bluuebunny/crossref_metadata_2025_split huggingface dataset:
ddRAD-seq generated genomic SNP dataset of Central and Southeast European Turkey oak (Quercus cerris L.) populations
Authors: Lados Botond B. , Cseke Klára , Benke Attila , Köbölkuti Zoltán A. , Molnár Csilla É. , Nagy László , Móricz Norbert , Németh Tamás M. , Borovics Attila , Mészáros Ilona , Tóth Endre Gy. 
Published: January 2024

AbstractTurkey oak (Quercus cerris L.) is one of the most ecologically and economically significant deciduous tree species in the Central and Southeast European regions. The species has long been known to exhibit high levels of genetic and phenotypic variation. Recent climate response predictions for Turkey oak suggest a significant distribution extension in Europe under climate change. Since Turkey oak has relative drought-tolerant behaviour, it is regarded as a potential alternative for other forest tree species during forestry climate adaptation efforts, not only in its native regions but also in Western Europe. For this reason, the survey of existing genetic variability, genetic resources, and adaptability of this species has great significance. Next-generation sequencing approaches, such as ddRAD-seq (Double digest restriction-site associated DNA sequencing), allow the attainment of high-resolution genome-wide single nucleotide polymorphisms (SNPs). This study provides the first highly variable genome-wide SNP data for Turkey oak generated by ddRAD-seq. The dataset comprises 17 607 de novo and 26 059 reference mapped SNPs for 88 individuals from eight populations, two from Bulgaria, one from Kosovo, and five from Hungary. Reference mapping was carried out by using cork oak’s (Quercus suber L.) reference genome. The obtained high-resolution genome-wide markers are suitable for investigating selection and local adaptation and inferring genetic diversity, differentiation, and population structure. The dataset is accessible at: https://doi.org/10.5281/zenodo.8091252
A Double-Blind Randomised Controlled Trial of Prebiotic Supplementation in Children with Autism: Effects on Parental Quality of Life, Child Behaviour, Gastrointestinal Symptoms, and the Microbiome
Authors: Palmer Jacqueline K. , van der Pols Jolieke C. , Sullivan Karen A. , Staudacher Heidi M. , Byrne Rebecca 
Published: January 2024

Abstract

Purpose
Modifying gut bacteria in children with autism may influence behaviour, with potential to improve family functioning. We conducted a randomised controlled trial to assess the effect of prebiotics on behaviour, gastrointestinal symptoms and downstream effects on parental quality of life.

Method
Children with autism (4-10yrs) were randomised to 2.4 g/d of prebiotic (GOS) or placebo for six weeks. Pre and post stools samples were collected, and validated questionnaires used to measure change in social and mealtime behaviours, GI symptoms and pQOL. Linear mixed models evaluated group differences for behavioural variables, and Mann Whitney U tests were used to compare change between-groups for GI symptoms, differential abundance of genera and alpha diversity of the microbiome.

Results
Thirty-three parent-child dyads completed the trial. No group difference was seen for behavioural variables but both groups improved significantly from baseline. There was a medium effect size between groups for GI symptoms (d = 0.47) and pQOL (d = 0.44) driven by greater improvements in the prebiotic group. Bifidobacterium increased threefold following prebiotics (1.4–5.9%, p < 0.001) with no change in controls. Supplements were well tolerated, compliance with dose 94%.

Conclusion
Prebiotics modify levels of Bifidobacterium and prove well tolerated but in this instance, resulted in only marginal effects on GI symptoms and pQOL. A larger sample of children with more severe symptoms could help to determine the potential of prebiotics in autism.

Trial Registration

https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12619000615189.
Deploying Machine Learning for Radiography of Aerospace Welds
Authors: Tyystjärvi Topias , Fridolf Peter , Rosell Anders , Virkkunen Iikka 
Published: January 2024

AbstractArtificial intelligence is providing new possibilities for analysis in the field of industrial radiography. As capabilities evolve, there is the need for knowledge concerning how to deploy these technologies in practice and benefit from the new automatically generated information. In this study, automatic defect recognition based on machine learning was deployed as an aid in industrial radiography of laser welds in an aerospace component, and utilized to produce statistics for improved quality control. A multi-model approach with an added weld segmentation step improved the inference speed and decreased false calls to improve field use. A user interface with visualization options was developed to display the evaluation results. A dataset of 451 radiographs was automatically analysed, yielding 10037 indications with size and location information, providing capability for statistical analysis beyond what is practical to carry out with manual annotation. The distribution of indications was modeled as a product of the probability of detection and an exponentially decreasing underlying flaw distribution, opening the possibility for model reliability assessment and predictive capabilities on weld defects. An analysis of the indications demonstrated the capability to automatically detect both large-scale trends and individual components and welds that were more at risk of failing the inspection. This serves as a step towards smarter utilization of non-destructive evaluation data in manufacturing.
Biodegradable polysaccharide aerogels based on tragacanth and alginate as novel drug delivery systems
Authors: Al-barudi Amenah , Sinani Genada , Ulker Zeynep 
Published: January 2024

