Determination of free desmosine and isodesmosine as urinary biomarkers of lung disorder by ultra performance liquid chromatography-ion mobility-mass spectrometry

The elastin degradation products, desmosine (DES) and isodesmosine (IDES) are highly stable, cross-linking amino-acids that are unique to mature elastin. The excretion of DES/IDES in urine, in the free form and with associated peptide fragments, provides an indicator of lung damage in chronic obstructive pulmonary disease (COPD). A quantitative ion mobility-mass spectrometry (IM-MS) method has been developed for the analysis of free DES/IDES in urine with deuterated IDES as an internal standard. Resolution of DES/IDES isomers was achieved in less than five minutes using ultra performance liquid chromatography (UPLC) combined with ion pairing. The optimized UPLC-IM-MS method provided a linear dynamic range of 10-300 ng/mL and a limit of quantitation of 0.028 ng/mL for IDES and 0.03 ng/mL for DES (0.55 ng and 0.61 ng on column respectively). The method reproducibility (%RSD) was < 4% for DES and IDES. The UPLC-IM-MS method was applied to the analysis of urine samples obtained from healthy volunteers and COPD patients. The DES/IDES concentrations in healthy and COPD urine showed an increase in DES (79%) and IDES (74%) in the COPD samples, relative to healthy controls. The incorporation of an IM separation prior to m/z measurement by MS was shown to reduce non-target ion responses from the bio-fluid matrix

Effects of cultivar and nitrogen application rate on lint, seed, oil, and protein yields of field-grown cotton

Article describes how the objective of this study was to evaluate the lint, seed, oil, and protein yield responses of cultivars with different seed mass and composition to N application rates (0–168 kg N ha−1) for field-grown cotton. The authors conclude that variability in the distribution of photosynthates to fiber and seed as well as seed oil and protein composition can significantly alter trends in fiber, seed, and seed component yields in response to cultivar or N application rates for field-grown cotton

Estimating yield-contributing physiological parameters of cotton using UAV-based imagery

Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton

A study of a cafeteria system using digital simulation

Digitized by Kansas Correctional Industrie

A Review on Potential Plant-Based Water Stress Indicators for Vegetable Crops

Area under vegetable cultivation is expanding in arid and semi-arid regions of the world to meet the nutritional requirements of an ever-growing population. However, water scarcity in these areas is limiting vegetable productivity. New water-conserving irrigation management practices are being implemented in these areas. Under these irrigation management practices, crops are frequently exposed to some extent of water stress. Vegetables are highly sensitive to water stress. For the successful implementation of new irrigation practices in vegetable crops, it is of immense importance to determine the threshold water deficit level which will not have a detrimental effect on plant growth and yield. Along with this, plant response and adaptation mechanisms to new irrigation practices need to be understood for the successful implementation of new irrigation practices. To understand this, water stress indicators that are highly responsive to water stress; and that can help in early detection of water stress need to be identified for vegetable crops. Plant-based water stress indicators are quite effective in determining the water stress level in plants because they take into account the cumulative effect of water stress due to declining soil moisture status and increased evaporative demand of the atmosphere while determining the water stress level in plant. Water stress quantification using plant-based approaches involves direct measurements of several aspects of plant water status and indirect measurements of plant processes which are highly sensitive to water deficit. In this article, a number of plant-based water stress indicators were critically reviewed for (1) their efficacy to determine the level of water stress, (2) their potential to predict the yield of a crop as affected by different water-deficit levels and (3) their suitability for irrigation scheduling in vegetable crops

IMPACT OF UNSCHEDULED INTERCHANGE PRICING IN COMPETITIVE ELECTRICITY MARKET

This paper proposes an approach for analysis and determination of unscheduled interchange based on Frequency Based Tariff for competitive electricity market. In this paper, combined model consisting of the pool, bilateral and multilateral transaction of deregulated power sector has been considered [11]. In competitive electricity market, power schedule is decided 24 hours ahead of real market but it has been observed that some of the market participants are diverted from their commitments regarding either supply of scheduled generation or withdrawal of scheduled load from the pool. Due to these reasons, the power system deviates from the optimized solutions. So there is an urgent need for determination of unscheduled interchange revenue paid by such defaulter market participants. In this paper, frequency based technique has been applied for determination of unscheduled interchange revenue paid by such defaulter market participants. The frequency based technique has been applied to double auction modified IEEE-30 bus test system. The whole transmission system has been divided into three sections; prevailing frequency in these regions can be assumed due to unscheduled interchange occurring in the system. In this paper, Frequency based tariff has been proposed for implementation to generalized deregulated model consisting of the pool, bilateral and multilateral transaction