User's guide to correlation coefficients

When writing a manuscript, we often use words such as perfect, strong, good or weak to name the strength of the relationship between variables. However, it is unclear where a good relationship turns into a strong one. The same strength of r is named differently by several researchers. Therefore, there is an absolute necessity to explicitly report the strength and direction of r while reporting correlation coefficients in manuscripts. This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients.

The relationship between health literacy and quality of life: a systematic review and meta-analysis

Background

Low health literacy often has an association with poor health outcomes such as low levels of self-efficacy, increased mortality, poor health status and reduced quality of life (QOL). The aim of the study was to quantitatively evaluate the relationship between health literacy (HL) and QOL based on a systematic review and meta-analysis.

Methods

EMBASE, PubMed, Web of Science, Elsevier, Cochrane Library, and Chinese electronic databases such as CNKI, and Wanfang were searched from 1970 until February 1, 2018. The pooled correlation coefficient (PCOR) and its 95% confidence interval (CI) between HL and QOL were estimated using R software. Potential sources of heterogeneity were explored using subgroup analysis, sensitivity analysis, and meta-regression.

Results

Twenty-three studies, with a total of 12,303 subjects,were included. The PCOR between HL and QOL was 0.35 (95%CI: 0.25–0.44). Considering different dimensions of HL, the PCOR between QOL and health knowledge, health behavior, health belief, and health skill were 0.36 (95% CI: 0.04–0.61), 0.36 (95%CI: 0.13–0.55), 0.39 (95%CI: 0.10–0.62), and 0.42 (95%CI: 0.03–0.69), respectively. The PCOR between HL and the two dimensions of QOL was lower than the total PCOR between HL and QOL. In subgroup analysis, the PCOR between HL and QOL was 0.46 (95%CI: 0.13, 0.69) among community residents, 0.45 (95%CI: 0.27, 0.61) in China, and 0.45 (95%CI: 0.24, 0.62) based on cohort studies. Sensitivity analyses showed that the stability of results had no significant after excluding the study (p < 0.001). Meta-regression showed that cohort study design, studies conducted in China, and publication before 2012 may be important influencing factors.

Conclusions

Health literacy was moderately correlated with quality of life, but this finding needs to be supported by more evidence.

Electronic supplementary material

The online version of this article (10.1186/s12955-018-1031-7) contains supplementary material, which is available to authorized users.

Burnout and depression in nurses: A systematic review and meta-analysis

BACKGROUND

Nurses work in stressful and demanding settings and often suffer from depression and burnout. Despite overlapping symptoms, research has been inconclusive regarding the discriminant validity of measures of burnout with regard to measures of depression. Such inconclusive discriminant validity might cause clinicians to fail to recognize and manage depression separately from burnout.

OBJECTIVES

This meta-analysis aimed to clarify the distinctiveness of burnout as a separate construct by examining the size of the relationship between burnout and depression among nurses as well as potential moderators.

METHOD

A stepwise method was used by searching 4 databases (PubMed, CINAHL, PsycINFO, and EMBASE) to retrieve published papers in English examining the relationship between burnout and depression among nurses and reporting the effect sizes of their findings.

RESULTS

We identified a total of 37 eligible studies. The pooled estimate showed a positive association between burnout and depression among nurses (r = 0.403, 95% CI [0.327, 0.474], p < 0.0001) and a slightly higher correlation coefficient for the Emotional Exhaustion subscale of the Maslach Burnout Inventory (MBI) measure (0.494, 95% CI [0.41, 0.57]).

CONCLUSIONS AND IMPLICATIONS

This review confirms a large burnout - depression correlation in nursing samples, adding to existing literature encompassing a variety of occupations. Future studies should focus on path analysis to assess the causal relationship as well as investigate potential moderators.