AbstractTragacanth, an anionic polysaccharide, is a natural material widely investigated for the synthesis of aerogels as drug delivery vehicles. Its biocompatibility, biodegradability, and affordability are all key features for its use in pharmaceutical applications. In this study, tragacanth and tragacanth alginate composite aerogels were prepared using the sol-gel technique followed by supercritical drying. Paracetamol was selected as a model drug for drug loading and release studies owing to its high solubility in ethanol and low solubility in supercritical carbon dioxide. The paracetamol loading into the aerogel pores was confirmed by infrared spectroscopy (IR) and x-ray diffraction (XRD) spectra of the resulting samples. Scanning electron microscopy (SEM) images showed that all aerogels were porous with a macroporous-mesoporous network. Due to the high porosity of the prepared aerogels, a loading of 99 wt% (mg drug/mg aerogel) for tragacanth and 114 wt% (mg drug/mg aerogel) for composite aerogels was achieved. Moreover, the release rate of the drug could be modified by manipulating the aerogel composition. Tragacanth aerogels had a faster release rate, while the addition of alginate prolonged the release rate of the model drug. Various empirical release models were investigated and the release rate was found to follow the Korsmeyer-Peppas (Power Law) model suggesting a diffusion-based release kinetics. Based on the results, the feasibility of utilizing tragacanth for the preparation of drug-loaded aerogels was shown.
Graphical Abstract
The effectiveness and safety of proton beam radiation therapy in children and young adults with Central Nervous System (CNS) tumours: a systematic review
Authors: Wilson Jayne S. , Main Caroline , Thorp Nicky , Taylor Roger E. , Majothi Saimma , Kearns Pamela R. , English Martin , Dandapani Madhumita , Phillips Robert , Wheatley Keith , Pizer Barry 
Published: January 2024

Abstract
Background
Central nervous system (CNS) tumours account for around 25% of childhood neoplasms. With multi-modal therapy, 5-year survival is at around 75% in the UK. Conventional photon radiotherapy has made significant contributions to survival, but can be associated with long-term side effects. Proton beam radiotherapy (PBT) reduces the volume of irradiated tissue outside the tumour target volume which may potentially reduce toxicity. Our aim was to assess the effectiveness and safety of PBT and make recommendations for future research for this evolving treatment.

Methods
A systematic review assessing the effects of PBT for treating CNS tumours in children/young adults was undertaken using methods recommended by Cochrane and reported using PRISMA guidelines. Any study design was included where clinical and toxicity outcomes were reported. Searches were to May 2021, with a narrative synthesis employed.

Results
Thirty-one case series studies involving 1731 patients from 10 PBT centres were included. Eleven studies involved children with medulloblastoma / primitive neuroectodermal tumours (n = 712), five ependymoma (n = 398), four atypical teratoid/rhabdoid tumour (n = 72), six craniopharyngioma (n = 272), three low-grade gliomas (n = 233), one germ cell tumours (n = 22) and one pineoblastoma (n = 22). Clinical outcomes were the most frequently reported with overall survival values ranging from 100 to 28% depending on the tumour type. Endocrine outcomes were the most frequently reported toxicity outcomes with quality of life the least reported.

Conclusions
This review highlights areas of uncertainty in this research area. A well-defined, well-funded research agenda is needed to best maximise the potential of PBT.
Systematic review registration.
PROSPERO-CRD42016036802.
Mitigating communications threats in decentralized federated learning through moving target defense
Authors: Martínez Beltrán Enrique Tomás , Sánchez Sánchez Pedro Miguel , López Bernal Sergio , Bovet Gérôme , Gil Pérez Manuel , Martínez Pérez Gregorio , Huertas Celdrán Alberto 
Published: January 2024

