Hepatitis C virus core antigen: A diagnostic and treatment monitoring marker of hepatitis C virus in Indian population

BACKGROUND

The diagnosis and treatment monitoring of hepatitis C is quite challenging. The screening test, i.e. antibody assay, is unable to detect acute cases, while the gold standard hepatitis C virus (HCV) reverse transcriptase polymerase chain reaction (RTPCR) assay is not feasible in resource-limited countries such as India due to high cost and infrastructure requirement. European Association for the Study of the Liver and World Health Organization have approved a new marker, i.e. HCV core antigen (HCVcAg) assay, as an alternative to molecular assay. In this study, we have evaluated HCVcAg assay for diagnosis and treatment monitoring follow-up in Indian population infected with hepatitis C.

METHODS

Blood specimen of 90 clinically suspected cases of acute hepatitis C were tested simultaneously for anti-HCV antibody assay via ELISA (enzyme-linked immunoassay), HCVcAg assay by chemiluminescence immune assay (CLIA) and HCV RTPCR VL (viral load) assay. Thirty-four HCV RTPCR positive patients were further enrolled in treatment monitoring group whose blood samples were tested at the beginning of treatment, two weeks, four weeks and 12 weeks via HCV core Ag assay and HCV RTPCR Viral Load assay.

RESULTS

Considering HCV RTPCR as gold standard, diagnostic performance of HCV core Ag assay and anti-HCV antibody assay was evaluated. The sensitivity and specificity of HCV core Ag assay were higher than that of anti-HCV Antibody assay, i.e. 88.3% and 100% vs. 23.3% and 83.3%, respectively. The overall diagnostic accuracy of HCV core Ag assay was 92.20%. Among treatment follow-up group, HCV core Ag levels correlated well with HCV viral load levels, at the beginning of treatment (baseline) till 12 weeks showing highly significant Spearman rank correlation coefficient of > 0.9 with HCV viral load levels.

CONCLUSIONS

HCV core Ag assay is a cost-effective, practically feasible substitute of HCV RTPCR viral load assay for diagnosis as well as long duration treatment monitoring of hepatitis C infection in resource-limited settings.

Quantitative analysis of lesion images to evaluate the efficiency of vascular-targeted photodynamic therapy for port-wine stains: Four case reports

SIGNIFICANCE

Vascular-targeted photodynamic therapy (V-PDT) is a clinically approved therapeutic approach for treating vascular-related diseases, such as port-wine stains (PWS). For accurate treatment, varying light irradiance is required for different lesions due to the irregularity of vascular size, shape and degree of disease, which commonly alters during different stages of V-PDT. This makes quantitative analysis of the treatment efficiency urgently needed.

APPROACH

Lesion images pre- and post- V-PDT treatment of patients with PWS were used to construct a quantitative method to evaluate the differences among lesions. Image analysis techniques were applied to evaluate the V-PDT efficiency for PWS by determining the Euclidean distances and two-dimensional correlation coefficients.

RESULTS

According to the image analysis, V-PDT with good treatment efficiency resulted in a larger Euclidean distance and a smaller correlation coefficient compared with the case having lower V-PDT efficiency.

CONCLUSIONS

A new method to quantify the Euclidean distances and correlation coefficients has been proposed, which is promising for the quantitative analysis of V-PDT efficiency for PWS.

A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for interval-valued pythagorean fuzzy hypersoft set

The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study.

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

This study predicts the parameters such as viscosity and thermal conductivity in silica-alumina-MWCN/water nanofluid using the artificial intelligence method and using design variables such as solid volume fraction and temperature. In this study, 6 optimization algorithms were used to predict and numerically model the μnf and TC of silica-alumina-MWCNT/water-NF. In this study, six measurement criteria were used to evaluate the estimates obtained from the coupling process of GMDH ANN with each of these 6 optimization algorithms. The results reveal that the influence of the φ is notably higher on both μnf and TC with values of 0.83 for μnf and 0.92 for TC, while Temp has a relatively weaker impact with -0.5 for μnf and 0.38 for TC. Among various algorithms, the coupling of the evolutionary algorithm NSGA II with ANN and GMDH performs best in predicting μnf and TC for the NF, with a maximum margin of deviation of -0.108 and an R2 evaluation criterion of 0.99996 for μnf and 1 for TC, indicating exceptional model accuracy. In the subsequent phase, a meta-heuristic Genetic Algorithm minimizes μnf and TC values. Four points (A, B, C, and D) along the Pareto front are selected, with point A representing the optimal state characterized by low values of φ and Temp (0.0002 and 50.8772, respectively) and corresponding target function values of 0.9988 for μnf and 0.6344 for TC. In contrast, point D represents the highest values of φ and Temp (0.49986 and 59.9775, respectively) and yields target function values of 2.382 for μnf and 0.8517 for TC. This analysis aids in identifying the optimal operating conditions for maximizing NF performance.

