bluuebunny/crossref_metadata_2025_split huggingface dataset:
Дослідження загроз безпеки великих мовних моделей за допомогою автоматизованих інструментів
Authors: Kolchenko Viktor , Khoma Volodymyr , Sabodashko Dmytro , Perepelytsia Pavlo 
Published: January 2025

Мета роботи: огляд та аналіз відомих підходів виявлення вразливостей великих мовних моделей (ВММ), розробка архітектури автоматизованої системи для тестування вразливостей, створення набору підказок для виконання практичного тестування ВММ для оцінювання їх безпеки..
Результати дослідження: дослідження показало, що автоматизована система з використанням утиліти Garak, може ефективно виявляти та запобігати атакам на великі мовні моделі. Застосування таких систем значно підвищує рівень безпеки мовних моделей.
Теоретична цінність дослідження: у статті представлено новий підхід до забезпечення безпеки мовних моделей шляхом автоматизації тестування вразливостей. Це доповнює існуючі теоретичні підходи у сфері кібербезпеки та моделювання.
Практична цінність дослідження: науковці та розробники можуть використовувати результати дослідження для створення безпечніших мовних моделей, а також для вдосконалення алгоритмів, які запобігають маніпуляціям і зловживанням.
Цінність дослідження: стаття пропонує нові технологічні рішення, зокрема впровадження автоматизованої системи на основі утиліти Garak, що дозволяє покращити безпеку, стійкість і ефективність мовних моделей. Це має значення для подальшого розвитку галузі штучного інтелекту та кібербезпеки.
Майбутні дослідження: результати можуть змінюватися залежно від конкретних архітектур мовних моделей або видів атак. Майбутні дослідження доцільно зосередити на вдосконаленні алгоритмів для виявлення нових видів атак та підвищенні ефективності автоматизованої системи в умовах змінних загроз.
IMPLEMENTASI PERATURAN DAERAH NOMOR 15 TAHUN 2019 TENTANG DESA WISATA DI DESA SOMONGARI
Authors: Affiangit Tesar , Dewi Mutiar Fitri 
Published: January 2025

ABSTRAK. Sebagai salah satu sektor unggulan, pariwisata perlu dikembangkan dengan serius supaya dapat berkontribusi nyata pada pendapatan negara dan daerah yang pada akhirnya dapat membantu menyejahterakan masyarakat. Tren saat ini yaitu pengembangan desa wisata yang banyak dilakukan di berbagai daerah di Indonesia termasuk di Desa Somongari Kecamatan Kaligesing Kabupaten Purworejo Jawa Tengah. Dalam penelitian ini, peneliti mencoba mendeskripsikan implementasi Peraturan Daerah Nomor 15 Tahun 2019 tentang Desa Wisata serta mengidentifikasi faktor pendukung dan penghambat serta upaya dalam mengatasi hambatan implementasi. Penelitian ini termasuk penelitian kualitatif dengan pendekatan deskriptif. Teori yang digunakan yaitu teori implementasi dari Van Meter & Van Horn (1975). Data dikumpulkan melalui in-depth interview, observasi, dan dokumentasi. Setelah dikumpulkan, data kemudian dianalisis dengan model analisis interaktif Miles, Huberman, Saldana (2014). Hasil penelitian menunjukkan implementasi kebijakan telah berjalan dengan baik yang didukung komitmen dan komunikasi yang baik dari apara pelaksana kebijakan dan masyarakat memiliki kesadaran tinggi dalam pengembangan desa wisata. Adapun yang menjadi faktor penghambat yaitu kurangnya pemahaman pelaksana kebijakan terhadap Peraturan Daerah Nomor 15 Tahun 2019 tentang Desa Wisata, sumber daya Pokdarwis yang belum memadai dan kompeten, serta pengelolaan dana desa yang belum optimal dan minimnya dukungan anggaran untuk pembangunan fasilitas penunjang.