AbstractThe rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model focused on DFL communications. In response to these identified risks, this work introduces a security module to counter communication-based attacks for DFL platforms. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform, Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario with physical and virtual deployments have been executed, encompassing three security configurations: (i) a baseline without security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks.The results showed an average F1 score of 95%, with the most secure configuration resulting in CPU usage peaking at 68% (± 9%) in virtual deployments and network traffic reaching 480.8 MB (± 18 MB), effectively mitigating risks associated with eavesdropping or eclipse attacks.
A winning formula: sustainable control of three stored-product insects through paired combinations of entomopathogenic fungus, diatomaceous earth, and lambda-cyhalothrin
Authors: Wakil Waqas , Kavallieratos Nickolas G. , Eleftheriadou Nikoleta , Haider Syed Adnan , Qayyum Mirza Abdul , Tahir Muhammad , Rasool Khawaja G. , Husain Mureed , Aldawood Abdulrahman S. 
Published: January 2024

AbstractThis research aimed to assess the effectiveness of Metarhizium robertsii, diatomaceous earth (Protect-It), and lambda-cyhalothrin, for the long-term protection of stored wheat against three destructive grain insect pests, Rhyzopertha dominica, Tribolium castaneum, and Trogoderma granarium. Different treatments were applied, both alone and in paired combinations in laboratory and persistence trials. Single treatments exhibited significantly lower mortality rates in comparison to the paired treatments for all tested insect species. Among the single treatments, lambda-cyhalothrin (Lamb) resulted in significantly higher mortality rates in laboratory trials, followed by diatomaceous earth (DE) and M. robertsii (Mr), with insignificant differences between Mr and DE. Evidently, DE exhibited the highest persistence after 120 days of storage for all insect species and initial exposures, although variations in mortality rates among treatments were mostly insignificant. Overall, the most effective treatment in terms of mortality in laboratory, and persistence trials, and progeny production was DE + Lamb, followed by Mr + Lamb, and Mr + DE for all tested insect species. In general, the most susceptible insect species was R. dominica, followed by T. castaneum and T. granarium. This research highlights the effectiveness of M. robertsii, DE, and lambda-cyhalothrin in providing prolonged protection of stored wheat against all the examined grain insect species.
The biological effects and thermal degradation of NPB-22, a synthetic cannabinoid
Authors: Takeda Akihiro , Doi Takahiro , Asada Akiko , Yuzawa Katsuhiro , Nagasawa Akemichi , Igarashi Kai , Maeno Tomokazu , Suzuki Atsuko , Shimizu Seiko , Uemura Nozomi , Nakajima Jun’ichi , Suzuki Toshinari , Inomata Akiko , Tagami Takaomi 
Published: January 2024

Abstract
Purpose
NPB-22 (quinolin-8-yl 1-pentyl-1H-indazole-3-carboxylate), Adamantyl-THPINACA (N-(1-adamantantyl)-1-[(tetrahydro-2H-pyran-4-yl)methyl]-1H-indazole-3-carboxamide), and CUMYL-4CN-B7AICA (1-(4-cyanobutyl)-N-(2-phenylpropan-2-yl)-1H- pyrrolo[2,3-b]pyridine-3-carboxamide), synthetic cannabinoids were evaluated in terms of CB1 (cannabinoid receptor type 1) and CB2 (cannabinoid receptor type 2) activities, and their biological effects when inhaled similar to cigarettes were examined.

Methods
The half maximal effective concentration values of the aforementioned synthetic cannabinoids at the CB1 and CB2 were investigated using [35S]guanosine-5’-O-(3-thio)-triphosphate binding assays. In addition, their biological effects were evaluated using the inhalation exposure test with mice. The smoke generated was recovered by organic solvents in the midget impingers, and the thermal degradation compounds of the smoke components were identified and quantified using a liquid chromatography–photo diode array detector.

Results
NPB-22 and Adamantyl-THPINACA had equivalent CB1 activity in in vitro assays. Meanwhile, NPB-22 had a weaker biological effect on some items on the inhalation exposure test than Adamantyl-THPINACA. When analyzing organic solvents in the midget impingers, it was revealed that NPB-22 was degraded to 8-quinolinol and pentyl indazole 3-carboxylic acid by combustion. In addition, these degradation compounds did not have CB1 activity.

