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Article ; Online: Reconciling the statistics of spectral reflectance and colour.

Lewis D Griffin

PLoS ONE, Vol 14, Iss 11, p e

2019 Volume 0223069

Abstract: The spectral reflectance function of a surface specifies the fraction of the illumination reflected by it at each wavelength. Jointly with the illumination spectral density, this function determines the apparent colour of the surface. Models for the ... ...

Abstract	The spectral reflectance function of a surface specifies the fraction of the illumination reflected by it at each wavelength. Jointly with the illumination spectral density, this function determines the apparent colour of the surface. Models for the distribution of spectral reflectance functions in the natural environment are considered. The realism of the models is assessed in terms of the individual reflectance functions they generate, and in terms of the overall distribution of colours which they give rise to. Both realism assessments are made in comparison to empirical datasets. Previously described models (PCA- and fourier-based) of reflectance function statistics are evaluated, as are improved versions; and also a novel model, which synthesizes reflectance functions as a sum of sigmoid functions. Key model features for realism are identified. The new sigmoid-sum model is shown to be the most realistic, generating reflectance functions that are hard to distinguish from real ones, and accounting for the majority of colours found in natural images with the exception of an abundance of vegetation green and sky blue.
Keywords	Medicine ; R ; Science ; Q
Subject code	333
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Categorical colour geometry.

Lewis D Griffin / Dimitris Mylonas

PLoS ONE, Vol 14, Iss 5, p e

2019 Volume 0216296

Abstract: Ordinary language users group colours into categories that they refer to by a name e.g. pale green. Data on the colour categories of English speakers was collected using online crowd sourcing - 1,000 subjects produced 20,000 unconstrained names for 600 ... ...

Abstract	Ordinary language users group colours into categories that they refer to by a name e.g. pale green. Data on the colour categories of English speakers was collected using online crowd sourcing - 1,000 subjects produced 20,000 unconstrained names for 600 colour stimuli. From this data, using the framework of Information Geometry, a Riemannian metric was computed throughout the RGB cube. This is the first colour metric to have been computed from colour categorization data. In this categorical metric the distance between two close colours is determined by the difference in the distribution of names that the subject population applied to them. This contrasts with previous colour metrics which have been driven by stimulus discriminability, or acceptability of a colour match. The categorical metric is analysed and shown to be clearly different from discriminability-based metrics. Natural units of categorical length, area and volume are derived. These allow a count to be made of the number of categorically-distinct regions of categorically-similar colours that fit within colour space. Our analysis estimates that 27 such regions fit within the RGB cube, which agrees well with a previous estimate of 30 colours that can be identified by name by untrained subjects.
Keywords	Medicine ; R ; Science ; Q
Subject code	401
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A note on the early transcriptional response in leaves and root of potato plants to cadmium exposure

M.F. Mengist / S.L. Byrne / D. Griffin / D. Milbourne

Irish Journal of Agricultural and Food Research, Vol 60, Iss 1, Pp 27-

2021 Volume 32

Abstract: Potato plants can accumulate a high amount of cadmium (Cd) in the tuber when grown in soils rich in Cd. The molecular mechanisms governing Cd accumulation in the potato plant are poorly understood. Here we performed an RNA-sequencing experiment to ... ...

Abstract	Potato plants can accumulate a high amount of cadmium (Cd) in the tuber when grown in soils rich in Cd. The molecular mechanisms governing Cd accumulation in the potato plant are poorly understood. Here we performed an RNA-sequencing experiment to identify genes differentially expressed in the leaf and root of potato during early stages of Cd exposure. Results did not identify any significant transcriptional response in leaves under 1 or 5 mg kg−1 Cd after 72 h. However, in the roots we did identify 2,846 genes that were significantly differentially expressed after 72 h between plants grown in 5 mg kg−1 Cd and controls. These included genes involved in photosynthesis and autophagy being up-regulated, and genes involved in intracellular transport being down-regulated. This study is the first report on the transcriptome-wide response of potato to Cd stress, providing insight into the molecular mechanisms involved in the response.
Keywords	cadmium ; gene expression ; potato ; rna-seq ; transcriptome ; Agriculture (General) ; S1-972 ; Nutrition. Foods and food supply ; TX341-641
Subject code	580
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	Compuscript Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Potatoes in Ireland

D. Griffin / L. Bourke / E. Mullins / M. Hennessy / S. Phelan / S. Kildea / D. Milbourne

Irish Journal of Agricultural and Food Research, Vol 61, Iss 1, Pp 184-

Sixty years of potato research and development, market evolution and perspectives on future challenges

2022 Volume 200

Abstract: Potato is often considered synonymous with Ireland, due to the great Irish famine in 1845, and remains the most important primary food crop in Ireland. Over the last 60 yr, the area of potatoes has reduced from 86,000 ha to 9,000 ha. This trend has ... ...

