bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncertainty quantification in real-time parameter updating for digital twins using Bayesian inverse mapping models
Authors: Kessels Bas M. , Fey Rob H.B. , de Wouw Nathan van 
Published: September 2024

Abstract

To reach its full predictive potential, a digital twin should accurately represent its physical copy in the real world during its entire service lifespan. To this extent, model parameter updating is employed using the inverse mapping parameter updating method. This method enables physically interpretable parameters to be updated for a wide range of (nonlinear) dynamical models. It does so by inferring, in real-time, parameter values based on features extracted from measured data. This is achieved by using a regression model (e.g., based on machine learning) that is trained in an offline phase on simulated data. To allow for the quantification of the uncertainty in the inferred updating parameters, this paper proposes to use probabilistic Bayesian neural networks. This type of neural network infers probability distributions for the updating parameter values instead of the point estimates obtained when employing traditional (deterministic) artificial neural networks. Consequently, the user gains insight in how certain the neural network is about the inferred parameter values and can adjust his/her decision-making based on this knowledge.Besides the features of measured responses, additionally, known so-called `input parameters' that characterize the performed experiments (e.g., specific settings on the physical setup) can be provided as input to the neural network. This allows for a broader applicability of the trained neural network.To validate the proposed methodology, it is applied, using both simulated and real measurements, to a real-world mechanical ventilation system.
Automatic Speech Recognition Predicts Contemporaneous Earthquake Fault Displacement
Authors: Johnson Christopher , Wang Kun , Johnson Paul 
Published: September 2024

Abstract

Significant progress has been made in probing the current state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec 2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kilauea volcano on the island of Hawai'i. We pre-train the Wav2Vec 2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligent models developed for encoding audio of speech may have important applications in studying active fault zones.
Frailty, Physical Function Impairment and Pulmonary Function in Aging Men with and without HIV from the Multicenter AIDS Cohort Study (MACS)
Authors: Abdo Mona , Kunisaki Ken M , Morris Alison , Stosor Valentina , Chang Dong , D’Souza Gypsyamber , Crothers Kristina , Abdel-Maksoud Madiha , DiGuiseppi Carolyn , Brown Todd T , MaWhinney Samantha , Erlandson Kristine M 
Published: September 2024

Abstract

Background
People aging with HIV (PAWH) experience greater impairment in physical and pulmonary function than individuals aging without HIV. We examined whether baseline physical function was associated with subsequent pulmonary impairments.
Methods
Associations of frailty and physical function (gait speed [m/sec], grip strength [kg]) with pulmonary function (< 80% predicted diffusing capacity for carbon monoxide [DLCO] and forced expiratory volume [FEV1]) 3 years later were modeled; age, HIV status, and smoking were assessed as effect modifiers.
Results
Among1,024 men, (54% PAWH, 10% frail, 51% pre-frail), mean (SD) age = 53 (12) years, cumulative smoking = 12 (19) pack-years, gait speed = 1.1 (0.2) m/sec, and grip strength = 36.6 (9.2) kg. Frailty, pre-frailty, and weak grip strength were associated with higher odds of subsequent impaired DLCO and FEV1. Slow gait speed was associated with higher odds of DLCO impairment but not FEV1. No statistically significant modifications were found.
Conclusion
Interventions to improve physical function may help preserve pulmonary function.
Multi-Modal Large Language Model Enables Protein Function Prediction
Authors: Xie Pengtao , Huo Mingjia , Guo Han , Cheng Xingyi , Singh Digvijay , Rahmani Hamidreza , Li Shen , Gerlof Philipp , Ideker Trey , Grotjahn Danielle , Villa Elizabeth , Song Le 
Published: September 2024

Abstract

Predicting the functions of proteins can greatly accelerate biological discovery and applications, where deep learning methods have recently shown great potential. However, these methods predominantly predict protein functions as discrete categories, which fails to capture the nuanced and complex nature of protein functions. Furthermore, existing methods require the development of separate models for each prediction task, a process that can be both resource-heavy and time-consuming. Here, we present ProteinChat, a versatile, multi-modal large language model that takes a protein's amino acid sequence as input and generates comprehensive narratives describing its function. ProteinChat is trained using over 1,500,000 (protein, prompt, answer) triplets curated from the Swiss-Prot dataset, covering diverse functions. This novel model can universally predict a wide range of protein functions, all within a single, unified framework. Furthermore, ProteinChat supports interactive dialogues with human users, allowing for iterative refinement of predictions and deeper exploration of protein functions. Our experimental results, evaluated through both human expert assessment and automated metrics, demonstrate that ProteinChat outperforms general-purpose LLMs like GPT-4, one of the flagship LLMs, by over ten-fold. In addition, ProteinChat exceeds or matches the performance of task-specific prediction models.
Optimization of Optical Systems for Background Oriented Schlieren
Authors: Zhou Tanbo , Gaskins Jonathan , Poggie Jonathan , Bane Sally 
Published: September 2024

