bluuebunny/crossref_metadata_2025_split huggingface dataset:
Radiotherapy biobanking: current landscape, opportunities, challenges, and future aspirations
Authors: Ward Tim H , Gilbert Duncan C , Higginbotham George , Morris Chris M , Speirs Valerie , Curtin Nicola J 
Published: October 2021

AbstractHalf of all cancer patients receive radiotherapy, which makes a substantial contribution to their long‐term disease control/cure. There are significant inter‐patient differences in response, both in terms of efficacy and toxicity (frequently delayed onset) which are difficult to predict. With the introduction of technological improvements (e.g. stereotactic body radiotherapy and proton therapy) and development of combination therapies (e.g. radiotherapy and immune checkpoint inhibition), predictive biomarkers are needed even more. Whilst genomic studies have contributed significantly to predictions of response to anticancer therapy, there is no doubt that more information can be gathered from patient tissue samples. Patients are willing to donate their tissues to biobanks and wish them to be used as widely as possible for high‐quality research. We report here a survey of the current practices in the UK from several groups collecting material from patients in radiotherapy trials and have identified barriers to collecting and sharing data and samples. We believe the current situation represents a significant missed opportunity to improve the personalisation of radiotherapy. We propose a greater involvement of patients and/or their advocates, a standardisation of the patient information leaflet, consent form content and data set, with easy linkage to clinical data, which would facilitate widespread sample and data discovery and availability to other researchers. The greater sharing of data and samples, nationally and internationally, would facilitate robust multicentre studies and avoid duplication of effort.
Forecasting annual maximum water level for the Negro River at Manaus
Authors: Chevuturi Amulya , Klingaman Nicholas P. , Rudorff Conrado M. , Coelho Caio A. S. , Schöngart Jochen 
Published: October 2021

AbstractMore frequent and stronger flood hazards in the last two decades have caused considerable environmental and socio‐economic losses in many regions of the Amazon basin. It is therefore critical to advance predictions for flood levels, with adequate lead times, to provide more effective and earlier warnings to safeguard lives and livelihoods. Water‐level variations in large, low‐lying, free‐flowing river systems in the Amazon basin, such as the Negro River, follow large‐scale precipitation anomalies. This offers an opportunity to predict maximum water levels using observed antecedent rainfall. This study aims to investigate possible improvements in the performance and extension of the lead time of existing operational statistical forecasts for annual maximum water level of the Negro River at Manaus, occurring between May and July. We develop forecast models using multiple linear regression methods, to produce forecasts that can be issued in March, February and January. Potential predictors include antecedent catchment rainfall and water levels, large‐scale modes of climate variability and the long‐term linear trend in water levels. Our statistical models gain one month of lead time against existing models for same skill level, but are only moderately better than existing models at similar lead times. All models lose performance at longer lead times, as expected. However, our forecast models can issue skilful operational forecasts in March or earlier. We show the forecasts for the Negro River maximum water level at Manaus for 2020 and 2021.
Evaluation and diagnostic value of next‐generation sequencing analysis of residual liquid‐based cytology specimens of pancreatic masses
Authors: Sekita‐Hatakeyama Yoko , Fujii Tomomi , Nishikawa Takeshi , Mitoro Akira , Sawai Masayoshi , Itami Hiroe , Morita Kouhei , Uchiyama Tomoko , Takeda Maiko , Sho Masayuki , Yoshiji Hitoshi , Hatakeyama Kinta , Ohbayashi Chiho 
Published: October 2021

