Optimal Index Research Articles

Application Model of Hyperspectral Technology Based on Novel Spectral Indices for Salinity Assessment in Soil Heritage Sites

Abstract. The Dunhuang murals are a precious treasure of China's cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive , circumventing issues of high costs, low efficiency. Building on this, our study developed a high-spectral feature inversion model for mural phosphate content using Fractional Order Differentiation (FOD), a novel three-band spectral index, and Partial Least Squares Regression (PLSR) algorithm. The specific research contents include: 1) Exploring the absorption mechanism of phosphates and their characteristic bands, combined with the optimal spectral index to construct a univariate linear regression model, providing a basis for rapid quantitative measurement of mural phosphate content. 2) By comparing the accuracy of the PSR and PNDI spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study's findings include: 1) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (R2) of 0.728. 2) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural phosphate content with a determination coefficient (R2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

Changes in dietary habits of medical and dental students and its impact on their quality of life and body mass index

<p>Background and Objective: Dietary habits are one of the most important factors that play a significant role in maintaining optimal health and body mass index (BMI). The objective of this study was to investigate and determine how changes in dietary pattern of medical and dental students can impact their BMI and quality of life.</p> <p>Method: This cross-sectional study included 190 medical and dental students from a local private medical and dental college in Lahore, Pakistan. All participants filled out a validated online questionnaire having 30 questions divided into four sections. The responses were scaled in three tiers and the data was collected and analysed using statistical software.</p> <p>Results: A total of 32.6% of students rated their quality of life as good with respect to dietary habits while 81.6% rated physical environment as moderately healthy with very many difficulties in concentrating (19.5%) and poor physical energy levels (31.1%). A notable percentage (12.6) reported not to have enough financial stability to meet their dietary needs. Satisfaction levels regarding their own health status varied with mixed responses. Negative feelings were frequent (70.5%), and a majority noted changes in eating habits and observed an increase in weight relating to BMI.</p> <p>Conclusion: Most of the medical and dental students after enrolling into their respective colleges face adverse changes in dietary habits. Taking educational stress and managing one’s daily activities show a limited balance with resultantly lower quality of life and a higher BMI.</p>

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Scalable chlorination of polypropylene for structural modification and flame retardancy as coating materials of insulation cable

ABSTRACT Chemical precipitation route has been demonstrated for in-situ chlorination of polypropylene and structural modification to integrate flame retardancy along with facile processing parameters, i.e. optimum melt flow index and mechanical properties. Thus, obtained chlorinated polypropylene (Cl-PP) was characterized for optimized composition and degree of chlorination, chemical structure, morphology, physical properties with the help of infra-red spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscope (SEM), melt flow index and other standard methods. The analytical results revealed the 74% chlorination of polypropylene chain increased with the molecular weight, comparable melt flow index and improved flame retardancy from V2 to V0 grade on UL-94 scale due to reduced heat release rate and improved limiting oxygen index from 17.5 to 27.1 due to structural changes and induced polycrystallinity. The evolved features of PP were explained on the basis of synergistic interaction, evolved crystallinity, size reduction and functionality during chemical treatments and reprecipitations. Further, the obtained Cl-PP was explored as the coating material for insulation of cables with electric insulation and durable properties.

Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index

Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 21 to 27, show the highest correlation with winter wheat chlorophyll content at a scale of 26, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 26 and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 26 processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R2) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture.

Index-based forest degradation mapping using high and medium resolution multispectral sensors

ABSTRACT Monitoring dense forest ecosystems, such as the laurel forest in Garajonay National Park, is vital for biodiversity conservation, carbon storage, and ecological balance. This study employs satellite remote sensing technologies to introduce a novel methodology, based on vegetation indices, aiming to assess and protect the health of the forest. Utilizing the Jeffries-Matusita distance and a histogram-based method, optimal indices to map forest degradation, like Wide Dynamic Range Vegetation Index (WDRVI) and Modified Simple Ratio (MSR), were identified among 19 generated indices. The study processed imagery from three satellite sensors (WorldView-2, PlanetScope and Sentinel-2), producing maps distinguishing healthy and degraded areas. The study’s practical significance lies in offering a method to assess the suitability of sensors and indices for effectively mapping forest degradation. This approach aids conservation efforts and provides valuable insights for environmental managers and policymakers, facilitating the implementation of targeted strategies to safeguard Garajonay National Park's unique laurel forest ecosystem. Emphasizing the role of remote sensing in practical vegetation protection endeavors, the study contributes to on-the-ground initiatives, ensuring the preservation and sustainability of the park's rich biodiversity.