Kata kunci: implementasi; kebijakan; pengembangan; pemerintah; desa wisata
Herbicide resistance prediction: a mechanistic model vs. a random forest model
Authors: Richter Otto , Lepke Janin , Herrmann Johannes , Beffa Roland 
Published: January 2025

IntroductionHerbicides are an important technology in the Integrated Weed Management (IWM) tool box aiming to control weeds in modern agriculture. Prediction tools to evaluate the risk of resistance evolution will greatly help to choose the best IWM strategy adapted to the local field situation. These comprise classical simulation models, mechanistic models (MMs), combining population dynamics and genetics, and recently artificial intelligence (AI) methods such as random forest. In this paper, both approaches are compared.Materials and methodsArtificial data were generated by an MM and used as training dataset for a random forest classifier. Field history information was taken from two previous studies. The data include the field histories and resistance status of Alopecurus myosuroides of 98 fields from the Hohenlohe area in Germany and 131 from the Champagne area in France.Results and discussionWith accuracies of approximately 80%, the results obtained by the random forest method applied to model-generated data and real field data, respectively, are well comparable. This concerns the ranking of prediction variables and the prediction of the resistance status of a real field and a “model field”. Predictions with model outcomes as training sets and, vice versa, predictions of a “model field” with real data as training sets and predictions by splitting of field data could be made with nearly the same accuracies.ConclusionComplementarity is shown between both approaches with the advantages of AI such as random forest to avoid approximations inherent to complex MMs.
Psychometric characteristics and feasibility of microinteraction-based Ecological Momentary Assessment in audiology research
Authors: Wu Yu-Hsiang , Stangl Elizabeth , Smith Samuel , Oleson Jacob , Miller Christi , Chipara Octav 
Published: January 2025

ObjectivesMicrointeraction-based Ecological Momentary Assessment (micro-EMA) is a smartwatch-based tool that delivers single-question surveys, enabling respondents to quickly report their real-time experiences. The objectives of the two studies presented here were to evaluate micro-EMA's psychometric characteristics and feasibility across three response formats (2-point, 5-point, and 10-point scales) for adults with hearing loss.DesignIn the first study, thirty-two participants completed a dual-task experiment aimed at assessing the construct validity, responsiveness, intrusiveness, and test-retest reliability of micro-EMA across the three response formats. Participants listened to sentences at five signal-to-noise ratios (SNRs) ranging from −3 to 9 dB relative to the SNR for 50% speech understanding, answered the question “Hearing well?” on smartwatches, and repeated the sentences. In the second study, twenty-one participants wore smartwatches over 6 days. Every 15 min, participants were prompted to answer the question “Hearing well?” using one of the three response formats for 2 days. Participants provided feedback on their experience with micro-EMA.ResultsIn the dual-task experiment, participants reported improved hearing performance in micro-EMA as SNRs and speech recognition scores increased across all three response formats, supporting the tool's construct validity. Statistical models indicated that the 5-point and 10-point scales yielded larger relative changes between SNRs, suggesting higher responsiveness, compared to the 2-point scale. Participants completed surveys significantly faster with the 2-point scale, indicating lower intrusiveness, compared to the 5-point and 10-point scales. Correlation analysis revealed that over two visits 1 week apart, the 2-point scale had the poorest test-retest reliability, while the 5-point scale had the highest. In the field trial, participants completed 79.6% of the prompted surveys, with each participant averaging 42.9 surveys per day. Although participants experienced interruptions due to frequent prompts, annoyance and distraction levels were low. Most participants preferred the 5-point scale.ConclusionsThe dual-task experiment suggested that micro-EMA using the 5-point scale demonstrated superior psychometric characteristics compared to the 2-point and 10-point scales at the tested SNRs. The field trial further supported its feasibility for evaluating hearing performance in adults with hearing loss. Additional research is needed to explore the potential applications of micro-EMA in audiology research.
Loading monocytes with magnetic nanoparticles enables their magnetic control without toxicity
Authors: Mödl Laura , Carnell Lucas R. , Stein Rene , Kerpes Andrea , Pfister Felix , Wirthl Barbara , Wall Wolfgang A. , Alexiou Christoph , Janko Christina 
Published: January 2025