Monitoring of rainwater quality in Kandy and Peradeniya, Sri Lanka

The composition of atmospheric deposition is a measure of air quality, an important aspect of the health of the ecosystem. Consequently, continuous monitoring of atmospheric deposition is crucial to obtain remedial measures to avoid undesirable aspects that would affect living things. In this context, the objective of this study was to determine the rainwater quality at selected locations in Kandy and Peradeniya area of Sri Lanka, namely, Kandy, Polgolla, and University of Peradeniya (UOP), and to identify possible correlations between quality parameters through statistical means. Forty (40) rainwater samples from the UOP site and seven (07) samples each from the Kandy and Polgolla sites were collected from 18 May 2020 to 28 April 2021. The volume-weighted average (VWA) pH values of UOP, Kandy, and Polgolla sites were determined to be 7.44, 7.19, and 7.19, respectively, and moreover, acid rain (pH < 5.6) occurrences were not detected during the sampling period. The VWA values of rainfall, conductivity, salinity, TDS, and hardness at the UOP site were 40.12 mm, 51.93 μS cm-1, 0.0300 ppt, 26.59 mg L-1, and 13.55 mg L-1, respectively. The corresponding values of the Kandy site were 16.52 mm, 64.04 μS cm-1, 0.0361 ppt, 30.80 mg L-1, and 19.49 mg L-1, respectively; and those of the Polgolla site were 33.10 mm, 53.90 μS cm-1, 0.0310 ppt, 25.76 mg L-1, and 19.31 mg L-1, respectively. The VWA values of conductivity, salinity, and TDS were the highest at the Kandy site. Further, the VWA values of hardness at Kandy and Polgolla sites were approximately equal, probably due to the spring of Ca2+ and Mg2+ particulates from the dolomite quarry located in Digana area. The most prominent anion was identified as Cl- in bulk deposition at all three sites, while NO3- showed the lowest concentration of all sites. Moreover, very strong significant positive correlations were identified between conductivity-TDS, conductivity-salinity, conductivity-hardness, TDS-hardness, TDS-salinity, salinity-hardness, SO42--Cl-, and NO3--Cl- according to the relevant Pearson correlation coefficients. It is thus concluded that the pollutants come from the same sources, either natural or anthropogenic.

Total organic halogen (TOX) in drinking water: Occurrence, correlation analysis, and precursor removal during drinking water treatment

Total organic halogen (TOX) in drinking water provides a measurement of the overall organic halogenated disinfection by-products (DBPs) formed during disinfection. Yangtze River Delta is one of the regions with the highest population density, the fastest urbanization process, and the most severe water pollution in China. Collecting water samples from full-scale drinking water treatment plants (DWTPs) in this region, this study firstly surveyed TOX occurrence in drinking water. Besides, the correlation of TOX formation potential (TOXFP) and trihalomethane formation potential (THMFP) with general water quality parameters (e.g., dissolved organic carbon [DOC], UV254, and specific ultraviolet absorbance) and the removal efficiencies of TOX precursors by different water treatment processes were also investigated. TOX levels in DWTP effluents (i.e., finished water) ranged from 29 to 165 μg/L (median 67 μg/L), and those in simulated distribution system waters ranged from 101 to 276 μg/L (median 158 μg/L). There were generally higher linear regression coefficient values for raw water (R2 = 0.51-0.88) than for treated water (R2 = 0.33-0.64) in terms of the relationship between DBP formation potentials and general parameters. However, a relatively stronger correlation between THMFP and TOXFP was observed for treated water (R2 = 0.80, p < 0.001) than for raw water (R2 = 0.64, p < 0.001). The overall treatment efficiencies of investigated parameters in DWTPs generally followed the order of UV254 > DOC > TOX precursors > THM precursors. Notably, the overall removal rates of DOC and TOX precursors in summer (averaging 59 % and 54 %, respectively) were obviously higher than those in winter (averaging 39 % and 38 %, respectively), which was assumed to be related to the seasonal variation of bioactivity in sand filter. These results could expand the knowledge of TOX in drinking water, and provide valuable perspectives to water industry and DBP research.

Variation in sediment sources and the response of suspended sediment grain size in the upper Changjiang River Basin following the large dam constructions

The amount, patterns, and particle size composition of suspended sediment in the upper Changjiang Basin has been altered significantly due to the cascade reservoirs construction. A dam breach may disrupt sedimentation in the reservoir, channel erosion downstream of the dam, and the cycle of nutrients and contaminants adherent to the dam. This study is based on the long time-series field data of water discharge, sediment flux, and suspended sediment grain size of the upper Changjiang River Basin from 1973 to 2019. Four significant stepwise reduction periods in sediment load were identified by employing the M-K test, namely, 1973-1992, 1993-2002, 2003-2012, and 2013-2019. Based on the results, sediment load reduction in the upper Changjiang River (CJR) is remarkably correlated with an increase in the capacity of large reservoirs. Jialingjiang River became the largest sediment source area for upper CJR in 2013-2019. Moreover, the variation range of suspended sediment median particle size gradually narrowed with decreasing sediment load during the four periods in the upper CJR. The silt content of suspended sediment increased while clay and sand contents decreased which would maybe a factor of decreasing of P flux in the upper CJR. After the impounding of the Xiangjiaba and Xiluodu Reservoirs in the Jinshajiang River, the fining trend of suspended sediment along the main stem of upper CJR was cut off in 2013-2019, which may be ascribed to sediment management of the cascade reservoirs regulation and the sediment resuspension from the channel erosion in the river downstream. These observations can also serve as a reference for future studies about the effects of cascade reservoirs on aquatic ecology and environmental changes.

Development and diagnostic evaluation of indirect enzyme linked immunosorbent assay for Epidermophyton floccosum infection in humans

BACKGROUND

Epidermophyton floccosum is an anthrophophilic dermatophyte widely distributed in the tropics and subtropics. It can invade keratinized tissues of humans and cause superficial mycoses called dermatophytosis (ringworm).

OBJECTIVE

The main objective of this study was to develop an in-house indirect enzyme-linked immunosorbent assay (ELISA) and to evaluate its performance for the immunological diagnosis of E. floccosum infection in humans.

METHODS

An in-house indirect ELISA was developed using partially purified E. floccosum antigens, pre immunized rabbit serum as negative control, immunized rabbit polyclonal antibodies as positive control, enzyme labeled goat anti rabbit antibodies and goat anti human antibodies. A total of 50 serum samples from E. floccosum infected patients as confirmed by direct microscopy and culture and 30 samples from humans without history of dermatophyte infection that served as controls were used to evaluate the performance of an in-house indirect ELISA developed in this study. Analytical and diagnostic performance characteristics were determined to evaluate its diagnostic value.

RESULTS

The diagnostic sensitivity, specificity, positive and negative predictive values of E. floccosum indirect ELISA were 90.00 %, 83.33 %, 90.83 %, 83.83 % respectively. The performance of indirect ELISA assay was compared with gold standard diagnostic tests such as KOH hydrolysis test and fungal culture. The correlation coefficients of E. floccosum indirect ELISA with KOH hydrolysis and fungal culture method were 0.612** and 0.826** (P < 0.01) respectively indicating significant correlation between these tests.