THM and HAA formation from NOM in raw and treated surface waters

The disinfection by-product (DBP) formation potential (FP) of natural organic matter (NOM) in surface water sources has been studied with reference to the key water quality determinants (WQDs) of UV absorption (UV₂₅₄), colour, and dissolved organic carbon (DOC) concentration. The data set used encompassed raw and treated water sampled over a 30-month period from 30 water treatment works (WTWs) across Scotland, all employing conventional clarification. Both trihalomethane (THM) and haloacetic acid (HAA) FPs were considered. In addition to the standard bulk WQDs, the DOC content was fractionated and analysed for the hydrophobic (HPO) and hydrophilic (HPI) fractions. Results were quantified in terms of the yield (dDBPFP/dWQD) and the linear regression coefficient R² of the yield trend. The NOM in the raw waters was found to comprise 30-84% (average 66%) of the more reactive HPO material, with this proportion falling to 18-63% (average 50%) in the treated water. Results suggested UV₂₅₄ to be as good an indicator of DBPFP as DOC or HPO for the raw waters, with R² values ranging from 0.79 to 0.82 for THMs and from 0.71 to 0.73 for HAAs for these three determinants. For treated waters the corresponding values were significantly lower at 0.52-0.67 and 0.46-0.47 respectively, reflecting the lower HPO concentration and thus UV₂₅₄ absorption and commensurately reduced precision due to the limit of detection of the analytical instrument. It is concluded that fractionation offers little benefit in attempting to discern or predict chlorinated carbonaceous DBP yield for the waters across the geographical region studied. UV₂₅₄ offered an adequate estimate of DBPFP based on a mean yield of ∼2600 and ∼2800 μg per cm^-1 absorbance for THMFP for the raw and treated waters respectively and ∼3800 and2900 μg cm^-1 for HAAFP, albeit with reduced precision for the treated waters.

FTIR spectroscopy supported by statistical techniques for the structural characterization of plastic debris in the marine environment: Application to monitoring studies

We inserted 190 FTIR spectra of plastic samples in a digital database and submitted it to Independent Component Analysis (ICA) to extract the "pure" plastic polymers present. These identified plastics were polypropylene (PP), high density polyethylene (HDPE), low density polyethylene (LDPE), high density polyethylene terephthalate (HDPET), low density polyethylene terephthalate (LDPET), polystyrene (PS), Nylon (NL), polyethylene oxide (OPE), and Teflon (TEF) and they were used to establish the similarity with unknown plastics using the correlation coefficient (r), and the crosscorrelation function (CC). For samples with r<0.8 we determined the Mahalanobis Distance (MD) as additional tool of identification. For instance, for the four plastic fragments found in the Carretta carretta, one plastic sample was assigned to OPE due to its r=0.87; for all the other three plastic samples, due to the r values ranging between 0.83 and0.70, the support of MD suggested LDPET and OPE as co-polymer constituents.

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

INTRODUCTION AND AIMS

The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India.

MATERIAL AND METHODS

A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases.

RESULTS

Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model R²_{tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way.} CONCLUSION: These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).

Association between health literacy and mortality: a systematic review and meta-analysis

BACKGROUND

To identify the relationship between health literacy (HL) and mortality based on a systematic review and meta-analysis.

METHODS

Literature published from database inception until July 2020 was searched using the PubMed and Web of Science databases, using relevant keywords and clear inclusion and exclusion criteria. The search was limited to English language articles. Two reviewers independently selected studies and extracted data. Pooled correlation coefficients and their 95% confidence intervals (CI) between HL and mortality were estimated using Stata 15.0 software. Potential sources of heterogeneity were explored using subgroup analysis, sensitivity analysis, and meta-regression. Quality of the original studies that were included in the meta-analysis was evaluated using the Newcastle-Ottawa Scale. A funnel plot and Egger's test were used to determine whether significant publication bias was present.

RESULTS

Overall, 19 articles were included, reporting on a total of 41,149 subjects. Eleven were prospective cohort studies, and all articles were considered "good" quality. The most used screening instruments were the short Test of Functional Health Literacy (S-TOFHLA) in Adults and the Brief Health Literacy Screen (BHLS). Among 39,423 subjects (two articles did not report the number of patients with low HL), approximately 9202 (23%) had inadequate or marginal HL. The correlation coefficient between HL and mortality was 1.25 (95%CI = 0.25-0.44).

CONCLUSION

Lower HL was associated with an increased risk of death. This finding should be considered carefully and confirmed by further research.