Abstract	Potato is often considered synonymous with Ireland, due to the great Irish famine in 1845, and remains the most important primary food crop in Ireland. Over the last 60 yr, the area of potatoes has reduced from 86,000 ha to 9,000 ha. This trend has occurred in most developed countries but in Ireland it is due to decreasing consumption, increasing yield, decline in seed production and potatoes no longer being use for animal feed. Significant specialisation occurred in the industry during the 1990s, with improvements in agronomy, on farm investment in storage and field equipment, consolidation of packing facilities, and a significant shift in cultivar choice, with Rooster becoming the dominant cultivar. These developments led to an increase in yield from 20 t/ha in the mid-1980s to over 40 t/ha today. Potato research in Ireland has focused on breeding, pathology and agronomy, while there have been significant changes in how knowledge is communicated to growers and the industry in this period. The industry faces many challenges in the future, largely framed by climate change, the need to reduce fertiliser and plant protection products as part of the EU Farm to Fork Strategy and industry size constraints. New superior potato varieties and novel breeding techniques will have potential to help address many challenges in combination with integrated pest management principles. Multi-actor approaches will be necessary to address all challenges but particularly to aid the industry grow and exploit emerging opportunities.
Keywords	breeding ; market ; pathology ; potato ; Agriculture (General) ; S1-972 ; Nutrition. Foods and food supply ; TX341-641
Subject code	941
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Compuscript Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Quantifying urban, industrial, and background changes in NO 2 during the COVID-19 lockdown period based on TROPOMI satellite observations

V. Fioletov / C. A. McLinden / D. Griffin / N. Krotkov / F. Liu / H. Eskes

Atmospheric Chemistry and Physics, Vol 22, Pp 4201-

2022 Volume 4236

Abstract: The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO 2 ). While the overall NO 2 decline over some large cities is well-established, understanding the details remains a challenge ... ...

Abstract	The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO 2 ). While the overall NO 2 decline over some large cities is well-established, understanding the details remains a challenge since multiple source categories contribute. In this study, a new method of isolation of three components (background NO 2 , NO 2 from urban sources, and NO 2 from industrial point sources) is applied to estimate the impact of the COVID-19 lockdown on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The tropospheric NO 2 vertical column density (VCD) values measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor over 261 urban areas for the period from 16 March to 15 June 2020 were compared with the average VCD values for the same period in 2018 and 2019. While the background NO 2 component remained almost unchanged, the urban NO 2 component declined by −18 % to −28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a −40 % to −50 % urban emission decline. In contrast, the decline over urban areas in China, where the lockdown was over during the analysed period, was, on average, only <math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>-</mo><mn mathvariant="normal">4.4</mn><mo>±</mo><mn mathvariant="normal">8</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="42pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="0aaf43c68cbc7557a2c43e98b6a90ba0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-4201-2022-ie00001.svg" ...<br />
Keywords	Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	511 ; 333
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine Learning of Raman Spectroscopy Data for Classifying Cancers

Nathan Blake / Riana Gaifulina / Lewis D. Griffin / Ian M. Bell / Geraint M. H. Thomas

Diagnostics, Vol 12, Iss 1491, p

A Review of the Recent Literature

2022 Volume 1491

Abstract: Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine ... ...

Abstract	Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.
Keywords	Raman Spectroscopy ; medical application ; disease screening and diagnosis ; machine learning ; cross-validation ; deep learning ; Medicine (General) ; R5-920
Subject code	006
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Repurposing clinically available drugs and therapies for pathogenic targets to combat SARS‐CoV‐2

Yiying Xue / Husheng Mei / Yisa Chen / James D. Griffin / Qingsong Liu / Ellen Weisberg / Jing Yang

MedComm, Vol 4, Iss 3, Pp n/a-n/a (2023)

2023

Abstract: Abstract The coronavirus disease 2019 (COVID‐19) pandemic has affected a large portion of the global population, both physically and mentally. Current evidence suggests that the rapidly evolving coronavirus subvariants risk rendering vaccines and ... ...