CONCLUSION

This revealed the great potentiality of E. floccosum indirect ELISA in early, specific and precise detection of E. floccosum infection in humans.

Optimization of biodiesel production, engine exhaust emissions, and vibration diagnosis using a combined approach of definitive screening design (DSD) and artificial neural network (ANN)

In this study, definitive screening design (DSD) optimization and artificial neural network (ANN) modelling techniques are applied for the production of palm oil biodiesel (POBD). These techniques are implemented to examine the vital contributing factors in achieving maximum POBD yield. For this purpose, seventeen experiments are conducted randomly by varying the four contributing factors. The results of DSD optimization reveal that a biodiesel yield of 96.06% is achieved. Also, the experimental results are trained in ANN for predicting the biodiesel yield. The results proved that the prediction capability of ANN is superior, with a high correlation coefficient (R2) and low mean square error (MSE). Furthermore, the obtained POBD is characterized by significant fuel properties and fatty acid compositions and observed within the standards (ASTM-D675). Finally, the neat POBD is examined for exhaust emissions and engine cylinder vibration analysis. The emissions results confirm a significant drop in NOx (32.46%), HC (40.57%), CO (44.44%), and exhaust smoke (39.65%) compared to diesel fuel at 100% load. Likewise, the engine cylinder vibration measured on top of the cylinder head reveals a low spectral density with low amplitude vibrations observed for POBD at measured loads.

The relationship between radiofrequency-electromagnetic radiation from cell phones and brain tumor: The brain tumor incidence trends in South Korea

INTRODUCTION

The aim of this study is to investigate the relationship between the nationwide cell phone subscription rate and the nationwide incidence of brain tumors in South Korea. The nationwide cell phone subscription rate was used as a proxy for the RF-EMR exposure assessment.

METHODS

The data for cell phone subscriptions per 100 persons from 1985 to 2019 were found in the Statistics, International Telecom Union (ITU). The brain tumor incidence data from 1999 to 2018 provided by the South Korea Central Cancer Registry operated by the National Cancer Center were used.

RESULTS

In South Korea, the subscription rate increased from 0 per 100 persons in 1991 to 57 per 100 persons in 2000. The subscription rate became 97 per 100 persons in 2009 and 135 per 100 persons in 2019. For the correlation coefficient between cell phone subscription rate before 10 years and ASIR per 100,000, a positive correlation coefficient with a statistical significance was reported in 3 benign brain tumors (International Classification of Diseases, ICD-10 code, D32, D33, and D32.0) and in 3 malignant brain tumors (ICD-10 code, C71.0, C71.1, and C71.2). Positive correlation coefficients with a statistical significance in malignant brain tumors ranged from 0.75 (95% CI 0.46-0.90) for C71.0 to 0.85 (95% CI 0.63-0.93) for C71.1.

DISCUSSION

In consideration of the fact that the main route for RF-EMR exposure has been through the frontotemporal side of the brain (the location of both ears), the positive correlation coefficient with a statistical significance in the frontal lobe (C71.1) and temporal lobe (C71.2) can be understood. Statistically insignificant results from recent cohort and large population international studies and contrasting results from many previous case-control studies could indicate a difficulty in identifying a factor as a determinant of a disease in ecological study design.

Biochemical investigation of Bangladeshi black tea and their correlation to organoleptic quality evaluation

Black tea samples were investigated for theaflavin (TF), thearubigin (TR), highly polymerized substances (HPS), total liquor color (TLC), color index (CI), caffeine (CAF), total polyphenol content (TPC), antioxidant activity (DPPH) and organoleptic evaluation. The objectives of the study was to perform the biochemical analysis and organoleptic test of various black tea and develop a correlation between them. Overall correlation study showed that TF:TR and total liquor color, are significantly (p < 0.01) and positively correlated with the total quality score where the correlation coefficient was 0.970 and 0.969 respectively. Additionally, statistical analysis revealed a significant (p < 0.01) and positive correlation between total phenol contents and antioxidant scavenging activity (0.986), supporting the notion that TPC accounts for most of the antioxidants in tea extract. Qualitative characteristics and organoleptic tests were shown identical outcomes in this study.

Assessment of the capability of cadmium accumulation and translocation among 31 willows: four patterns of willow biomass variation response to cadmium

Cadmium (Cd) pollution threatens food security and the environment. Willow species (Salix, Salicaceae) exhibit a remarkable potential to restore Cd-polluted sites due to their high biomass production and high Cd accumulation capacities. This study examined the Cd accumulation and tolerance in 31 genotypes of shrub willow in hydroponic conditions at varying Cd levels (0 μM Cd, 5 μM Cd, and 20 μM Cd). The root, stem, and leaf biomass of 31 shrub willow genotypes showed significant differences to Cd exposure. Among 31 willow genotypes, four patterns of biomass variation response to Cd were identified: insensitive to Cd; growth inhibition due to excessive Cd supply (high Cd inhibition); low Cd causing inhibited growth, whereas high Cd leading to increased biomass (U-shape); and growth increment with excessive Cd exposure (high Cd induction). The genotypes belonging to the "insensitive to Cd" and/or "high Cd induction" were candidates for the utilization of phytoremediation. Based on the analysis of Cd accumulation of 31 shrub willow genotypes at high and low Cd levels, genotypes 2372, 51-3, and 1052 obtained from a cross between S. albertii and S. argyracea grew well and accumulated relatively more Cd levels than other genotypes. In addition, for Cd-treated seedlings, root Cd accumulation was positively correlated with shoot Cd accumulation and total Cd uptake, demonstrating that Cd accumulation in roots could serve as a biomarker for evaluating the Cd extraction capacity of willows, especially in hydroponics screening. The results of this study screened out willow genotypes with high Cd uptake and translocation capacities, which will provide valuable approaches for restoring Cd-contaminated soils with willows.

