bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data
Authors: Handa Koichi , Thomas Morgan C. , Kageyama Michiharu , Iijima Takeshi , Bender Andreas 
Published: November 2023

AbstractWhile a multitude of deep generative models have recently emerged there exists no best practice for their practically relevant validation. On the one hand, novel de novo-generated molecules cannot be refuted by retrospective validation (so that this type of validation is biased); but on the other hand prospective validation is expensive and then often biased by the human selection process. In this case study, we frame retrospective validation as the ability to mimic human drug design, by answering the following question: Can a generative model trained on early-stage project compounds generate middle/late-stage compounds de novo? To this end, we used experimental data that contains the elapsed time of a synthetic expansion following hit identification from five public (where the time series was pre-processed to better reflect realistic synthetic expansions) and six in-house project datasets, and used REINVENT as a widely adopted RNN-based generative model. After splitting the dataset and training REINVENT on early-stage compounds, we found that rediscovery of middle/late-stage compounds was much higher in public projects (at 1.60%, 0.64%, and 0.21% of the top 100, 500, and 5000 scored generated compounds) than in in-house projects (where the values were 0.00%, 0.03%, and 0.04%, respectively). Similarly, average single nearest neighbour similarity between early- and middle/late-stage compounds in public projects was higher between active compounds than inactive compounds; however, for in-house projects the converse was true, which makes rediscovery (if so desired) more difficult. We hence show that the generative model recovers very few middle/late-stage compounds from real-world drug discovery projects, highlighting the fundamental difference between purely algorithmic design and drug discovery as a real-world process. Evaluating de novo compound design approaches appears, based on the current study, difficult or even impossible to do retrospectively.Scientific Contribution This contribution hence illustrates aspects of evaluating the performance of generative models in a real-world setting which have not been extensively described previously and which hopefully contribute to their further future development.
Components of professional satisfaction among novice nurses
Authors: Savitsky Bella , Shvartsur Rachel , Findling Yifat , Ereli Anat , Hendel Tova 
Published: November 2023

Abstract
Background
In Israel there are only 6.53 nurses per 1000 citizens, compared to 8.8 nurses per 1000 citizens in the OECD countries. The nursing shortage is even more severe in peripheral areas, especially in southern Israel. Nurses` professional satisfaction is crucial for preserving the nursing workforce. This study aimed to assess job satisfaction among novice nurses and identify components of professional satisfaction.

Methods
Cross-sectional study of 216 novice nurses who graduated in 2018–2022 and were approached ten months after graduation. Job satisfaction components were constructed using factor analysis.

Results
Professional satisfaction was based mainly on the intrinsic characteristics of the occupation related to personal accomplishment and organizational culture. In a multivariable model, a one-point increase in mean satisfaction with the training period during studies in the nursing department was associated with a more than a three-fold elevation in the odds for high and very high professional satisfaction (OR 3.0, 95% CI 1.7–5.1). Odds for high and very high professional satisfaction were more than four-fold and two-fold higher among graduates who rated their level of control over work schedule as high and medium vs. low (OR 4.2, 95% CI 1.0–16.7 and OR 2.8, 95% CI 1.2–6.3, respectively). Work-life balance without disturbance to daily life by work was found significantly associated with higher odds for high and very high satisfaction. Nurses who plan to continue professional development, i.e., an advanced professional course or Master’s degree, had significantly higher mean professional satisfaction scales than others (4.2 vs. 3.7, p = .009 and 4.2 vs. 3.9, p < .001, respectively).

Conclusion
The most important components of professional satisfaction among novice nurses are self-accomplishment, which was built from work-related challenges, interest and variety of tasks, personal growth and development, and the possibility of contributing to patients` care and organizational culture, which was built from relationships with co-workers. Persons who manage nurses should cultivate an atmosphere of support and guidance, provide new nurses with interesting work tasks, and increase their ability to control their work schedule. Young nurses should be encouraged to continue their professional and academic education.
Algorithm selection using edge ML and case-based reasoning
Authors: Ali Rahman , Zada Muhammad Sadiq Hassan , Khatak Asad Masood , Hussain Jamil 
Published: November 2023