Performance of spleen stiffness measurement in prediction of clinical significant portal hypertension: A meta-analysis

AIM

Our purpose was to evaluate the correlation between spleen stiffness (SS) measured by ultrasound-based elastography and hepatic venous pressure gradient (HVPG) and assess the accuracy of SS in detecting clinical significant portal hypertension (CSPH) and severe portal hypertension.

METHOD

Nine studies were included from thorough literature research and selection processes. A random model was used to analyze the correlation between HVPG and SS. We adopted the bivariate mixed effects model to assess the diagnostic performance.

RESULTS

Regarding to correlation between SS and HVPG, the summary correlation coefficient was 0.72 (95% confidence interval [CI], 0.63-0.80). In detection of CSPH, the sensitivity, specificity, AUC and DOR were: 0.88 (0.70-0.96), 0.84 (0.72-0.92), 0.92 (0.89-0.94) and 38 (17-84) for CSPH, respectively; and 0.92 (0.82-0.96), 0.79 (0.72-0.85), 0.87 (0.84-0.90) and 41 (17-100) for severe portal hypertension, respectively.

CONCLUSION

Correlation between SS and HVPG was good. Although SS showed good sensitivity and specificity, the different cut-off values and techniques among studies might limit the impact of our results on clinical practice. Therefore, more high-quality prospective studies are required to evaluate the role of SS in predicting portal hypertension.

Phenotyping for drought resistance in bread wheat using physiological and biochemical traits

Drought is one of the most prominent limiting factors that negatively affect crop productivity by manipulating its physiological pathway. One hundred twenty diverse bread wheat genotypes were used in a pot experiment to explore the relationship among their fifteen physio-biochemical traits (PBT) by using multivariate analysis, heatmapping and stress tolerance index (STI) for grain yield as a marker trait to identify high yielding genotype with maximum stress tolerance capability. Increased proline and sugar accumulation were observed from control to moisture deficient environments by 159% and 122%, respectively. Moreover, leaf membrane stability index (LMSI), leaf relative water content (LRWC), relative dry weight (RDW), chlorophyll content, leaf surface area (LSA), Leaf succulence (LS), canopy temperature depression (CTD), relative excised leaf water loss (RELWL) and leaf osmotic potential (LOP) showed significantly decreasing trend in drought stress treatment as compared to well-watered plants by -21%, -21%, -34%, -22%, -38%, -37%, -46%, -18% and -35% respectively. Additionally, principal component analysis and genotype by trait biplot analysis showed that initial 7 principal components (PC1 to PC7) represented 77.27% and 79.02% of total cumulative variation under control and drought stress respectively. Genotypic-Phenotypic correlation revealed that most of the attributes were higher in case of genotypic correlation component (rg) as compared to the phenotypic correlation component (rp) indicating more genetic association between traits. The darker and lighter colour scale produced by heatmap exhibited contrasting nature of genotypes, as positive side with higher values represented drought resistance while values on the negative side with lower values showed susceptible performance of genotypes. Our results concluded that the studied PBT associated with STI for grain yield are the main factors which may contribute in improved productivity of wheat crop and if these traits show appropriate performance under stress condition the crop will show the more productive returns under changing climate.

Estimation of length-weight relationship and condition factor of spotted snakehead Channa punctata (Bloch) under different feeding regimes

Comparative study was conducted to observe the efficacy of different feeding regimes on growth of Channa punctata. Six iso- proteinous diets were prepared by using different agro industrial by-products. Maximum weight gain was recorded with diet having 66.75% rice bran, 11.50% mustard cake, 23.0% groundnut cake, 5% molasses, 1.5% vitamin-mineral mixture and 0.5% salt with specific growth rate of 0.408. The experimental fish recorded the value of exponent ‘b’ in the range of 2.7675 to 4.3922. The condition factor ‘K’ of all experimental fish was above 1.0 (1.094- 1.235) indicating robustness or well being of experimented fish.

Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine

Accurate estimation of soil organic matter (SOM) is essential in understanding the spatial distribution of SOM to identify areas that need fertilization and the required grade of those fertilizers. Visible and near-infrared spectroscopy is a promising alternative to time consuming and costly conventional soil assessment methods. However, this approach is highly dependent on selecting suitable preprocessing strategies and data mining techniques for regression analysis. In this study, 2D correlation coefficients, including ratio, difference, and normalized difference indices, were introduced to select sensitive spectral parameters. The performance of extreme learning machine (ELM) was evaluated via comparison with that of support vector machine (SVM) for SOM estimation. A total of 257 soil samples were collected from Hubei Province, Central China, with SOM contents and reflectance spectra measured in the laboratory. Five spectral pretreatments, except for the raw spectra, were applied. SVM and ELM models were calibrated on spectral parameters selected by one-dimensional and 2D correlation coefficients and subsequently applied to predict SOM. Results showed that 2D correlation coefficient can effectively highlight the detailed SOM information compared with that of one-dimensional correlation coefficient. The ELM models yielded superior predictability relative to SVM models in all eight established models. The most excellent estimation accuracy was obtained by 2D ratio index and ELM (TRI-ELM) method, with an independent validation R² and a ratio of performance to interquartile range of 0.83 and 3.49, respectively. The SOM fertility levels of predicted SOM showed that TRI-ELM method presented the largest similarity to laboratory-measured SOM levels, and misclassified samples were all concentrated within one error level. In summary, our study indicates that the TRI-ELM model is a rapid, inexpensive, and relatively accurate method for identifying SOM fertility level.

Transcutaneous bilirubinometry is not influenced by term or skin color in neonates

BACKGROUND

The utility of transcutaneous bilirubin measurements (TcB) in screening for hyperbilirubinemia in preterm infants (<34 weeks) and in non-white infants remains a matter of debate.

AIM

To evaluate accuracy of TcB in preterm and term infants of different ethnic backgrounds, using a second generation bilirubinometer.

STUDY DESIGN

The Draeger JM-103® device was used to measure TcB. Eighty five measurements of TcB and total serum bilirubin (TSB) were retrospectively compared. Neonates were stratified into groups according to gestational age: <34 weeks (group 1, n=44) and >34 weeks (group 2, n=41), and according to ethnic origin: Caucasians (group A, n=49) and non-Caucasians (group B, n=36). Statistical analysis, using Pearson's correlation coefficient (r) and Bland-Altman analysis were performed to evaluate correlation and agreement between TSB and TcB values. Multiple linear regression was used to control for confounders for TSB values.

RESULTS

Correlation between TSB and TcB was high. Pearson's correlation coefficients were over 0.9 in all groups (0.910, 0.908, 0.916 and 0.934, p<0.0001 in groups 1, 2, A, and B respectively). Bland-Altman plots showed acceptable and close limits of agreements (56.8/-57.7, 54.2/-67.2, 57.7/-55.8, and 51.3/-69.9μmol/L in groups 1, 2, A and B respectively) with a trend for TcB to overestimate TSB in groups 2 and B. Birth term and skin color were not identified as confounding factors for predicting TSB in multiple linear regression.

CONCLUSIONS

TcB measurements using the Draeger JM-103® device correlate significantly with TSB, regardless of term and skin color. Transcutaneous bilirubinometry seems to be a safe and cost-effective screening method for severe hyperbilirubinemia in newborns of different terms and ethnic origins.

Cryo_fit: Democratization of flexible fitting for cryo-EM

Cryo-electron microscopy (cryo-EM) is becoming a method of choice for describing native conformations of biomolecular complexes at high resolution. The rapid growth of cryo-EM in recent years has created a high demand for automated solutions, both in hardware and software. Flexible fitting of atomic models to three-dimensional (3D) cryo-EM reconstructions by molecular dynamics (MD) simulation is a popular technique but often requires technical expertise in computer simulation. This work introduces cryo_fit, a package for the automatic flexible fitting of atomic models in cryo-EM maps using MD simulation. The package is integrated with the Phenix software suite. The module was designed to automate the multiple steps of MD simulation in a reproducible manner, as well as facilitate refinement and validation through Phenix. Through the use of cryo_fit, scientists with little experience in MD simulation can produce high quality atomic models automatically and better exploit the potential of cryo-EM.

