Relative growth rate (RGR) has a long history of use in biology. In its logged form, RGR = ln[(M + ΔM)/M], where M is size of the organism at the commencement of the study, and ΔM is new growth over time interval Δt. It illustrates the general problem of comparing non-independent (confounded) variables, e.g. (X + Y) vs. X. Thus, RGR depends on what starting M(X) is used even within the same growth phase. Equally, RGR lacks independence from its derived components, net assimilation rate (NAR) and leaf mass ratio (LMR), as RGR = NAR × LMR, so that they cannot legitimately be compared by standard regression or correlation analysis.
The mathematical properties of RGR exemplify the general problem of \'spurious\' correlations that compare expressions derived from various combinations of the same component terms X and Y. This is particularly acute when X >> Y, the variance of X or Y is large, or there is little range overlap of X and Y values among datasets being compared. Relationships (direction, curvilinearity) between such confounded variables are essentially predetermined and so should not be reported as if they are a finding of the study. Standardizing by M rather than time does not solve the problem. We propose the inherent growth rate (IGR), lnΔM/lnM, as a simple, robust alternative to RGR that is independent of M within the same growth phase.
Although the preferred alternative is to avoid the practice altogether, we discuss cases where comparing expressions with components in common may still have utility. These may provide insights if (1) the regression slope between pairs yields a new variable of biological interest, (2) the statistical significance of the relationship remains supported using suitable methods, such as our specially devised randomization test, or (3) multiple datasets are compared and found to be statistically different. Distinguishing true biological relationships from spurious ones, which arise from comparing non-independent expressions, is essential when dealing with derived variables associated with plant growth analyses.

Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people\'s health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019-2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from \"very good\" (MAPE of 8.64% at the Liwa station with the closed loop) to \"acceptable\" (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values.

The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums.

Introduction Diabetic foot infection is a condition that affects the patient\'s life, may cause limb loss, and has a high mortality. Too many parameters were used for predicting early mortality but the gold standard method wasn\'t described. Neutrophil lymphocyte ratio (NLR) is universally accepted as a predictive value for amputation-free survival and mortality. NLR increases due to inflammation-induced neutrophilia and lymphopenia related to cortisol-induced stress. Increasing in the neutrophil albumin ratio is expected due to decreasing albumin levels because albumin is a negative acute-phase reactant. The aim of this study is to investigate the sensitivity and value of the neutrophil albumin ratio (NAR) for early mortality after major lower extremity amputation (LEA). Methods  Following the approval of the ethics committee, 87 patients who underwent major LEA between May 2018 and May 2020 were analyzed for the study. White blood cell (WBC), neutrophil, lymphocyte, C-reactive protein (CRP), creatinine, albumin, platelet, and hemoglobin values on the day prior to surgery were recorded. NLR was calculated as the ratio of neutrophil count to lymphocyte count, NAR as the ratio of neutrophil count to albumin value, CRP/albumin ratio (CAR) as the ratio of CRP value to albumin value, and platelet lymphocyte ratio (PLR) as the ratio of platelet count to lymphocyte count. Each parameter was also recorded in the postoperative second week. Results Of the patients included in the study, 52 were men (59.8%) and 35 were women (40.2%). It was determined that 29 of 87 patients (33.3%) died within the first year. The relationship between post-operative NAR value and early mortality is examined. The area under the curve was calculated as 0.873. When the cut-off value was applied as 0.265, the sensitivity was found as 88% and specificity as 76%. Conclusions Higher neutrophil/albumin ratio after lower extremity amputation was associated with early mortality after extremity amputation. This parameter can help predict mortality. The cut-off value was determined as 0.265, the sensitivity was found as 88%, and specificity as 76%.

Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.
We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters.
The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.
Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

Pro-inflammatory mediators play an important role in the pathogenesis of pulmonary tuberculosis. Consecutively, 26 pulmonary tuberculosis patients were enrolled in our study based on the exclusion criteria. We have used Spearman\'s correlation analysis, hierarchical clustering and regression modelling to evaluate the association of 11 biomarkers with culture status after antituberculosis treatment. The results of our study demonstrated that six inflammatory biomarkers of 11, C-reactive protein (CRP), white blood cells (WBC), neutrophils, interferon gamma inducible protein 10, C-reactive protein (CRP) to albumin ratio (CAR) and neutrophil to albumin ratio (NAR), were significantly associated with culture negativity. The predictive ability of a composite model of seven biomarkers was superior to that of any single biomarker based on area under the receiver operating characteristic curve (AUC) analysis, indicating an excellent prediction efficacy (AUC:0.892; 95% CI:0.732-1.0). We also found that the highest significant trends and lower levels of CRP and IP-10 were observed in the two-month treated tuberculosis (TB) patients. We believe that our study may be valuable in providing preliminary results for an additional strategy in monitoring and management of the clinical outcome of pulmonary tuberculosis. Using a panel of predictors added a superior value in predicting culture status after anti-TB therapy.

Social media platform like Twitter is one of the primary sources for sharing real-time information at the time of events such as disasters, political events, etc. Detecting the resource tweets during a disaster is an essential task because tweets contain different types of information such as infrastructure damage, resources, opinions and sympathies of disaster events, etc. Tweets are posted related to Need and Availability of Resources (NAR) by humanitarian organizations and victims. Hence, reliable methodologies are required for detecting the NAR tweets during a disaster. The existing works don\'t focus well on NAR tweets detection and also had poor performance. Hence, this paper focus on detection of NAR tweets during a disaster. Existing works often use features and appropriate machine learning algorithms on several Natural Language Processing (NLP) tasks. Recently, there is a wide use of Convolutional Neural Networks (CNN) in text classification problems. However, it requires a large amount of manual labeled data. There is no such large labeled data is available for NAR tweets during a disaster. To overcome this problem, stacking of Convolutional Neural Networks with traditional feature based classifiers is proposed for detecting the NAR tweets. In our approach, we propose several informative features such as aid, need, food, packets, earthquake, etc. are used in the classifier and CNN. The learned features (output of CNN and classifier with informative features) are utilized in another classifier (meta-classifier) for detection of NAR tweets. The classifiers such as SVM, KNN, Decision tree, and Naive Bayes are used in the proposed model. From the experiments, we found that the usage of KNN (base classifier) and SVM (meta classifier) with the combination of CNN in the proposed model outperform the other algorithms. This paper uses 2015 and 2016 Nepal and Italy earthquake datasets for experimentation. The experimental results proved that the proposed model achieves the best accuracy compared to baseline methods.

In patients with locally advanced rectal cancer, restaging pelvis magnetic resonance imaging (MRI) after neoadjuvant concurrent chemoradiotherapy is recommended despite its limited accuracy in predicting pathologic T (ypT) and N (ypN) stage. Neoadjuvant rectal (NAR) score is a novel short-term surrogate endpoint for disease-free survival (DFS) and overall survival (OS). We tested the agreement between restaging MRI T (yT) and N (yN) with ypT and ypN stages, respectively, and explored the prognostic significance of restaging MRI NAR (mNAR) score.
Between 2014 and 2018, 43 patients with locally advanced rectal cancer completed neoadjuvant concurrent chemoradiotherapy, had a restaging MRI, and underwent surgery. Weighted kappa was used to test the agreement between yT and yN with ypT and ypN, respectively. A kappa value of less than 0.5 was deemed unacceptable. Paired t test was used to compare NAR and mNAR mean scores. Survival was estimated by Kaplan-Meier curves.
Restaging MRI could not predict ypT stage (slight agreement, κ = 0.111) or ypN stage (fair agreement, κ = 0.278). The mean mNAR score was higher than the mean NAR score (20 vs. 16, P = .0079). The median DFS for patients with low-intermediate NAR and high NAR was not reached vs. 30 months (P = .0063). The median OS for patients with low-intermediate NAR and high NAR was not reached vs. 40 months (P = .0056). There was a trend for longer DFS and OS in patients with low-intermediate mNAR scores (not reached in both groups, P = .058) compared to patients with high mNAR scores (not reached in both groups, P = .15).
Restaging MRI could not predict ypT and ypN stage. The mean mNAR score was higher than the mean NAR score. There was a trend for longer DFS and OS in patients with low-intermediate mNAR scores compared to patients with high mNAR scores.

