Tag: statistics

Environ Sci Pollut Res Int. 2022 Jan 22. doi: 10.1007/s11356-022-18639-8. Online ahead of print.

ABSTRACT

Groundwater quality management is a priority in arid and semi-arid zones where water is scarce. Leachate from open dumping of municipal solid wastes may threaten groundwater quality. This research aimed at assessing groundwater quality of the aquifer of Shur river basin in Tehran province, Iran. The pollution potential of leachate from a landfill, located at the center of the basin, was estimated to assess its impact on the aquifer. Samples from 38 wells and 2 leachate ponds around the landfill were analyzed for their physico-chemical parameters and heavy metals. Leachate Pollution Index (LPI) and Water Quality Index (WQI) were calculated and multivariate statistical techniques were employed through geostatistical models to predict the spatial variability of groundwater quality and assess its contamination sources. The groundwater quality map was developed by GIS Interface. LPI indicated that leachate from the closed cell (LPI = 36) was more contaminating than that of the active site (LPI = 25). Kriging and cokriging geostatistical interpolation methods were applied to groundwater quality parameters. The best interpolation model was then identified through cross-validation with RMSE and GSD criteria. Cokriging yielded more accurate results than kriging. Spatial distribution maps showed high groundwater contamination and degraded water quality mainly in the central part of the basin, where the landfill was. Also, 293.7 ha of the study area possessed poor and very poor water quality, unsuitable for drinking. This study implicated multiple approaches for groundwater quality assessment and estimated its spatial structure as an effort toward effective groundwater quality management in Shur river basin.

PMID:35064881 | DOI:10.1007/s11356-022-18639-8

J Psycholinguist Res. 2022 Jan 22. doi: 10.1007/s10936-022-09836-0. Online ahead of print.

ABSTRACT

Simultaneous interpreting (SI) is a cognitively complex activity due to the concurrent nature of receiving and producing messages. Previous research confirms that SI is profoundly influenced by cognitive, attitudinal, and psychological mechanisms. Following this line of enquiry, the present investigation proposes a unique model by integrating cognitive and psychological factors related to the professional performance in SI. Specifically, this study examined a model to test the predictive and mediational effects of emotional intelligence, creativity, self-efficacy, and field dependence/independence (FD/FI) on simultaneous interpreting. A total of 248 university students majoring in Translation Studies completed measures of General Self-Efficacy Scale (GSES), Emotional-Quotient Inventory (EQ-I), Torrance Test of Creative Thinking (TTCT), General Embedded Figures Test (GMFT), and two SI tasks, namely the oral cloze test (OCT) and the listening and memory recall exercise (LMRE). The path analysis supported the direct effect of creativity and its indirect effects mediated by FD/FI on SI. Emotional intelligence made only a significant indirect effect on SI through FD/FI. Self-efficacy, on the other hand, made only a significant direct effect on SI. Emotional intelligence and creativity also contributed significantly to the prediction of FD/FI. The analyses also revealed a significant correlation between emotional intelligence and self-efficacy and also between creativity and emotional intelligence. Finally, FD/FI directly predicted simultaneous interpreting. Other hypothesized associations were not found to be statistically significant. The findings suggest that psychological attributes can have a great impact on students’ performance in simultaneous interpreting training exercises. Implications of the study and the research avenues are discussed.

PMID:35064859 | DOI:10.1007/s10936-022-09836-0

Neuroinformatics. 2022 Jan 22. doi: 10.1007/s12021-022-09561-y. Online ahead of print.

ABSTRACT

Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ) patients. We show that, by using Support Vector Machines or selected Artificial Neural Networks trained on image-based features associated with somas of hiPSCs derived neurons, we can predict very reliably SCZ and healthy control cells. In addition, our method reveals that [Formula: see text]III tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.

PMID:35064871 | DOI:10.1007/s12021-022-09561-y

JA Clin Rep. 2022 Jan 22;8(1):7. doi: 10.1186/s40981-022-00498-4.

ABSTRACT

BACKGROUND: Systemic inflammatory response occurs by sepsis and invasive surgery. Recent articles suggest that not only CRP but also procalcitonin, presepsin, and neutrophil gelatinase-associated lipocalin may reflect the severity of systemic inflammation. In addition, as systemic inflammation could degenerate orexin neurons, plasma orexin A might also be a good biomarker to predict the severity. Thus, we have determined relation between plasma biomarker and severity of illness score in patients with systemic inflammation.

METHODS: Previous database (UMIN000018427) was used to secondly determine which plasma biomarkers may predict the severity of illness in the ICU patients with systemic inflammation (n = 57, 31 non-sepsis surgical patients and 26 sepsis patients). We measured plasma levels of orexin A, CRP, procalcitonin, presepsin, and neutrophil gelatinase-associated lipocalin were measured, and APACHE II score was assessed in these patients at their admission to the ICU. Data are shown as mean ± SD. Statistical analyses were done with unpaired t test. The correlation between APACHE II score and plasma biomarkers were examined using Pearson’s correlation coefficient and a least squares linear regression line.