BackgroundLiquid‐based cytology (LBC) is a widely used method for processing specimens obtained by endoscopic biopsy. This study evaluated next‐generation sequencing (NGS) analysis of LBC specimens to improve the diagnostic accuracy of pancreatic lesions.MethodsUpon the diagnosis of a suspected pancreatic mass, LBC residues were used retrospectively. The quantity and quality of DNA extracted from residual LBC samples were evaluated, and an NGS analysis targeting 6 genes (KRAS, GNAS, TP53, CDKN2A, SMAD4, and PIK3CA) was performed.ResultsThe library was prepared from LBC specimens taken from 52 cases: 44 were successful, and 8 preparations failed. An analysis of DNA quantity and quality suggested that the success or failure of NGS implementation depended on both properties. The final diagnosis was achieved by a combination of the pathological analysis of the surgical excision or biopsy material with clinical information. Among the 33 cases of pancreatic ductal adenocarcinoma (PDAC), KRAS, TP53, CDKN2A, and SMAD4 mutations were identified in 31 (94%), 16 (48%), 3 (9%), and 2 (6%), respectively. Among the 11 benign cases, only a KRAS mutation was identified in 1 case. On the basis of NGS results, 18 of 33 PDACs (55%) were classified as highly dysplastic or more, and 10 of 11 benign lesions were evaluated as nonmalignant, which was consistent with the final diagnosis.ConclusionsNGS analysis using LBC specimens from which DNA of appropriate quantity and quality has been extracted could contribute to improving the assessment of pancreatic tumor malignancies and the application of molecular‐targeted drugs.
In‐vivo estimation of tissue electrical conductivities of a rabbit eye for precise simulation of electric field distributions during ocular iontophoresis
Authors: Lee Sangjun , Lee Chany , Kim Euijin , Ko Song Ah. , Kim Se‐Na , Choy Young Bin , Im Chang‐Hwan 
Published: October 2021

AbstractPrecise estimation of electrical conductivity of the eyes is important for the accurate analysis of electric field distributions in the eyes during ocular iontophoresis. In this study, we estimated the tissue electrical conductivities of a rabbit eye, which has been widely employed for neuro‐ophthalmological experiments, through an in vivo experiment for the first time. Electrical potentials were measured at multiple locations on the skin, while weak currents were transmitted into the skin via two surface electrodes attached to the skin around the eye. A finite element model was constructed to calculate the electric potentials at the measurement locations. The conductivity values of different tissues were then estimated using an optimization procedure to minimize the difference between the measured and calculated electric potentials. The accuracy of the estimated tissue conductivity values of the rabbit eye was validated by comparing the measured and calculated electric potential values for different electrode montages. Further multi‐physical analyses of iontophoretic drug delivery to the rabbit eye showed a significant influence of the conductivity profile on the resultant particle distribution. Overall, our results provide an important reference for the tissue electrical conductivity values of the rabbit eye, which could be further utilized for designing new medical devices for delivering electric fields to the eyes, such as transorbital and transscleral electrical stimulations.
Perspectives of pediatric oncologists and palliative care physicians on the therapeutic use of cannabis in children with cancer
Authors: Oberoi Sapna , Protudjer Jennifer L. P. , Rapoport Adam , Rassekh Shahrad R. , Crooks Bruce , Siden Harold , Decker Kathleen , Ananth Prasanna , Chapman Stacy , Balneaves Lynda G. , Vanan Magimairajan Issai , Kelly Lauren E. 
Published: October 2021

AbstractBackgroundChildren with cancer are increasingly using cannabis therapeutically.AimThe purpose of this study was to determine the perspectives and practices of pediatric oncologists and palliative care physicians regarding the use of cannabis for medical purposes among children with cancer.MethodsA self‐administered, voluntary, cross‐sectional, deidentified online survey was sent to all pediatric oncologists and palliative care physicians in Canada between June and August 2020. Survey domains included education, knowledge, and concerns about cannabis, views on its effectiveness, and the importance of cannabis‐related research. Data were analyzed using descriptive statistics.ResultsIn total, 122/259 (47.1%) physicians completed the survey. Although 62.2% of the physicians completed some form of training about medical cannabis, nearly all (95.8%) desired to know more about the dosing, side effects, and safety of cannabis. Physicians identified a potential role of cannabis in the management of nausea and vomiting (85.7%), chronic pain (72.3%), cachexia/poor appetite (67.2%), and anxiety or depression (42.9%). Only four (0.3%) physicians recognized cannabis to be potentially useful as an anticancer agent. Nearly all physicians reported that cannabis‐related research for symptom relief is essential (91.5%) in pediatric oncology, whereas 51.7% expressed that future studies are necessary to determine the anticancer effects of cannabis.ConclusionsOur findings indicate that most pediatric oncologists and palliative care physicians recognize a potential role for cannabis in symptom control in children with cancer. Well‐conducted studies are required to create evidence for cannabis use and promote shared decision making with pediatric oncology patients and their caregivers.
A sweeping optimization algorithm for the global cosine fitting energy image segmentation model
Authors: Zou Le , Chen Yan‐Ping , Wu Zhi‐Ze , Huang Qian‐Jing , Wang Xiao‐Feng 
Published: October 2021

