bluuebunny/crossref_metadata_2025_split huggingface dataset:
Imaging for treatment planning
Authors: Lavender Frances , Whitelaw Gemma 
Published: November 2022

Abstract
This chapter, ‘Imaging for treatment planning’, describes, compares, and contrasts the key imaging modalities used to support the treatment planning processes. These are computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). The advantages and disadvantages of each modality in respect of their roles in treatment planning are presented. The basic principles of CT image acquisition are described, including four-dimensional CT imaging which accounts for organ movement during the scan. The most significant physical factors which affect the quality of CT images are stated. The use of registration/fusion, using rigid body and deformable methods, to combine datasets from different imaging sources is introduced.
Imaging for treatment delivery: Image-guided radiotherapy
Authors: Lavender Frances 
Published: November 2022

Abstract
This chapter, ‘Imaging for treatment delivery: Image-guided radiotherapy’, discusses the technology and workflows used to verify the geometric accuracy of treatment delivery. A review of the relevant photon interaction processes (photoelectric effect and Compton scattering) precedes a discussion on cone beam CT imaging (CBCT), how this differs from fan-beam CT imaging and how it is used in clinical practice. The importance of patient positioning is highlighted. Other techniques used to improve treatment accuracy, such as gating and tracking are described. The use of adaptive radiotherapy is outlined and the chapter closes with a brief introduction to the possibilities presented by MR-linac technology.
Radiation protection in radiotherapy
Authors: Thurston Jim 
Published: November 2022

Abstract
This chapter, ‘Radiation Protection’, discusses the reduction of the risk of harm from the use of radiation while retaining the required beneficial effects in the diagnosis and treatment of cancer. Mechanisms of cell damage are covered and comparisons are made between doses received due to natural and manmade radiations. The deterministic and stochastic effects of radiation on tissue are described. The current UK legislation (IRR, IR(ME)R) in place to protect staff, patients, and members of the public from unnecessary exposure to radiation is outlined. This chapter also covers the management of incidents involving radiation and the key factors essential to the design of a safe radiation treatment facility are considered.
Coronary plaque progression is greater in systemic lupus erythematosus than rheumatoid arthritis
Authors: Moore Jeff , Lakshmanan Suvasini , Manubolu Venkat Sanjay , Kinninger April , Stojan George , Goldman Daniel W. , Petri Michelle , Budoff Matthew , Karpouzas George A. 
Published: November 2022

Background
Systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are associated with a high incidence of cardiovascular disease. Coronary atherosclerosis, particularly total plaque and noncalcified plaque on coronary computed tomography angiography (CCTA) has been correlated with cardiovascular events. We compared baseline coronary plaque burden and progression by serial CCTA in SLE and RA patients.

Methods
We prospectively evaluated 44 patients who underwent serial CCTA examinations to quantify coronary plaque progression, 22 SLE patients, and 22 age- and sex-matched RA patients. Semiautomated plaque software was used for quantitative plaque assessment. Linear regression examined the effect of SLE diagnosis (versus RA) on annualized change in natural log-transformed total normalized atheroma volume (ln-TAVnorm) for low-attenuation, fibrofatty, fibrous, total noncalcified, densely calcified, and total plaque.

Results
No quantitative differences for any plaque types were observed at baseline between SLE and RA patients (P = 0.330–0.990). After adjustment for baseline plaque and cardiovascular risk factors, the increase in ln-TAVnorm was higher in SLE than RA patients for fibrous [Exp-β: 0.202 (0.398), P = 0.0003], total noncalcified [Exp-β: 0.179 (0.393), P = 0.0001], and total plaque volume [Exp-β: 0.154 (0.501), P = 0.0007], but not for low-attenuation, fibrofatty, or densely calcified plaque (P = 0.103–0.489). Patients with SLE had 80% more fibrous, 82% more noncalcified, and 85% more total plaque increase than those with RA.

Conclusion
Coronary plaque volume was similar in RA and SLE at baseline. Progression was greater in SLE, which may explain the greater cardiovascular risk in this disease. Further research to evaluate screening and management strategies for cardiovascular disease in these high-risk patients is warranted.
Immunotherapy approaches for rare pediatric solid tumors: advances and future directions
Authors: Fierro Pineda Juan C. , Wedekind Mary Frances , Glod John W. 
Published: November 2022

Purpose of review
Immunotherapy for pediatric tumors is rapidly evolving. From major successes in pediatric hematologic malignancies, immunotherapy utility increased in the pediatric solid tumor landscape. Numerous pediatric solid tumors are defined as rare with limitations in diagnosis and treatment. This review will describe four major immunotherapies used in pediatrics and discuss results seen in rare pediatric tumors. We will also briefly review the challenges of immunotherapy in solid tumors and opportunities to drive this therapy forward.

