bluuebunny/crossref_metadata_2025_split huggingface dataset:
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems
Authors: Mazumdar Atanu , López-Ibáñez Manuel , Chugh Tinkle , Hakanen Jussi , Miettinen Kaisa 
Published: May 2023

Abstract
For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data, and an optimizer, for example, a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to provide uncertainty information. However, building GPRs becomes computationally expensive when the size of the dataset is large. Using sparse GPRs reduces the computational cost of building the surrogates. However, sparse GPRs are not tailored to solve offline data-driven MOPs, where good accuracy of the surrogates is needed near Pareto optimal solutions. Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with continuous decision variables are proposed in this paper. The proposed surrogates first split the decision space into subregions using regression trees and build GPRs sequentially in regions close to Pareto optimal solutions in the decision space to accurately approximate tradeoffs between the objective functions. TGPR-MO surrogates are computationally inexpensive because GPRs are built only in a smaller region of the decision space utilizing a subset of the data. The TGPR-MO surrogates were tested on distance-based visualizable problems with various data sizes, sampling strategies, numbers of objective functions, and decision variables. Experimental results showed that the TGPR-MO surrogates are computationally cheaper and can handle datasets of large size. Furthermore, TGPR-MO surrogates produced solutions closer to Pareto optimal solutions compared to full GPRs and sparse GPRs.
Upgrades of Genetic Programming for Data-Driven Modeling of Time Series
Authors: Murari A. , Peluso E. , Spolladore L. , Rossi R. , Gelfusa M. 
Published: May 2023

Abstract
In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of symbolic regression (SR) via genetic programming (GP) for the investigation of time series, with the objective of extracting mathematical models directly from the available signals. The main task is not simply prediction but consists of identifying interpretable equations, reflecting the nature of the mechanisms generating the signals. The implemented improvements involve almost all aspects of GP, from the knowledge representation and the genetic operators to the fitness function. The unique capabilities of genetic programming, to accommodate prior information and knowledge, are also leveraged effectively. The proposed upgrades cover the most important applications of empirical modeling of time series, ranging from the identification of autoregressive systems and partial differential equations to the search of models in terms of dimensionless quantities and appropriate physical units. Particularly delicate systems to identify, such as those showing hysteretic behavior or governed by delayed differential equations, are also addressed. The potential of the developed tools is substantiated with both a battery of systematic numerical tests with synthetic signals and with applications to experimental data.
Demonstrating the Potential Impacts of Assimilating FY-4A Visible Radiances on Forecasts of Cloud and Precipitation with a Localized Particle Filter
Authors: Zhou Yongbo , Liu Yubao , Han Wei 
Published: May 2023

Abstract
The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4A (FY-4A) satellite provides visible radiances that contain critical information on clouds and precipitation. In this study, the impact of assimilating FY-4A/AGRI all-sky visible radiances on the simulation of a convective system was evaluated with an observing system simulation experiment (OSSE) using a localized particle filter (PF). The localized PF was implemented into the Data Assimilation Research Testbed (DART) coupled with the Weather Research and Forecasting (WRF) Model. The results of a 2-day data assimilation (DA) experiment generated encouraging outcome at a synoptic scale. Assimilating FY-4A/AGRI visible radiances with the localized PF significantly improved the analysis and forecast of cloud water path (CWP), cloud coverage, rain rate, and rainfall areas. In addition, some positive impacts were produced on the temperature and water vapor mixing ratio in the vicinity of cloudy regions. Sensitivity studies indicated that the best results were achieved by the localized PF configured with a localization distance that is equivalent to the model grid spacing (20 km) and with an adequately short cycling interval (30 min). However, the localized PF could not improve cloud vertical structures and cloud phases due to a lack of related information in the visible radiances. Moreover, the localized PF was compared with the ensemble adjustment Kalman filter (EAKF) and it was indicated that the localized PF outperformed EAKF even when the number of ensemble members was doubled for the latter, indicating a great potential of the localized PF in assimilating visible radiances.
Reason vs. Religion in Medieval India: Mainly from Evidence in Persian
Authors: Habib Irfan 
Published: May 2023