AbstractIn practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications.
Efficacy and safety of baricitinib for the treatment of hospitalized adults with COVID-19: a systematic review and meta-analysis
Authors: Sun Jing , Wang Shufang , Ma Xin , Wei Qingqing , Peng Yujuan , Bai Ying , Miao Guobin , Meng Chang , Liu Peng 
Published: November 2023

Abstract
Objectives
Several clinical trials have evaluated the efficacy and safety of baricitinib in COVID-19 patients. Recently, there have been reports on critical patients, which are different from previous research results. The meta-analysis was performed to investigate the effects of baricitinib in COVID-19, by pooling data from all clinically randomized controlled trials (RCTs) available to increase power to testify.

Methods
Studies were searched in PubMed, Embase, and Cochrane Library databases on January 31, 2023. We performed a meta-analysis to estimate the efficacy and safety of baricitinib for the treatment of hospitalized adults with COVID-19. This study is registered with INPLASY, number 202310086.

Results
A total of 3010 patients were included in our analyses. All included studies were randomized controlled trials or prospective study. There was no difference in 14-day mortality between the two groups [OR 0.23 (95% CI 0.03–1.84), I2 = 72%, P = 0.17]. In subgroup analyses we found that baricitinib did not seem to improve significantly in 24-day mortality critically ill patients [OR 0.60 (95% CI 0.35–1.02), I2 = 0%, P = 0.06]. Fortunately, baricitinib have led to faster recovery and shorter hospital stays for COVID-19 patients. There were no difference in infections and infestations, major adverse cardiovascular events, deep vein thrombosis and pulmonary embolism.

Conclusions
Baricitinib did not increase the incidence of adverse reactions. At the same time, we can find that it reduces the mortality of COVID-19 patients, not including the critically ill.
“It’s a feeling of complete disconnection”: experiences of existential loneliness from youth to older adulthood
Authors: McKenna-Plumley Phoebe E. , Turner Rhiannon N. , Yang Keming , Groarke Jenny M. 
Published: November 2023

Abstract
Background
Existential loneliness is a feeling which stems from a sense of fundamental separation from others and the world. Although commonly mentioned in the loneliness literature, there is relatively little empirical work on this construct, and existing work tends to focus on older and seriously ill individuals. The present study aimed to understand how people experience existential loneliness without specific constraints on precipitating factors like illness or age.

Methods
A qualitative online survey collected data from 225 adults aged 16 to 72 years old. Participants were asked to write about their experiences of existential loneliness and how these experiences compared to non-existential loneliness. Data were analysed using reflexive thematic analysis.

Results
Of 225 participants, 51% knew the meaning of “existential loneliness” upon accessing the survey and in total, 83% had experienced existential loneliness. 93% of these participants had also experienced loneliness that was not existential in nature. 175 participants provided qualitative data regarding their experiences of existential loneliness, from which four themes were identified: Existential loneliness is (1) A deeper form of loneliness, and (2) A feeling of deep disconnection, in which (3) Cognitive evaluations and negative emotions are central elements, and (4) Stress and mental health issues are perceived as relevant factors.

Conclusions
Existential loneliness is a deeply rooted and impactful form of loneliness which involves feelings of profound separateness. This aspect of loneliness is deserving of further attention. Future research directions are suggested.
Microbial tapestry of the Shulgan-Tash cave (Southern Ural, Russia): influences of environmental factors on the taxonomic composition of the cave biofilms
Authors: Gogoleva Natalia , Chervyatsova Olga , Balkin Alexander , Kuzmina Lyudmila , Shagimardanova Elena , Kiseleva Daria , Gogolev Yuri 
Published: November 2023

Abstract
Background
Cave biotopes are characterized by stable low temperatures, high humidity, and scarcity of organic substrates. Despite the harsh oligotrophic conditions, they are often inhabited by rich microbial communities. Abundant fouling with a wide range of morphology and coloration of colonies covers the walls of the Shulgan-Tash cave in the Southern Urals. This cave is also famous for the unique Paleolithic painting discovered in the middle of the last century. We aimed to investigate the diversity, distribution, and potential impact of these biofilms on the cave’s Paleolithic paintings, while exploring how environmental factors influence the microbial communities within the cave.