A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance

In response to the traditional Dempster-Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (SALSA) algorithm combined with Lance-Williams distance is proposed. Firstly, the degree of conflict between evidences is calculated based on the number of correlation coefficients between evidences. Then, the evidences with a number of correlation coefficients greater than the average number of correlation coefficients of evidence are connected to construct an evidence association network. The authority weight of the evidence is calculated based on the number of citations in the concept of SALSA algorithm combined with the support of the evidence. Subsequently, the Lance-Williams distance between the evidences is calculated and transformed into support of the evidence. Next, the authority weight and support of evidence are combined to jointly construct a novel correction coefficient to correct the evidence. Finally, the corrected evidence is fused using the D-S combination rule to obtain the final fusion result. The numerical results verify that the method proposed in this paper can effectively solve the problem of the traditional D-S combination rule being unable to handle highly conflicting evidence.

The role of transportation in developing the tourism sector at high altitude destination, Kinnaur

As a supporting factor, transportation is an important element of destination image that provides a base for the successful tourism industry. It is like the blood vessels of an area and is considered a determinant in developing a tourist destination. The article aims to characterize the status or problem of transportation accessibility in Kinnaur. GARMIN hand GPS (Global Positioning System) has been used to identify the damaged roads from their start to endpoints. In addition, a simple random sample technique has been used to register the opinion of 280 tourists about the transport facilities. Study results suggest that the bad condition of National Highway-22 is one of the barriers to tourism development in Kinnaur. There were no significant differences found between the selected destinations. Overall, the district headquarters, Kalpa, has been perceived significantly higher agreements by tourists. The government should ensure that the Border Road Organization, the organization entrusted with the responsibility of construction and maintenance of roads in international border areas, has sufficient resources to invest in transport development and its maintenance.

A novel correlation Gaussian process regression-based extreme learning machine

An obvious defect of extreme learning machine (ELM) is that its prediction performance is sensitive to the random initialization of input-layer weights and hidden-layer biases. To make ELM insensitive to random initialization, GPRELM adopts the simple an effective strategy of integrating Gaussian process regression into ELM. However, there is a serious overfitting problem in kernel-based GPRELM (kGPRELM). In this paper, we investigate the theoretical reasons for the overfitting of kGPRELM and further propose a correlation-based GPRELM (cGPRELM), which uses a correlation coefficient to measure the similarity between two different hidden-layer output vectors. cGPRELM reduces the likelihood that the covariance matrix becomes an identity matrix when the number of hidden-layer nodes is increased, effectively controlling overfitting. Furthermore, cGPRELM works well for improper initialization intervals where ELM and kGPRELM fail to provide good predictions. The experimental results on real classification and regression data sets demonstrate the feasibility and superiority of cGPRELM, as it not only achieves better generalization performance but also has a lower computational complexity.

A crossed-polarized four port MIMO antenna for UWB communication

This paper presents a compact, crossed-polarized, ultra-wideband (UWB) four-ports multiple-input-multiple-output (MIMO) printed antenna. The proposed antenna is constructed from four microstrip circular patch elements fed by a 50-Ω microstrip line. Two metamaterial cell elements, in the form of a rectangular concentric double split ring resonator (SRR), are placed at the upper plane of the substrates for bandwidth improvement and isolation enhancement. The ultra-wideband frequency response is achieved using a defective ground plane. Surface current flow between the antenna's four elements is limited to ensure maximum isolation. The four-port MIMO system is designed with orthogonal antenna elements orientation on an FR4 substrate with a loss tangent of 0.02 and an overall size of 30 mm × 30 mm × 1.6 mm. Such orientation resulted in less than -17dB port-to-port isolation and an impedance bandwidth of 148% (3.1-12 GHz). The proposed UWB-MIMO antenna achieved a maximum realized gain of 6.2dBi with an efficiency of 87%. The measured and simulated results are in good agreement over the operating frequency band (3.1-12 GHz). The results also provide overall good diversity performance with the TARC < -10 dB, ECC < 0.001, DG > 9.9, MEG < -3 dB and CCL <0.1. The proposed antenna is well-suited for applications in WLAN, WIMAX and GPRs.

Validity of using ambient concentrations as surrogate exposures at the individual level for fine particle and black carbon: A systematic review and meta-analysis

Exposure measurement error is an important source of bias in epidemiological studies. We assessed the validity of employing ambient (outdoor) measurements as proxies of personal exposures at individual levels focusing on fine particles (PM_2.5) and black carbon (BC)/elemental carbon (EC) on a global scale. We conducted a systematic review and meta-analysis and searched databases (ISI Web of Science, Scopus, PubMed, Ovid MEDLINE®, Ovid Embase, and Ovid BIOSIS) to retrieve observational studies in English language published from 1 January 2006 until 5 May 2021. Correlation coefficients (r) between paired ambient (outdoor) concentration and personal exposure for PM_2.5 or BC/EC were standardized as effect size. We used random-effects meta-analyses to pool the correlation coefficients and investigated the causes of heterogeneity and publication bias. Furthermore, we employed subgroup and meta-regression analyses to evaluate the modification of pooled estimates by potential mediators. This systematic review identified thirty-two observational studies involving 1744 subjects from ten countries, with 28 studies for PM_2.5 and 11 studies for BC/EC. Personal PM_2.5 exposure is more strongly correlated with ambient (outdoor) concentrations (0.63, 95% confidence interval [CI]: 0.57-0.68) than personal BC/EC exposure (0.49, 95% CI: 0.38-0.59), with significant differences in ṝ (0.14, 95% CI: 0.03-0.25; p < 0.05). The results demonstrated that the health status of participants was a significant modifier of pooled correlations. In addition, the personal to ambient (P/A) ratio for PM_2.5 and average ambient BC/EC levels were potential effect moderators of the pooled ṝ. The funnel plots and Egger's regression test indicated inevident publication bias. The pooled estimates were robust through sensitivity analyses. The results support the growing consensus that the validity coefficient of proxy measures should be addressed when interpreting results from epidemiological studies to better understand how strong health outcomes are affected by different levels of PM_2.5 and their components.

