bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
Authors: Lao Chuanqi , Zheng Pengfei , Chen Hongyang , Liu Qiao , An Feng , Li Zhao 
Published: December 2023

Abstract
Motivation: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.
Results: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing Simplified Molecular Input Line Entry Specification (SMILES) data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules.Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.
VB-T PHD-SLAM: Efficient SLAM under heavy-tailed noise
Authors: Zou Han , Wu Sunyong , Wang Bin , Xue Qiutiao , Sun Xiyan 
Published: December 2023

Abstract
To address the challenge of simultaneous localization and mapping (SLAM) in the presence of heavy-tailed noise, this papper introduces a robust probability hypothesis density (PHD) SLAM algorithm. This algorithm models measurement noise using the Student's t-distribution, which better captures the heavy-tailed nature of the noise. Since the prior density is assumed to be Gaussian mixture form, the posterior density is no longer Gaussian mixture form after the likelihood update of the t-distribution. A variational Bayesian approach is employed to ensure computable multi-target densities during filtering, minimizing the Kullback-Leibler divergence to obtain an approximate solution for the new marginal likelihood function. Then a new closed-form recursion of PHD-SLAM is derived by using t-distribution. Simulation results and real-world validations demonstrate that the proposed algorithm outperforms PHD-SLAM 1.0 and PHD-SLAM 2.0 in terms of both localization and mapping accuracy while maintaining computational efficiency in SLAM scenarios affected by heavy-tailed noise.
The SDS2 study (SmartFlat for Dysexecutive Syndrome Stratification - a prospective study quantifying cognitive and behavioral executive dysfunction in patients with acquired brain lesions): statistical analysis plan
Authors: Chapelle Rémy , Bompaire Flavie , Bargiotas Ioannis , Perochon Sam , Vayatis Nicolas , Oudre Laurent , Ricard Damien 
Published: December 2023

Abstract
Background:
Evaluating executive dysfunction in brain-damaged patients remains a significant challenge in neurology. Current standard neuropsychological tests, although internally valid, often lack ecological validity. The SDS2 study aims to address this limitation and to improve the understanding of executive dysfunction by collecting multi-modal data including results from ecological and semi-ecological tests. These tests are designed to be more reflective of real-world challenges faced by individuals with executive dysfunction.
Methods:
The study relies on a longitudinal, non-randomized design, focusing on the results of the Arc de Triomphe Construction Test as the primary outcome. Various statistical methods, including Receiver Operating Characteristic curves, logistic regression models, and intraclass correlation coefficients, will be used to assess the performance and validity of this test. Missing data will be handled using multiple imputation techniques. Secondary, subgroup, and exploratory analyses will focus on various aspects of the collected dataset.
Discussion:
The SDS2 study's comprehensive statistical framework will contribute to refining the understanding of executive dysfunction. The proposed use of established statistical methods originating from various fields and state-of-the-art machine learning algorithms seems particularly suited for the complex and heterogeneous multi-modal data involved. These data also represent a valuable opportunity to develop new statistical and machine learning analysis methods.
Trial registration:
ClinicalTrials.gov Identifier: NCT05017051. IDRCB: 2021-A00087-34.
Reversal of dexamethasone induced insulin resistance by Apocynin in L6 myotubes and 3T3L1 adipocytes – a mechanistic approach
Authors: Nagarajan Priyashree , Thandapani Prabhu , Maganti Harika , Manikandan Thanikes , Kailasam Ragul Prasath , Mendonca Angel , Sundaresan Sujatha 
Published: December 2023