Results
The cave’s biofilm morphotypes were categorized into three types based on the ultrastructural similarities. Molecular taxonomic analysis identified two main clusters of microbial communities, with Actinobacteria dominating in most of them and a unique “CaveCurd” community with Gammaproteobacteria prevalent in the deepest cave sections. The species composition of these biofilms reflects changes in environmental conditions, such as substrate composition, temperature, humidity, ventilation, and CO2 content. Additionally, it was observed that cave biofilms contribute to biocorrosion on cave wall surfaces.

Conclusions
The Shulgan-Tash cave presents an intriguing example of a stable extreme ecosystem with diverse microbiota. However, the intense dissolution and deposition of carbonates caused by Actinobacteria pose a potential threat to the preservation of the cave’s ancient rock paintings.
Integrated risk measurement and control for stochastic energy trading of a wind storage system in electricity markets
Authors: Xiao Dongliang , Chen Haoyong , Cai Weijun , Wei Chun , Zhao Zhendong 
Published: November 2023

AbstractTo facilitate wind energy use and avoid low returns, or even losses in extreme cases, this paper proposes an integrated risk measurement and control approach to jointly manage multiple statistical properties of the expected profit distribution for a wind storage system. First, a risk-averse stochastic decision-making framework and multi-type risk measurements, including the conditional value at Risk (CVaR), value at risk (VaR) and shortfall probability (SP), are described in detail. To satisfy the various needs of multi-type risk-averse decision makers, integrated risk measurement and control approaches are then proposed by jointly considering the expected, boundary and probability values of the extreme results. These are managed using CVaR, VaR and SP, respectively. Finally, the effectiveness of the proposed risk control strategy is verified by conducting case studies with realistic market data, and the results of different risk control strategies are analyzed in depth. The impacts of the risk parameters of the decision maker, the energy capacity of the battery storage and the price difference between the day-ahead and real-time markets on the expected profits and risks are investigated in detail.
CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease
Authors: Hu Na , Yan Gang , Tang Maowen , Wu Yuhui , Song Fasong , Xia Xing , Chan Lawrence Wing-Chi , Lei Pinggui 
Published: November 2023

AbstractMetabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy “quantitative” CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.
Graphical Abstract
Close Reading/Mass Media: I. A. Richards’s Screen Tests
Authors: Foltz Jonathan 
Published: November 2023

Abstract
This article explores the overlapping histories of close reading and mass media by attending to the late-career film and television experiments of I. A. Richards. A lifelong critic of the entertainment industry and (arguably reactionary) media theorist, Richards also spent decades experimenting with the exacting and humbling realities of media production. Spanning his abandoned collaboration with Disney, a series of Basic English teaching films (made with the artist and filmmaker Len Lye), his two educational television programs on poetry, and a number of unrealized television adaptations of Plato and Homer, this article surveys the extensive and ambivalent record of Richards’s ventures into media production. It details his painstaking efforts to reimagine and remediate the experience of poetry and the act of reading in terms of complex multisensory channels of audiovisual stimuli, positioning film and television as ideal media for poetry and as unlikely saviors of a Western humanism in crisis. Literary history, Richards suggests, can no longer be considered merely literary; if it survives, it will do so only as part of an evolving history of media.
Modern Humanities and the Ancient Book: Karl Kerényi’s Media Theory
Authors: Kiséry András 
Published: November 2023

Abstract
Karl Kerényi is now mostly remembered for his monographs on Greek mythological figures in the Bollingen Series and for his collaboration with Carl Gustav Jung from the 1940s on. The radical approach to mythology reflected in this work was Kerényi’s solution to what he perceived as a crisis of the humanities: a disciplinary fragmentation combined with the growing influence of the social sciences in the study of culture. Before he would have turned to the study of myth, Kerényi proposed media history as the foundation for the renewal of classical studies and the humanities in general. In a series of essays written in the 1930s, Kerényi theorized the media of ancient texts as central to cultural hermeneutics. His understanding of textual media as expressive of the essential characteristics of a culture was underpinned by a conservative-humanist critique of modernity. It shows strong affinities with the work of such figures as the cultural theorist Oswald Spengler. Offered as an alternative to what Kerényi considered the scientistic preoccupations rampant in modern academe, this vision is also clearly at odds with most later media-historical research and its interest in the affordances and social ramifications of the materiality of communication technologies.
Polarized Perspectives on Health Equity: Results from a Nationally Representative Survey on US Public Perceptions of COVID-19 Disparities in 2023
Authors: Gollust Sarah E. , Gansen Chloe , Fowler Erika Franklin , Moore Steven T. , Nagler Rebekah H. 
Published: November 2023

