bluuebunny/crossref_metadata_2025_split huggingface dataset:
Visualization of dose distribution and basic study of dose estimation using plastic scintillator and digital camera
Authors: Yoshitani Hiroshi , Fujibuchi Toshioh , Anam Choirul 
Published: June 2022

Abstract
Radiation can be visualized using a scintillator and a digital camera. If the amount of light emitted by the scintillator increases with dose, the dose estimation can be obtained from the amount of light emitted. In this study, the basic performance of the scintillator and digital camera system was evaluated by measuring computed tomography dose index (CTDI). A circular plastic scintillator plate was sandwiched between polymethyl methacrylate (PMMA) phantoms, and x-rays were irradiated to them while rotating the x-ray tube to confirm changes in light emission. In addition, CTDI was estimated from the amount of light emitted by the scintillator during the helical scan and compared with the value measured from dosimeter. The scintillator emitted light while changing its distribution according to the movement of the x-ray tube. The measured CTDIvol was 33.20 mGy, the CTDIvol estimated from the scintillation light was approximately 46 mGy, which was 40% larger. In particular, when the scintillator was directly irradiated, the dose was overestimated compared with the value measured from the dosimeter. This overestimation can be because of the reproducibility of the position and the difference between the sensitivity of the scintillator to detect light emission and the sensitivity of the dosimeter, and the non-uniformity of position sensitivity due to the wide-angle lens.
Mean Arc Distance (MAD): a quantity to compare trajectory 4π sampling in single target cranial stereotactic radiotherapy
Authors: MacDonald R Lee , Lincoln John , Church Cody , Thomas Christopher , Syme Alasdair 
Published: June 2022

Abstract

Purpose. C-arm linac-based radiotherapy has seen a recent interest in 4π methods of delivery using simultaneous rotations of couch and gantry to reduce doses to organs-at-risk (OARs) and increase dose compactness. While many methods use heuristics to generate trajectories that avoid OARs, combined with arbitrary trajectory restrictions to prevent oversampling, a quantity has not yet been developed to succinctly compare sampling of the 4π space for candidate trajectories as a surrogate for dosimetric compactness. Methods. Evenly spaced sampling points were distributed across a 4π sphere centred on isocentre. A metric, mean arc distance (MAD), was defined that quantifies the average arc distance between all sampling points and their nearest field in a radiotherapy trajectory. The relationship between isodose volume and MAD was examined in 2,047 plans: 900 unique trajectories of fixed port DCA plans, 900 unique trajectories of contiguous field DCA plans, 192 VMAT plans (eight volumes in four locations, each with six trajectories) in matRad with 5 VMAT plans repeated for validation in a clinical planning system, and 10 clinical VMAT cases replanned with five trajectories in a clinical treatment planning system. Results. All isodose volumes greater than 10% of the prescription dose decreased with decreasing MAD in all comparisons. In the range of 10% to 100% of the prescription dose, the rate of isodose volume decrease was exponential as a function of MAD in all comparisons. Reduction of absolute isodose volume is seen with increased 4π sampling, with larger target volumes exhibiting larger absolute reductions. Very low isodoses (0% to 10% of prescription) increased with decreasing MAD. Conclusions. MAD is a 4π sampling quantity useful in quantifying the decrease of isodose volume, relevant for sparing normal tissues. By quantifying this feature, candidate dynamic trajectories can be efficiently compared for 4π sampling. This quantity is explored here for single target cranial radiotherapy but may have applications to other radiotherapy treatment sites.
Urban soils in a historically industrial city: patterns of trace metals in Pittsburgh, Pennsylvania
Authors: Maxim Alexandra , Bain Daniel J , Burgess Jonathan 
Published: June 2022

Abstract
The distribution of legacy heavy metals in industrial city soils is not well documented. Therefore, fundamental details such as the ‘background’ (i.e., non-road/non-dripline) concentration of trace metals in urban soils are uncertain. While there has been a strong focus on mapping lead contamination near roads and residences, these studies are generally not placed in the context of the urban background. In this study, ‘background’ distributions of urban relevant trace metals: arsenic, cadmium, copper, lead, and zinc were mapped based on soil samples collected throughout Pittsburgh. Distinct spatial patterns were revealed: contamination is elevated in the eastern portion of the study area, driven by dominant wind patterns and historical coking activities in low-lying areas (paleochannels), areas subject to atmospheric temperature inversions that focus air contamination. The mixing analysis revealed spatial structures in contributions of industrial activities to metal soil contamination. In particular, regions enriched in cadmium relative to zinc (i.e., Zn:Cd<317) were located near historical coking operations, and areas enriched in lead relative to zinc (Pb:Zn>1) were located in areas with historical secondary lead smelters. These results suggest a comprehensive accounting of the trace metals concentrations in background soils has important implications for the assessment of exposure risk in populations residing in historically industrial areas. Relatively sparse sampling of background conditions in urban systems can indicate patterns of legacy contamination and attribute this contamination to historical sources.
Advantages of binary stochastic synapses for hardware spiking neural networks with realistic memristors
Authors: Sulinskas Karolis , Borg Mattias 
Published: June 2022