Rolling discrete grey periodic power model with interaction effect under dual processing and its application

Time series characterized by periodic fluctuations are widespread in reality behavioural systems, and accurate forecasting of them has important practical application value. In this paper, based on the idea of discrete grey model, dual interaction effects and the nonlinear characteristics of the system are considered under the perspective of dual processing, and the fluctuating trend of the periodic sequence is portrayed through the introduction of the periodic dummy variable, and at the same time, combined with the extended periodic rolling time window method, the rolling discrete grey periodic power model with the interaction effect under dual processing is proposed, namely DIE-RDGM(1,1,T)α model. Differential evolution algorithm (DE) are employed to determine the optimum power index. Finally, the new model was compared with nine other forecasting models by three instances and applied to the problem of agricultural drought forecasting in Zhoukou City and Anyang City, Henan Province. The results show: (1) The new model has demonstrated strong competitiveness, confirming its rationality, validity, and superiority. (2) The DE is suitable for power index optimization search. (3) The new model can better describe the periodic change rule of seasonal-scale soil moisture in the two cities, and the forecast results are consistent with the actual drought conditions.

Design of Selective PARP-1 Inhibitors and Antitumor Studies.

Designing selective PARP-1 inhibitors has become a new strategy for anticancer drug development. By sequence comparison of PARP-1 and PARP-2, we identified a possible selective site (S site) consisting of several different amino acid residues of α-5 helix and D-loop. Targeting this S site, 140 compounds were designed, synthesized, and characterized for their anticancer activities and mechanisms. Compound I16 showed the highest PARP-1 enzyme inhibitory activity (IC50 = 12.38 ± 1.33 nM) and optimal selectivity index over PARP-2 (SI = 155.74). Oral administration of I16 (25 mg/kg) showed high inhibition rates of Hela and SK-OV-3 tumor cell xenograft models, both of which were higher than those of the oral positive drug Olaparib (50 mg/kg). In addition, I16 has an excellent safety profile, without significant toxicity at high oral doses. These findings provide a novel design strategy and chemotype for the development of safe, efficient, and highly selective PARP-1 inhibitors.

External validation of three diabetes prediction scores in a Spanish cohort: does adding high risk for depression improve the validation of the FINDRISC score (FINDRISC-MOOD)?

ObjectivesTo evaluate the external validity of the FINDRISC, DESIR and ADA risk scores for the prediction of diabetes in a Spanish population aged >45 years and to test the possible...

Associations between time in range and insulin secretory capacity in Japanese patients with type 2 diabetes

Impaired insulin secretory capacity is associated with high glycemic variability in patients with type 2 diabetes (T2DM). However, there are no existing reports on the association between insulin secretory capacity and time in range (TIR). This retrospective study involved 330 T2DM admitted for diabetes education who underwent intermittently scanned continuous glucose monitoring (isCGM) and had their fasting serum C-peptide immunoreactivity (S-CPR) measured within 5 days of admission. The baseline characteristics were as follows: age, 60.2 years; glycated hemoglobin (HbA1c), 9.2%; S-CPR, 2.2 ng/mL; S-CPR index (S-CPR [ng/mL]/fasting plasma glucose [mg/dL] × 100), 1.6; and TIR, 60.3%. TIR correlated significantly with the S-CPR index, which was confirmed by multivariate analysis that included various factors such as HbA1c. Receiver operating characteristic (ROC) analysis showed that 1.88 was the optimal S-CPR index level to predict TIR ≥ 70%. In addition to HbA1c and biguanide use, the S-CPR index was a significant factor associated with TIR > 70%. S-CPR index values of ≥ 1.88 also correlated significantly with TIR > 70%. In conclusion, insulin secretory capacity is associated with TIR in Japanese T2DM, suggesting that the S-CPR index might be a potentially useful biomarker insulin secretory capacity, in association with TIR.Trial registration UMIN0000254333.

Linear System Identification-Oriented Optimal Tampering Attack Strategy and Implementation Based on Information Entropy with Multiple Binary Observations

With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker’s point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense’s discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective.