Abstract

Background oriented schlieren (BOS) is a non-intrusive optical method for measuring density gradients in a fluid flow based on variations of the local refractive index. The type of BOS optical system used, i.e., entocentric vs. telecentric, and the system design determine the accuracy and quality of the measurement. This work aims to optimize both types of optical systems to minimize the error for measurements of high-speed compressible turbulent boundary layers. Claims of the advantages offered by telecentric optical systems over entocentric systems are investigated, as well as the out-of-focus effects for types of systems. Numerical ray tracing simulations are performed using density fields from large eddy simulations (LES) of a Mach 2 turbulent boundary layer to generate synthetic but realistic BOS images. The results show that telecentric systems have lower overall error and less sensitivity. The optimal standoff distance between the BOS background and the density gradient object is found to be 300% and 200% of the depth-of-field for entocentric and telecentric systems, respectively.
Perception of breastfeeding mothers’ perceptions of donating breast milk for nutritional
Authors: Kuenzang  , Vaswani Vina 
Published: September 2024

Abstract

Background: Rapid advancements in technology have improved the premature survival rate globally. However, the majority of neonatal deaths occur in third-world countries during the neonatal period due to infections and complications. Vulnerablepremature infants need to meet basic nutritional needs, such as breast milk.
Research aim: We aimed to explore mothers’ perceptions ofdonating breast milk fornutrition vis-à-vis research and nurses as a facilitator of donation.
Methods: A qualitative exploratory study was conducted in 2022 with breastfeeding mothers and nurses as facilitators atJigme Dorji Wangchuck National Referral Hospital, Bhutan.
Results:The findings concerningdonation for nutrition werefavorable and revealed the importance and benefits of donation, current practices related to breastfeeding and motives for sharing. Unfavorable conditions includedfear of pathogen transmission; unfavorable experiences with donating; cultural practices; family pressure; and undecided-no experience. The study identified the enablers and barriers perceived by breastfeeding mothers for donating breast milk for research purposes. The benefits that the research can offer for infant survival were enabler, while sociocultural and religious beliefs wereconsidered barriers to donation. The nurses who facilitated donation were involved in the current practice of breastfeeding, the importance of breastfeeding and its technique, and the consent process. Barriers to internal factors include difficulty communicating, beliefs, research hesitancy and disadvantages in research use. The external factor includes beliefs. Nurses toward research had advantages and essential qualities.
Conclusions: Breastfeeding mothers were aware of the importance of donation as a source of nutrition, and these research outcomes could also be beneficial to society. As facilitators, nurses who obtainverbal consent during the donation process and research could improvethe clinical field. Establishing ahuman milk bank is necessary.
Management of trihalomethanes in water by ZnO@kaolinite composite: integrated experimental and modeling studies
Authors: Ezzat Enas , Mishaqa El-Sayed I. , Mohamed O. A. , Shehata Nabila 
Published: September 2024

ABSTRACT

The adsorption of trihalomethanes (THMs) from drinking water was investigated in the current study through comparison studies of kaolinite and ZnO@kaolinite nanocomposites. The clay structural network's successful immobilization on the zincite hexagonal structure of ZnO nanoparticles’ lattice layers was verified by the SEM/EDX analysis. Under the optimum conditions, the maximum removal of THMs was achieved by kaolinite and ZnO@kaolinite nanocomposites after 60 min. The adsorption performance of the ZnO@kaolinite nanocomposites was greater than that of kaolinite because the former had a larger surface area than the latter. The Freundlich isotherm model best matched the adsorption experimental data, which also reveals the existence of multilayer adsorption on a diverse surface with the greatest correlation (R2 = 0.956 and 0.954, respectively) for both nanoadsorbents using the pseudo-first-order (PFO), pseudo-second-order (PSO), mixed 1, 2-order (MFSO), and intraparticle diffusion (IPD) models. The mechanism by which THMs in drinking water adsorb onto nanoadsorbents was examined. This revealed that both intraparticle and film diffusion were involved in the adsorption process. Kaolinite and ZnO@kaolinite nanocomposites can be used in water treatment to remove THMs due to their great recyclable and reusable properties, even after six cycles.
ANGPTL4-the Link Binding Lipid Metabolism and Inflammation
Authors: Zhang Yueqi , liang Jingwen , Li Zhi , Zuo Yuyue , Dai Lei 
Published: September 2024

Background:
Angiopoietin-like 4 (ANGPTL4) belongs to the family of angiopoietin-
like proteins. The involvement of ANGPTL4 in various aspects of lipid
metabolism and inflammation has become an important area of research.