RESULTS: Demographic data did not differ between sepsis and non-sepsis groups. However, APACHE-II score was significantly higher in sepsis group than those in non-sepsis group (20.9 ± 6.6 vs 15.8 ± 3.2, p < 0.01). There were significant correlations between APACHE II score and plasma CRP (r = 0.532, p < 0.01), procalcitonin (r = 0.551, p < 0.01), presepsin (r = 0.510, p < 0.01), and neutrophil gelatinase-associated lipocalin (r = 0.466, P < 0.01) except orexin A.

CONCLUSION: All plasma biomarkers tested except orexin A may reflect the severity of illness in patients with systemic inflammation.

PMID:35064847 | DOI:10.1186/s40981-022-00498-4

Trop Anim Health Prod. 2022 Jan 22;54(1):72. doi: 10.1007/s11250-022-03075-0.

ABSTRACT

Pastoral and agro-pastoral farming are extensively practised in Ethiopia, and the main livestock kept are cattle, goats, sheep, poultry, and camels. The livestock sector is faced with complex challenges including limited availability of well-trained and skilled animal health professionals. The objective of this study was to identify and prioritise areas for training with the goal of providing evidence to guide strategies to improve the skills, delivery, and governance of veterinary services across Ethiopia. A cross-sectional survey was developed and administered electronically to veterinary professionals in Ethiopia using the Qualtrics platform. Data were collected on select parameters including demographics, diseases of economic significance, diagnosis, disease prevention, biosecurity, disease control, treatment, epidemiology, One Health, disease reporting, and the participants’ opinions about training. The survey data was downloaded in Microsoft Excel and descriptive statistics performed. A total of 234 veterinary professionals completed the survey. Most participants were male (89.7%) and aged between 26 and 35 years (81.2%). Of the total respondents, 56.4% worked in government and 8.5% in private practice. Most participants perceived training on laboratory diagnostic testing, disease prevention, antimicrobial resistance, antibiotic sensitivity testing, basic epidemiology, and clinical procedures, as most beneficial. In addition, most respondents would like to receive training on diseases affecting cattle, poultry, and small ruminants. The findings from this study provide baseline information on priority training areas for veterinary professionals and could potentially contribute to national efforts to develop and implement a continuing professional development programme in the veterinary domain, in view of improving veterinary service delivery.

PMID:35064854 | DOI:10.1007/s11250-022-03075-0

Oecologia. 2022 Jan 22. doi: 10.1007/s00442-022-05112-z. Online ahead of print.

ABSTRACT

In a recent paper, Renner et al. (Oecologia 195:825-831, 2021) concluded, without supporting statistical evidence, that increased density of managed honey-bee hives between 2019 and 2020 intensified competitive effects of honey bees on non-Apis bee species in the Munich Botanic Garden. Analysis of Renner et al.’s observations revealed that, contrary to their assumption, the change in hive numbers did not statistically alter honey-bee visitation to 29 plant species within or between years. Given this consistency, changes in the proportion of non-Apis bees among visitors of the surveyed plant species between years likely represent their responses to reduced overall availability of floral resources during 2020. Thus, Renner et al.’s observations do not provide convincing evidence that honey bees competitively reduced the abundance of non-Apis bees in the Munich Botanic Garden.

PMID:35064820 | DOI:10.1007/s00442-022-05112-z

Int Arch Occup Environ Health. 2022 Jan 22. doi: 10.1007/s00420-022-01835-x. Online ahead of print.

ABSTRACT

PURPOSE: The effects of electromagnetic fields of mobile phones on headaches have attracted researchers during the last decades. However, contradictory results have been reported so far.

METHODS: In this systematic review and meta-analysis, major databases including PubMed, Scopus and Web of Science were searched using suitable search terms and PRISMA guidelines to retrieve eligible studies for the effect of mobile phone use on headache. After the abstract and full-text screening, 33 studies were retrieved and the effect size in terms of odds ratio (OR) was extracted. Between-study heterogeneity was assessed using I² statistic and Q test, while publication bias was evaluated by funnel plot and Egger’s and Begg’s tests.

RESULTS: Among 33 eligible studies, 30 eligible studies were included in the meta-analysis. When considering all studies, the pooled effect size of OR = 1.30(95% CI 1.21-1.39) was obtained, while the heterogeneity between studies was significant. Subgroup analyses by considering the age of participants and EMF exposure duration were performed to find the source of heterogeneity. The odds ratios when the age of participants was the variable were 1.33 (95% CI 1.14-1.53) and 1.29 (95% CI 1.20-1.37), for ages > 18 and age ≤ 18 years, respectively. When EMF exposure duration was considered, subgroup analysis obtained the pooled effect size of OR = 1.41(95% CI 1.22-1.61) and 1.23(95% CI 1.12-1.34), for EMF exposure duration > 100 and ≤ 100 minutes per week, respectively. The pooled effect sizes emphasized the effect of mobile phone use on headaches for all ages and exposure durations.

CONCLUSION: Results revealed that age and exposure duration (mainly call duration), both were the source of heterogeneity between studies. Furthermore, results showed that increasing call duration and mobile phone use in older individuals increased the risk of headache.

