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MEASUREMENT OF 224Ra, 226Ra AND 228Ra IN NATURAL WATERS THROUGH GAMMA-RAY SPECTROMETRY
No full textMEASUREMENT OF 224Ra, 226Ra AND 228Ra IN NATURAL WATERS THROUGH GAMMA-RAY SPECTROMETRY Feng-Yun J. Huanga,*, Feng-Chih Changb, Jiunn-Hsing Chaoc aDepartment of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology: No.666, Buzih Road, Beitun District, Taichung City, 40605, Taiwan; bChemical Division, Institute of Nuclear Energy Research: No.1000, Wenhua Road, Jiaan Village, Longtan District, Taoyuan City, 32546, Taiwan; cNuclear Science and Technology Development Center, National Tsing Hua University: No.101, Section 2, Kuang-Fu Road, Hsinchu City, 30013, Taiwan; *Corresponding Author Email Address: [email protected] Introduction Radium in drinking water may expose the public to significant doses of radiation. In this study, a gamma-ray spectroscopic technique was established to determine radium isotopes, which were preconcentrated from natural waters as barium sulfate. Additionally, the concurrent determination of radium isotopes (224Ra, 226Ra, and 228Ra) in hot spring waters and associated sludge was performed in the Beitou hot spring area in Taiwan. Description of the Work A field survey utilizing gamma-ray spectroscopic technique was conducted in the Beitou hot spring area, where radium activity in hot spring waters and sludge was found to be higher than elsewhere in Taiwan. According to the results, the activity of 224Ra in spring waters was highly correlated with 228Ra due to their identical chemical behavior and original decay chain series. Additionally, concentrations of these radium isotopes (226Ra, 228Ra) and some chemical analogues in sludge were linearly related to one another, revealing their similar chemical behavior and that they may transport and distribute together in the environment. Conclusions Gamma-ray spectroscopic technique was sensitive and alternative way to determine radium isotopes in natural and drinking waters. Radioactivity of radium isotopes in Beitou hot spring waters was relatively high levels compared with elsewhere in Taiwan and not safe for use as drinking water. Concentration of 226Ra/228Ra was correlated with selected chemical analogues in sludge from moderate (Ba and Sr) to strong (Pb) correlation. Keywords: Beitou; radium; gamma-ray; hot spring; natural waters References Huang, F. Y. J., Hsu, F. Y., & Chao, J. H. (2019). Radiation dose due to naturally occurring radionuclides in soils from varying geological environments. Health Phys, vol.116, 657-663
Green Spaces with Fewer People Improve Self-Reported Affective Experience and Mood
No full text© 2023 by the authors.
Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Social Sciences and Humanities Research Council of Canada under grant SSHRC 430-2017-0072, the Ramón y Cajal Fellowship (RyC-2019-027279-I) and the Spanish Ministry of Science, Innovation and Universities through the “María de Maeztu” program for Units of Excellence (CEX2019-000940-M)Peer ReviewedCalm and quiet green spaces provide health benefits for urban residents. Yet as cities become more densely populated, increasing public users to green spaces may reduce or moderate these benefits. We examine how increased pedestrian density in a green street changes self-reported wellbeing. We use a between subject experimental design that added public users as confederates in randomly selected periods over three weeks. We collect data on mood and affective response from pedestrians moving through the green street (n = 504), with and without our public user treatment in randomly selected periods. Mood and affective response are improved when experiencing the green street with fewer people. We find that an increased number of public users in the green space has a negative effect on mood, especially among women. We provide experimental evidence that self-reported wellbeing in urban green spaces depends on social context, and that there are gender inequities associated with changes in affective response. Although we only measure immediate impacts, our results imply that the health benefits of green spaces may be limited by the total number of users. This research contributes additional evidence that greener cities are also healthier cities, but that the benefits may not be equally shared between women and men and will depend on the social context of use
Modelling of Cracked Reinforced Concrete Corrosion in Service Environment
No full textThe abstract of this item is unavailable due to an embargo
Image-based Microplot Segmentation/Detection and Deep Learning in Plant Breeding Experiments