Recent findings
Despite rare success employing immunotherapy for pediatric solid tumors, recently there have been several successes in pediatric rare solid tumors. After describing the evolving landscape of rare pediatric tumors, we will demonstrate the successes or disappointments of immunotherapy. We will describe the mechanism of four immunotherapies used in the pediatrics, followed by the published results. Finally, we will discuss the challenges and opportunities for immunotherapies in pediatric rare tumors.

Summary
Pediatric rare tumors are lacking in treatment options. Despite numerous disappointments utilizing immunotherapies in the more common pediatric solid tumors, there have been several successes within the pediatric rare tumor landscape. Much work is still needed to enhance our understanding and knowledge on utilizing these immunotherapies for pediatric rare solid tumors.
Exploring the association between Body Mass Index, Sex and Gene Expression in human colorectal epithelium
Authors: Lemler L. , Donnelly K. , Tomlinson I. P. M. , Timofeeva M. , Theodoratou E. , Rozadilla C. Fernández , Fernandez-Tajes J. , Grimes Graeme , Farrington Susan M. , Dunlop M. G. 
Published: November 2022

AbstractIntroductionColorectal cancer (CRC) is the second most common cause of cancer death globally. Genome-wide association studies have established that cancer risk mediated through common genetic variants can be linked to variation in gene expression. Since obesity and male sex impart substantially elevated CRC risk, we studied transcriptional profiles of normal colorectal mucosa using RNA sequencing to better understand the relationship of these risk factors with gene expression levels.MethodsNormal colorectal mucosa was sampled from 365 participants (208 males, 157 females) either during surgery (n=103) or through endoscopic biopsy (n=262) from cancer patients and patients with other unrelated conditions. In total, 238 samples were used for our discovery dataset and 380 samples were obtained for the validation of our findings. The transcription analysis was done using paired-end total RNA sequencing. Data processing and gene filtering followed the Genotype-Tissue Expression (GTEx) Project pipeline v8. Differential Expression Analysis (DEA) was performed on normalised counts to evaluate effects of sex and body mass index on the total gene expression, as well as possible confounding effects of cancer presence on the gene expression in normal colorectal tissue.ResultsFollowing filtering, there were 15,465 genes available for analysis. DEA identified two genes that were significantly associated with sex and five associated with body mass index. However, whilst these nominal signals are of interest, none of the genes associated with sex remained significant in a replication dataset. Due to the missing BMI information, replication of DEA by BMI was not possible.ConclusionWe found no systematic differences in gene expression in normal colorectal epithelium between males and females, nor did we find a strong association between gene expression and BMI. Although sample size may limit our analysis, the results suggest no or limited confounding effects of BMI and sex on gene expression in normal colorectal mucosa samples.
Feedback brings scene information to the representation of occluded image regions in area V1 of monkeys and humans
Authors: Papale Paolo , Wang Feng , Morgan A. Tyler , Chen Xing , Gilhuis Amparo , Petro Lucy S. , Muckli Lars , Roelfsema Pieter R. , Self Matthew W. 
Published: November 2022

AbstractNeuronal activity in the primary visual cortex (V1) is driven by feedforward input from within the neurons’ receptive fields (RFs) and modulated by contextual information in regions surrounding the RF. The effect of contextual information on spiking activity occurs rapidly, and is therefore challenging to dissociate from feedforward input. To address this challenge, we recorded the spiking activity of V1 neurons in monkeys viewing either natural scenes or scenes where the information in the RF was occluded, effectively removing the feedforward input. We found that V1 neurons responded rapidly and selectively to occluded scenes. V1 responses elicited by occluded stimuli could be used to decode scene identity and could be predicted from those elicited by non-occluded images, indicating that there is overlap between visually-driven and contextual responses. We used representational similarity analysis to show that the structure of V1 representations of occluded scenes measured with electrophysiology in monkeys correlates strongly with the representations of the same scenes in humans measured with fMRI. Our results reveal that contextual influences alter the spiking activity of V1 in monkeys across large distances on a rapid time scale, carry information about scene identity and resemble those in human V1.
“The Free Association Sessions”. Perspectives on a Novel Teaching Platform by Final Year Medical Students and Basic Specialist Trainees in Psychiatry
Authors: McFarland John , Brar Gurjot , Hayes Peter , Abdalla De Mohamed Elhassan 
Published: November 2022