AbstractImage segmentation plays a pivotal role in image processing. Level set model is a traditional variation image segmentation method. In order to achieve level set evolution equation, the level set energy functionals are minimized with the gradient descent methods and then the partial differential equation (PDE) was solved by finite difference scheme. Slow speed is one of its disadvantages. We propose a sweep optimization algorithm based on global cosine fitting (GCF) energy. Instead of calculating the PDE and the curvature, the proposed sweeping algorithm directly calculates the energy change when a pixel moves from one side of evolving contour to the other. It checks whether the GCF energy is decreased or not. The proposed algorithm has many advantages. For example, independent of initial level set contour positions and parameters, need not consider the Courant Friedrichs Lew condition and the regularization energy term. The proposed algorithm can be easily extended to high dimension image segmentation. The experiments on synthetic images, noise images and real images demonstrate the effectiveness of the proposed sweeping optimization algorithm.
Effect of Temperature and pH on Calcium Phosphate Precipitation
Authors: Vilela Handially Santos , Rodrigues Marcela Charantola , Fronza Bruna Marin , Trinca Rafael Bergamo , Vichi Flávio Maron , Braga Roberto Ruggiero 
Published: October 2021

AbstractCalcium orthophosphates (CaP) synthesis involves several chemical equilibria that define the phases present in the final product. From the biomaterials standpoint, it is important to gain knowledge on how synthesis parameters affect phase formation and particle size. This study evaluates the interaction between temperature (24 or 45 °C) and pH conditions (4.5, 6.5, or drifting) on CaP precipitation in terms of yield, phase formation, density, morphology, and size distribution. Calcium and phosphate solutions (Ca/P = 1.0) are mixed and kept under stirring for 3 h. The precipitate is freeze‐dried and characterized. Under drifting pH and pH 4.5, dicalcium phosphate dihydrate (DCPD, CaHPO4 × 2H2O) is the predominant phase at both temperatures; however, some samples also present peaks ascribed to dicalcium phosphate anhydrous (DCPA, CaHPO4). At pH 6.5, diffractograms reveal a mixture of low‐crystallinity DCPD and DCPA (24 °C) or low crystallinity hydroxyapatite [HAP, Ca10(OH)2(PO4)6] (45 °C). In spite of the different morphologies (plates or aggregates), particle size remains within a relatively narrow range (D50 = 12–28 µm). DCPD precipitation is favored under more acidic or drifting pH, while HAP is formed under nearly neutral pH 6.5.
Exploring multitrophic interactions in oilseed rape fields reveals the prevailing role of Carabidae
Authors: Serée Lola , Gardarin Antoine , Crouzet Olivier , Barbottin Aude , Valantin‐Morison Muriel , Chiron François 
Published: October 2021