Synchronization study in ring-like and grid-like neuronal networks

In this paper, we study the synchronization status of both two gap-junction coupled neurons and neuronal network with two different network connectivity patterns. One of the network connectivity patterns is a ring-like neuronal network, which only considers nearest-neighbor neurons. The other is a grid-like neuronal network, with all nearest neighbor couplings. We show that by varying some key parameters, such as the coupling strength and the external current injection, the neuronal network will exhibit various patterns of firing synchronization. Different types of firing synchronization are diagnosed by means of a mean field potential, a bifurcation diagram, a correlation coefficient and the ISI-distance method. Numerical simulations demonstrate that the synchronization status of multiple neurons is much dependent on the network patters, when the number of neurons is the same. It is also demonstrated that the synchronization status of two coupled neurons is similar with the grid-like neuronal network, but differs radically from that of the ring-like neuronal network. These results may be instructive in understanding synchronization transitions in neuronal systems.

Chemical profiling and adulteration screening of Aquilariae Lignum Resinatum by Fourier transform infrared (FT-IR) spectroscopy and two-dimensional correlation infrared (2D-IR) spectroscopy

As a kind of expensive perfume and valuable herb, Aquilariae Lignum Resinatum (ALR) is often adulterated for economic motivations. In this research, Fourier transform infrared (FT-IR) spectroscopy is employed to establish a simple and quick method for the adulteration screening of ALR. First, the principal chemical constituents of ALR are characterized by FT-IR spectroscopy at room temperature and two-dimensional correlation infrared (2D-IR) spectroscopy with thermal perturbation. Besides the common cellulose and lignin compounds, a certain amount of resin is the characteristic constituent of ALR. Synchronous and asynchronous 2D-IR spectra indicate that the resin (an unstable secondary metabolite) is more sensitive than cellulose and lignin (stable structural constituents) to the thermal perturbation. Using a certified ALR sample as the reference, the infrared spectral correlation threshold is determined by 30 authentic samples and 6 adulterated samples. The spectral correlation coefficient of an authentic ALR sample to the standard reference should be not less than 0.9886 (p=0.01). Three commercial adulterated ALR samples are identified by the correlation threshold. Further interpretation of the infrared spectra of the adulterated samples indicates the common adulterating methods - counterfeiting with other kind of wood, adding ingredient such as sand to increase the weight, and adding the cheap resin such as rosin to increase the content of resin compounds. Results of this research prove that FT-IR spectroscopy can be used as a simple and accurate quality control method of ALR.

Genetic diversity and structure of tea plant in Qinba area in China by three types of molecular markers

Background

Qinba area has a long history of tea planting and is a northernmost region in China where Camellia sinensis L. is grown. In order to provide basic data for selection and optimization of molecular markers of tea plants. 118 markers, including 40 EST-SSR, 40 SRAP and 38 SCoT markers were used to evaluate the genetic diversity of 50 tea plant (Camellia sinensis.) samples collected from Qinb. tea germplasm, assess population structure.

Results

In this study, a total of 414 alleles were obtained using 38 pairs of SCoT primers, with an average of 10.89 alleles per primer. The percentage of polymorphic bands (PPB), polymorphism information content (PIC), resolving power (Rp), effective multiplex ratio (EMR), average band informativeness (Ib_av), and marker index (MI) were 96.14%, 0.79, 6.71, 10.47, 0.58, and 6.07 respectively. 338 alleles were amplified via 40 pairs of SRAP (8.45 per primer), with PPB, PIC, Rp, EMR, Ib_av, and MI values of 89.35%, 0.77, 5.11, 7.55, 0.61, and 4.61, respectively. Furthermore, 320 alleles have been detected using 40 EST-SSR primers (8.00 per primer), with PPB, PIC, Rp, EMR, Ib_av, and MI values of 94.06%, 0.85, 4.48, 7.53, 0.56, and 4.22 respectively. These results indicated that SCoT markers had higher efficiency.

Mantel test was used to analyze the genetic distance matrix generated by EST-SSRs, SRAPs and SCoTs. The results showed that the correlation between the genetic distance matrix based on EST-SSR and that based on SRAP was very small (r = 0.01), followed by SCoT and SRAP (r = 0.17), then by SCoT and EST-SSR (r = 0.19).