Abstract
Republicans and Democrats responded to the COVID-19 pandemic in starkly different ways, from their attitudes in 2020 about whether the virus posed a threat to whether the pandemic ended in 2023. The consequences of COVID-19 for health equity have been a central concern in public health, and the concept of health equity has also been beset by partisan polarization. In this article, the authors present and discuss nationally representative survey data from 2023 on US public perceptions of disparities in COVID-19 mortality (building on a previous multiwave survey effort) as well as causal attributions for racial disparities, the contribution of structural racism, and broader attitudes about public health authority. The authors find anticipated gulfs in perspectives between Democrats on the one hand and independents and Republicans on the other. The results offer a somewhat pessimistic view of the likelihood of finding common ground in how the general public understands health inequities or the role of structural racism in perpetuating them. However, the authors show that those who acknowledge racial disparities in COVID-19 are more likely to support state public health authority to act in response to other infectious disease threats. The authors explore the implications of these public opinion data for advocacy, communication, and future needed research.
Supplemental information for "Effectiveness of Residue and Tillage Management on Runoff Pollutant Reduction from Agricultural Areas"
Authors: Prasad Laxmi , Thompson Anita , Arriaga Francisco J , Koropeckyj-Cox Lydia , Yuan Yongping 
Published: November 2023

<p> </p>
<p>This file presents data for cost-effectiveness analysis and summary statistics of annual runoff, sediment, nitrogen, and phosphorus losses of the "Effectiveness of Residue and Tillage Management on Runoff Pollutant Reduction From Agricultural Areas" meta-analysis manuscript published in the "ACPE Special Collection II" of the <em>Journal of ASABE</em>.</p>
Modeling understory shrub diversity related to environmental gradients using Akaike Information Criterion (AIC) in an urban forest in Jakarta, Indonesia
Authors: KIRANA GABRIELLA RIA , WIBOWO ANDRIO A. , NURDIN ERWIN , WARDHANA WISNU , BASUKRIADI ADI 
Published: November 2023

Abstract. Kirana GR, Wibowo AA, Nurdin E, Wardhana W, Basukriadi A. 2023. Modeling understory shrub diversity related to environmental gradients using Akaike Information Criterion (AIC) in an urban forest in Jakarta, Indonesia. Asian J For 7: 74-80. The urban forest is one form of human-made ecosystem in urban environments. One of the most important components of urban forest ecosystem is understory shrubs. The sustainability of understory shrub community is supported by environmental variables suitable to its growth. Nonetheless, there has been limited information on how environmental variables contribute to the presence of shrubs in particular urban settings. This study aims to model the relationships between understory shrub diversity and environmental covariates, including air temperature, humidity, light intensity and wind speed, using the Akaike Information Criterion (AIC) in Srengseng Urban Forest, Jakarta, Indonesia. The result showed that there were 20 species and 12 families with the most common shrub species being Rivina humilis, followed by Acalypha siamensis, Cordyline fruticosa, Syzigium paniculatum and Caesalpinia pulcherrima. The average Shannon-Wiener diversity index was 0.671 (95% CI: 0.441, 0.901). The AIC models showed that understory shrub diversity was negatively correlated with humidity and positively correlated with light gradients with AIC values of 38.696 and 41.679, respectively. The diversity of understory shrubs in urban forests was significantly supported by sufficient light intensity (R2 = 0.29) and limited by an increase in air humidity (R2 = -0.44). The humidity and light intensity combinations also affect the understory shrub diversity (AIC = 38.900, R2 = 0.256). The results of these studies can help urban forest managers manage urban forests if aiming for biodiversity conservation, especially understory shrub species.