bluuebunny/crossref_metadata_2025_split huggingface dataset:
Frieder Nake and the Ethics of Cold War Computer Art
Authors: Evans Cindy 
Published: August 2023

Abstract
In 1971, artist Frieder Nake denounced the production of computer art. Paradoxically, Nake, a pioneer of computer art, participated in groundbreaking exhibitions across Western and Eastern Europe in the 1960s. These shows blurred boundaries between artists and scientists to evaluate the viability of art as visual research for its aesthetic and social potential. This article reexamines Nake’s position in context, from his initial understanding of generative art’s redemptive political role and his later view post-1968, following revolutions that demonstrated computers’ entanglement with the North Atlantic capitalist military-corporate research complex.
Conceptual Bankruptcy in Comprehending and Addressing the Marine Resources Question in Tanzania: The Need for the Social Organisation
Authors: Ishengoma Nelson M. 
Published: August 2023

AbstractAccording to the current World-Fish Centre’s estimates, all around the world, marine resources are experiencing a significant decline. Numerous political and technocratic remedies deployed to abate the problem have primarily been in vain. This analytical paper believes that this widely-acknowledged failure is due to their preoccupation with observable symptoms and not the processes and mechanisms upon which the phenomenon is elevated and resides. It is this conceptual deficiency and ill-conceptions that this paper is set to address. Therefore, the social organisation as a conceptual, diagnosing, and remedial framework is engaged and proposed in light of marine resource depletion in Tanzania. The paper argues that any intervention outside the prevailing socio-political and economic outfit (social organisation) is untenable.
The Mean Wind and Potential Temperature Flux Profiles in Convective Boundary Layers
Authors: Liu Luoqin , Gadde Srinidhi N. , Stevens Richard J. A. M. 
Published: August 2023

Abstract
We develop innovative analytical expressions for the mean wind and potential temperature flux profiles in convective boundary layers (CBLs). CBLs are frequently observed during daytime as Earth’s surface is warmed by solar radiation. Therefore, their modeling is relevant for weather forecasting, climate modeling, and wind energy applications. For CBLs in the convective-roll-dominated regime, the mean velocity and potential temperature in the bulk region of the mixed layer are approximately uniform. We propose an analytical expression for the normalized potential temperature flux profile as a function of height, using a perturbation method approach in which we employ the horizontally homogeneous and quasi-stationary characteristics of the surface and inversion layers. The velocity profile in the mixed layer and the entrainment zone is constructed based on insights obtained from the proposed potential temperature flux profile and the convective logarithmic friction law. Combining this with the well-known Monin–Obukhov similarity theory allows us to capture the velocity profile over the entire boundary layer height. The proposed profiles agree excellently with large-eddy simulation results over the range of −L/z0 ∈ [3.6 × 102, 0.7 × 105], where L is the Obukhov length and z0 is the roughness length.
Using Object-Based Verification to Assess Improvements in Forecasts of Convective Storms between Operational HRRR Versions 3 and 4
Authors: Duda Jeffrey D. , Turner David D. 
Published: August 2023

Abstract
The object-based verification procedure described in a recent paper by Duda and Turner was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast–observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced overforecasting bias for medium- and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely overforecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts.

Significance Statement
This work builds upon the authors’ prior work in assessing model forecast quality using an alternative verification method—object-based verification. In this paper we verified two versions of the same model (one an upgrade from the other) that were making forecasts covering the same time window, using the object-based verification method. We found that the updated model was not statistically significantly better, although there were indications it performed better in certain aspects such as capturing the change in the number of storms during the daytime. We were able to identify specific problem areas in the models, which helps us direct model developers in their efforts to further improve the model.
Detecting the temporal trend of cultivated soil organic carbon content using visible near infrared spectroscopy
Authors: Zayani Hayfa , Fouad Youssef , Michot Didier , Kassouk Zeineb , Lili-Chabaane Zohra , Walter Christian 
Published: August 2023

Monitoring changes in soil properties is essential to ensure ecosystem function and agricultural productivity. This study evaluated the ability of visible near infrared (Vis-NIR) spectroscopy to detect the temporal trend in soil organic carbon (SOC) content after 5 years in a 12 km2 agricultural catchment in western France. Partial least squares regression models were developed using soil samples from a local dataset collected in 2013 at two depths (198 samples at 0–15 cm and 196 samples at 15–25 cm) to predict SOC content of 111 new samples collected in 2018 at the same locations and at similar depths (0–15 cm and 15–25 cm). Two approaches, which differed in whether or not they considered the SOC content variability that can result from collecting soil samples at two depths, were applied. For both approaches, the potential benefit of “temporal spiking” was evaluated by adding 10% of 2018 samples to the 2013 dataset. The results showed that removing outliers and stratifying the calibration dataset by depth yielded the highest accuracy, with SOC RMSEP of 4.1 and 2.7 g.kg−1 for 0–15 and 15–25 cm, respectively. Moreover, temporal spiking improved five of eight predictions (stratifying or not the calibration dataset by depth, removing or not poorly predicted outliers), with increases in the ratio of performance to deviation of 0.10–0.44. Furthermore, comparing observed and predicted changes in SOC content showed that Vis-NIR spectroscopy estimated its trend over time in most cases.
Understanding who benefits most from cognitive rehabilitation for multiple sclerosis: A secondary data analysis
Authors: Taylor LA , Mhizha-Murira JR , Law G , Evangelou N , das Nair R 
Published: August 2023