Abstract	Abstract The coronavirus disease 2019 (COVID‐19) pandemic has affected a large portion of the global population, both physically and mentally. Current evidence suggests that the rapidly evolving coronavirus subvariants risk rendering vaccines and antibodies ineffective due to their potential to evade existing immunity, with enhanced transmission activity and higher reinfection rates that could lead to new outbreaks across the globe. The goal of viral management is to disrupt the viral life cycle as well as to relieve severe symptoms such as lung damage, cytokine storm, and organ failure. In the fight against viruses, the combination of viral genome sequencing, elucidation of the structure of viral proteins, and identifying proteins that are highly conserved across multiple coronaviruses has revealed many potential molecular targets. In addition, the time‐ and cost‐effective repurposing of preexisting antiviral drugs or approved/clinical drugs for these targets offers considerable clinical advantages for COVID‐19 patients. This review provides a comprehensive overview of various identified pathogenic targets and pathways as well as corresponding repurposed approved/clinical drugs and their potential against COVID‐19. These findings provide new insight into the discovery of novel therapeutic strategies that could be applied to the control of disease symptoms emanating from evolving SARS‐CoV‐2 variants.
Keywords	combination therapy ; drug resistance ; pathogenic targets ; repurposing therapies ; SARS‐CoV‐2 ; Medicine ; R
Subject code	610
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Irrigated corn grain yield prediction in Florida using active sensors and plant height

Diego A. H. de S． Leitão / Sudeep S． Sidhu / Winniefred D． Griffin / Uzair Ahmad / Lakesh K． Sharma

Smart Agricultural Technology, Vol 5, Iss , Pp 100276- (2023)

2023

Abstract: Remote sensing is widely utilized in agriculture for estimating corn (Zea mays L.) grain yield (CGY). Few studies have determined if the Normalized Difference Vegetation Index (NDVI) and/or Soil Plant Analysis Development (SPAD) can estimate CGY in ... ...

Abstract	Remote sensing is widely utilized in agriculture for estimating corn (Zea mays L.) grain yield (CGY). Few studies have determined if the Normalized Difference Vegetation Index (NDVI) and/or Soil Plant Analysis Development (SPAD) can estimate CGY in Florida. From April to August 2022, in Live Oak, Florida, a field-scale experiment was conducted in two sites with irrigated corn using a complete randomized block design with six nitrogen (N) rates and four replicates per site. This study aimed to estimate CGY using NDVI alone or in combination with SPAD, plant height (PH), and N rate. CGY response curve served as a comparison standard. Fifteen data subsets were selected, and stepwise selection multiple linear regression analysis was utilized to generate each reduced equation (Model). In addition, the relative significance of the predictor variables was evaluated. The strongest correlations with CGY were demonstrated by N rate (r = 0.93), PH103 (r = 0.91), NDVI39 (r = 0.81), and SPAD60 (r = 0.93). Models with multiple variables showed a better fit than single-variable models. Model 15 (variables until tasseling - 60 DAP) demonstrated comparable performance with 92.8% of variance explained and RMSE = 1,315.685 kg ha−1. Regardless of the model, the N rate has always contributed the most to CGY. Although Model 1 had the best overall performance, it may not be feasible for growers to utilize a model with multiple terms. Consequently, Model 15 could estimate CGY in Florida based on PH and NDVI at 60 and 32 DAP, respectively.
Keywords	Multiple regression ; NDVI ; Nitrogen ; Remote sensing ; SPAD ; Zea mays ; Agriculture (General) ; S1-972 ; Agricultural industries ; HD9000-9495
Subject code	310
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effects of a high protein diet and liver disease in an in silico model of human ammonia metabolism

Jeddidiah W. D. Griffin / Patrick C. Bradshaw

Theoretical Biology and Medical Modelling, Vol 16, Iss 1, Pp 1-

2019 Volume 14

Abstract: Abstract Background After proteolysis, the majority of released amino acids from dietary protein are transported to the liver for gluconeogenesis or to peripheral tissues where they are used for protein synthesis and eventually catabolized, producing ... ...