Optimisation and Modelling of Soil Pulverisation Index Using Response Surface Methodology for Disk Harrow Under Different Operational Conditions

Abstract The study aimed to determine the optimal pulverisation index of soil for disk harrow by modelling. A mathematical model was developed using a Design-Expert software and response surface methodology. Experiments were carried out in silty loamy soil with three different levels of soil moisture content of 9.25%, 17.56%, and 22.32%, operating depths of 10 cm, 15 cm, and 20 cm, and operating speeds of 3.17, 4.85, and 5.47 km·h-1. The quadratic model proposed by the Design-Expert software was statistically significant (P &lt;0.01), with a strong correlation relationship (R 2 = 0.989) between actual and predicted soil pulverisation index values. The adequacy precision achieved at 41.84 showed the models‘ ability to navigate the design space. However, statistical analysis, using the t-test and P-value, showed the actual and predicted values have no significant differences in the pulverisation index of soil. The optimal soil pulverisation index (8.61 mm) was achieved with a desirability of 1.00, at a soil moisture content of 14.43%, an operating depth of 11.64 cm, and a forward speed of 5.30 km·h-1. Model validation confirmed acceptability (R 2 = 0.974) and a 99% accuracy in predicting the soil pulverisation index.

Optimization Design of Semi-active Suspension for Vehicles Based on LQR Optimal Control

Abstract The suspension system is crucial for providing a comfortable ride and ensuring smooth driving. This article presents a random road surface model and a 1/4 vehicle two-degree-of-freedom semi-active suspension model. The optimal control performance index function was obtained by using the minimum principle, and the optimal controller for semi-active suspension was designed. Simulations were conducted in MATLAB/Simulink, using vehicle acceleration, suspension dynamic deflection, and tire dynamic deformation as evaluation indicators. The study found that the vehicle’s acceleration, suspension dynamic deflection, and tire dynamic deformation were reduced by 21.79%, 21.86%, and 9.88%, respectively, with the use of the designed semi-active suspension optimization controller. This resulted in improved driving smoothness and riding comfort.

Evaluation index system for carbon information disclosure quality in China's electric power sector based on a mutual information and back propagation neural network model

This paper employs the Mutual Information (MI) and Back Propagation (BP) neural network model to screen the preliminarily constructed evaluation index system for carbon information disclosure (CID) quality of public companies in China's electric power sector (EPS), which is subsequently incorporated into the fuzzy comprehensive evaluation (FCE) method for evaluation application. The results show that (1) after screening, 19 out of the 31 preliminarily constructed indicators constitute an optimal index set, and (2) the evaluation application of the screened index system validates the feasibility and applicability of the index system. Simultaneously, the evaluation results reveal a generally low CID quality in the EPS.

Optimal body mass index for protecting middle-aged and elderly patients with fatty liver from future fractures.

Previous studies have suggested that body mass index (BMI) should be considered when assessing the relationship between fatty liver (FL) and osteoporosis. The aim of this study was to investigate future fracture events in people with FL, focusing on the effect of BMI in both sexes. This retrospective cohort study from 2011 to 2019 enrolled 941 people, including 441 women and 500 men, aged 50 years or older who underwent liver imaging (ultrasound, computed tomography, or magnetic resonance image) and dual-energy X-ray absorptiometry (DXA, for bone mineral density measurements). The study examined predictors of osteoporosis in both sexes, and the effect of different ranges of BMI (18.5-24, 24-27, and ≥27 kg/m2 in women; 18.5-24, 24-27, 27-30 and ≥30 kg/m2 in men) on the risk of future fractures in FL patients. The average follow-up period was 5.3 years for women and 4.2 years for men. Multivariate analysis identified age and BMI as independent risk factors for osteoporosis in both sexes. Each unit increase in BMI decreased the risk of osteoporosis by ≥10%. In both women and men with FL, a BMI of 24-27 kg/m2 offered protection against future fractures, compared to those without FL and with a BMI of 18.5-24 kg/m2. The protective effect of a higher BMI against future fractures in middle-aged and elderly women and men with FL is not uniform and decreases beyond certain BMI ranges.