The 50 tea samples were divided into two sub-populations using STRUCTURE, Neighbor-joining (NJ) method and principal component analyses (PCA). The results produced by STRUCTURE were completely consistent with the PCA analysis. Furthermore, there is no obvious relationship between the results produced using sub-populational and geographical data.

Conclusion

Among the three types of markers, SCoT markers has many advantages in terms of NPB, PPB, Rp, EMR, and MI. Nevertheless, the values of PIC showed different trends, with the highest values generated with EST-SSR, followed by SCoT and SRAP. The average band informativeness showed similar trends. Correlation between genetic distances produced by three different molecular markers were very small, thus it is not recommended to use a single marker to evaluate genetic diversity and population structure. It is hence suggested that combining of different types of molecular markers should be used to evaluate the genetic diversity and population structure. It also seems crucial to screen out, for each type of molecular markers, core markers of Camellia sinensis. This study revealed that genes of exotic plant varieties have been constantly integrated into the gene pool of Qinba area tea. A low level of genetic diversity was observed; this is shown by an average coefficient of genetic similarity of 0.74.

El Niño Southern Oscillation as an early warning tool for malaria outbreaks in India

BACKGROUND

Risks of malaria epidemics in relation to El Niño and Southern Oscillation (ENSO) events have been mapped and studied at global level. In India, where malaria is a major public health problem, no such effort has been undertaken that inter-relates El Niño, Indian Summer Monsoon Rainfall (ISMR) and malaria. The present study has been undertaken to find out the relationship between ENSO events, ISMR and intra-annual variability in malaria cases in India, which in turn could help mitigate the malaria outbreaks.

METHODS

Correlation coefficients among 'rainfall index' (ISMR), '+ winter ONI' (NDJF) and 'malaria case index' were calculated using annual state-level data for the last 22 years. The 'malaria case index' representing 'relative change from mean' was correlated to the 4 month (November-February) average positive Oceanic Niño Index (ONI). The resultant correlations between '+ winter ONI' and 'malaria case index' were further analysed on geographical information system platform to generate spatial correlation map.

RESULTS

The correlation between '+ winter ONI' and 'rainfall index' shows that there is great disparity in effect of ENSO over ISMR distribution across the country. Correlation between 'rainfall index' and 'malaria case index' shows that malaria transmission in all geographical regions of India are not equally affected by the ISMR deficit or excess. Correlation between '+ winter ONI' and 'malaria case index' was found ranging from -0.5 to + 0.7 (p < 0.05). A positive correlation indicates that increase in El Niño intensity (+ winter ONI) will lead to rise in total malaria cases in the concurrent year in the states of Orissa, Chhattisgarh, Jharkhand, Bihar, Goa, eastern parts of Madhya Pradesh, part of Andhra Pradesh, Uttarakhand and Meghalaya. Whereas, negative correlations were found in the states of Rajasthan, Haryana, Gujarat, part of Tamil Nadu, Manipur, Mizoram and Sikkim indicating the likelihood of outbreaks in La Nina condition.

CONCLUSIONS

The generated map, representing spatial correlation between ' + winter ONI' and 'malaria case index', indicates positive correlations in eastern part, while negative correlations in western part of India. This study provides plausible guidelines to national programme for planning intervention measures in view of ENSO events. For better resolution, district level study with inclusion of IOD and 'epochal variation of monsoon rainfall' factors at micro-level is desired for better forecast of malaria outbreaks in the regions with 'no correlation'.

Mediative fuzzy logic mathematical model: A contradictory management prediction in COVID-19 pandemic

This paper presents a model based on mediative fuzzy logic in this COVID-19 pandemic. COVID-19 (novel coronavirus respiratory disease) has become a pandemic now and the whole world has been affected by this disease. Different methodologies and many prediction techniques based on various models have been developed so far. In the present article, we have developed a mediative fuzzy correlation technique based on the parameters for COVID-19 patients from different parts of India. The proposed mediative fuzzy correlation technique provides the relation between the increments of COVID-19 positive patients in terms of the passage of increment with respect to time. The peaks of infected cases in connection with the other condition are estimated from the available data. The mediative fuzzy logic mathematical model can be utilized to find a good fit or a contradictory model for any pandemic model. The proposed approach to the prediction in COVID-19 based on mediative fuzzy logic has produced promising results for the continuous contradictory prediction in India.

Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage

In order to control the spread of COVID-19, China had implemented strict lockdown measures. The closure of cities had had a huge impact on human production and consumption activities, which had greatly reduced population mobility. This article used air pollutant data from 341 cities in mainland China and divided these cities into seven major regions based on geographic conditions and climatic environment. The impact of urban blockade on air quality during COVID-19 was studied from the perspectives of time, space, and season. In addition, this article used Normalized Difference Vegetation Index (NDVI) to systematically analyze the characteristics of air pollution in the country and used the Pearson correlation coefficient to explore the relationship between NDVI and the air pollutant concentrations during the COVID-19 period. Then, linear regression was used to find the quantitative relationship between NDVI and AQI, and the fitting effect of the model was found to be significant through t test. Finally, some countermeasures were proposed based on the analysis results, and suggestions were provided for improving air quality. This paper has drawn the following conclusions: (1) the concentration of pollutants varied greatly in different regions, and the causes of their pollution sources were also different. The region with the largest decline in AQI was the Northeast China (60.01%), while the AQI in the southwest China had the smallest change range, and its value had increased by 1.72%. In addition, after the implementation of the city blockade, the concentration of NO2 in different regions dropped the most, but the increase in O3 was more obvious. (2) Higher vegetation coverage would have a beneficial impact on the atmospheric environment. Areas with higher NDVI values have relatively low AQI. There is a negative correlation between NDVI and AQI, and an average increase of 0.1 in NDVI will reduce AQI by 3.75 (95% confidence interval). In the case of less human intervention, the higher the vegetation coverage, the lower the local pollutant concentration will be. Therefore, the degree of vegetation coverage would have a direct or indirect impact on air pollution.

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children or early adolescents with an estimated worldwide prevalence of 7.2%. Numerous articles related to ADHD have been published in the literature. However, which articles had ultimate influence is still unknown, and what factors affect the number of article citations remains unclear as well. This bibliometric analysis (1) visualizes the prominent entities with 1 picture using the top 100 most-cited articles, and (2) investigates whether medical subject headings (i.e., MeSH terms) can be used in predicting article citations.

METHODS

By searching the PubMed Central^® (PMC) database, the top 100 most-cited abstracts relevant to ADHD since 2014 were downloaded. Citation rank analysis was performed to compare the dominant roles of article types and topic categories using the pyramid plot. Social network analysis (SNA) was performed to highlight prominent entities for providing a quick look at the study result. The authors examined the MeSH prediction effect on article citations using its correlation coefficients (CC).

RESULTS

The most frequent article types and topic categories were research support by institutes (56%) and epidemiology (28%). The most productive countries were the United States (42%), followed by the United Kingdom (13%), Germany (9%), and the Netherlands (9%). Most articles were published in the Journal of the American Academy of Child and Adolescent Psychiatry (15%) and JAMA Psychiatry (9%). MeSH terms were evident in prediction power on the number of article citations (correlation coefficient = 0.39; t = 4.1; n = 94; 6 articles were excluded because they do not have MeSH terms).

CONCLUSIONS

The breakthrough was made by developing 1 dashboard to display 100 top-cited articles on ADHD. MeSH terms can be used in predicting article citations on ADHD. These visualizations of the top 100 most-cited articles could be applied to future academic pursuits and other academic disciplines.