Abstract	Abstract Background After proteolysis, the majority of released amino acids from dietary protein are transported to the liver for gluconeogenesis or to peripheral tissues where they are used for protein synthesis and eventually catabolized, producing ammonia as a byproduct. High ammonia levels in the brain are a major contributor to the decreased neural function that occurs in several pathological conditions such as hepatic encephalopathy when liver urea cycle function is compromised. Therefore, it is important to gain a deeper understanding of human ammonia metabolism. The objective of this study was to predict changes in blood ammonia levels resulting from alterations in dietary protein intake, from liver disease, or from partial loss of urea cycle function. Methods A simple mathematical model was created using MATLAB SimBiology and data from published studies. Simulations were performed and results analyzed to determine steady state changes in ammonia levels resulting from varying dietary protein intake and varying liver enzyme activity levels to simulate liver disease. As a toxicity reference, viability was measured in SH-SY5Y neuroblastoma cells following differentiation and ammonium chloride treatment. Results Results from control simulations yielded steady state blood ammonia levels within normal physiological limits. Increasing dietary protein intake by 72% resulted in a 59% increase in blood ammonia levels. Simulations of liver cirrhosis increased blood ammonia levels by 41 to 130% depending upon the level of dietary protein intake. Simulations of heterozygous individuals carrying a loss of function allele of the urea cycle carbamoyl phosphate synthetase I (CPS1) gene resulted in more than a tripling of blood ammonia levels (from roughly 18 to 60 μM depending on dietary protein intake). The viability of differentiated SH-SY5Y cells was decreased by 14% by the addition of a slightly higher amount of ammonium chloride (90 μM). Conclusions Data from the model suggest decreasing protein consumption may be one simple strategy to decrease blood ammonia levels and minimize the risk of developing hepatic encephalopathy for many liver disease patients. In addition, the model suggests subjects who are known carriers of disease-causing CPS1 alleles may benefit from monitoring blood ammonia levels and limiting the level of protein intake if ammonia levels are high.
Keywords	Ammonia ; Hepatic encephalopathy ; Liver cirrhosis ; Carbamoyl phosphate synthetase 1 ; Nitrogen ; Urea cycle ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2019-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Demographic and Risk-Factor Differences between Users and Non-Users of Unscheduled Healthcare among Pediatric Outpatients with Persistent Asthma

Pavani Rangachari / Dixie D. Griffin / Santu Ghosh / Kathleen R. May

International Journal of Environmental Research and Public Health, Vol 17, Iss 2704, p

2020 Volume 2704

Abstract: This study assesses differences between users and non-users of unscheduled healthcare for persistent childhood asthma, with regard to select demographic and risk factors. The objectives are to provide important healthcare utilization information and a ... ...

Abstract	This study assesses differences between users and non-users of unscheduled healthcare for persistent childhood asthma, with regard to select demographic and risk factors. The objectives are to provide important healthcare utilization information and a foundation for future research on self-management effectiveness (SME), informed by a recently developed “holistic framework” for measuring SME in childhood asthma. An 18-month retrospective chart review was conducted on 59 pediatric outpatients with persistent asthma—mild, moderate, or severe, to obtain data on various demographic and risk factors, and healthcare use for each child. The study examined five types of “unscheduled” healthcare use. Users had non-zero encounters (at least one) in any of the five types; non-users had zero encounters (not even one) in all five types. Differences between users and non-users were assessed using contingency table and logistic regression analysis. There were 25 users and 34 non-users of unscheduled healthcare. Each severity category contained users and non-users. The only statistically significant finding was that the mild persistent category had fewer users than severe persistent ( p < 0.05). There were no significant differences between users and non-users for any other demographic or risk factor examined. After adjusting for asthma severity, there were no other significant differences between users and non-users of unscheduled healthcare. This is a crucial finding which suggests that something else is driving unscheduled healthcare use in these children, given there were users and non-users in each asthma severity category. These results provide impetus for future research on the role of other aspects of the "holistic framework" in explaining differences in uses of unscheduled healthcare in persistent childhood asthma.
Keywords	pediatric asthma ; self-management effectiveness ; healthcare utilization ; evidence-based practice guidelines ; asthma management ; persistent asthma ; Medicine ; R
Subject code	306
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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