Collateral Capacity Assessment : Robustness and Interobserver Agreement of Two Grading Scales and Agreement with Quantitative Scoring

BACKGROUND AND PURPOSE

Intracranial collateral capacity is conducive to imply parenchymal perfusion of affected territory after acute vessel occlusion. The Tan collateral score is commonly used to assess the intracranial collateral capacity; however, this score is coarsely grained and interobserver agreement is low, which reduces prognostic value and clinical utility. We introduce and evaluate an alternative extended Tan score based on the conventional Tan scale and assess the agreement with a quantitative score.

METHODS

We included 100 consecutive patients with a proven acute single large vessel occlusion of the proximal anterior circulation. Collaterals were graded with the conventional and extended Tan score and an automated quantitative score. The extended Tan score is a finer 6‑scale manual score based on the conventional 4‑point Tan scale. The quantitative score is calculated by an automatic software package (StrokeViewer). Interobserver agreement of the manual scores was assessed with the weighted kappa. The Spearman correlation coefficient was calculated to determine the agreement between the manual and automated collateral scores.

RESULTS

The interobserver agreement was higher for the extended score than for the conventional score with a weighted kappa of 0.70 and 0.65, respectively. For the extended and conventional score, the Spearman correlation coefficient for the agreement with the automated score was 0.78 and 0.76, respectively.

CONCLUSION

Because of the good interobserver agreement and good agreement with quantitative assessment, the extended collateral score is a strong candidate to improve prognostic value of collateral assessment and implementation in clinical practice.

Heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation in a semiarid region of China

Wetlands in semiarid regions have important ecological functions and provide services that are partly dependent on the landscape pattern and structure; therefore, it is important to assess the heterogeneity of wetland landscape conditions for wetland conservation and management. Widespread wetland degradation resulting from main factors in semiarid regions highlights the need for exploring the heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation. Wetlands in our study referred to natural wetlands, including marshes, rivers, and lakes. We used overall landscape metrics to assess wetland landscape conditions and used grid landscape metrics at a scale of 30 km using a moving window method to explore the heterogeneity of wetland landscapes during the period (1986-2019) for western Jilin Province. We performed Spearman correlation analyses of the landscape metrics with the Hemeroby index (HI) and mean annual precipitation (MAP). We suggested that wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED and AI) had positive correlations with HI by 2019 (p < 0.01); in addition, wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED) were negatively correlated with MAP during 1986-2019. In conclusion, wetland landscapes have become increasingly fragmented over the past few decades (1986-2019) in western Jilin Province and the reasons for this result mainly include the household contract responsibility system, along with rural productivity, severe overgrazing and reclamation, and climate change. Wetland landscape composition was positively correlated with human interference and negatively correlated with mean annual precipitation.

Machine learning based personalized promotion strategy of piglets weaned per sow per year in large-scale pig farms

Background

The purpose of this study was to analyze the relationship between different productive factors and piglets weaned per sow per year (PSY) in 291 large-scale pig farms and analyze the impact of the changes in different factors on PSY. We chose nine different algorithm models based on machine learning to calculate the influence of each variable on every farm according to its current situation, leading to personalize the improvement of the impact in the specific circumstances of each farm, proposing a production guidance plan of PSY improvement for every farm. According to the comparison of mean absolute error (MAE), 95% confidence interval (CI) and R², the optimal solution was conducted to calculate the influence of 17 production factors of each pig farm on PSY improvement, finding out the bottleneck corresponding to each pig farm. The level of PSY was further analyzed when the bottleneck factor of each pig farm changed by 0.5 standard deviation (SD).

Results

17 production factors were non-linearly related to PSY. The top five production factors with the highest correlation with PSY were the number of weaned piglets per litter (WPL) (0.6694), mating rate within 7 days after weaning (MR7DW) (0.6606), number of piglets born alive per litter (PBAL) (0.6517), the total number of piglets per litter (TPL) (0.5706) and non-productive days (NPD) (− 0.5308). Among nine algorithm models, the gradient boosting regressor model had the highest R², smallest MAE and 95% CI, applied for personalized analysis. When one of 17 production factors of 291 large-scale pig farms changed by 0.5 SD, 101 pig farms (34.7%) can increase 1.41 PSY (compared to its original value) on average by adding the production days, and 60 pig farms (20.6%) can increase 1.14 PSY on average by improving WPL, 45 pig farms (15.5%) can increase 1.63 PSY by lifting MR7DW.

Conclusions

The main productive factors related to PSY included WPL, MR7DW, PBAL, TPL and NPD. The gradient boosting regressor model was the optimal method to individually analyze productive factors that are non-linearly related to PSY.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40813-022-00280-z.

[Comprehensive evaluation method of real-time non-reference ultrasound image involving soft tissue deformation]

Ultrasound guided percutaneous interventional therapy has been widely used in clinic. Aiming at the problem of soft tissue deformation caused by probe contact force in robot-assisted ultrasound-guided therapy, a real-time non-reference ultrasound image evaluation method considering soft tissue deformation is proposed. On the basis of ultrasound image brightness and sharpness, a multi-dimensional ultrasound image evaluation index was designed, which incorporated the aggregation characteristics of the organization. In order to verify the effectiveness of the proposed method, ultrasound images of four different models were collected for experiments, including prostate phantom, phantom with cyst, pig liver tissue, and pig liver tissue with cyst. In addition, the correlation between subjective and objective evaluations was analyzed based on Spearman's rank correlation coefficient. Experimental results showed that the average evaluation time of a single image was 68.8 milliseconds. The evaluation time could satisfy real-time applications. The proposed method realizes the effective evaluation of real-time ultrasound image quality in robot-assisted therapy, and has good consistency with the evaluation of supervisors.

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.

Effect of different walking speeds on joint and muscle force estimation using AnyBody and OpenSim

BACKGROUND

To be able to use muscluloskeletal models in clinical settings, it is important to understand the effect of walking speed on joint and muscle force estimations in different generic musculoskeletal models.

