The performance of phenomic selection depends on the genetic architecture of the target trait

Key message The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Abstract Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation

The ability to measure and monitor wildlife populations is important for species management and conservation. The use of near-infrared spectroscopy (NIRS) to rapidly detect physiological traits from wildlife scat and other body materials could play an important role in the conservation of species. Previous research has demonstrated the potential for NIRS to detect diseases such as the novel COVID-19 from saliva, parasites from feces, and numerous other traits from animal skin, hair, and scat, such as cortisol metabolites, diet quality, sex, and reproductive status, that may be useful for population monitoring. Models developed from NIRS data use light reflected from a sample to relate the variation in the sample’s spectra to variation in a trait, which can then be used to predict that trait in unknown samples based on their spectra. The modelling process involves calibration, validation, and evaluation. Data sampling, pre-treatments, and the selection of training and testing datasets can impact model performance. We review the use of NIRS for measuring physiological traits in animals that may be useful for wildlife management and conservation and suggest future research to advance the application of NIRS for this purpose.

Development of the hyperspectral near infrared spectroscopy for monitoring tissue oxygen metabolism

Near Infrared Spectroscopy (NIRS) employs optical window of 650-1100 nm to measure the concentrations of different chromophores such as, oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (Hb) and water. In addition, changes in the redox cytochrome c oxidase (Cyt-ox) can be measured using hyperspectral NIRS. Cyt-ox has a relatively low concentration compared to HbO2 and HHb therefore its measurements is very challenging. The main objective of this thesis was to identify the best signal-processing algorithm for deconvolution of the Cyt-ox changes. In this thesis two different studies have been done; in the first study, functional broadband NIRS during simulated driving (on 16 adult subjects) has been recorded and the best data processing algorithm for deconvolution of the chromophores was identified. In the second series of experiments broadband NIRS data during cardiac arrest and resuscitation was recorded on 19 pigs. A novel signal-processing algorithm was introduced to deconvolve Cyt-ox. The Introduction section covers relevant aspects of the theory and mathematical concepts. The Journal Manuscript part contains the two projects mentioned above. The last part is The Journal Manuscript part contains the two projects mentioned above. The last part is allocated for the overall conclusion of the entire thesis.

Development of the hyperspectral near infrared spectroscopy for monitoring tissue oxygen metabolism

Near Infrared Spectroscopy (NIRS) employs optical window of 650-1100 nm to measure the concentrations of different chromophores such as, oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (Hb) and water. In addition, changes in the redox cytochrome c oxidase (Cyt-ox) can be measured using hyperspectral NIRS. Cyt-ox has a relatively low concentration compared to HbO2 and HHb therefore its measurements is very challenging. The main objective of this thesis was to identify the best signal-processing algorithm for deconvolution of the Cyt-ox changes. In this thesis two different studies have been done; in the first study, functional broadband NIRS during simulated driving (on 16 adult subjects) has been recorded and the best data processing algorithm for deconvolution of the chromophores was identified. In the second series of experiments broadband NIRS data during cardiac arrest and resuscitation was recorded on 19 pigs. A novel signal-processing algorithm was introduced to deconvolve Cyt-ox. The Introduction section covers relevant aspects of the theory and mathematical concepts. The Journal Manuscript part contains the two projects mentioned above. The last part is The Journal Manuscript part contains the two projects mentioned above. The last part is allocated for the overall conclusion of the entire thesis.

The use of Near-Infrared Spectroscopy to evaluate the effect of exercise on peripheral muscle oxygenation in patients with lower-extremity artery disease: a systematic review

Funding Acknowledgements Type of funding sources: None. Background Near-Infrared Spectroscopy (NIRS) has been suggested as a new diagnostic tool in patients with lower-extremity artery disease (LEAD). The aim of this systematic review was i) to summarize the impact of exercise therapy on lower-limb muscle oxygenation, evaluated by means of NIRS, in patients with LEAD and ii) to give an overview on NIRS instruments and methodology. Methods A systematic search was conducted in MEDLINE and Embase, from the earliest date available until 16th of March 2020, to identify peer-reviewed studies involving the use of NIRS in the evaluation of exercise training on muscle oxygenation in patients with LEAD. Primary outcomes were NIRS-derived variables. Effect sizes were calculated as standardized mean differences. Assessment of methodological quality was done using a combined checklist from the Cochrane bias and the quality assessment tool for before-after studies without control group. Results Eleven original trials were included involving 16 exercise groups and 4 control groups. Tissue saturation index (TSI) at rest remained unchanged following the exercise interventions. Yet, exercise training increased time-to-minimum TSI during exercise (range effect sizes: +0.172 to +0.927). In addition, exercise training led to a faster recovery to half and full TSI rest values in most intervention groups (range effect sizes: -0.046 to -0.558 and -0.269 to -0.665 respectively). Finally, NIRS data reproducibility and analytic methods were underreported in the included studies. To illustrate, only four studies reported appropriate measures to ensure repeated reproducible probe positioning. The use of raw or filtered data for analysis was reported in none of the included articles. Conclusion The available NIRS data suggest that exercise training improves deoxygenation and reoxygenation patterns in patients with LEAD. Whereas NIRS is a promising tool in the evaluation of LEAD, the low number of RCT’s as well as large heterogeneity in NIRS assessment methods, outcome measures and instrumentation, warrants more research to better understand the role of muscle oxygenation associated with exercise-induced improvements in walking capacity. In particular the role of NIRS to study underlying mechanisms and determinants related to exercise progression in LEAD patients is appealing. Abstract Figure. NIRS during treadmill test