Abstract
Hardware implementing spiking neural networks (SNNs) has the potential to provide transformative gains in energy efficiency and throughput for energy-restricted machine-learning tasks. This is enabled by large arrays of memristive synapse devices that can be realized by various emerging memory technologies. But in practice, the performance of such hardware is limited by non-ideal features of the memristor devices such as nonlinear and asymmetric state updates, limited bit-resolution, limited cycling endurance and device noise. Here we investigate how stochastic switching in binary synapses can provide advantages compared with realistic analog memristors when using unsupervised training of SNNs via spike timing-dependent plasticity. We find that the performance of binary stochastic SNNs is similar to or even better than analog deterministic SNNs when one considers memristors with realistic bit-resolution as well in situations with considerable cycle-to-cycle noise. Furthermore, binary stochastic SNNs require many fewer weight updates to train, leading to superior utilization of the limited endurance in realistic memristive devices.
Two sparsities are better than one: unlocking the performance benefits of sparse–sparse networks
Authors: Hunter Kevin , Spracklen Lawrence , Ahmad Subutai 
Published: June 2022

Abstract
In principle, sparse neural networks should be significantly more efficient than traditional dense networks. Neurons in the brain exhibit two types of sparsity; they are sparsely interconnected and sparsely active. These two types of sparsity, called weight sparsity and activation sparsity, when combined, offer the potential to reduce the computational cost of neural networks by two orders of magnitude. Despite this potential, today’s neural networks deliver only modest performance benefits using just weight sparsity, because traditional computing hardware cannot efficiently process sparse networks. In this article we introduce Complementary Sparsity, a novel technique that significantly improves the performance of dual sparse networks on existing hardware. We demonstrate that we can achieve high performance running weight-sparse networks, and we can multiply those speedups by incorporating activation sparsity. Using Complementary Sparsity, we show up to 100× improvement in throughput and energy efficiency performing inference on FPGAs. We analyze scalability and resource tradeoffs for a variety of kernels typical of commercial convolutional networks such as ResNet-50 and MobileNetV2. Our results with Complementary Sparsity suggest that weight plus activation sparsity can be a potent combination for efficiently scaling future AI models.
Electric bicycles sharing: opportunities and environmental impacts
Authors: Kontar Wissam , Ahn Soyoung , Hicks Andrea 
Published: June 2022

Abstract
Electric bicycles (E-bikes) are an emerging transportation technology with the potential to replace other available modes. In this work, we investigate the ability of an E-bike sharing program to compete with different modes of transportation and the resulting use-phase environmental impacts. A survey study on users of an E-bike program in Madison, Wisconsin was conducted to reveal modal shifts before and after access to the program’s membership. An environmental investigation based on well-to-wheel life cycle analysis, coupled with mode choice modeling reveals the users of this technology, the underlying modal shifts triggered by its usage, and the cascading environmental implications. The analysis reveals E-bike’s ability in attracting users, which translates into beneficial environmental impacts across five studied categories: energy consumption, greenhouse gas emission, particulate matter, sulfate and nitrate emissions. We further explore the implications of trip distance on the ability of E-bikes to compete with other modes of transportation, and the resultant environmental impacts. Finally, the electricity generation scheme is analyzed to showcase the dependency between environmental benefits of E-bike and the energy infrastructure it is operating under.
Introducing ‘Environmental Research: Climate’—a new journal devoted to understanding the causes, consequences and solutions of climate variability and change
Authors: Diffenbaugh Noah S 
Published: June 2022