Get PDFIn the coming years, the agricultural sector will encounter significant challenges from population growth, climate change, and evolving consumer demands. To address these challenges, farmers and plant breeders actively develop advanced plant varieties with enhanced productivity and resilience to harsh environmental conditions. However, the current methods for evaluating plant traits, such as manual operations and visual assessment by breeders, are time-consuming and subjective. A promising solution to this issue is image-based phenotyping, which leverages image-processing and machine-learning techniques to facilitate rapid and objective monitoring of numerous plants, enabling breeders to make more informed decisions. In order to perform per-microplot phenotypic analysis from the imagery and extract phenotypic traits from the field, it is necessary to identify and segment individual microplots (a small subdivided area within a field) in the orthomosaics. Nonetheless, the current procedures for segmenting and identifying microplots within aerial imagery used in agricultural field experiments necessitate manual operations, resulting in considerable time and labour investments. By automating this process, the evaluation of microplot phenotypes, such as physical traits, can be expedited, facilitating automated monitoring and quantification of plant characteristics. Our objective is to develop novel phenotyping algorithms to segment, detect, and classify microplots using image-processing and machine-learning techniques to achieve the goal. The thesis comprises four projects such as a comprehensive review of vegetation and microplot segmentation methods, the development of algorithms for the detection of both rectangular and non-rectangular microplots, and the utilization of deep learning techniques to predict lodging on microplots and highlighting the impact of deep learning on microplot phenotyping. These innovative approaches possess broad applicability in remote sensing field trials, encompassing diverse applications such as weed detection, crop row identification, plant recognition, height estimation, yield prediction, and lodging detection. Moreover, our proposed methods hold great potential for streamlining microplot phenotyping efforts by reducing the need for labour-intensive manual procedures
Root2Graph: A Graph-based Semantic Segmentation Architecture For Plant Roots
Get PDFPlant growth is significantly dependent on roots because roots play a crucial role in water and nutrient uptake. Traditional convolutional neural network (CNN) models extract binary segmentation masks, distinguishing root pixels from background pixels. However, it is important to advance further and classify different levels of roots, such as primary and lateral roots. Extracting primary and lateral roots from plant root images enables the calculation of root traits like primary and lateral root length and their branching angles. Computation of such traits can aid in breeding more stress-tolerant plants and crops, resulting in better yields. Existing approaches for the extraction of primary and lateral roots from plant root images often employ pixel-based semantic segmentation, which may not consider the structural information of plant roots and could lead to disconnected root structures. A plant root system is essentially a tree/graph-like structure with branching points as nodes and the root segments as edges. Leveraging this graph-like structure of plant roots, this thesis proposes a graph-based semantic segmentation approach using graph neural networks (GNN), which is named as 'Root2Graph' architecture. The Root2Graph architecture represents a shift from pixel-wise to graph-based classification.
Despite the GNN model displaying slightly lower performance in terms of F1 score and AUC-ROC metric in comparison to baseline pixel-based CNN model, it effectively addresses the issue of disconnected root structures observed in the pixel-based baseline model. We conduct a comprehensive analysis to identify the strengths and weaknesses of Root2Graph architecture in comparison to the baseline pixel-based CNN model and validate it's effectiveness in distinguishing between primary and lateral roots. The findings provide valuable insights for future advancements in root system analysis
Evaluating the Effectiveness of the Mozambique-Canada Maternal Health Abstraction Tool (MCMH Tool) in the Identification of Maternal Near Miss (MNM) Events
No full textBackground: Maternal morbidity and mortality has long been of great developmental concern globally. In 2005, the WHO defined Maternal Near-Miss (MNM) as a woman who nearly dies from obstetrical complications during pregnancy or up to 42 days after birth but survives the event. It also developed an abstraction tool that identifies these events. The tool is divided into 3 criteria (Disease, Intervention, and Organ-dysfunction criteria). Earlier studies suggested that the Organ-dysfunction criterion was the best yardstick for identifying MNMs. However, growing research shows that this criterion is not as effective within LMICs due to the lack of necessary laboratory capacity and skilled personnel to diagnose organ system failures. Instead, countries are increasingly relying on the disease-based criterion and have adapted the original WHO tool to suit their local needs. The Mozambique-Canada Maternal Health Project near-miss abstraction tool (MCMH near-miss tool) was tailored for the local resource availability in Mozambique as part of a wider initiative aimed at reducing maternal and neonatal morbidity. The tool contains all three (3) criteria of the WHO tool in addition to two (2) additional clinical criteria, namely, “Expanded Disease” and “Co-morbidities”. It also contains important socio-demographic indicators concerning MNM patients. It is important to examine if the added clinical criteria improve the ability of the original disease criterion to identify MNMs.