The pursuit of reason may be defined as the drawing of logical deductions from a study of actual phenomena, and thus be essentially confined to the results gained from access to the various branches of science. It was in Greece where, from the fifth century BCE onward, rational thought was deemed to have developed most. Greek texts exercised undeniable influence on early thought in the Islamic world, Alberūnī’s Kitabu’l Hind being a remarkable product of that influence. Muslim theologians mounted a tirade against rationality (‘aql), in which the ṣūfīs joined; but since ṣūfic moral thought often tended to override Muslim theology, there could arise figures (even if partly imaginary) like Rābi‘a of Basra, who stood up against theology and its fictions. The conflict between ma‘qūlāt (reason) and manqūlāt (theology) was duly imported into India, along with the arrival of the Arabic–Persian sciences in the 13th and 14th centuries. Here poetry in Persian also became a major vehicle undermining theology. The tendency is partly present in Amīr Khusrau of Delhi (extolling love above theology!), but especially in the Iranian poet Ḥāfiz Shīrāzī, where the sāqī and ale-house constituted the major alternative to the pulpit and the mosque. In Indo-Persian poetry the same role is often ascribed to the but (idol) and the butkhāna (temple). It was under Akbar (r. 1556–1605) that a detailed inquiry (1574 onward) into Islamic beliefs and the doctrines of other religions led to disquiet about their validity. Abū’l Faẓl (d. 1602) became the leading light of a revival of rationality. Akbar’s own critique of Islam was similarly extended to aspects of Hinduism. ‘Urfī represents best the shift to reason, by the boldness of his poetry, rejecting religion for its inadequacy and looking forward to a just world. The seventeenth century did not fulfil the promise of the 16th. There was continuing interest in religion, shown by Jahāngīr’s formula: Tasawwuf = Vedānta; Dārā Shukoh’s translation of the Upanishads; and Mobad’s unique work Dabistān. But there was no corresponding assertion of rationality, whose votaries were reduced to a small band, last described, c. 1655.
The association between Patient Assessment of Chronic Illness Care Scores and quality of life in type 2 diabetes patients
Authors: Akan Gülçin , Kartal Asiye 
Published: May 2023

Background: Chronic illness care is an important factor for health promotion in diabetes since it affects health outcomes such as quality of life. Aims: The aim of this study was to investigate the relationship between the Patient Assessment of Chronic Illness Care and the quality of life in type 2 diabetes patients. Methods: The study used a cross-sectional and correlational design. The sample included 317 patients with type 2 diabetes. A socio-demographic and disease-related questionnaire form, the Patient Assessment of Chronic Illness Care (PACIC) scale and the WHOQOL-BREF Quality of Life Scale were used to collect data. Results: According to the regression analysis, the stronger predictor affecting all domains of quality of life was the overall PACIC. This study demonstrated the importance of satisfaction level of chronic illness care in the improvement of quality of life. Therefore, the factors affecting the satisfaction level with chronic care services should be determined to improve the quality of life of patients. In addition, healthcare based on the chronic-care model should be provided in the care of the patients. Conclusions: PACIC had an important effect on quality of life of the patients. This study demonstrated the importance of satisfaction level on chronic illness care in the improvement of quality of life.
Gluteal muscle and subcutaneous tissue thicknesses in adults: a systematic review and meta-analysis
Authors: Gulnar Emel , Ordu Yadigar , Biyik Bayram Sule , Çalışkan Nurcan 
Published: May 2023

Background: Nurses often administer intramuscular injections at the gluteal site. This study aimed to determine gluteal muscle and subcutaneous tissue thicknesses in adults. Methods: Systematic review and meta-analysis. The databases Turkish Medline, Ulakbim, National Thesis Center, Cochrane, Web of Science, Science Direct, PubMed, CINAHL Plus with Full text (EBSCO host), OVID and SCOPUS were screened using the keywords ‘intramuscular injection’, ‘subcutaneous tissue thickness’, ‘muscle tissue thickness’ and ‘needle length’ between April and May 2021. The studies were evaluated with ultrasound. This study was reported according to the PRISMA recommendations. Results: Six studies met the eligibility criteria. The total sample size was 734 (women: 432, men: 302). The V method revealed that the ventrogluteal site had a muscle and subcutaneous tissue thickness of 38.071 ± 2.119 and 19.927 ± 2.493 mm, respectively. The geometric method revealed that the ventrogluteal site had a muscle and subcutaneous tissue thickness of 35.989 ± 4.190 and 19.661 ± 3.992 mm, respectively. The geometric method also revealed that the dorsogluteal site had a thickness of 42.560 ± 8.840 mm. According to the V method, females had thicker subcutaneous tissue at the ventrogluteal site than males ( Q = 5.37, df = 1, p = 0.0204). Body mass index did not affect the subcutaneous tissue thicknesses at the ventrogluteal site. Conclusion: The results show that gluteal muscle, subcutaneous and total tissue thicknesses vary across injection sites.
Effects of Opioid-Limiting Legislation and Increased Provider Awareness on Postoperative Opioid Use and Complications After Hip Arthroscopy
Authors: Strony John T. , Raji Yazdan , Ina Jason G. , Yu Jiao , Megerian Mark F. , McCollum Samuel W. , Mather Richard C. , Nho Shane J. , Salata Michael J. 
Published: May 2023