RESEARCH QUESTION

The aim of the current study is to compare estimated joint and muscle forces as a function of walking speed between two standard approaches offered in two different modelling environments (AnyBody and OpenSim).

METHODS

Experimental data of 10 healthy participants were recorded at three different walking speeds (self-selected, 25 % slower, 25 % faster) using a ten-camera motion capture system together with four force plates embedded into a ten-meter walkway. Joint compression forces and muscle forces were calculated with a generic model in AnyBody and OpenSim. Trend analyses, mean absolute error (MAE) and correlation coefficients were used to compare joint compression forces and muscle forces between the two approaches. A one-way and two-way ANOVA with repeated measures were used to compare MAE and trend analysis changes, respectively (α = 0.05, Bonferroni corrected post-hoc tests).

RESULTS

Trend analyses showed the same speed effect for AnyBody and OpenSim. MAEs increased significantly from slow to fast walking for knee joint compression forces, biceps femoris long head, gluteus maximus, gluteus medius and vastus intermedius. Lower correlation coefficients during slower walking were found for quadriceps muscles, gluteus maximus and biceps femoris compared to normal and faster walking.

SIGNIFICANCE

Lower correlation coefficients during slower walking are assumed to be due to a higher amount of solutions solving the muscle recruitment in musculoskeletal models. This indicates that decreasing walking speed is more prone to speed dependent differences regarding variability, while the absolute error increased with increasing walking speed. To conclude, different modelling environments can react differently to changes in walking speed, but overall results are promising regarding the generalization across different generic musculoskeletal models.

Predicting Pyrodinium bahamense occurrence using weather pattern data in Eastern Visayas, Philippines

Pyrodinium bahamense, a harmful alga which causes paralytic shellfish poisoning (PSP), has been found in seawater samples collected along bays in Eastern Visayas in the Philippines. Due to its negative impacts and uncertainty in its occurrence, there is a need to develop a real-time monitoring device of the harmful algal bloom (HAB) occurrence. This study aims to determine whether there is significant relationship between the dependent variable, the P. bahamense cell density, and the independent variables, namely, temperature, rainfall and humidity, bays in Eastern Visayas, and sampling month. This was done through data mining technique of P. bahamense cell density and weather patterns and statistically analyzing the datasets, with P. bahamense cell density as the dependent variable and weather patterns, bays and sampling month as independent variables using M5P regression analysis. Through the regression analysis, the usefulness of the predictive model as an initial development can be assessed. Results of the study showed that the correlation coefficient of P. bahamense with the five independent variables is 0.725 which signified a moderate correlation between P. bahamense cell density and bay, sampling month, temperature, rainfall and humidity. Maximum cell density, which was 20 cells/L occurred during months of wet season in the bays in Eastern Visayas, except in Cancabato bay while the minimum cell density, which was 0 cell/L occurred during months of dry season. It can be concluded from the results that there is enough data to say that there is a significant relationship between P. bahamense occurrence and temperature, rainfall and humidity, so the model is meaningful and useful at alpha = 0.05 or 95% and 0.01 or 99% confidence interval. The data can be used to generate a predictive model for P. bahamense occurrence relative to its cell density.

Comparison of ISO work of breathing and NIOSH breathing resistance measurements for air-purifying respirators

The National Institute for Occupational Safety and Health's methods and requirements for air-purifying respirator breathing resistance in 42 CFR Part 84 do not include work of breathing. The International Organization for Standardization Technical Committee 94, Subcommittee 15 utilized work of breathing to evaluate airflow resistance for all classes of respiratory protective devices as part of their development of performance standards regarding respiratory protective devices. The objectives of this study were: (1) to evaluate the relationship between the International Organization for Standardization's work of breathing measurements and the National Institute for Occupational Safety and Health's breathing resistance test results; (2) to provide scientific bases for standard development organizations to decide if work of breathing should be adopted; and (3) to establish regression equations for manufacturers and test laboratories to estimate work of breathing measurements using breathing resistance data. A total of 43 respirators were tested for work of breathing at minute ventilation rates of 10, 35, 65, 105, and 135 liters per minute. Breathing resistance obtained at a constant flow rate of 85 liters per minute per National Institute of Occupational Safety and Health protocol was correlated to each of the parameters (total work of breathing, inhalation, and exhalation) obtained from the work of breathing tests. The ratio of work of breathing exhalation to work of breathing inhalation for all air-purifying respirators is similar to the ratio of exhalation to inhalation resistance when tested individually. The ratios were about 0.8 for filtering facepiece respirators, 0.5 for half-masks, and 0.25 for full-facepiece respirators. The National Institute for Occupational Safety and Health's breathing resistance is close to work of breathing's minute ventilation of 35 liters per minute, which represents the common walking/working pace in most workplaces. The work of breathing and the National Institute of Occupational Safety and Health's breathing resistance were found to be strongly and positively correlated (r values of 0.7-0.9) at each work rate for inhalation and exhalation. In addition, linear and multiple regression models (R-squared values of 0.5-0.8) were also established to estimate work of breathing using breathing resistance. Work of breathing was correlated higher to breathing resistance for full-facepiece and half-mask elastomeric respirators than filtering facepiece respirators for inhalation. For exhalation, filtering facepiece respirators were correlated much better than full-facepiece and half-mask elastomeric respirators. Therefore, the National Institute for Occupational Safety and Health's breathing resistance may reasonably be used to predict work of breathing for air-purifying respirators. The results could also be used by manufacturers for product development and evaluation.