Predicting brain edema and outcomes after thrombectomy in stroke: Frontal delta/alpha ratio as an optimal quantitative EEG index

ObjectiveWe aimed to determine whether quantitative electroencephalography (QEEG) measures have predictive value for cerebral edema (CED) and clinical outcomes in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion who underwent mechanical thrombectomy (MT). MethodsA total of 105 patients with AIS in the anterior circulation were enrolled in this prospective study. The occurrence and severity of CED were assessed through computed tomography conducted 24 h after MT. Clinical outcomes were evaluated based on early neurological deterioration (END) and 3-month functional status, as measured by the modified Rankin scale (mRS). Electroencephalography (EEG) recordings were performed 24 h after MT, and QEEG indices were calculated from the standard 16 electrodes and 2 frontal channels (F3-C3, F4-C4). The delta/alpha ratio (DAR), the (delta + theta) / (alpha + beta) ratio (DTABR), and relative delta power were averaged over all electrodes (global) and the F3-C3 and F4-C4 channels (frontal). The predictive effect and value of QEEG indices for CED and clinical outcomes were assessed using ordinal and logistic regression models, as well as receiver operating characteristic (ROC) curves. ResultsSignificantly, both global and frontal DAR were found to be associated with the severity of CED, END, and poor functional outcomes at 90 days, while global and frontal DTABR and relative delta power were not associated with outcomes. In ROC analysis, the best predictive effect was observed in frontal DAR, with an area under the curve of approximately 0.80. It exhibited approximately 75% sensitivity and 71% specificity for radiological and clinical outcomes when a threshold of 3.3 was used. ConclusionsQEEG techniques may be considered an efficient bedside monitoring method for assessing treatment efficacy, identifying patients at higher risk of severe CED and END, and predicting long-term functional outcomes. SignificanceQEEG can help identify patients at risk of severe neurological complications that can impact long-term functional recovery in AIS patients who underwent MT.

Zero‐sum game‐based H∞ optimal finite‐time prescribed performance control for nonlinear multiagent systems with actuator faults

AbstractThis article proposes an optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error‐related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult‐to‐verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real‐time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed‐loop system are bounded and the expected finite‐time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single‐link robots manipulator system is executed to validate the efficacy of the developed scheme.

Delineating the uranium anomalous zones using remote sensing and radiometric data: a case study from Gabal Umm Tinassib area, North Eastern Desert, Egypt

AbstractRemote sensing (RS) and airborne gamma-ray spectrometric (AGS) methods are utilized to delineate significant uranium zones and altered mineralization areas in Gabal Umm Tinassib and its surrounding region, situated in the northern section of the Egyptian Eastern Desert. AGS serves as a valuable tool for mapping surface geology and conducting mineral exploration. It assesses the concentrations of radioactive elements such as potassium (K), equivalent uranium (eU), and equivalent thorium (eTh). The concentration of radioelements exhibits measurable and significant variation according to lithology. On the other hand, several processing steps are employed for the RS data to generate high-quality images for geological mapping and to identify the mineralized alteration zones. The analysis of RS and AGS data in this study led to insightful conclusions. The utilization of False Color Composite (FCC) with the three best bands derived from the Optimum Index Factor (OIF), Principal Component Analysis (PCA) to extract two highly informative datasets, and the application of two band ratios contributed to accurate geological mapping. These band ratios notably identified identical alteration locations on both younger and older granite basement rocks. Additionally, the constrained energy minimization (CEM) technique effectively pinpointed alterations across these strata. The statistical analysis of AGS data revealed that radioactivity levels in the region range from 1.3 to 19.3 Ur for the total-count (TC), 0.2–3.6% for K, 0.09–11.6 ppm for eU, and 1.1–30.0 ppm for eTh. The estimated coefficient of variability (CoV) demonstrated that the three radio-elements exhibited normal distribution patterns across different rock units, with CoV values of less than 100%, except for K in the Malha Formation. High radiometric readings are observed in the outcroppings of younger and older granites. However, the lowest readings are recorded over undifferentiated Upper-Cretaceous sediments, Abu Rimth Formation, Galala Formation, and some parts of Quaternary sediments. The derived ternary radio-elements map highlights significant radiometric and related uranium anomalous zones as bright white regions. A strong correlation was found between high radiometric anomalous zones and the presumed occurrence of alteration zones in the study area.