PMID:35064837 | DOI:10.1007/s00420-022-01835-x

Environ Monit Assess. 2022 Jan 22;194(2):115. doi: 10.1007/s10661-022-09749-4.

ABSTRACT

This research aimed to determine the background and precautionary values of Cd, Cr, Cu, Ni, Pb, Zn, Co, As, and Hg and provide the spatial distribution of these metals in Albania’s soils using statistical and geostatistical methods. Since the distribution of the data was commonly not Gaussian, they were transformed into their natural logarithm for deriving background concentrations and precautionary values. Background concentrations were defined as antilog of the median. Precautionary concentration values for soil protection were defined as antilog of 90th percentile of the ln-transformed data. Background concentrations in forest soils were larger compared to those in soils under other land use types. The largest background concentrations for Cd, Cr, Ni, Cu, Co, and Zn were found in forest soils formed on magmatic rocks, while largest concentrations of Pb and Hg were found in soils formed on flysch and molasse. The background values and precautionary concentration values (in brackets) (mg kg^-1) for agricultural soils across flysch/molasses and quaternary deposits were as follows: Cd 0.24 (0.82), Cr 131.63 (527.42), Cu 41.26 (72.75), Ni 287.15 (632.70), Pb 19.11 (284.86), Zn 81.80 (113.90). The largest background values for Cd and Zn were found in Phaeozems, for Cr, Pb, and Co in Luvisols and Cambisols, and for Cu, Hg, and Ni in Fluvisols. The proposed background concentrations and precautionary values complete the picture of metal contents in European soils, can be used as reference concentrations for the Albanian environmental legislation, and allow the identification of potential contamination hot spots.

PMID:35064814 | DOI:10.1007/s10661-022-09749-4

Eur Radiol. 2022 Jan 22. doi: 10.1007/s00330-021-08459-8. Online ahead of print.

ABSTRACT

OBJECTIVES: To compare the image quality and radiation dose of a deep learning image reconstruction (DLIR) algorithm compared with iterative reconstruction (IR) and filtered back projection (FBP) at different tube voltages and tube currents.

MATERIALS AND METHODS: A customized body phantom was scanned at different tube voltages (120, 100, and 80 kVp) with different tube currents (200, 100, and 60 mA). The CT datasets were reconstructed with FBP, hybrid IR (30% and 50%), and DLIR (low, medium, and high levels). The reference image was set as an image taken with FBP at 120 kVp/200 mA. The image noise, contrast-to-noise ratio (CNR), sharpness, artifacts, and overall image quality were assessed in each scan both qualitatively and quantitatively. The radiation dose was also evaluated with the volume CT dose index (CTDI_vol) for each dose scan.

RESULTS: In qualitative and quantitative analyses, compared with reference images, low-dose CT with DLIR significantly reduced the noise and artifacts and improved the overall image quality, even with decreased sharpness (p < 0.05). Despite the reduction of image sharpness, low-dose CT with DLIR could maintain the image quality comparable to routine-dose CT with FBP, especially when using the medium strength level.

CONCLUSION: The new DLIR algorithm reduced noise and artifacts and improved overall image quality, compared to FBP and hybrid IR. Despite reduced image sharpness in CT images of DLIR algorithms, low-dose CT with DLIR seems to have an overall greater potential for dose optimization.

KEY POINTS: • Using deep learning image reconstruction (DLIR) algorithms, image quality was maintained even with a radiation dose reduced by approximately 70%. • DLIR algorithms yielded lower image noise, higher contrast-to-noise ratios, and higher overall image quality than FBP and hybrid IR, both subjectively and objectively. • DLIR algorithms can provide a better image quality, much better than FBP and even better than hybrid IR, while facilitating a reduction in radiation dose.

PMID:35064803 | DOI:10.1007/s00330-021-08459-8

Environ Manage. 2022 Jan 22. doi: 10.1007/s00267-021-01588-2. Online ahead of print.

ABSTRACT

This study sought to assess how smallholder farmers have been living and responding to impacts of climate change in Hanang’ District, Tanzania. Qualitative and quantitative data were collected using key informant interviews, household surveys, focus group discussions (FGDs) and field observations. Quantitative data from the questionnaire survey were analyzed using Statistical Package for Social Sciences (SPSS), whilst, qualitative data were exposed to content analysis. Rainfall and temperature trends were analyzed using Microsoft Excel and the significance of the trends determined using Mann-Kendall and CUSUM analysis. Most respondents (78%) revealed decreased rainfall amounts and changed onset, and 94% reported increased temperature. Farmers disclosed that droughts and floods are major climatic stresses in the area; this was substantiated by observed increasing and decreasing temperature and rainfall trends respectively. This corroborated with most respondents who perceived decreased rainfall amounts and changed onset, and reported increased temperature levels. Response strategies include crop diversification and drought-resistant crop varieties, migration, abandoning some crops, and short-cycle crops. However, smallholder farmers have been failing to effectively address climatic challenges. We argue that they are still heavily reliant on social, economic, and policy support to improve their adaptive capacity, particularly, transformative responses.

PMID:35064806 | DOI:10.1007/s00267-021-01588-2