An AHP-based regional COVID-19 vulnerability model and its application in China

Since the COVID-19 outbreak, four cities-Wuhan, Beijing, Urumqi and Dalian-have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making

Picture fuzzy set is an efficient tool for dealing with uncertainty and vagueness, particularly in situations that require assimilation of more dimensions of linguistic assessment such as human voting, feature selection, etc. The correlation coefficient of picture fuzzy sets is a tool to determine the association of two picture fuzzy sets. It has several applications in various disciplines like science, engineering, and management. The prominent applications include decision-making, pattern recognition, clustering analysis, medical diagnosis, etc. In this paper, we introduce a new correlation coefficient for picture fuzzy sets with the justification of its advantages. This correlation coefficient is better than the existing correlation coefficients and other such measures in the picture fuzzy theory because it considers the picture fuzzy set as a whole and also expresses the nature (positive or negative) as well as the extent of association between two PFSs. By performing some comparative analysis based on the computation of correlation degree and linguistic hedges, we establish the effectiveness of the suggested correlation measure over some available correlation measures in a picture fuzzy environment. Further, in the context of pattern recognition, we examine the performance of the proposed correlation measure over some existing picture fuzzy correlation measures. Finally, we apply the suggested picture fuzzy correlation coefficient to a decision-making problem involving the selection of an appropriate COVID-19 mask.

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.

Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing

Reliable loss estimation is crucial for flood risk management. As the current standard form of flood loss assessment, it is difficult to fit the Flood Inundated Depth-Loss Rate Function (FILF) due to the lack of historical data in most inland arid and semi-arid plain cities. To address the current trend of increasing flood risk, it has become increasingly important to develop a scientific and reasonable loss assessment function or model for these cities. Therefore, the flood loss rate data of several cities were transferred through amplified characteristic indices to form a loss rate transfer vector of cities lacking disaster data based on the analogy principle. Three-dimensional data processing rules were then set, including the priority sequence of object dimensional variance and the greatest correlation coefficient (CC) of the joint dimension of function and array. Finally, a FILF of cities lacking disaster data was constructed after three-level optimization. The FILF of eight property types was calculated taking Zhengzhou City, China, as the study area. The optimal function and array dimensions were F₆ (Biquadratic) and D₄-D₆, respectively. All CCs exceeded 0.9935, with an average of 0.9971. The joint fitting results also showed that the function dimension was more sensitive to the FILF than the array dimension. The simulated total flood loss of the Jinshui District in 20 years was 2.46 billion yuan, and there was clear spatial disparity in economic loss. This study is expected to resolve the problem of the absence of a loss function in cities or regions lacking data to support urban flood risk management.

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.

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.

Impact of the inversion and air pollution on the number of patients with Covid-19 in the metropolitan city of Tehran

There is a downward curve between increasing inversion altitude and the number of coronavirus patients during all periods. As temperature inversion altitude increases, the pollutants are dispersed in a greater thickness of the atmosphere and the concentration of the pollutants decreases on the earth's surface. At the same time, the number of patients with Covid-19 reduces. Although investigation of the effect of severity of pollutants on the number of coronavirus patients showed poor significance level during the periods, a decreasing and increasing relationship was shown. in 1- and 9-14-day periods, the correlation coefficient was negative. As a result, the effect of the severity of pollutants and Covid-19 is not observed on 1- and9-14-day periods. Conversely, during2-8-day periods, a positive correlation coefficient was observed. Therefore, the time between infection with the virus and the onset of symptoms of this disease is between 2 and 8 days, in which the 3-day period showed the highest correlation. Considering the relationship between inversion altitude, the severity of pollutants and the number of patients during 2-5-day periods, it can be concluded that in the metropolitan city of Tehran, the maximum infection of this virus and the onset of symptoms is between 2 and 5 days.

Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel

BACKGROUND

Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds-namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined.

RESULTS

Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson's |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson's |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson's |r|≥ 0.84.

CONCLUSIONS

Our analysis corrected the previously stated Pearson's correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.

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.

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.

Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data

Gene coexpression networks (GCNs) are useful tools for inferring gene functions and understanding biological processes when properly constructed. Traditional microarray analysis is being more frequently replaced by bulk-based RNA-sequencing as a method for quantifying gene expression. This new technology requires improved statistical methods for generating GCNs. This chapter explores several popular methods for constructing GCNs using bulk-based RNA-Seq data, such as distribution-based methods and normalization techniques, implemented using the statistical programming language R.

Examining the correlation between the weather conditions and COVID-19 pandemic in India: A mathematical evidence

In this article, for the analysis of Covid-19 progression in India, we present new insights to formulate a data-driven epidemic model and approximation algorithm using the real data on infection, recovery and death cases with respect to weather in the view of mathematical variables.

Glycoconjugate vaccine batch consistency assessed by objective comparison of circular dichroism spectra

Circular dichroism (CD) spectra of biopharmaceutical protein, or protein conjugate, products contain information about their secondary and tertiary structures, which can answer to increasing regulatory interest in demonstrating consistent higher order structures of production batches. Widespread routine use of CD in a regulatory environment requires objective, statistically based, and validated methods to analyse and compare spectra against product specifications. Correlation approaches to compare spectra, developed and tested on monoclonal antibodies, are here used to assess the consistency of Hib PRP-CRM197 glycoconjugate immunogen batches, by analysis of historical data sets. Deconvolution of spectra into Gaussian peaks was used to model the spectrum and allow a more detailed description of spectral differences. Two groups of spectra [and hence samples] were distinguished. The analyses are discussed in the context of spectral comparison approaches, inter-laboratory studies, potential regulatory use and sources of uncertainty between spectra. Data analysis methods implemented here can also support stability and formulation studies.

Some physicochemical parameters and 226Ra concentration in groundwater samples of North Guilan, Iran

The ²²⁶Ra concentration and some physicochemical parameters have been measured in thermal spring waters used for medical therapy and drinking purposes in the Astara basin of North Guilan, Iran. The radon emanation method was performed using the AB-5 photomultiplier tube to measure the ²²⁶Ra concentration in water samples. Also, the physicochemical parameters of the water were measured in situ using a portable multimeter-VWR multi. The average concentrations of ²²⁶Ra were ranged between 3.4 ± 0.06 to 38.2 ± 0.08 mBq l^-1. For all samples, the ²²⁶Ra concentration values range is lower than the maximum admissible value recommended by the WHO report. The relation between the physicochemical parameters and ²²⁶Ra activity concentration of groundwater was assessed. The results indicate a significant correlation coefficient between ²²⁶Ra concentration and T, as well as acidity pH. Anomalously high ²²⁶Ra concentrations in groundwater are preferentially found in high temperate and electric conductivity along with the acidic environment.