Background: Up to 70% of people with multiple sclerosis (MS) experience cognitive difficulties. Cognitive rehabilitation is a type of therapy that helps manage cognitive problems. Objective: The Cognitive Rehabilitation for Attention and Memory in MS (CRAMMS) trial showed some evidence of effectiveness of cognitive rehabilitation in improving cognitive function, with some participants benefitting more than others. We therefore conducted a secondary analysis of the CRAMMS data to understand who benefits most. Methods: We grouped baseline data into four categories of possible predictors. We used regression models to identify specific factors/characteristics that could predict the likelihood that an individual will benefit from cognitive rehabilitation. Results: The models predicted whether a participant improved or did not improve in neuropsychological function following cognitive rehabilitation in up to 86% of participants. Results suggest that younger participants with medium to high education, diagnosed with relapsing-remitting multiple sclerosis (RRMS) and primary-progressive multiple sclerosis (PPMS) who have not experienced any recent relapses, with mild to moderate cognitive difficulties were most likely to benefit from cognitive rehabilitation. Conclusion: We can predict which participants are most likely to demonstrate significant improvements in neuropsychological function following group-based cognitive rehabilitation. Clinically, this allows us to optimise limited neuropsychology resources by offering such cognitive rehabilitation to those most likely to benefit.
A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning
Authors: Atek Sofiane , Bianchini Filippo , De Vito Corrado , Cardinale Vincenzo , Novelli Simone , Pesaresi Cristiano , Eugeni Marco , Mecella Massimo , Rescio Antonello , Petronzio Luca , Vincenzi Aldo , Pistillo Pasquale , Giusto Gianfranco , Pasquali Giorgio , Alvaro Domenico , Villari Paolo , Mancini Marco , Gaudenzi Paolo 
Published: August 2023

Objective Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Methods Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. Results The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Conclusions Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.
Molecular complex detection in protein interaction networks through reinforcement learning
Authors: Palukuri Meghana V. , Patil Ridhi S. , Marcotte Edward M. 
Published: August 2023

Abstract
Background
Proteins often assemble into higher-order complexes to perform their biological functions. Such protein–protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein–protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

Results
The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

Conclusions
Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.
Common antibiotics, azithromycin and amoxicillin, affect gut metagenomics within a household
Authors: Chopyk Jessica , Cobián Güemes Ana Georgina , Ramirez-Sanchez Claudia , Attai Hedieh , Ly Melissa , Jones Marcus B. , Liu Roland , Liu Chenyu , Yang Kun , Tu Xin M. , Abeles Shira R. , Nelson Karen , Pride David T. 
Published: August 2023

Abstract
Background
The microbiome of the human gut serves a role in a number of physiological processes, but can be altered through effects of age, diet, and disturbances such as antibiotics. Several studies have demonstrated that commonly used antibiotics can have sustained impacts on the diversity and the composition of the gut microbiome. The impact of the two most overused antibiotics, azithromycin, and amoxicillin, in the human microbiome has not been thoroughly described. In this study, we recruited a group of individuals and unrelated controls to decipher the effects of the commonly used antibiotics amoxicillin and azithromycin on their gut microbiomes.

Results
We characterized the gut microbiomes by metagenomic sequencing followed by characterization of the resulting microbial communities. We found that there were clear and sustained effects of the antibiotics on the gut microbial community with significant alterations in the representations of Bifidobacterium species in response to azithromycin (macrolide antibiotic). These results were supported by significant increases identified in putative antibiotic resistance genes associated with macrolide resistance. Importantly, we did not identify these trends in the unrelated control individuals. There were no significant changes observed in other members of the microbial community.

Conclusions
As we continue to focus on the role that the gut microbiome plays and how disturbances induced by antibiotics might affect our overall health, elucidating members of the community most affected by their use is of critical importance to understanding the impacts of common antibiotics on those who take them.
Clinical Trial Registration Number NCT05169255. This trial was retrospectively registered on 23–12-2021.
Biofilm stimulating activity of solanidine and Solasodine in Pseudomonas aeruginosa
Authors: Noori Hadi Ghoomdost , Tadjrobehkar Omid , Moazamian Elham 
Published: August 2023

Abstract
Background
Biofilm formation has reported as an important virulence associated properties of Pseudomonas aeruginosa that is regulated by quorum-sensing associated genes. Biofilm and quorum-sensing interfering properties of steroidal alkaloids, Solanidine and Solasodine were investigated in the present study.