BackgroundWith the help of superparamagnetic iron oxide nanoparticles (SPIONs), cells can be magnetically directed so that they can be accumulated at target sites. This principle can be used to make monocytes magnetically steerable in order to improve tumor accumulation, e.g., for immunotherapy with chimeric antigen receptor (CAR) monocytes. Here, we investigated the loading of monocytic THP-1 cells with SPIONs, analyzed their impact on the viability and cellular reactive oxygen species (ROS) generation and their magnetic enrichment. Finally, we compared and confirmed the experimentally generated magnetic enrichment data with computational simulations.Methods and resultsWhen THP-1 cells were incubated with citrate-coated SPIONs (SPIONCitrate) or citrate-stabilized gold-coated SPIONs (SPIONGold), cells ingested the particles, as determined via transmission electron microscopy and atomic emission spectroscopy. Flow cytometry showed that the particles were biocompatible and produced hardly any ROS. With SPION-loading, cells accumulated in Ibidi flow slides at the edge of a Neodym magnet, where the magnetic field and force were maximal, as calculated by our computational model.ConclusionTHP-1 cells were successfully loaded with SPIONs, which exhibited excellent biocompatibility and provided the cells with magnetic steerability. The computational model predicted the actual magnetic accumulation of the SPION-loaded cells, enabling a more systematic and faster exploration of the design space in the future.
Enhanced production of ginsenoside compound K by synergistic conversion of fermentation with Aspergillus tubingensis and commercial cellulase
Authors: Lee Yong-In , Song Woo-Seok , Oh Deok-Kun 
Published: January 2025

Fermentation of ginseng extract is limited by the low concentration of compound K (CK), a bioactive ginsenoside. In this study, a novel approach combining Aspergillus tubingensis fermentation with Aspergillus niger cellulase conversion was used to enhance CK production from high concentrations of American ginseng extract (AGE). The reaction conditions, including the feeding rate and concentrations of carbon source, enzyme type, AGE and enzyme concentrations, temperature, pH, and timing of enzyme addition, were optimized. Under optimized conditions, this combined method achieved an enhanced CK production of 8.06 g/L (13.0 mM) after 168 h, with a productivity of 48 mg/L/h. This approach led to a 2.0-fold increase in concentration and a 1.7-fold increase in productivity when compared with traditional fermentation using the same strain. The findings of this study demonstrate the synergistic effect of combining fermentation with enzyme conversion to improve CK production.
Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study
Authors: Shen Li , Wu Jiaqiang , Lan Jianger , Chen Chao , Wang Yi , Li Zhiping 
Published: January 2025

BackgroundSepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. The primary objective was to construct and evaluate a machine learning (ML) model for forecasting 28-day all-cause mortality among ICU sepsis patients.MethodsData for the study was sourced from the eICU Collaborative Research Database (eICU-CRD) (version 2.0). The main outcome was 28-day all-cause mortality. Predictor selection for the final model was conducted using the least absolute shrinkage and selection operator (LASSO) regression analysis and the Boruta feature selection algorithm. Five machine learning algorithms including logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (lightGBM) were employed to construct models using 10-fold cross-validation. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, recall, and F1 score. Additionally, we performed an interpretability analysis on the model that showed the most stable performance.ResultsThe final study cohort comprised 4564 patients, among whom 568 (12.4%) died within 28 days of ICU admission. The XGBoost algorithm demonstrated the most reliable performance, achieving an AUC of 0.821, balancing sensitivity (0.703) and specificity (0.798). The top three risk predictors of mortality included APACHE score, serum lactate levels, and AST.ConclusionML models reliably predicted 28-day mortality in critically ill sepsis patients. Of the models evaluated, the XGBoost algorithm exhibited the most stable performance in identifying patients at elevated mortality risk. Model interpretability analysis identified crucial predictors, potentially informing clinical decisions for sepsis patients in the ICU.
Genetic insights and therapeutic avenues: unraveling the role of polyunsaturated fatty acids as mediators between hypothyroidism and Von Willebrand disease through Mendelian randomization
Authors: Zhou Wenting , He Rui , Ou Ruwei 
Published: January 2025