Methods:
A thorough search on PubMed related to ANGPTL4, lipid metabolism, and inflammation
was performed.

Results:
Over the past two decades, the recognition of ANGPTL4 as a potent regulator
of lipid metabolism has substantially increased. As part of the senescence-associated secretory
phenotype, ANGPTL4 also serves as an inflammatory mediator. Considering the
advancements in ANGPTL4 research, we have highlighted that ANGPTL4 acts as a key
node linking lipid metabolism and inflammation. ANGPTL4 impacts inflammation by
regulating lipid metabolism. It affects critical enzymes (lipoprotein lipase, hepatic lipase,
endothelial lipase, and acetyl-CoA carboxylase), regulatory factors (AMPK,
cAMP, SLC7A11, GPX4, and mTOR), and receptors (LepR, CD36, and PPARγ) of
lipid oxidation, synthesis, and peroxidation, thereby affecting immune cells and inflammatory
pathways.

Conclusion:
Understanding the potential association and the therapeutic value of
ANGPTL4 for regulating lipid metabolism and inflammation could contribute to drug
discovery and therapeutic development.
Analyzing Comorbidity Patterns in Patients With Thyroid Disease Using Large-Scale Electronic Medical Records: Network-Based Retrospective Observational Study
Authors: Huang Yanqun , Chen Siyuan , Wang Yongfeng , Ou Xiaohong , Yan Huanhuan , Gan Xin , Wei Zhixiao 
Published: September 2024

Background
Thyroid disease (TD) is a prominent endocrine disorder that raises global health concerns; however, its comorbidity patterns remain unclear.

Objective
This study aims to apply a network-based method to comprehensively analyze the comorbidity patterns of TD using large-scale real-world health data.

Methods
In this retrospective observational study, we extracted the comorbidities of adult patients with TD from both private and public data sets. All comorbidities were identified using ICD-10 (International Classification of Diseases, 10th Revision) codes at the 3-digit level, and those with a prevalence greater than 2% were analyzed. Patients were categorized into several subgroups based on sex, age, and disease type. A phenotypic comorbidity network (PCN) was constructed, where comorbidities served as nodes and their significant correlations were represented as edges, encompassing all patients with TD and various subgroups. The associations and differences in comorbidities within the PCN of each subgroup were analyzed and compared. The PageRank algorithm was used to identify key comorbidities.

Results
The final cohorts included 18,311 and 50,242 patients with TD in the private and public data sets, respectively. Patients with TD demonstrated complex comorbidity patterns, with coexistence relationships differing by sex, age, and type of TD. The number of comorbidities increased with age. The most prevalent TDs were nontoxic goiter, hypothyroidism, hyperthyroidism, and thyroid cancer, while hypertension, diabetes, and lipoprotein metabolism disorders had the highest prevalence and PageRank values among comorbidities. Males and patients with benign TD exhibited a greater number of comorbidities, increased disease diversity, and stronger comorbidity associations compared with females and patients with thyroid cancer.

Conclusions
Patients with TD exhibited complex comorbidity patterns, particularly with cardiocerebrovascular diseases and diabetes. The associations among comorbidities varied across different TD subgroups. This study aims to enhance the understanding of comorbidity patterns in patients with TD and improve the integrated management of these individuals.
Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis
Authors: Pérez-Pérez Martín , Fernandez Gonzalez María , Rodriguez-Rajo Francisco Javier , Fdez-Riverola Florentino 
Published: September 2024

Background
Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecasts indicate that within 15 years 50% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change.

Objective
The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, the research sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing our understanding of how pollen-related information spreads and its implications for public health awareness.

Methods
Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency–inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant themes and patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter.

Results
The months between March and August had the highest volume of messages. The percentage of patient tweets appeared to increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is a global issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses.

Conclusions
The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services.
Associations of Abnormal Fluid Status, Plasma Sodium Disorders, and Low Dialysate Sodium with Mortality in Patients on Hemodialysis
Authors: Pinter Jule , Canaud Bernard , Mayne Kaitlin J. , Stuard Stefano , Moissl Ulrich , Kooman Jeroen , Jager Kitty J. , Chesnaye Nicholas C. , Smyth Brendan , Genser Bernd 
Published: September 2024

Key Points

This multicenter observational study followed 68,196 patients on hemodialysis over a decade and investigated the effect of cumulative exposure burden of abnormal fluid and sodium concentrations and dialysate sodium on mortality.Of >2 million patient-months, 61% were spent in any degree of fluid overload, 28% in severe fluid overload, and 4% in fluid depletion; 11% were spent in hyponatremia and 1% in hypernatremia; and 64% were spent on low dialysate sodium.Cumulative exposure to even mild fluid overload was associated with higher risk of death, and this association was independent of abnormal plasma sodium and low dialysate sodium.