Hybrid algorithm for elastography to visualize both solid and fluid-filled lesions

We propose a novel strain estimation technique that can produce reliable strain images of both solid and fluid-filled lesions. In our method, a kernel-based correlation coefficient technique and a speckle tracking-based strain estimation technique are combined into a single algorithm. The elegance of our algorithm is that fluid-filled lesions are first automatically identified by three selection criteria, and strain in those parts is estimated using the kernel-based correlation coefficient technique. Strain where fluid-filled lesions have not been detected is estimated using a speckle tracking-based algorithm, and then these two estimates are merged to form the final image. Any speckle tracking algorithm can be used in our proposed technique; however, we used a modified version of the direct average spectral strain estimation technique to describe our algorithm. We modified the direct average spectral strain estimation algorithm to track smaller strain variation and to facilitate strain calculation from multiple frames. We describe the performance of our proposed hybrid algorithm using in vivo patient data. Both the solid and fluid-filled lesions are clearly visible in the strain images produced by our proposed approach and are of better quality in terms of contrast-to-noise ratio and border sharpness than the strain images generated by other reported techniques. We also validate the performance of our proposed multiframe technique using experimental phantom data and in vivo patient data. The results reveal that the quality of the strain image can be improved using the multiframe technique compared with its dual-frame counterpart.

Reliability of preoperative measurement with standardized templating in Total Knee Arthroplasty

AIM: To investigate the correlation between preoperative measurement in total knee arthroplasty and the prosthetic size implanted.

METHODS: A prospective double-blind study of 50 arthroplasties was performed. Firstly, the reliability and correspondence between the size of said measurement and the actual implant utilized was determined. Secondly, the existing correlation between the intra- and interobserver determinations with the intraclass correlation coefficient was analyzed.

RESULTS: An overall correspondence of 54%, improving up to 92% when the measured size admitted a difference of one size, was found. Good intra- and interobserver reliability with an intraclass correlation coefficient greater than 0.90 (P < 0.001) was also discovered.

CONCLUSION: Agreement between the preoperative measurement with standardized acetate templates and the prosthetic size implanted can be considered satisfactory. We thus conclude it is a reproducible technique.

Investigating the relationships among lung function variables in chronic obstructive pulmonary disease in men

Background

In patients with chronic obstructive pulmonary disease (COPD), the independent contributions of individual lung function variables to outcomes may be lower when they are modelled together if they are collinear. In addition, lung volume measurements may not be necessary after spirometry data have been obtained. However, these hypotheses depend on whether forced vital capacity (FVC) can predict total lung capacity (TLC). Moreover, the definitions of hyperinflation and air trapping according to lung function variables overlap and need be clarified. Therefore, the aim of this study was to evaluate the relationships among various lung function parameters to elucidate these issues.

Methods

Demographic data and 26 parameters of full lung function were measured in 94 men with COPD and analyzed using factor and correlation analyses.

Results

Factor analysis revealed five latent factors. Inspiratory capacity (IC)/TLC and residual volume (RV)/TLC were most strongly correlated with all other lung volumes. IC/TLC, RV/TLC, and functional residual capacity (FRC)/TLC were collinear and were potential markers of air trapping, whereas TLC%, FRC%, and RV% were collinear and were potential markers of hyperinflation. RV/TLC >0.4 (or IC/TLC <0.4) was comparable with the ratio of forced expiratory volume in one second (FEV₁) and FVC <0.7. FVC% and FEV₁% were poorly correlated with TLC%. The correlation study showed that TLC%, RV/TLC, and FEV₁% could be used to represent individual latent factors for hyperinflation, air trapping, inspiration, expiration, and obstruction. Combined with diffusion capacity%, these four factors could be used to represent comprehensive lung function.

Conclusions

This study identified collinear relationships among individual lung function variables and thus selecting variables with close relationships for correlation studies should be performed with caution. This study also differentiated variables for air trapping and lung hyperinflation. Lung volume measurements are still required even when spirometry data are available. Four out of 26 lung function variables from individual latent factors could be used to concisely represent lung function.

Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.

Similarity analysis between chromosomes of Homo sapiens and monkeys with correlation coefficient, rank correlation coefficient and cosine similarity measures

In this paper, we consider correlation coefficient, rank correlation coefficient and cosine similarity measures for evaluating similarity between Homo sapiens and monkeys. We used DNA chromosomes of genome wide genes to determine the correlation between the chromosomal content and evolutionary relationship. The similarity among the H. sapiens and monkeys is measured for a total of 210 chromosomes related to 10 species. The similarity measures of these different species show the relationship between the H. sapiens and monkey. This similarity will be helpful at theft identification, maternity identification, disease identification, etc.