Purpose: The purpose of this study was to determine how the additional clinical criteria, namely, the “Expanded Disease” and “Co-morbidities” criteria of the MCMH abstraction tool improve the capacity of the Original WHO Disease criterion in the identification of MNM cases in the Inhambane province of Mozambique. It also aimed to examine how specific health system, geographic, and socio-demographic factors influence the identification of MNMs in Inhambane, Mozambique.
Methods: The study utilized data obtained from the MNM 1.0 study, which was conducted across two (2) hospitals in the Inhambane province in Mozambique between August 2021 and February 2022 by researchers in the Mozambique-Canada Maternal Health Project. Approximately 2057 respondent samples were analyzed for this study. To estimate the association between the additional clinical criteria and the original disease criterion, both chi-square test of independence and kappa estimates were performed. Furthermore, multivariable logistic regression was performed to determine the
association between various socio-demographic factors and the identification of MNMs based on all 3 clinical criteria.
Results: Generally, the additional clinical criteria identified more MNMs than the original WHO Disease group. There were stronger associations between the Expanded Disease criterion markers and the WHO disease category. Out of this, hypertension was the most strongly associated and was the only marker with a moderate level of agreement with the original disease group. Contrastingly, the Co-morbidities group showed weak or no associations with the original disease group. Of note, HIV/AIDs had no significant overlap with the original WHO Disease criterion although it contributed the most to the Co-morbidities category. Concerning the socio-demographic indicators, distance from the health facility was consistently associated with MNMs regardless of the clinical criterion. Other factors like education, age, and type of hospital showed varying levels of association with MNMs depending on the clinical criterion. No associations were observed between MNMs and profession or religion.
Conclusion: In conclusion, the Expanded Disease criterion can be a useful category in expanding the ability of the original WHO Disease criterion to identify MNMs. Additionally, the study provides evidence that factors such as distance from the hospital, type of hospital, and age, could be strong predictors for recognizing MNMs especially in rural areas. Overall, this study provides information to help assess the effectiveness of MCMH near-miss tool within the Inhambane province of Mozambique. Further research is however needed to understand its usefulness across different provinces throughout Mozambique
Effects of Repeated Herbicide Use for Leafy Spurge (Euphorbia esula L.) Control on Rangeland Functioning
Get PDFInvasive species management poses a significant challenge to ecosystem restoration. Leafy spurge (Euphorbia esula L.) is an invasive weed in North America that can lead to declines in native plant diversity, forage productivity and have large effects on microbial communities, nutrient cycling, and overall ecosystem functioning. Herbicides are frequently used to control leafy spurge but can have non-target impacts on ecosystems and often need to be re-applied to maintain control, which may worsen effects. The objective of this study was to determine if repeated herbicide applications of a broadleaf specific herbicide (active ingredients: aminocyclopyrachlor and metsulfuron-methyl) for leafy spurge control negatively affects non-target plant species and alters microbial abundance and community structure and nutrient retention. We established an experiment in a leafy spurge infested mixed grass prairie to test the effects of three herbicide rates – never, once, and in two consecutive years – in areas both invaded and uninvaded with leafy spurge, on plant community composition and production, microbial abundance and community structure and soil carbon (C) and nitrogen (N) concentrations. With a single application we found that: leafy spurge was effectively reduced for two growing seasons but was recovering by the third, forbs and broadleaf species richness declined, and plant community composition was altered. A second application worsened these effects and significantly reduced shrubs. There was no improvement in grass production. Herbicide did not have significant effects on bacterial abundance and microbial community structure but with a second application did lead to a decline in fungal and AMF abundance and an increase in the Gram-negative stress indicator. We also saw an initial increase in inorganic N, but a reduction in water-extractable organic carbon (WEOC) with a repeated application. These effects were most likely due to reductions in leafy spurge and native forbs and shrubs. Our results show that herbicides can have detrimental effects on non-target species and the plant community, which can lead to changes in the microbial community and nutrient concentrations and that these effects can be more pronounced with a repeated application
INTRASPECIES VARIATION IN MYCORRHIZAL RESPONSE OF MEDICAGO SATIVA TO RHIZOPHAGUS IRREGULARIS UNDER ABIOTIC STRESS
Get PDFArbuscular mycorrhizal fungi are considered beneficial for their host plants, contributing to better growth, especially in stressful conditions. Actual plant outcomes can vary from beneficial to detrimental depending on both participant’s identity and the environmental context. Understanding plant-AMF symbiosis is a key component to understanding plant functioning. Additionally, there is potential for AMF use in developing sustainable agricultural practices. There are multiple methods that seek to explain the mechanisms behind variation in AMF symbiosis, and predict outcomes, such as using resource economics, or plant root morphology. While broad differences in AMF responsiveness between plant species can be explained these ways with varying success, intraspecific differences are not well understood.