Abstract
Objective The current study aims to decipher the putative interaction between skeletal muscle and adipose tissue in response to apocynin (APO) mediated glucose uptake in Dexamethasone (DXM) induced insulin resistance.
Methods The gene expression of the molecular markers pertaining to insulin signaling pathway including IRTK, IRS-1, PI3K and GLUT-4 along with adipogenic markers like PPARα and adiponectin were studied. The results from the study aided in understanding the molecular effects of apocynin favoring the investigation of the crosstalk among the 3T3-L1 adipocytes and L6 myotubes.
Results After confirming 100 nM DXM was ideal for inducing insulin resistance without much alteration in cell viability. This is taken further for inducing insulin resistance in both the chosen cell lines. The gene expression of IRTK and IRS-1 was found to be increased in APO treated, DXM+APO treated cells and was found to be equivalent to that expressed in 100 nM insulin treated cells. In DXM+APO treated L6 myotubes and 3T3-L1 adipocytes, IRTK showed a 2- fold and 3.7-fold increase compared to control respectively. Whereas IRS-1 showed 2.3-fold and 3.5-fold increase in L6 myotubes and 3T3-L1 adipocytes respectively. In addition to this PI3K exhibited a 2.6-fold increase in L6 myotubes and 3.3-fold change in adipocytes. The maximum fold increase of 7.3 was exhibited by GLUT-4 in adipocytes while it showed a 2.8-fold increase in myotubes. The impact of apocynin on adipogenic markers were also analyzed and it was found that DXM+APO treated cells show 3.6-fold and 3.4-fold decrease in the expression of adiponectin and PPAR-α respectively in 3T3-L1 adipocytes.
Conclusion The results substantiate that APO (1 μM) has both anti-diabetic effect in L6 myotubes along with anti-adipogenic properties in 3T3-L1 adipocytes.
On the knowledge production function
Authors: Dolgonosov Boris 
Published: December 2023

Abstract
Knowledge amount is an integral indicator of the development of society. Humanity produces knowledge in response to challenges from nature and society. Knowledge production depends on population size and human productivity. Productivity is a function of knowledge amount. The purpose of this study is to find this function and verify it on empirical material, including global demographic and information data. The productivity function is a basic element of the theory that results in the dynamic equations of knowledge production and population growth. A separate problem is the quantitative assessment of knowledge. To solve it, we consider knowledge representations in the form of patents, articles and books. Knowledge is stored in various types of devices, which together form a global informational storage. Storage capacity is increasing rapidly as digital technology advances. We compare storage capacity with the memory occupied by the forms of knowledge representation. The results obtained in this study contribute to the theory of knowledge production and related demographic dynamics and allow us to deepen our understanding of civilization development.
Prediction of internal friction angle of Q2-Q3 mixed loess as affected by weight ratio based on modified adhesion theory
Authors: HE Jianhui , Zheng Jianguo , Jiang Feng 
Published: December 2023

Abstract
Due to geography and construction technique considerations, Q2-Q3 mixed loess has been used for some loess high-fill slopes in Yan'an, China. The predicting formula for weight ratio and internal friction angle of Q2-Q3 mixed loess with optimal water content is essential for determining the stability of these slopes. This study assumed that the Q2-Q3 mixed loess was a homogeneous, single-species equivalent loess to simplify the derivation of the equation without impacting the research outcomes. This study utilized the stress synthesis law to present a hypothetical stress formula. Two limit cases were considered to determine the formula parameters. Then, the study derived the sliding conditions of equivalent loess soil particles according to the law of bound water. Finally, the study established a formula for predicting the internal friction angle of Q2-Q3 mixed loess based on the definition of the friction factor and the relationship between the water content and the bound water thickness. The results indicate that the predicted internal friction angle of Q2-Q3 mixed loess has minor relative errors, which confirms the accuracy of the formulas developed in this paper. Additionally, as the weight ratio increased, the internal friction angle of the Q2-Q3 mixed loess increased.
Reconstruction of Carrington Rotation means of Open Solar Flux over the past 154 years
Authors: Lockwood Mike , Owens Mat J. 
Published: December 2023