Conclusion
It was estimated that the biological effects of NPB-22 on the inhalation exposure test weakened because it underwent thermal degradation by combustion, and the resultant degradation compounds did not have any CB1 activity in vitro.
3-D probability density imaging of Euler solutions using gravity data: a case study of Mount Milligan, Canada
Authors: Cao Shujin , Deng Yihuai , Yang Bo , Lu GuangYin , Zhu Ziqiang , Chen Peng , Xie Jingtao , Chen Xinyue 
Published: January 2024

AbstractEuler deconvolution is a widely used automatic or semi-automatic method for potential field data. However, it yields many spurious solutions that complicate interpretation and must be reduced, eliminated, recognized, or ignored during interpretation. This study proposes a post-processing algorithm that converts Euler solutions produced by tensor Euler deconvolution of gravity data with an unprescribed structural index into probability values (p values) using the B-spline series density estimation (BSS) method. The p values of the Euler solution set form a probability density distribution on the estimation grid. The BSS method relies on the fact that while spurious solutions are sparse and ubiquitous, Euler deconvolution yields many similar or duplicate solutions, which may tightly cluster near real sources. The p values of the Euler solution clusters form multi-layered isosurfaces that can be used to discriminate neighboring target sources because the p values of spurious solutions are vanishingly small, making it simple to remove their interference from the probability density distribution. In all synthetic cases, the geometric outlines of anomaly sources are estimated from probability density isosurfaces approximating synthetic model parameters. The BSS method was then applied to airborne gravity data from Mount Milligan, British Columbia, Canada. Subsequently, results from synthetic models and field data show that the proposed method can successfully localize meaningful geological targets.
Sepsis in elderly patients: the role of neutrophils in pathophysiology and therapy
Authors: Ramoni Davide , Tirandi Amedeo , Montecucco Fabrizio , Liberale Luca 
Published: January 2024

AbstractSepsis is among the most important causes of mortality, particularly within the elderly population. Sepsis prevalence is on the rise due to different factors, including increasing average population age and the concomitant rise in the prevalence of frailty and chronic morbidities. Recent investigations have unveiled a "trimodal" trajectory for sepsis-related mortality, with the ultimate zenith occurring from 60 to 90 days until several years after the original insult. This prolonged temporal course ostensibly emanates from the sustained perturbation of immune responses, persevering beyond the phase of clinical convalescence. This phenomenon is particularly associated with the aging immune system, characterized by a broad dysregulation commonly known as "inflammaging." Inflammaging associates with a chronic low-grade activation of the innate immune system preventing an appropriate response to infective agents. Notably, during the initial phases of sepsis, neutrophils—essential in combating pathogens—may exhibit compromised activity. Paradoxically, an overly zealous neutrophilic reaction has been observed to underlie multi-organ dysfunction during the later stages of sepsis. Given this scenario, discovering treatments that can enhance neutrophil activity during the early phases of sepsis while curbing their overactivity in the later phases could prove beneficial in fighting pathogens and reducing the detrimental effects caused by an overactive immune system. This narrative review delves into the potential key role of neutrophils in the pathological process of sepsis, focusing on how the aging process impacts their functions, and highlighting possible targets for developing immune-modulatory therapies. Additionally, the review includes tables that outline the principal potential targets for immunomodulating agents.
The development and flux of the University of Minnesota Survivorship Program: progress, challenges, and opportunities
Authors: Rick Tara J. , Lee Jill , Skendzel Sasha , Verich Kaia , Schempp Ashley , Napurski Char , Kreuser Holly , Turcotte Lucie , Holtan Shernan , Sadak Karim , Blaes Anne 
Published: January 2024

Abstract
For the past 30 years, the University of Minnesota’s Cancer Survivorship Program has been dedicated to providing exceptional care to patients who have lived the cancer experience. Our model is consultative, risk-stratified, and oncologist-led but executed predominately by advanced practice providers. Care is personalized and serves three survivor populations: children, adults, and patients who received BMT with over 500 new patients evaluated annually. As guidelines and survivorship standards have changed, our clinical programs have evolved from a focus on survivorship care plans to supportive care. The program offers a wide range of supportive services from acupuncture to nutritional services as well as several educational programs for patients. The program has a strong research legacy, notably as the birthplace of research that led to the Children’s Oncology Group Guidelines as well as advancements in cardio-oncology and frailty after bone marrow transplantation. In 2021, we hosted the first annual Survivorship Research Forum, providing the opportunity and space for experts across disciplines to exchange ideas on a broad range of survivorship topics not possible at other national cancer-related conferences. With successes and challenges, we have identified opportunities for growth as our program continues to evolve and grow in our goal to improve cancer outcomes along a wide spectrum of physical, emotional, functional, and social dimensions.

Implications for Cancer Survivors
The University of Minnesota Cancer Survivorship Program provides care, education, and research opportunities for patients across the cancer continuum.
Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring
Authors: Barnova Katerina , Martinek Radek , Vilimkova Kahankova Radana , Jaros Rene , Snasel Vaclav , Mirjalili Seyedali 
Published: January 2024

AbstractElectronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.