Abstract

Environmental Research: Climate is a new open-access journal focused on the causes, consequences and solutions of climate variability and change. The field of climate research is at a critical juncture, driven by a broad and heterogeneous community that is seeking to respond to both the rapidly changing climate system and the rapidly evolving needs of society to manage climate-related risks. While there are already many journals publishing research in different areas of this broad field, Environmental Research: Climate aims to fill a unique role by convening the multidisciplinary communities working across the full scope of causes, consequences and solutions, in a fully Open Access publishing venue. The inaugural Editorial Board comprises a broad array of backgrounds, expertise and perspectives that span the physical, biogeochemical and human dimensions that shape climate and its impacts. The Editorial Board is committed to fair, ethical, transparent editorial practices, and the journal offers a suite of modern publishing features that will enhance the journal experience for authors, reviewers and readers. Through these practices and features, Environmental Research: Climate aims to provide the climate research community with a single publishing venue that is sufficiently broad, focused and open to deeply and comprehensively probe the causes, consequences and solutions of climate variability and change—and in doing so coalesce and catalyze the next era of climate research.
Robust identification of temporal biomarkers in longitudinal omics studies
Authors: Metwally Ahmed A , Zhang Tom , Wu Si , Kellogg Ryan , Zhou Wenyu , Contrepois Kevin , Tang Hua , Snyder Michael 
Published: June 2022

AbstractMotivationLongitudinal studies increasingly collect rich ‘omics’ data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject.ResultsIn this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups.Availability and implementationWe provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use.Supplementary informationSupplementary data are available at Bioinformatics online.
Improving candidate Biosynthetic Gene Clusters in fungi through reinforcement learning
Authors: Almeida Hayda , Tsang Adrian , Diallo Abdoulaye Baniré 
Published: June 2022

AbstractMotivationPrecise identification of Biosynthetic Gene Clusters (BGCs) is a challenging task. Performance of BGC discovery tools is limited by their capacity to accurately predict components belonging to candidate BGCs, often overestimating cluster boundaries. To support optimizing the composition and boundaries of candidate BGCs, we propose reinforcement learning approach relying on protein domains and functional annotations from expert curated BGCs.ResultsThe proposed reinforcement learning method aims to improve candidate BGCs obtained with state-of-the-art tools. It was evaluated on candidate BGCs obtained for two fungal genomes, Aspergillus niger and Aspergillus nidulans. The results highlight an improvement of the gene precision by above 15% for TOUCAN, fungiSMASH and DeepBGC; and cluster precision by above 25% for fungiSMASH and DeepBCG, allowing these tools to obtain almost perfect precision in cluster prediction. This can pave the way of optimizing current prediction of candidate BGCs in fungi, while minimizing the curation effort required by domain experts.Availability and implementationhttps://github.com/bioinfoUQAM/RL-bgc-components.Supplementary informationSupplementary data are available at Bioinformatics online.
A unifying network modeling approach for codon optimization
Authors: Karaşan Oya , Şen Alper , Tiryaki Banu , Cicek A Ercument 
Published: June 2022

AbstractMotivationSynthesizing genes to be expressed in other organisms is an essential tool in biotechnology. While the many-to-one mapping from codons to amino acids makes the genetic code degenerate, codon usage in a particular organism is not random either. This bias in codon use may have a remarkable effect on the level of gene expression. A number of measures have been developed to quantify a given codon sequence’s strength to express a gene in a host organism. Codon optimization aims to find a codon sequence that will optimize one or more of these measures. Efficient computational approaches are needed since the possible number of codon sequences grows exponentially as the number of amino acids increases.ResultsWe develop a unifying modeling approach for codon optimization. With our mathematical formulations based on graph/network representations of amino acid sequences, any combination of measures can be optimized in the same framework by finding a path satisfying additional limitations in an acyclic layered network. We tested our approach on bi-objectives commonly used in the literature, namely, Codon Pair Bias versus Codon Adaptation Index and Relative Codon Pair Bias versus Relative Codon Bias. However, our framework is general enough to handle any number of objectives concurrently with certain restrictions or preferences on the use of specific nucleotide sequences. We implemented our models using Python’s Gurobi interface and showed the efficacy of our approach even for the largest proteins available. We also provided experimentation showing that highly expressed genes have objective values close to the optimized values in the bi-objective codon design problem.Availability and implementationhttp://alpersen.bilkent.edu.tr/NetworkCodon.zip.Supplementary informationSupplementary data are available at Bioinformatics online.
Decreased taste sensitivity to sucrose in dopamine D3 receptor mutant mice
Authors: Sun Huiling , Li Jinrong , Yan Junbao , Sun Bo , Wei Xiaojing , Song Lin , Yan Jianqun 
Published: June 2022

