse (GH) model as drying method for crops-based product is extremely important in industrial scale agriculture, especially in developed country. The drying model is considered environmentally friendly since it utilizes solar power as the main drying energy. However, the fundamental operational aspect of the basic GH is less discussed according to the drying mechanism of the dried product. In this work, three small-scale drying model are evaluated: open drying (OD), natural convection GH (NCGH), and forced convection GH (FCGH). The drying product is potato, which can be taken as an essential agricultural product in modern world. The OD model has the lowest mass loss rate which less than 50 % of the water from product can be evaporated. The operation of both GH is satisfactory, resulting more than 60 % moisture evaporation. The drying characteristic indicates the two-step failing rate which makes the mass loss fluctuation from the process. The highest mass loss is observed during the constant rate period around 21 % for FCGH and 18 % for NCGH. In addition, the detailed analysis on the effect of each process is discussed in this work. For example, the presence of hair-like structure and the movement of the pith of dried potato during the process. Also, a higher drying rate from NCGH and FCGH initiate the gelatinization and compartmentalization, resulting a substantial water evaporation of the product. It confirms the important correlation of the drying process and water diffusion mechanism of the dried product. The finding from this study can be taken as a vital reference for improving the operation of GH solar dryer
Investigation on the influence of waste-based fillers on the mechanical and thermal characteristics of rigid polyurethane foams
Authors: Muhsen Muhsen Chelab , Mohammed Ahmed J. , AL-Malki Ziyad T. , Thbayh Dalal K. 
Published: August 2024

An investigation was conducted to analyze the impact of incorporating coal powder particles at different weight ratios (5, 10, 15, 25, and 35) on the mechanical properties and thermal conductivity coefficient of the polyurethane polymer. The thermal conductivity coefficient of the samples was calculated using Holmarc's Lee's Disc apparatus device. The mechanical properties like compressive, tensile, and bending strengths were measured using a universal machine. The results indicated that increasing the coal powder ratio leads to an improvement in the thermal insulation ability due to a decrease in the value of thermal conductivity. Also, the addition of these percentages led to a rise in the values of the mechanical qualities represented by the compressive strength, especially at the ratio of 25 wt. %, with a value equal to 2.79 MPa (MPa). The flexural resistance and tensile strength increase at a ratio of 35 wt. %, with values equal to 20.4 MPa and 2.86 MPa respectively. The results indicate that the addition of coal powder enhances the ability of thermal conductivity at the ratios (5 %, 10) wt. %, with values equal to 0.119 W/m ºC and 0.114 W/m ºC, respectively, by increasing the thermal conductivities of the samples. The aim of this study is, investigate the effect of filler used coal powder waste on the mechanical and thermal properties of PU. The filler materials show the advantages of recycling waste. Filler influences the morphology and strengthens the brittleness. Additionally, the technology of polyurethane materials conforms to the use of coal powder. The overall amount of energy used to produce PU composites is decreased when waste of filler is used to partially replace petrochemical components
Reactivation of public–private partnership (PPP) models for rural water supplies in Rwanda: strengthening operation and maintenance systems
Authors: Ogata Ryuji , Yoshikawa Takeshi , Mugwaneza Vincent de Paul , Sakamaki Yukie 
Published: August 2024

ABSTRACT
The responsibility for operation and maintenance (O&M) in rural piped water supply systems in Rwanda has been transferred from the community to private operators (POs) during 2004. However, water supply services were suspended in many cases because of a lack of proper rehabilitation and regular maintenance due to technical and institutional capacity gaps of POs and other key stakeholders. As countermeasures, this project used the following six approaches: elaborating the O&M framework including the adoption of a licensing system for POs, establishing a drinking water quality monitoring framework, developing stakeholder capacity, developing inventory data and facility mapping, strengthening data management systems, and installing the necessary equipment for water supply management. As a result, the annual downtime of water supply services in the model districts was reduced by 41%. The result was inferred to be mainly related to the establishment of a licensing system for POs, improved reporting mechanism and royalty payment strategy, dedicated bank accounts for water, and the development of an asset inventory. To reactivate the public–private partnership models, it was important to identify specific issues from the national guidelines and contents of the contract between asset owners and POs to activities in the field and to work on improving them realistically.
Examining the Relationship Between Social Support, Self-Efficacy, Diabetes Self-Management, and Quality of Life Among Rural Individuals With Type 2 Diabetes in Eastern China: Path Analytical Approach
Authors: Wang Lizhu , Li Li , Qiu Yang , Li Sihan , Wang Zhonghua 
Published: August 2024

Background
Patients with type 2 diabetes (T2D) in rural China frequently exhibit inadequate diabetes self-management (DSM) and a reduced quality of life (QoL). Social support and self-efficacy are known to influence DSM and QoL. However, the pathways through which social support and self-efficacy impact DSM and QoL among patients with T2D in rural China has yet to be fully elucidated.

Objective
This study offers a foundation for developing policies in rural chronic disease management, thereby, contributing to the improvement of T2D prevention and control in China and other transitional countries.

Methods
This study used a cross-sectional design, collecting data from a survey conducted between May and July 2021 on DSM and QoL among rural patients diagnosed with T2D in 2 townships in East China. All patients with T2D were enrolled through cluster sampling from the township health center database, and a questionnaire survey was administered by investigators. Structural equation modeling and multiple regression analyses were used to explore the pathways through which social support influences DSM and QoL, as well as the mediating role of self-efficacy.

Results
It was found that the DSM score (mean 37.42, SD 7.70) was less than half of the maximum theoretical score. The QoL score (mean 48.92, SD 8.88) accounted for 36% of the maximum theoretical score. Social support directly and positively affected the DSM and QoL of Chinese rural patients with T2D (P<.01); an increase of 1 unit in social support was associated with a direct increment of 0.339 units in DSM and 0.397 units in QoL. Self-efficacy played a positive mediating role (P<.01), further increasing DSM and QoL by 0.147 and 0.159 units, respectively. The mediating effect of self-efficacy accounted for 30.2% and 28.6% of the total effect of social support on DSM and QoL. Furthermore, the family and friend dimension of social support, along with the symptom and disease management dimensions of self-efficacy, were significantly associated with DSM or QoL (P<.01).

Conclusions
The study confirmed the direct and indirect influences of social support on DSM and QoL and elucidated the mediating effect of self-efficacy among rural patients with T2D in eastern China. Interventions should be developed to enhance both social support and self-efficacy, creating a positive cycle of mutual reinforcement to improve DSM and QoL among this group.
Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study
Authors: Hirosawa Takanobu , Harada Yukinori , Tokumasu Kazuki , Ito Takahiro , Suzuki Tomoharu , Shimizu Taro 
Published: August 2024

Background
Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user’s login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown.

Objective
This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series.

Methods
We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02.

Results
In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002).

Conclusions
The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence–enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.