Background
Abnormal fluid and plasma sodium concentrations are established prognostic factors for patients on hemodialysis. However, the cumulative effects of abnormal salt and water and potential effect modifications and the effect of dialysate sodium remain incompletely understood.

Methods
The study followed 68,196 patients on incident hemodialysis from 875 dialysis clinics in 25 countries over 10 years (2010–2020) investigating dose-response patterns between cumulative exposure time of fluid overload/depletion (measured by bioimpedance spectroscopy using the Fresenius Body Composition Monitor), abnormal plasma sodium levels, low dialysate sodium, and all-cause mortality. We calculated time-varying cumulative exposure (in months) of relative fluid overload (any degree; >7% or severe; >13 or >15% in women or men, respectively) and fluid depletion (<−7%), hypo- or hypernatremia (sodium <135 or >145 mmol/L, respectively), low dialysate sodium (≤138 mmol/L), and estimated hazard ratios for all-cause mortality using a multivariable Cox model.

Results
Of 2,123,957 patient-months, 61% were spent in any degree of fluid overload, 4% in fluid depletion, 11% in hyponatremia, and 1% in hypernatremia. Any degree of fluid overload was associated with higher all-cause mortality (hazard ratio peak at 3.42 [95% confidence intervals, 3.12 to 3.75] relative to no exposure), and this association with all-cause mortality seemed to be stronger with severe fluid overload. The risk pattern associated with hyponatremia was approximately linear in the first four patient-months and then plateaued after the fourth patient-month. We did not observe effect modification between fluid overload and hyponatremia.

Conclusions
Even mild fluid overload was associated with higher mortality in patients on hemodialysis. Whether more stringent fluid management results in clinical improvement requires further investigation.
Designing and Synthesis of Flavonoids Derivatives and Screening of their Antimicrobial Activity
Authors: Sharma Mukesh , Ahuja Dharmendra , Jain Anurekha , Goyal Rakesh 
Published: September 2024

Antimicrobial drugs either kill microbes (microbicidal) or prevent the growth of microbes (microbistatic). The streptococcus mutans is a bacteria that found in the human mouth cavity. This bacterial strain produces plaque and acids that break down tooth enamel and cause dental caries. Gram positive cocci, facultatively anaerobic bacteria that forms rod-like chains. the chemical reaction of 2- hydroxyacetophenones with aromatic acylchloride occurs to form 1,3-diketones. This rearrangement reaction proceeds via enolate formation followed by acyl transfer. Then it cyclises into flavone.13 As the same of above scheme can be worked out as 2- Methoxybenzoyl Chloride is prepared by reaction of 2- methoxybenzoic acid with Thionyl chloride and DMF. 2-Methoxybenzoyl Chloride then added to mixture of 2- hydroxyacetophenone and pyridine, 2-[(2-Methoxybenzoyl)oxy]acetophenone thus obtained is treated with pyridine and KOH which gives1-(2-Hydroxyphenyl)-3-(2- methoxyphenyl)-propan1,3-dione. The result of study indicated that C5 [1-(2- hydroxyphenyl)-5-phenylpenta-2,4-dien-1-one]; is only inactive against Streptococcus mutans. All 3-hydroxyflavone derivatives exhibited their MIC to be in range of 250-125 µg/ml., 2,3-dihydroflavan-3-ol derivatives exhibited their MIC to be in range of 1000- 500 µg/ml. The chalcone derivatives exhibited their MIC to be at 250 µg/ml.
Keywords: Streptococcus mutans, flavonoids derivatives, MIC, 2,3-dihydroflavan-3-ol.
Potential control of Echinochloa crus-galli (Barnyard grass) by Curvularia lunata as a mycoherbicide
Authors: Nandhini Chandrasekaran , Ganesh Punamalai , Yoganathan Kamaraj 
Published: September 2024

Echinochloa crus- galli (Barnyard grass) is a world’s most harmful weed species. Echinochloa crus-galli weed species compete with many economically important crops and causes serious problem in rice production. The virulent strain LD2 (leaf disease) of Curvularia lunata was screened out from 25 fungus strains isolated from the diseased leaves of barnyard grass (Echinochloa crus-galli). Greenhouse pot studies were conducted to the feasibility of the strain being as a mycoherbicide for barnyard grass control. The results of pathogenicity experiments showed that this strain was highly pathogenic to barnyard grass at the 2 to 3 leaf stages. Strain LD2 provided excellent barnyard grass control when it was applied at the concentration of 1×104, 1×106 conidia ml1. This strain was very safe to rice and the most plant species except wheat, barley and corn. Findings of this study indicated that this strain could be a potential mycoherbicide for barnyard grass control in paddy fields in the future.
Keywords: Barnyard grass, Bioherbicides, Curvularia lunata, Echinochloa crus-galli, Mycoherbicide,