Results
Biofilm formation capacity and relative expression level of five studied genes(lasI, lasR, rhlI, rhlR and algD) were significantly increased dose-dependently after treatment with sub-inhibitory concentrations (32 and 512 µg/ml) of the both Solanidine and Solasodine. Biofilm formation capacity was more stimulated in weak biofilm formers(9 iaolates) in comparison to the strong biofilm producers(11 isolates). The lasI gene was the most induced QS-associated gene among five investigated genes.

Conclusion
Biofilm inducing properties of the plants alkaloids and probably medicines derived from them has to be considered for revision of therapeutic guidelines. Investigating the biofilm stimulating properties of corticosteroids and other medicines that comes from plant alkaloids also strongly proposed.
Tracking of serum lipids in healthy children on a year-to-year basis
Authors: Maidowski Ludwig , Kiess Wieland , Baber Ronny , Dathan-Stumpf Anne , Ceglarek Uta , Vogel Mandy 
Published: August 2023

Abstract
Objectives
To assess the stability of lipid profiles throughout childhood and evaluate their onset and dynamic.

Materials and methods
Lipid markers were longitudinally measured in more than 1300 healthy children from the LIFE Child study (Germany) and categorized into normal, at-risk, or adverse. Year-to-year intra-person persistence of the categories during follow-ups was examined and Pearson’s correlation coefficient was calculated.

Results
We found strong positive correlations for TC, LDL-C and ApoB (r > 0.75, p < 0.001) from the age of four years. Correlations were lowest during the first two years of life. Most children with normal levels also had normal levels the following year. Children with at-risk levels showed a tendency towards normal levels at the follow-up visit. Adverse levels of TC, LDL-C, ApoB (all ages), and HDL-C (from age 15) persisted in more than half of the affected children. Age-dependent patterns of stability were most pronounced and similar for TC, LDL-C, and ApoB.

Conclusions
Normal levels of serum lipids show high stability and adverse levels stabilized in early childhood for TC, LDL, and ApoB. At-risk and adverse levels of TC, LDL-C or ApoB may warrant further or repeated diagnostic measurements with regards to preventing CVD in the long run.
Improving paramedic responses for patients dying at home: a theory of change-based approach
Authors: Simpson Jane , Remawi Bader Nael , Potts Kieran , Blackmore Tania , French Maddy , Haydock Karen , Peters Richard , Hill Michael , Tidball Oliver-Jon , Parker Georgina , Waddington Michelle , Preston Nancy 
Published: August 2023

Abstract
Background
Paramedics are increasingly being called to attend patients dying from advanced incurable conditions. However, confidence to deal with such calls varies, with many feeling relatively unskilled in this aspect of their role. A number of interventions have been piloted to improve their skills in end-of-life care (EoLC) but without a fully specified theoretical model. Theory of Change models can provide theoretical and testable links from intervention activities to proposed long-term outcomes and indicate the areas for assessment of effectiveness. This study aimed to develop an intervention for improving paramedic EoLC for patients in the community.

Methods
A Theory of Change approach was used as the overarching theoretical framework for developing an intervention to improve paramedic end-of-life skills. Nine stakeholders - including specialist community paramedics, ambulance call handlers and palliative care specialists - were recruited to five consecutive online workshops, ranging between 60 and 90 min. Each workshop had 2–3 facilitators. Over multiple workshops, stakeholders decided on the desired impact, short- and long-term outcomes, and possible interventions. During and between these workshops a Theory of Change model was created, with the components shared with stakeholders.

Results
The stakeholders agreed the desired impact was to provide consistent, holistic, patient-centred, and effective EoLC. Four potential long-term outcomes were suggested: (1) increased use of anticipatory and regular end-of-life medications; (2) reduced end-of-life clinical and medication errors; (3) reduced unnecessary hospitalisations; (4) increased concordance between patient preferred and actual place of death. Key interventions focused on providing immediate information on what to do in such situations including: appraising the situation, developing an algorithm for a treatment plan (including whether or not to convey to hospital) and how to identify ongoing support in the community.

Conclusions
A Theory of Change approach was effective at identifying impact, outcomes, and the important features of an end-of-life intervention for paramedics. This study identified the need for paramedics to have immediate access to information and resources to support EoLC, which the workshop stakeholders are now seeking to develop as an intervention.
Evaluating and improving real-world evidence with Targeted Learning
Authors: Gruber Susan , Phillips Rachael V. , Lee Hana , Concato John , van der Laan Mark 
Published: August 2023

Abstract
Background
The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE.

Methods
We applied the roadmap to a published observational study of the dose–response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the study's authors, and to explore potential options for overcoming the barriers that robustify results.

Results
Following the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose–response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data.

Conclusions
Before RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process.