AbstractIn cropped fields, birds are often at the highest position in the food chain, feeding on pest arthropods and their intermediate predators in a process known as intraguild predation. The net effects of bird predation on phytophagous insect populations (feeding on plants) are difficult to predict without comprehensively describing prey–predator communities and their complex interplay. We sampled bird and arthropod communities in 30 oilseed rape fields in the spring of 2019 and 2020 in France. To assess the top‐down control of arthropods by birds, we used a vertebrate exclusion experiment. Using a taxonomic and functional trait‐based approach, we determined the direct and indirect influences of birds on arthropod predators and phytophagous insect populations in arable crops. We observed a negative relationship between the abundance of Carabidae and phytophagous insects but not with the other predator group suggesting the key role of Carabidae on phytophagous insects in agroecosystem. We found no statistical evidence of intraguild predation from birds toward intermediate predators. Despite the lack of overall effect of predator functional diversity on their prey, we highlighted the negative relationship between the functional complementarity (through functional evenness) of Carabidae and the abundance of phytophagous insects. This result suggests that functional complementarity between Carabidae species could help to reduce phytophagous insect populations. We analyzed the effect of agricultural practices on these multitrophic interactions, showing that pesticide intensity only had detrimental effects on Carabidae abundance, while the frequency of tillage did not affect the studied communities. Complementary indices used to depict communities are helpful to better understand the mechanisms underlying trophic relationships.
Adaptive cluster‐based heuristic approach in cognitive radio networks for5Gapplications
Authors: Devaraj S. Allwin , Aruna T. , Muthukumaran N. , Roobert A. Andrew 
Published: October 2021

AbstractThe main objective of cognitive radio network is to provide flexible spectrum management, by permitting the secondary users (SUs) to temporarily access the licensed spectrum in the absence of a primary user. In the existing backward induction approach, each SU performs spectrum sensing and consequently reports the sensing details to the centralized controller. In the proposed work, an adaptive cluster‐based heuristic approach (ACBHA) with cooperative spectrum sensing (CSS) scheme has been proposed for cognitive radio medium access control networks for 5G applications. To identify the availability of unused spectrum, the proposed CSS‐based ACBHA algorithm adopts a cluster‐based selection scheme. Simulation results show that the proposed CSS‐based ACBHA provides a better result compared with existing techniques. The performance parameters such as false alarm probability, the probability of detection, spectrum sensing time, slot length, noise density have been analyzed using MATLAB R2012a.
Chemical hazards in smoked meat and fish
Authors: Iko Afé Ogouyôm Herbert , Kpoclou Yénoukounmè Euloge , Douny Caroline , Anihouvi Victor Bienvenu , Igout Ahmed , Mahillon Jacques , Hounhouigan Djidjoho Joseph , Scippo Marie‐Louise 
Published: October 2021

AbstractThis review aims to give an insight into the main hazards currently found in smoked meat and fish products. Literature research was carried out on international databases such as Access to Global Online Research in Agriculture (AGORA) database, Science direct, and Google scholar to collect and select 92 relevant publications included in this review. The smoking process was described and five hazards mostly found in smoked fish and meat were presented. The heat‐induced compounds such as polycyclic aromatic hydrocarbons, heterocyclic amines, and nitrosamines were found in smoked fish and meat. Other hazards such as biogenic amines and heavy metals were also present in smoked fish and meat. The levels of these hazards reported from the literature exceeded the maximal limits of European Union. A brief description of risk assessment methodology applicable to such toxic compounds and risk assessment examples was also presented in this review. As most of the hazards reported in this review are toxic and even carcinogenic to humans, actions should be addressed to reduce their presence in food to protect consumer health and to prevent public health issue.
Topological features in addition to radiomics signature predict 1p19q status and tumor grade in low‐grade gliomas
Authors: Paul Rahul 
Published: October 2021

AbstractRadiomics is a procedure to access quantitative features from medical images for predictive analysis. Radiomics is used extensively for malignancy and survival analysis, genomic expression analysis, cancer progression, and assessment. But radiomics does not obtain the connected components, loops, or voids information from a region of interest. Topological data analysis is a newer approach that uses persistent homology to generate multiscale features from an image. We investigated in this study whether the topological features can be as useful as the radiomics features. Glioma grading and 1p19q codeletion assessment requires surgical biopsy, which can be error prone. To reduce biopsy error, MRI‐based radiomics and topological features–based analysis were performed in our study. Topological features produced the best results for 1p19q status and tumor‐grade prediction, with 94.3% (0.93 AUC) and 97.3% (0.97 AUC), respectively, which was an improvement over radiomics. Codes and partial data are available in https://github.com/hellorp1990/TDA-analysis.
#SayHerName: Addressing Anti‐Blackness and Patriarchy in Language and Literacy Curricula
Authors: Austin Tasha , Hsieh Betina 
Published: October 2021