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).

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.

BTrackS limits of stability test is a reliable assessment of volitional dynamic postural control

BACKGROUND

Unconstrained limits of stability assessment reveals aspects of volitional postural sway control that are inaccessible by other means. Prior versions of this assessment include instructions to sway towards predefined targets, and may not capture the full capability of the individual.

RESEARCH QUESTION

This study sought to establish the test-retest reliability of a novel limits of stability protocol.

METHODS

Volitional sway area was determined during unconstrained trials, where participants were instructed to explore their ability to sway towards the perimeter of their base of support. Visual feedback was provided via computer monitor. Forty healthy young adults (mean age = 20.2 ± 1.3, 15 males, 25 females) participated in this study. Trials were collected in three sessions, repeated at the same time of the same day, with one week between. Reliability was assessed using IntraClass Correlation Coefficients (ICC), considering the total area of sway as well as quadrant level area.

RESULTS

Reliability was moderate between the first and second session (0.583), and much higher 0.921) between the second and third session. The quadrant level reliability was poor to excellent (0.183-0.791), with similar trends between the three sessions.

SIGNIFICANCE

Ultimately, these results indicate that the novel limits of stability test is reliable. However, it is recommended that a practice trial be conducted prior to baseline establishment.

The role of climate in the trend and variability of Ethiopia's cereal crop yields

Food security has been and will continue to be a major challenge in Ethiopia. The country's smallholder, rainfed agriculture renders its food production system extremely vulnerable to climate variability and extremes. In this study, we investigate the impact of past climate variability and change on the yields of five major cereal crops in Ethiopia-barley, maize, millet, sorghum, and wheat-during the period 1979-2014 using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model is calibrated at both the site and agroecological-zone scales. At the sites studied, the model results suggest that climate in the past four decades may have contributed to an increasing trend in maize yield, a decreasing trend in wheat yield, and no clear trend in the yields of barley and millet; cereal crop yield is positively correlated with growing season solar radiation and temperature, but negatively correlated with growing season precipitation. For modeled cereal crops across the nation during the study period, yield in western Ethiopia is positively correlated with solar radiation and day time temperature; in the eastern and southeastern Ethiopia where water is a limiting factor for growth, yield is positively correlated with precipitation but negatively correlated with solar radiation and both day time and night time temperature. The national average of simulated yields of most crops (except maize) showed an overall decreasing (although not statistically significant) trend induced by past climate variability and changes. Over a large portion of the highly productive areas where there is a negative correlation between yield and temperature, yield is simulated to have significantly decreased over the past four decades, an indication of adverse climate impact in the past and potential food security concern in the future.

Fast and effective removal of contamination from scalp electrical recordings

OBJECTIVE

To present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings.

METHOD

We used spectral and temporal characteristics of different sources recorded during a typical scalp electrical recording in order to improve a fast and effective artefact removal approach. Our experiments show that correlation coefficient and spectral gradient of brain components differ from artefactual components. We trained two binary support vector machine classifiers such that one separates brain components from muscle components, and the other separates brain components from mains power and environmental components. We compared the performance of the proposed approach with seven currently used alternatives on three datasets, measuring mains power artefact reduction, muscle artefact reduction and retention of brain neurophysiological responses.

RESULTS

The proposed approach significantly reduces the main power and muscle contamination from scalp electrical recording without affecting brain neurophysiological responses. None of the competitors outperformed the new approach.

CONCLUSIONS

The proposed approach is the best choice for artefact reduction of scalp electrical recordings. Further improvements are possible with improved component analysis algorithms.

SIGNIFICANCE

This paper provides a definitive answer to an important question: Which artefact removal algorithm should be used on scalp electrical recordings?

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.

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.

Using Google Maps to display the pattern of coauthor collaborations on the topic of schizophrenia: A systematic review between 1937 and 2017

Schizophrenia is a severe mental disorder affecting more than 21 million people worldwide. Scientific collaborations are required to research schizophrenia. However, there have been limited publications to date investigating scientific collaborations in schizophrenia research or reporting individual researchers' achievements(IRA) for authors. This study aimed to investigate the pattern of coauthor collaborations in schizophrenia research. We conducted a bibliometric study of international scientific publications on schizophrenia. About 57,964 abstracts were identified and downloaded from MEDLINE. All were examined using social network analysis (SNA) on February 20, 2018. The clusters of author nationalities, the authors, and the medical subject headings (MESH) terms were presented on Google Maps. A total of 36,934 articles met the inclusion criteria. The mean number of authors per article increased from 4.5 in 2008 to 6.4 in 2017. The proportion of published articles decreased in North America from 46.7% in 2008, to 32.3% in 2017. In contrast, the proportion of published articles in Asia increased from 14.5% in 1998 to 23.9% in 2017. Among the countries generating schizophrenia research the most prominent is China (corr. = 0.98), followed by India (corr. = 0.94), and France (corr. = 0.93). The representative of the biggest cluster is the author Michael F Green from the United States. The top three MESH terms are physiopathology, schizophrenic psychology, and complications. The scientific interest in schizophrenia remains significant. The application of bibliometric indicators of production is evident in the growth of scientific literature on the topic of schizophrenia.

Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data

Single-cell RNA-Sequencing is a pioneering extension of bulk-based RNA-Sequencing technology. The "guilt-by-association" heuristic has led to the use of gene co-expression networks to identify genes that are believed to be associated with a common cellular function. Many methods that were developed for bulk-based RNA-Sequencing data can continue to be applied to single-cell data, and several of the most widely used methods are explored. Several methods for leveraging the novel time information contained in single-cell data when constructing gene co-expression networks, which allows for the incorporation of directed associations, are also discussed.

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.

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.