BackgroundPrevious observational studies have shown that Hypothyroidism is associated with Von Willebrand Disease (VWD), but the causal relationship has not been confirmed because of conflicting findings and confounding by mixing factors. There are also some studies suggesting that polyunsaturated fatty acids (PUFA) may be one of the potential mediators. In this study, we used a Mendelian randomization study to analyze the causal relationship between Hypothyroidism and VWD and to investigate whether polyunsaturated fatty acids mediate the effects of Hypothyroidism on VWD.MethodsUsing a large publicly available genome-wide association study of predominantly European ancestry to obtain data on Hypothyroidism, VWD, and PUFA, we conducted a two-sample Mendelian randomization study to assess the causal relationship between Hypothyroidism and VWD and assess the potential role of Polyunsaturated fatty acids in mediating the causal pathway between Hypothyroidism and VWD. Finally, we also inferred reverse causality between VWD and Hypothyroidism. Inverse variance weighting (IVW) was the primary analytical method.ResultsWe found that Hypothyroidism may be negatively causally associated with the development of VWD and that PUFA have a role in mediating role in this process (the ratio of the mediating effect: 24.33%). The causal effects of Hypothyroidism and PUFA on VWD remained significant (p &lt; 0.05) after correction of each other by MVMR.ConclusionOur study unveils a novel negative correlation between hypothyroidism and VWD, further enriched by the discovery of partial mediation by PUFA. This groundbreaking finding not only advances our comprehension of VWD etiology but also opens promising avenues for its control and treatment. By elucidating the intricate interplay between hypothyroidism, PUFA, and VWD, our research pioneers a paradigm shift in therapeutic approaches, offering fresh perspectives for the management of this complex disorder.
Comprehensive analysis of the prognostic, immunological, and diagnostic roles of SIRT1 in pan-cancer and its validation in KIRC
Authors: Liu Qi , Sun Songxian , Zhou Chunxiang , Xu Houxi 
Published: January 2025

BackgroundDisturbances in DNA damage repair may lead to cancer. SIRT1, an NAD+-dependent deacetylase, plays a crucial role in maintaining cellular homeostasis through the regulation of processes such as histone posttranslational modifications, DNA repair, and cellular metabolism. However, a comprehensive exploration of SIRT1’s involvement in pan-cancer remains lacking. Our study aimed to analyze the role of SIRT1 in pan-cancer to gain a more comprehensive understanding of its role in multiple malignancies.MethodsWe systematically examined the role of SIRT1 in pan-cancer by analyzing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Various tools, including R, Cytoscape, HPA, Archs4, TISIDB, cBioPortal, STRING, GSCALite, and CancerSEA, were used to integrate and analyze SIRT1 gene expression, prognosis, protein interactions, signaling pathways, immune infiltration, and other relevant information. Furthermore, we validated the differential expression of SIRT1 in normal human kidney cells and kidney cancer cell lines via experimental verification.ResultsSIRT1 expression was significantly reduced in various cancers and was different across molecular and immune subtypes. SIRT1 is intricately linked to numerous cancer pathways. In most cancer types, increased SIRT1 expression is positively associated with eosinophils, helper T cells, central memory T cells, effector memory T cells, γδ T cells, and Th2 cells. SIRT1 expression is significantly correlated with immune regulatory factors across various cancer types. Quantitative reverse transcription polymerase chain reaction (qRT–PCR) and Western blot (WB) analyses confirmed that SIRT1 is differentially expressed in kidney renal clear cell carcinoma (KIRC).ConclusionsUsing an integrative approach involving bioinformatics analysis and experimental validation, we clarified the potential roles and mechanisms of SIRT1 in pan-cancer, providing a theoretical basis for the development of SIRT1-targeted therapies in clinical applications.
Identification of an immunological signature of long COVID syndrome
Authors: Guerrera Gisella , Sambucci Manolo , Timperi Eleonora , Picozza Mario , Misiti Andrea , Placido Roberta , Corbisiero Silvia , D’Orso Silvia , Termine Andrea , Fabrizio Carlo , Gargano Francesca , Eleuteri Sharon , Marchioni Luisa , Bordoni Veronica , Coppola Luigi , Iannetta Marco , Agrati Chiara , Borsellino Giovanna , Battistini Luca 
Published: January 2025