AbstractBlack women have been and continue to be objectified, mistrusted to voice their own realities, and pushed to the margins even among other people of color, yet expected to lead and contribute their lives and labor. In this piece, we focus on curricular and epistemic violence evidenced through omission and distortions of Black women in English Language Arts and World Languages and advocate for a centering of Black women’s voices and perspectives in ways that affirm their humanity across both contexts. We come together as literacy educators to offer strategies to support the inclusion and integration of Black women’s voices and experiences throughout secondary language and literacy learning spaces.
Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach
Authors: Long Danny K. , Bangerth Wolfgang , Handwerk Derek R. , Whitehead Christopher B. , Shipman Patrick D. , Finke Richard G. 
Published: October 2021

AbstractIn order to quantitatively predict nano‐ as well as other particle‐size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism‐enabled population balance models (ME‐PBM) of nanoparticle growth. The Bayesian approach addresses the question of “how well do we know our parameters, along with their uncertainties?.” The results reveal that Bayesian inversion statistical analysis on an example, prototypenanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model's parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. The paper can also be seen as a tutorial with the additional goal of achieving a “Gold Standard” Bayesian inversion ME‐PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.
Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning
Authors: Ling Jianmin , Liao Tongzhou , Wu Yanqing , Wang Zhaohua , Jin Hai , Lu Feng , Fang Minghao 
Published: October 2021

AbstractBackgroundSepsis‐associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution width (RDW) can reflect body response to inflammation and oxidative stress. We try to investigate the relationship between the RDW and the prognosis of patients with SAT through machine learning.Methods809 patients were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC‐III) database. The eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to analyze the impact of each feature. Logistic regression analysis, propensity score matching (PSM), receiver‐operating characteristics (ROC) curve analysis, and the Kaplan‐Meier method were used for data processing.ResultsThe patients with thrombocytopenia had higher 28‐day mortality (48.2%). Machine learning indicated that RDW was the second most important in predicting 28‐day mortality. The RDW was significantly increased in non‐survivors by logistic regression and PSM. ROC curve shows that RDW has moderate predictive power for 28‐day mortality. The patients with RDW>16.05 exhibited higher mortality through Kaplan‐Meier analysis.ConclusionsInterpretable machine learning can be applied in clinical research. Elevated RDW is not only common in patients with SAT but is also associated with a poor prognosis.
Zinc chloride activated carbon from Pleurotus floridanus biomass for piroxicam adsorption
Authors: Chenthamara Dhrisya , Ramakrishnan Sankar Ganesh , Robert Becky , Murugan Prasathkumar , Subramaniam Sadhasivam 
Published: October 2021

AbstractBACKGROUNDPharmaceuticals and personal care products (PPCPs) are growing families of chemicals that are regarded as emerging contaminants and are becoming a serious threat to the environment. Adsorptive removal of micropollutants has been extensively studied using activated carbons derived from agro‐poultry waste biomass and byproducts. The non‐sustainable resources and steep supply chain costs further accelerate the search for finding new renewable materials for adsorbent preparation. In the present study, the carbon prepared from Pleurotus floridanus biomass obtained as a value‐added product from laccase production was used as an adsorbent for the removal of a nonsteroidal anti‐inflammatory drug, namely piroxicam (PRX).RESULTSThe prepared carbon was activated using zinc chloride (ZAC) and showed an excellent Brunauer–Emmett–Teller surface area of about 285.1 m2 g−1. ZAC displayed a rapid adsorption capacity for PRX of about 6.08 mg g−1 with an adsorption equilibrium time of 90 min. Both Langmuir and Freundlich isotherm equations were best fitted to the experimental data. The results of kinetic studies were fitted best with pseudo‐first‐order kinetic models.ConclusionsFungal biomass obtained as a value‐added product of fermentation media can be converted into carbon, which is effective in adsorbing PPCPs such as PRX. © 2021 Society of Chemical Industry (SCI).