Hypoxic indices for obstructive sleep apnoea severity and cardiovascular disease risk prediction: A comparison and application in a community population.

The apnoea-hypopnoea index (AHI) and oxygen desaturation index (ODI) encounter challenges in capturing the intricate relationship between obstructive sleep apnoea (OSA) and cardiovascular disease (CVD) risks. Although novel hypoxic indices have been proposed to tackle these limitations, there remains a gap in comprehensive validation and comparisons across a unified dataset. Samples were derived from the Sleep Heart Health Study (SHHS), involving 4485 participants aged over 40 years after data quality screening. The study compared several key indices, including AHI, ODI, the reconstructed hypoxic burden (rHB), the percentage of sleep time with the duration of respiratory events causing desaturation (pRED_3p) and the sleep breathing impairment index (SBII), in relation to CVD mortality and morbidity risks. Adjusted Cox proportional models were employed to calculate hazard ratios (HRs) for each index, and comparisons were performed. SBII and pRED_3p exhibited significant correlations with both CVD mortality and morbidity, with SBII showing the highest adjusted HR (95% confidence interval) for mortality (2.04 [1.25, 3.34]) and pRED_3p for morbidity (1.43 [1.09-1.88]). In contrast, rHB was only significant in predicting CVD mortality (1.63 [1.05-2.53]), while AHI and ODI did not show significant correlations with CVD outcomes. The adjusted models based on SBII and pRED_3p exhibited optimal performance in the CVD mortality and morbidity datasets, respectively. This study identified the optimal indices for OSA-related CVD risks prediction, SBII for mortality and pRED_3p for morbidity. The open-source online platform provides the computation of the indices.

Dynamic responses of blood metabolites to nutrient depletion and repletion in broiler chicken nutrition

A total of 720 male Cobb 500 broiler chicks were used in a 5 treatment and 8 replicate experiment to explore dynamic changes in blood metabolites in response to short-term nutrient depletion and repletion. Day old chicks were offered a corn and soybean meal-based common starter diet from d1 to 14 that was formulated to meet all nutrient requirements of the birds. From d15 to 17, the experimental diets were offered, before returning all groups to a common diet from d18 to 20, at which point the experiment was terminated. A total of 5 experimental diets were designed. A standard grower diet served as a control and was offered to 1 of the 5 groups of chicks. The additional 4 experimental groups comprised diets that were low in digestible phosphorus (P), total calcium (Ca), crude protein and digestible amino acids (AA) or apparent metabolizable energy (AME). The common grower diet that was offered from d18-20 was designed to be nutritionally complete and was intended to explore dynamic response to nutrient repletion. Blood was drawn from 8 chicks per treatment at time 0 (immediately prior to introduction of the experimental diets) and then again 3, 6, 12, 24, and 48h after introduction of the nutrient depleted diets. Additionally, blood was drawn 3, 6, 12, 24, and 48h after the introduction of the nutritionally complete common grower diet. Chicks were not sampled more than once. Feed intake, body weight and feed conversion ratio (FCR) were assessed on d14, 17, and 20. Blood metabolites were analyzed using the iSTAT Alinity V handheld blood analyzer, the Vetscan VS2 Chemistry Analyzer and the iCheck Carotene Photometer. Live performance metrics were not affected by the short-term nutrient depletion and all chicks grew normally throughout the experiment. The diet with low digestible P generated a rapid temporary decrease in plasma P and an increase in plasma Ca, that were returned to baseline following the re-introduction of the common grower feed. Introduction of the diet with low total Ca resulted in a significant increase in plasma P, effects which were also mitigated during the nutrient repletion phase. Total plasma protein, albumin and uric acid (UA) were decreased, and plasma glucose increased, in the chicks that received the diet with low crude protein and digestible AA. There was a delayed increase in aspartate amino transaminase (AST) associated with the diets with low digestible P and low AME. These results demonstrate the capacity of blood biochemistry to adapt to quantitative and qualitative changes in nutrient intake. Point-of-care analysis of blood biomarkers offers nutritionists a valuable opportunity to calibrate nutritional matrices for common dietary ingredients, zootechnical feed additives and to optimize diet phase changes. It can be concluded that many blood biomarkers are plastic to changes in diet nutrient density and offer an objective index for optimization of nutritional programs for commercial broiler production.