IntroductionAcute COVID-19 infection causes significant alterations in the innate and adaptive immune systems. While most individuals recover naturally, some develop long COVID (LC) syndrome, marked by persistent or new symptoms weeks to months after SARS-CoV-2 infection. Despite its prevalence, there are no clinical tests to distinguish LC patients from those fully recovered. Understanding the immunological basis of LC is essential for improving diagnostic and treatment approaches.MethodsWe performed deep immunophenotyping and functional assays to examine the immunological profiles of LC patients, individuals with active COVID-19, recovered patients, and healthy donors. This analysis assessed both innate and adaptive immune features, identifying potential biomarkers for LC syndrome. A Binomial Generalized Linear Model (BGLM) was used to pinpoint immune features characterizing LC.ResultsCOVID-19 patients exhibited depletion of innate immune cell subsets, including plasmacytoid and conventional dendritic cells, classical, non-classical, and intermediate monocytes, and monocyte-derived inflammatory dendritic cells. Elevated basal inflammation was observed in COVID-19 patients compared to LC patients, whose immune profiles were closer to those of healthy donors and recovered individuals. However, LC patients displayed persistent immune alterations, including reduced T cell subsets (CD4, CD8, Tregs) and switched memory B cells, similar to COVID-19 patients. Through BGLM, a unique adaptive immune signature for LC was identified, featuring memory CD8 and gd T cells with low proliferative capacity and diminished expression of activation and homing receptors.DiscussionThe findings highlight a unique immunological signature associated with LC syndrome, characterized by persistent adaptive immune dysregulation. While LC patients displayed recovery in innate immune profiles comparable to healthy and Recovered individuals, deficits in T cell and memory B cell populations were evident, differentiating LC from full recovery. These findings provide insights into LC pathogenesis and may support the development of diagnostic tools and targeted therapies.
Hook, line, and social media: crowd-sourced images reveal size and species patterns of ocean sunfishes (Tetraodontiformes, Molidae) from California to Alaska
Authors: Mowatt-Larssen Tor , Thys Tierney M. , Hildering Jackie , Caldera Eric J. , Biesack Ellen E. , McDowell Jan R. , Nyegaard Marianne 
Published: January 2025

Taxonomic confusion and limited data have impeded species-level biogeographic analyses of the world’s largest bony fishes, ocean sunfishes (Molidae; ‘molids’), in many ecosystems. However, recent advances in molid taxonomy and the emergence of photo-based community-science platforms provide an opportunity to revisit species-level biogeography. In this study, we use crowd-sourced images of 1,213 ocean sunfishes to determine if molid morphology visible in citizen-science images permits reliable species determination. From the ensuing data, we describe patterns in molid size structure and species composition from 1,178 molids observed in the Alaska and California Current Systems (ACS and CCS, respectively). Molids &lt;1 m total length (TL) were commonly reported in the CCS, particularly off the central coast of California, suggesting this area may function as a molid nursery. Molids &gt;1 m TL were more commonly observed in both the CCS and cooler ACS, which suggests larger molids occupy a larger thermal range (ontogenetic habitat expansion) than smaller individuals. Overall, Mola mola was the most frequently observed species in both the ACS and CCS; however, the persistent occurrence of Mola tecta in both current systems suggests a range extension for this otherwise Southern Hemisphere species. The species identity of six M. tecta specimens from California and Alaska were verified with genetic analysis. Finally, two Mola alexandrini confirmed in the southern portion of the CCS represent the first records of this species in the Northeast Pacific Ocean.
Frailty index, frailty phenotype and 6-year mortality trends in the FRASNET cohort
Authors: Damanti Sarah , De Lorenzo Rebecca , Citterio Lorena , Zagato Laura , Brioni Elena , Magnaghi Cristiano , Simonini Marco , Ruggiero Maria Pia , Santoro Simona , Senini Eleonora , Messina Marco , Farina Francesca , Festorazzi Costanza , Vitali Giordano , Manunta Paolo , Manfredi Angelo Andrea , Lanzani Chiara , Rovere-Querini Patrizia 
Published: January 2025

BackgroundFrailty, a geriatric syndrome associated with adverse outcomes, lacks a universal definition. No consensus exists on the most effective frailty scale for predicting mortality.MethodsThis prospective observational study followed community-dwelling volunteers for 6 years. Frailty was measured with the Frailty Index (FI) and the Frailty Phenotype (FP). Concordance was assessed using Cohen’s Kappa coefficients. Age-and sex-adjusted Cox regression analyses were conducted to evaluate the association with mortality.ResultsOut of 1,114 participants (median age 72 years, IQR 69–77), 186 were classified as frail by the FI, 13 by the FP and 48 by both definitions. The concordance between the two measures was fair (κ = 0.26). Thirty-nine individuals died during the follow-up period. The FI showed a stronger association with mortality (HR 75.29, 95% CI 8.12–697.68, p &lt; 0.001) compared to the FP (HR 3.3, 95% CI 1.45–7.51, p = 0.004). Individuals classified as frail by both definitions had the highest mortality risk and the highest FI scores (median 0.36).ConclusionDefinitions of frailty identify different individuals as frail. The FI was more closely related to mortality than the FP. Individuals classified as frail according to both definitions displayed the highest complexity (corresponding also ho higher FI scores) and the greatest mortality. The FI demonstrated a more accurate ability to predict mortality due to its comprehensive nature.