To discover the association between eating alone and diet quality among Korean adults who eat alone measured by the mean adequacy ratio (MAR), METHODS: The cross-sectional study in diet quality which was measured by nutrient intakes, indicated as MAR and nutrient adequacy ratio (NAR) with the Korean National Health and Nutrition Examination Survey (KNHANES) VI 2013-2015 data. Study population was 8523 Korean adults. Multiple linear regression was performed to identify the association between eating behaviour and MAR and further study analysed how socioeconomic factors influence the diet quality of those who eat alone.
We found that the diet quality of people who eat alone was lower than that of people who eat together in both male (β: - 0.110, p = 0.002) and female participants (β: - 0.069, p = 0.005). Among who eats alone, the socioeconomic factors that negatively influenced MAR with the living arrangement, education level, income levels, and various occupation classifications.
People who eat alone have nutrition intake below the recommended amount. This could lead to serious health problems not only to those who are socially disadvantaged but also those who are in a higher social stratum. Policy-makers should develop strategies to enhance diet quality to prevent potential risk factors.

Nitrate respiration is a widespread mode of anaerobic energy generation used by many bacterial pathogens, and the respiratory nitrate reductase, Nar, has long been known to reduce chlorate to the toxic oxidizing agent chlorite. Here, we demonstrate the antibacterial activity of chlorate against Pseudomonas aeruginosa, a representative pathogen that can inhabit hypoxic or anoxic host microenvironments during infection. Aerobically grown P. aeruginosa cells are tobramycin sensitive but chlorate tolerant. In the absence of oxygen or an alternative electron acceptor, cells are tobramycin tolerant but chlorate sensitive via Nar-dependent reduction. The fact that chlorite, the product of chlorate reduction, is not detected in culture supernatants suggests that it may react rapidly and be retained intracellularly. Tobramycin and chlorate target distinct populations within metabolically stratified aggregate biofilms; tobramycin kills cells on the oxic periphery, whereas chlorate kills hypoxic and anoxic cells in the interior. In a matrix populated by multiple aggregates, tobramycin-mediated death of surface aggregates enables deeper oxygen penetration into the matrix, benefiting select aggregate populations by increasing survival and removing chlorate sensitivity. Finally, lasR mutants, which commonly arise in P. aeruginosa infections and are known to withstand conventional antibiotic treatment, are hypersensitive to chlorate. A lasR mutant shows a propensity to respire nitrate and reduce chlorate more rapidly than the wild type does, consistent with its heightened chlorate sensitivity. These findings illustrate chlorate\'s potential to selectively target oxidant-starved pathogens, including physiological states and genotypes of P. aeruginosa that represent antibiotic-tolerant populations during infections.IMPORTANCE The anaerobic growth and survival of bacteria are often correlated with physiological tolerance to conventional antibiotics, motivating the development of novel strategies targeting pathogens in anoxic environments. A key challenge is to identify drug targets that are specific to this metabolic state. Chlorate is a nontoxic compound that can be reduced to toxic chlorite by a widespread enzyme of anaerobic metabolism. We tested the antibacterial properties of chlorate against Pseudomonas aeruginosa, a pathogen that can inhabit hypoxic or anoxic microenvironments, including those that arise in human infection. Chlorate and the antibiotic tobramycin kill distinct metabolic populations in P. aeruginosa biofilms, where chlorate targets anaerobic cells that tolerate tobramycin. Chlorate is particularly effective against P. aeruginosalasR mutants, which are frequently isolated from human infections and more resistant to some antibiotics. This work suggests that chlorate may hold potential as an anaerobic prodrug.