AbstractIntroductionNotwithstanding the many advantages of outcomes-based education within Psychiatry placements in Medical School and Basic Specialist Training within the College of Psychiatry of Ireland, there is limited protected time for broad Reflective Practice that appreciates the complexity of working in the Mental Health setting. Furthermore, there are limitations to the current model of restricting Reflective Practice sessions to the Balint Group Format.MethodsA novel programme of structured Reflective Practice was offered to students in the School of Medicine in The University of Limerick and trainees in the Mid-West Deanery. Six student and fourteen trainee participants were subsequently invited to provide perspectives on the programme via Focus Groups. The study employed an inductive latent phenomenological approach for analysis of qualitative data.ResultsFive major themes emerged: These related to the teaching environment, personal identity, complexity, awareness of cognitive dissonance and the structure of the sessions. There were a number of different perceptions, relating to the participants’ stage of training.ConclusionsThere was evidence that the structured reflective sessions created a comfortable environment, addressed hierarchy issues and facilitated wide-ranging opportunities for reflective practice, with an observed increased appreciation for complexity in Psychiatry. There was apparent tension between controllingcontentand facilitating theformof group process. Nonetheless, the structure appeared more approachable for medical students and those early in training.
The role of vertical and horizontal transmission in the assembly of seed fungal endophyte communities in wheat and wheat wild relatives
Authors: Sharon Or , Sun Xiang , Ezrati Smadar , Kagan-Trushina Naomi , Sharon Amir 
Published: November 2022

AbstractPlants acquire fungal endophytes either from the environment or from their progenitors. These transmission modes are central in shaping the community as they affect species composition and balance. We studied fungal endophyte communities (FEC) and their seed-to-seed transmission in three Triticeae plant species: bread wheat (Triticum aestivum), wild emmer wheat (Triticum turgidum dicoccoides) and wild barley (Hordeum spontaneum). The FECs in the three plant species contained similar fungal taxa, however they were overall different. The most prevalent class of fungi was Dothideomycetes, which was dominated by the taxon Alternaria infectoria. In field collected plants, the number of taxa in the seeds was less than half the number in stems, with close to 90% of the taxa found in seeds also found in stems. Growing the same plant species in a controlled environment infection greatly affected their FEC composition; the FECs in the stems and seeds of these plants were richer and more diverse than in the original seeds, they were not dominated by a single taxon, and FECs in the new seeds had a similar richness and diversity to the stem FECs, with only 40% overlap. The controlled environment experiment confirmed vertical transmission of certain species, but also showed that external infection of the seeds is the main source for specific taxa, including A. infectoria. Collectively, our results show that many taxa can reach the seeds through the internal pathway, albeit in different abundance, and both internal and external sources significantly affect the composition of seed FECs.
Database of recurrent mutations (DORM), a web tool to browse recurrent mutations in cancers
Authors: Chakroborty Deepankar , Paatero Ilkka , Kurppa Kari J. , Elenius Klaus 
Published: November 2022

ABSTRACTAdvances in sequencing technologies have facilitated the genetic characterization of large numbers of clinical cancer samples, leading to accumulation of extensive amounts of data. While potentially very useful for directing research and for clinical decision making, the increasing quantity of data generates challenges in its optimal management, and translation to informing clinical and research questions. Here, we presentDatabaseOfRecurrentMutations (DORM), a database listing recurrent mutations (tissue-agnostic population frequency > 1) identified from cancer samples analyzed with whole genome or whole exome sequencing. The DORM database is a fast and feature-rich database supporting searching for several proteins, amino acid substitutions as well as queries using regular expressions.
Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning
Authors: Cappelletti Luca , Rekerle Lauren , Fontana Tommaso , Hansen Peter , Casiraghi Elena , Ravanmehr Vida , Mungall Christopher J , Yang Jeremy , Spranger Leonard , Karlebach Guy , Harry Caufield J. , Carmody Leigh , Coleman Ben , Oprea Tudor , Reese Justin , Valentini Giorgio , Robinson Peter N 
Published: November 2022

AbstractGraph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. We show here that this sampling strategy typically leads to sets of positive and negative edges with imbalanced edge degree distributions. Using representative homogeneous and heterogeneous biomedical knowledge graphs, we show that this strategy artificially inflates measured classification performance. We present a degree-aware node sampling approach for sampling negative edge examples that mitigates this effect and is simple to implement.
Differential metabolic adaptations define responses of winner and loser oncogenic mutant stem cells in skin epidermisin vivo
Authors: Hemalatha Anupama , Li Zongyu , Tai Karen , Gonzalez David G. , Lathrop Elizabeth , Gil Daniel , Matte-Martone Catherine , Ganesan Smirthy , Yun Sangwon , Gonzalez Lauren E. , Skala Melissa , Perry Rachel J. , Greco Valentina 
Published: November 2022