Abstract

We generate reconstructions of signed Open Solar Flux (OSF) for the past 154 years using observations of geomagnetic activity. Previous reconstructions have been limited to annual resolution but this is here increased by a factor of more than 13 by using averages over Carrington Rotation (CR) intervals. We use two indices of geomagnetic activity: the homogeneous aa geomagnetic index, aaH, and the IDV(1d) composite index and a combination of the two is fitted to OSF estimates from near-Earth interplanetary satellite data. For 1995-2022 these are corrected for excess flux (i.e., orthogardenhose flux and switchbacks) using strahl electrons. For 1970-2022 we also use the absolute values of the radial component of the near-Earth interplanetary magnetic field ⟨|⟨Br⟩τ|⟩CR, where the excess flux is allowed for by using the optimum averaging interval τ of 20 hrs. However, in the interval 1970-1995 data gaps in the interplanetary data are a serious problem. The errors that these missing data cause in CR averages of OSF are evaluated by synthetically masking data for CRs that have a full complement of data, using the same number and time series of data gaps as for the CR in question. Given these errors can be large, we use the near-continuous 1995-2022 data to derive the best-fit combination of the geomagnetic data and employ the 1970-1995 data for testing that can allow for the errors caused by data gaps. Errors caused by inaccuracies in the geomagnetic data are shown to be considerably smaller than the uncertainties due to the polynomial fitting. It is shown that the new reconstructions are consistent with the previous annual estimates and that there is considerable variability in the OSF values from one CR to the next: in particular, in high-activity solar cycles there can be individual CRs in which the OSF exceeds that for adjacent CRs by a factor as large as two.
Exploitation copyrights for a work created by an employee: trends, shortcomings and advantages of legal regulation
Authors: Yarkina Natalia 
Published: December 2023

The problems of legal protection of rights to works created by employees in connection with the performance of an employment contract are related to the definition of their legal regime and subjects of exploitation rights. The relevance of the study of these issues is due to the prevalence of legal conflicts between the author - employee and employer; the lack of an established practice of concluding agreements on the distribution of exploitation rights to a work between these entities; inconsistency of legislative provisions and repeated changes in legal regulation. When conducting a scientific study, the tasks were to analyze the process of transformation of the legal regime of works created by an employee under the legislation of Ukraine, to determine the advantages and disadvantages of legal regulation, to study the experience of regulating similar relations in European countries. The purpose of the work is reflected in the formulated conclusions regarding the optimal model of regulation of these relations in the context of balanced protection of the rights of their subjects. The study of the outlined relationships was carried out using a number of scientific methods, in particular, dialectical, formal-logical, analysis and synthesis, system-structural, formal-legal, comparative-legal, logical-legal, method of interpreting legal norms, prognostic, method of legal modeling, logical and legal method. Studying the issues of practical application of the provisions of the legislation, judicial protection of the relevant rights, analysis of the positions of scientists and practitioners made it possible to draw a conclusion about the ineffectiveness of the model of regulation of joint copyrights of the employer and the employee. The current state of the studied relations, the experience of their regulation in European countries, the need to harmonize the law of Ukraine with the law of European countries prove the expediency of providing exploitation copyrights for works to the employer, as well as the possibility of including the copyright fee in the employee's salary. At any stage of the relationship, the employee and the employer retain the legal opportunity to enter into an agreement on the legal regime of works, the legal fate of exploitation rights to them, payment of royalties for the performance of certain tasks, etc. In order to eliminate conflicts between legal norms, in accordance with the established principles of copyright, amendments to the legislative provisions are proposed. In particular, it is proposed to provide for the emergence (rather than the transfer) of exploitation copyrights from the employer, as the primary subject, from the moment of creation of the work. The need to exclude from the law the norm on the employer's right to instruct other employees to complete, supplement and change works without the consent of their author is substantiated, since such actions can violate the author's moral right and harm his creative reputation.
Bias correction of ERA5-Land temperature data using standalone and ensemble machine learning models: a case of northern Italy
Authors: Niazkar Majid , Piraei Reza , Menapace Andrea , Dhawan Pranav , Torre Daniele Dalla , Larcher Michele , Righetti Maurizio 
Published: December 2023