Background: On August 31, 2017, Ohio passed legislation that regulates how opioids can be prescribed postoperatively. Studies have shown that such legislation is successful in reducing the morphine milligram equivalents (MMEs) prescribed after certain orthopaedic procedures. Purpose: (1) To determine if the opioid prescription–limiting legislation in Ohio reduced the cumulative MMEs prescribed after hip arthroscopy without significantly affecting the rates of emergency department (ED) visits, hospital readmissions, and reoperations within 90 days postoperatively, and (2) to assess risk factors associated with increased postoperative opioid dosing. Study Design: Cohort study; Level of evidence, 3. Methods: This study included patients who underwent primary and revision hip arthroscopy at a single institution over a 4-year period. The prelegislation (PRE) and postlegislation (POST) groups were defined as patients who underwent surgery before August 31, 2017, and on/after this date, respectively. The Ohio Automated Rx Reporting System was queried for controlled-substance prescriptions from 30 days preoperatively to 90 days postoperatively, and patient medical records were reviewed to collect demographic, medical, surgical, and readmission data. Inverse probability weighting–adjusted mean treatment effect regression models were used to measure the difference in mean outcomes between the PRE and POST cohorts. Results: A total of 546 patients (228 PRE, 318 POST) were identified. There was a 25% reduction in the cumulative MMEs prescribed to the POST group as compared with the PRE group during the first 90 days postoperatively (840 vs 1125 MME, respectively; P < .01). The legislation was associated with a significant decrease in the cumulative MMEs prescribed in the first 90 postoperative days (mean treatment effect = –280.6; P < .01), and there were no significant between-group differences in the frequency of ED encounters (8.8% PRE, 11.6% POST; P = .32), hospital readmissions (1.3% PRE, 0.9% POST; P = .70), or reoperations (0.9% PRE, 0.6% POST; P ≥ .99) during this period. Preoperative opioid use was a significant independent risk factor for increased cumulative MMEs in the first 90 days postoperatively (β = 275; P < .01). Conclusion: Opioid prescription–limiting legislation in Ohio was associated with significant reductions in opioid MMEs dosing in the 90-day period following hip arthroscopy. This legislation had no significant effect on ED utilization, hospital readmissions, or reoperations within the same period. Preoperative opioid use was a significant risk factor for increased MME dosing after hip arthroscopy.
Circulating tumor DNA adds specificity to PET after axicabtagene ciloleucel in large B-cell lymphoma
Authors: Dean Erin A. , Kimmel Gregory J. , Frank Matthew J. , Bukhari Ali , Hossain Nasheed M. , Jain Michael D. , Dahiya Saurabh , Miklos David B. , Altrock Philipp M. , Locke Frederick L. 
Published: May 2023

Abstract
We examined the meaning of metabolically active lesions on 1-month restaging nuclear imaging of patients with relapsed/refractory large B-cell lymphoma receiving axicabtagene ciloleucel (axi-cel) by assessing the relationship between total metabolic tumor volume (MTV) on positron emission tomography (PET) scans and circulating tumor DNA (ctDNA) in the plasma. In this prospective multicenter sample collection study, MTV was retrospectively calculated via commercial software at baseline, 1, and 3 months after chimeric antigen receptor (CAR) T-cell therapy; ctDNA was available before and after axi-cel administration. Spearman correlation coefficient (rs) was used to study the relationship between the variables, and a mathematical model was constructed to describe tumor dynamics 1 month after CAR T-cell therapy. The median time between baseline scan and axi-cel infusion was 33 days (range, 1-137 days) for all 57 patients. For 41 of the patients with imaging within 33 days of axi-cel or imaging before that time but no bridging therapy, the correlation at baseline became stronger (rs, 0.61; P &lt; .0001) compared with all patients (rs, 0.38; P = .004). Excluding patients in complete remission with no measurable residual disease, ctDNA and MTV at 1 month did not correlate (rs, 0.28; P = .11) but correlated at 3 months (rs, 0.79; P = .0007). Modeling of tumor dynamics, which incorporated ctDNA and inflammation as part of MTV, recapitulated the outcomes of patients with positive radiologic 1-month scans. Our results suggested that nonprogressing hypermetabolic lesions on 1-month PET represent ongoing treatment responses, and their composition may be elucidated by concurrently examining the ctDNA.
A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator
Authors: Doherty Trevor , Dempster Emma , Hannon Eilis , Mill Jonathan , Poulton Richie , Corcoran David , Sugden Karen , Williams Ben , Caspi Avshalom , Moffitt Terrie E. , Delany Sarah Jane , Murphy Therese M. 
Published: May 2023

AbstractBackgroundThe field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine–guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons.ResultsWe found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general—suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities.ConclusionsThe variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.
Faster and more accurate pathogenic combination predictions with VarCoPP2.0
Authors: Versbraegen Nassim , Gravel Barbara , Nachtegael Charlotte , Renaux Alexandre , Verkinderen Emma , Nowé Ann , Lenaerts Tom , Papadimitriou Sofia 
Published: May 2023

Abstract
Background
The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture.

Results
We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database (https://olida.ibsquare.be). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels.

Conclusions
Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform (https://orval.ibsquare.be) to apply VarCoPP2.0 on their data.
Genomic benchmarks: a collection of datasets for genomic sequence classification
Authors: Grešová Katarína , Martinek Vlastimil , Čechák David , Šimeček Petr , Alexiou Panagiotis 
Published: May 2023

Abstract
Background
Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition.

Results
Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package ‘genomic-benchmarks’, and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks.

Conclusions
Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.