Our study aimed to target the context dependency and intraspecific variation of mycorrhizal relationships by using nine alfalfa cultivars (Medicago sativa) to determine how different cultivar attributes or trait expression might alter the plant-AMF, and plant-AMF-pollinator relationships in different stress contexts. We performed a greenhouse trial on alfalfa plants inoculated with Rhizophagus irregularis, exposing them to drought, salt, or low nutrient stress, to compare to alfalfa grown under unstressed conditions. We measured how bee visitation, flower number, seed production, biomass, N and P content changed with stress and AMF inoculation. We also measured how variation in specific root length, root tissue density, and root diameter interacted with mycorrhizal effects.
Our study showed growth conditions mattered more for determining AMF affects on growth and stress response than cultivar identity did. Biomass and nutrient concentrations were fairly consistent across cultivars in each stress treatment group, and AMF had largely neutral or negative effects on biomass across treatments. AMF Increased biomass stress responses to drought and saline soil, marginally improved nutrient uptake, but did not ultimately determine plant seed production. AMF effects did not correlate with root trait expression. Plants attained sufficient nutrients without AMF, and it is likely that the negative affects seen in inoculated plants were a result of stressed AMF acting as a drain on resources. Our study highlights the context specificity of mycorrhizal interactions with plants, and the lack of understanding of the role fungi identity and origin plays in this relationship
The Economically Optimal Nitrogen Rate for Spring Wheat Production in West-Central Saskatchewan
Get PDFWheat is an essential crop for global food security serving as a staple food crop in households worldwide, providing a significant portion of the daily calorie needs. While significant wheat yield advancements have been achieved over time, a gap between farmer-realized wheat yields and their genetic potential persists. Several studies have estimated that average wheat yields range from 20 to 70% of their potential, suggesting that significant contributions to global wheat production are possible. Additionally, many yield gap studies have identified fertilizer deficiencies as a major contributor to the yield gap. These studies, however, have failed to examine if it is economically desirable at the farm level to increase fertilizer use in order to reduce the yield gap. This study examines how much of the yield gap for spring wheat in west-central Saskatchewan is economically exploitable with respect to synthetic nitrogen fertilizer use. A spring wheat yield response function was estimated using field-level yield, input, and management data to determine the impact of various input levels and management characteristics on yields. Focusing specifically on nitrogen use, observed nitrogen rates were compared to estimated economically optimal nitrogen rates to determine that spring wheat fields on the most productive soils were observed having received suboptimal nitrogen rates while those on the least productive soils had received nitrogen rates beyond what was estimated to be economically optimal. Spring wheat produced on fields that followed a pulse crop exhibited the largest economically exploitable yield gap, indicating an increased yield response to applied nitrogen for subsequent crops following pulses. Producers may be able to reduce a portion of the yield gap for wheat in west-central Saskatchewan by increasing nitrogen rates when producing wheat on pulse stubble. Observed application rates of nitrogen appeared to follow recommended agronomic yield targets rather than yield potential. Additionally, spring wheat variety and the management ability of the producer significantly impacted estimated spring wheat yields
Lattice Models of RNA-DNA R-loop Complexes
Get PDFNSERC (RGPIN-2020-06339), NSERC USRATwo models are combined to explore the formation and stability of DNA-RNA structures called
R-loops. The first is a formal grammar model (FGM) developed by Ferrari and coworkers. The
second is a simplified lattice model developed for studying R-loop formation and geometry by
Soteros and coworkers for a PIMS VXML project. We combine these into one model and explore
the model both theoretically and via computer simulation using Markov chains. The general
goals are to explore the probability of R-loop formation and geometric properties of the R-loops