Abstract

Using the global climate model outputs without any adjustment may bring errors in water resources and climate change investigations. This study tackles the critical issue of bias correction temperature in ERA5-Land reanalysis for 10 ground stations in northern Italy using nine machine learning (ML) techniques. Among standalone ML models, XGBoost regression emerged as the most effective standalone ML model, outperforming others across 6 out of 10 stations, while random forest regression, Gaussian process regression, and support vector regression obtained the second to fourth places. In contrast, AdaBoost regression (ABR) achieved the least favorable performance. Furthermore, nine ensemble ML models are proposed to correct bias of the reanalysis of temperature data. The results indicated that the K-nearest neighbors-based ensemble model excelled and secured the top rank in 7 out of 10 stations, while the multiple linear regression-based ensemble model achieved the highest precision in 4 out of 10 stations. Furthermore, other ML-based ensemble models displayed satisfactory results. On the other hand, the ABR-based ensemble model exhibited the lowest accuracy among ML-based ensemble models. The findings highlight the potential of ML-based ensemble models in effectively addressing bias correction in climate data.
Bonum familiae w kontekście przemówienia papieża Franciszka na rozpoczęcie roku sądowego Trybunału Roty Rzymskiej z dnia 29 stycznia 2021 roku
Authors: Dappa Rafał 
Published: December 2023

Pojęcie bonum familiae, choć samo w sobie nie jest kategorią prawną, poja- wia się coraz częściej w kontekście procesów o stwierdzenie nieważności małżeństwa. Stało się również przedmiotem przemówienia Ojca Świętego Franciszka do pracowni- ków Trybunału Roty Rzymskiej na inaugurację roku sądowego (29 stycznia 2021 roku). Papież w swoim przemówieniu zwraca uwagę na dwie kwestie: z jednej strony bonum familiae nie jest i nie może być rozpatrywane jako przyczyna nieważności małżeństwa, z drugiej jednak strony może i powinno być uwzględniane przez sędziów kościelnych w rozstrzygnięciach procesów o nieważność małżeństwa.
Niniejszy artykuł, po zdefiniowaniu pojęcia bonum familiae, podejmuje próbę interpretacji papieskiego przemówienia w kontekście kan. 1691 § 1 KPK/83. Praktyczną aplikację stanowi zachęta do przypominania stronom procesów o nieważność małżeństwa o ich moralnych i cywilnych zobowiązaniach względem siebie i potomstwa.
Rho Kinase (ROCK) Inhibitors for the Treatment of Glaucoma
Authors: Wu Junhui , Wei Jing , Chen Haoliang , Dang Yalong , Lei Fang 
Published: December 2023

Abstract:
Glaucoma is the most common cause of irreversible blindness worldwide. It is characterized
by progressive optic nerve degeneration and loss of visual field. Pathological increased intraocular
pressure is its main modifiable risk factor. Rho kinase inhibitors are developed as a new
class of glaucoma medication that increases outflow facility from the conventional aqueous humor
outflow pathway. Additionally, they also have neuroprotective and anti-scarring effects that can
might increase the success rate of glaucoma filtration surgery. This review aims to summarize the
current concept of Rho kinase inhibitors in the treatment of glaucoma from beach to bedside.
Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation
Authors: Ma Shaoying , Jiang Shuning , Yang Olivia , Zhang Xuanzhi , Fu Yu , Zhang Yusen , Kaareen Aadeeba , Ling Meng , Chen Jian , Shang Ce 
Published: December 2023

Background
From 2016 to 2021, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations.

Objective
In order to expedite evidence synthesis and research gap discoveries, this pilot study has the following three aims: (1) to compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) to develop natural language processing (NLP) algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) to compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations.

Methods
We built a tobacco research domain–specific semantic database using data from 2993 paper abstracts from 4 leading tobacco-specific journals, with enrichment from other publicly available sources. We then trained an NLP model to extract named entities after learning patterns and relationships between words and their context in text, which further enriched the semantic database. Using this iterative process, we extracted and assessed studies relevant to LGBTQ+ tobacco control issues, further comparing our findings with an ongoing systematic review that also focuses on evidence synthesis for this demographic group.

Results
In total, 33 studies were identified as relevant to sexual and gender diverse individuals’ nicotine and tobacco product use. Consistent with the ongoing systematic review, the NLP results showed that there is a scarcity of studies assessing policy impact on this demographic using causal inference methods. In addition, the literature is dominated by US data. We found that the product drawing the most attention in the body of existing research is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults, consistent with the research needs identified by the US health agencies.

Conclusions
Our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. While future research is needed to statistically test the NLP tool’s performance, there is potential for NLP tools to fundamentally transform the approach to evidence synthesis.