Abstract
Dopamine plays a key role in food rewards and sweet-taste stimulation. We examined the basis for behavioral responses to sweet taste in dopamine D3 receptor-deficient (D3−/−) mice by determining whether the absence of D3 receptors affects the sensitivity to dilute sucrose solutions. In experiment 1, we measured the intensity generalization threshold of conditioned taste aversion (CTA) to a 0.2 M sucrose solution. Results showed that the generalization thresholds were 0.025–0.05 M in D3−/− mice and 0.0025–0.005 M in wild-type (WT) mice. In experiment 2, we found that D3−/− and WT mice had similar capabilities to form and extinguish CTAs. Since the intensity generalization threshold is mainly due to a combination of sweet-taste sensitivity and the robust nature of CTA formation, the results showed that taste sensitivity to sucrose in D3−/− mice was lower than that in WT mice. In experiment 3, to test whether the peripheral sensory signaling may also be affected by the disruption of the dopamine D3 receptors, the mRNA expression levels of sweet-taste-related proteins in taste buds of D3−/− mice were determined. The T1R1 and BDNF mRNA expression levels in D3−/− mice were higher than the controls, whereas T1R2, T1R3, α-gustducin, and TRPM5 mRNA were similar. These findings suggest that disruption of dopamine D3 receptor-mediated signaling decreases the sweet-taste sensitivity and alters the mRNA expression levels of some taste-related molecules.
A concise history of echocardiography: timeline, pioneers, and landmark publications
Authors: Fraser Alan G , Monaghan Mark J , van der Steen Antonius F W , Sutherland George R 
Published: June 2022

AbstractEchocardiography is less than 70 years old, and many major advances have occurred within living memory, but already some pioneering contributions may be overlooked. In order to consider what circumstances have been common to the most successful innovations, we have studied and here provide a timeline and summary of the most important developments in transthoracic and transoesophageal ultrasound imaging and Doppler techniques, as well as in intravascular ultrasound and imaging in paediatric cardiology. The entries are linked to a comprehensive list of first publications and to a collection of first-hand historical accounts published by early investigators. Review of the original manuscripts highlights that it is difficult to establish unequivocal precedence for many new imaging methods, since engineers were often working independently but simultaneously on similar problems. Many individuals who are prominently linked with particular developments were not the first in their field. Developments in echocardiography have been highly dependent on technological advances, and most likely to be successful when engineers and clinicians were able to collaborate with open exchange between centres and disciplines. As with many other new medical technologies, initial responses were sceptical and introduction into clinical practice required persistence and substantial energy from the first adopters. Current developments involve advances in software as much as in equipment, and progress will depend on continuing collaborations between engineers and clinical scientists, for example to identify unmet needs and to investigate the clinical impact of particular imaging approaches.
Regional longitudinal strain patterns according to left ventricular hypertrophy in the general population
Authors: Frimodt-Møller Katrine Emilie , Olsen Flemming Javier , Biering-Sørensen Sofie Reumert , Lassen Mats Christian Højbjerg , Møgelvang Rasmus , Schnohr Peter , Jensen Gorm , Gislason Gunnar , Marcus Gregory Maurice , Biering-Sørensen Tor 
Published: June 2022

Abstract

Aims
A pattern of reduced basal longitudinal strain (BLS) is often observed with left ventricular (LV) hypertrophy (LVH). Whether this pattern is associated with poor outcome is unclear. We aimed to evaluate the prognostic value of regional longitudinal strain according to LV geometry.

Methods and results
We investigated participants in the 4th Copenhagen City Heart Study who had an echocardiogram with speckle tracking performed. Participants were stratified according to the presence of LVH (LV mass index ≥116 g/m2 for men and ≥96 g/m2 for women). The outcome was major adverse cardiovascular events (MACE) defined as a composite of myocardial infarction, heart failure, and/or cardiovascular death. The study population consisted of 1090 participants. Mean LVEF was 60% and 160 (15%) had LVH. During a median follow-up of 14.7 years, there were 137 events. Both BLS and midventricular strain, but not apical strain, became incrementally impaired in the spectrum from normal to hypertensives subjects without LVH, and to participants with hypertension and LVH. After multivariable adjustment, BLS and midventricular strain were independently associated with MACE in participants with LVH (BLS: HR 1.08, 95% CI 1.00–1.17, P = 0.041; midventricular strain: HR 1.10, 95% CI 1.00–1.21, P = 0.041) but not in participants without LVH (BLS: HR 0.96, 95% CI 0.90–1.01, P = 0.13; midventricular strain: HR 0.97, 95% CI 0.91–1.03, P = 0.36).

Conclusion
BLS and midventricular strain, but not apical strain, become incrementally impaired in the spectrum from normal geometry to LVH, and are independently associated with MACE in participants with LVH.