AbstractSkin epithelial stem cells detect and correct aberrancies induced by oncogenic mutations. Different oncogenes invoke different mechanisms of epithelial tolerance: while wild-type cells outcompete β-catenin-Gain-of-Function (βcatGOF) mutant cells, HrasG12Vmutant cells outcompete wild-type cells1,2. Here we ask how metabolic states change as wild-type stem cells interface with mutant cells, and how this ultimately drives different cell competition outcomes. By adapting our live-imaging platform to track endogenous redox ratio (NAD(P)H/FAD) with single cell resolution in the same mice over time, we show that wild-type epidermal stem cells maintain robust redox ratio despite their heterogeneous cell cycle states. We discover that both βcatGOF and HrasG12Vmodels lead to a rapid drop in redox ratios. However, the “winner” cells in each model (wild-type in βcatGOF and mutant in HrasG12V), rapidly recover their redox ratios, irrespective of the mutation induced. Using mass spectrometry (13C-LC-MS/MS)3, we find that both mutants increase flux through the oxidative tricarboxylic acid cycle, but the “winner” HrasG12Vcells and the “loser” βcatGOF cells modulate glycolytic flux differently. Hence, we reveal the metabolic adaptations that define the hallmarks of winners and losers during cell competitionin vivoand uncover the nodes of regulation unique to each cell fate.
Relationship between Students’ Attitude towards, and Performance in Mathematics Word Problems
Authors: Wakhata Robert , Balimuttajjo Sudi , Mutarutinya Védaste 
Published: November 2022

AbstractThe study explored the direct and indirect relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their attitude towards linear programming (LP) linear programming word tasks (ATLPWTs). Tools for data collection were: the adapted Attitude towards Mathematics Inventory-Short Form (ATMI-SF), (α=.75) as a multidimensional measurement tool, and linear programming achievement tests (pre-test and post-test). A quantitative approach with a quasi-experimental pre-test, post-test, and non-equivalent control group study design was adopted. A sample of 60811th-grade grade Ugandan students (291 male and 317 female) from eight secondary schools (both public and private) participated. Data were analyzed using PROCESS macro (v.4) for SPSS version 26. The results revealed a direct significant positive relationship between students’ performance and their ATLPWTs. Thus, it is likely that students’ attitude positively and directly impacted their performance. The present study contributes to the literature on performance and attitude towards learning mathematics. Overall, the findings carry useful practical implications that can support the theoretical framework for enhancing students’ performance and attitude towards mathematics word problems.
Ecological boundaries and constraints on viable eco-evolutionary pathways
Authors: Coblentz Kyle E. , DeLong John P. 
Published: November 2022

AbstractEvolutionary dynamics are subject to constraints ranging from limitations on what is physically possible to limitations on the pathways that evolution can take. One set of evolutionary constraints, known as ‘demographic constraints’, constrain what can occur evolutionarily due to the population demographic or population dynamical consequences of evolution leading to conditions that make populations susceptible to extinction. These demographic constraints can limit the strength of selection or rates of environmental change populations can experience while remaining extant and the trait values a population can express. Here we further hypothesize that the population demographic and population dynamical consequences of evolution also can constrain the eco-evolutionary pathways that populations can traverse by defining ecological boundaries represented by areas of likely extinction. We illustrate this process using a model of predator evolution. Our results show that the populations that persist over time tend to be those whose eco-evolutionary dynamics have avoided ecological boundaries representing areas of likely extinction due to stochastic deviations from a deterministic eco-evolutionary expectation. We term this subset of persisting pathways viable eco-evolutionary pathways. The potential existence of ecological boundaries constraining evolutionary pathways has important implications for predicting evolutionary dynamics, interpreting past evolution, and understanding the role of stochasticity and ecological constraints on eco-evolutionary dynamics.
Assessment of Emerging Pretraining Strategies in Interpretable Multimodal Deep Learning for Cancer Prognostication
Authors: Azher Zarif L. , Suvarna Anish , Chen Ji-Qing , Zhang Ze , Christensen Brock C. , Salas Lucas A. , Vaickus Louis J. , Levy Joshua J. 
Published: November 2022

AbstractDeep learning models have demonstrated the remarkable ability to infer cancer patient prognosis from molecular and anatomic pathology information. Studies in recent years have demonstrated that leveraging information from complementary multimodal data can improve prognostication, further illustrating the potential utility of such methods. Model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and trust in the technology. However, while prior works have presented novel multimodal neural network architectures as means to improve prognostication performance, these approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to “pretrain” models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. Here, we develop an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology (i.e., tissue slides) data, and we compare the performances of crossmodal pretraining, contrastive learning, and transfer learning versus the standard procedure in this context. Our models outperform the existing state-of-the-art method (average 11.54% C-index increase), and baseline clinically driven models. Our results demonstrate that the selection of pretraining strategies is crucial for obtaining highly accurate prognostication models, even more so than devising an innovative model architecture, and further emphasize the all-important role of the tumor microenvironment on disease progression.