Plant Growth-Promoting Rhizobacteria: Effects on Yield and Nutritive Value of ‘Russell’ Bermudagrass and ‘KY 31’ Tall Fescue

Commercial N fertilizer are labor intensive and expensive for many forage producers. Alternative N fertility options are necessary for the long-term sustainability of forage systems. A 2-yr experiment evaluating plant growth-promoting rhizobacteria (PGPR) as an alternative N source for ‘Russell’ bermudagrass (C. dactylon) and ‘KY 31’ tall fescue (L. arundinaceum) was conducted in Alabama. Fourteen, 3-m2 plots were treated with full N (56 kg/ha) and ½ N (28 kg/ha), Accomplish LM (AMS), AMS + ½ N, DH44, Blend 20, and a control. DH44 is a single strain of Paenibacillus sonchi, while Blend 20 contains 2 strains of Bacillus pumilus and 1 strain of B. spaericus. Forage samples were taken every 4 weeks with a 0.1-m2 quadrat then analyzed for NDF, ADF, CP, and yield using NIRS. Data were analyzed using Proc GLIMMIX of SAS 9.4 (SAS Inst., Cary, NC) as a completely randomized design (n = 2). For both forages, full N had greater (P < 0.0139) yield when compared to the control (tall fescue: 8,295 vs. 7,353 kg/ha; bermudagrass: 9,329 vs. 8,109 kg/ha). There were no differences (P ≥ 0.05) in NDF concentration for either forage. Full and ½ N had greater CP than AMS in bermudagrass (11.5, 11.4%; P ≤ 0.0490). Blend 20 treated bermudagrass had greater ADF than full and ½ N (32.7, 30.7, and 30.9%, respectively; P < 0.0313). Blend 20 treated tall fescue had greater ADF compared to AMS + N and DH44 (35.8, 12.1, and 33.9%, respectively; P < 0.0227). Full N tall fescue had greater CP) compared to AMS (12.1, AMS CP %, respectively; P < 0.0082). DH44 had greater CP compared to AMS and Blend 20 (11.9, 11.0, and 11.0%, respectively; P < 0.0423). For both forages, PGPR treated plots produced yields and maintained forage nutritive value not different than the commercial fertilizer.

Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications

Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, a multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the representative multi-way models which were used for analyzing NIRS data, as well as the advances, advantages and limitations of different multi-way models. The applications of a multi-way analysis in NIRS for the food industry in terms of food process control, quality evaluation and fraud, identification and classification, prediction and quantification, and image analysis are also reviewed. It is evident from this report that a multi-way analysis is presently an attractive tool for modeling complex NIRS data in the food industry while its full potential is far from reached. The combination of a multi-way analysis with NIRS will be a promising practice for turning food data information into operational knowledge, conducting reliable food analyses and improving our understanding about food systems and food processes. To the best of our knowledge, this is the first paper that systematically reports the advances on models and applications of a multi-way analysis in NIRS for the food industry.

Using a Portable Near-infrared Spectroscopy Device to Estimate The Second Ventilatory Threshold

A breakpoint in a portable near-infrared spectroscopy (NIRS) derived deoxygenated haemoglobin (deoxy[Hb]) signal during an incremental VO2max running test has been associated with the second ventilatory threshold (VT2) in healthy participants. Thus, the aim was to examine the association between this breakpoint (NIRS) and VT2 in well-trained runners. Gas exchange and NIRS data were collected during an incremental VO2max running test for 10 well-trained runners. The breakpoint calculated in oxygen saturation (StO2) and the VT2 were determined and compared in terms relative to %VO2max, absolute speed, VO2, and maximum heart rate (HRmax). There were no significant differences (p>0.05) between the breakpoint in StO2 and VT2 relative to %VO2max (87.00±6.14 and 88.28 ± 3.98 %), absolute speed (15.70±1.42 and 16.10±1.66 km·h−1), VO2 (53.71±15.17 and 54.66±15.57 ml·kg−1·min−1), and%HRmax (90.90±4.17 and 91.84±3.70%). There were large and significant correlations between instruments relative to%VO2max (r=0.68, p<0.05), absolute speed (r=0.86, p<0.001), VO2 (r=0.86, p<0.001), and %HRmax (r=0.69; p<0.05). A Bland and Altman analysis of agreement between instruments resulted in a mean difference of − 1.27±4.49%, −0.40±0.84 km·h−1,−0.90±3.07 ml·kg−1·min−1, and − 0.94±3.14 for %VO2max, absolute speed, VO2, and %HRmax, respectively. We conclude that a portable NIRS determination of the StO2 breakpoint is comparable with VT2 using gas exchange and therefore appropriate for use in determining exercise training above VT2 intensity. This is the first study to analyze the validity with the running mode using a NIRS portable device.