Development and validation of a classification model for boar taint detection in pork fat samples

This study aims to characterize a complete volatile organic compound profile of pork neck fat for boar taint prediction. The objectives are to identify specific compounds related to boar taint and to develop a classification model. In addition to the well-known androstenone, skatole and indole, 10 other features were found to be discriminant according to untargeted volatolomic analyses were conducted on 129 samples using HS-SPME-GC×GC-TOFMS. To select the odor-positive samples among the 129 analyzed, the selection was made by combining human nose evaluations with the skatole and androstenone concentrations determined using UHPLC-MS/MS. A comparison of the data of the two populations was performed and a statistical model analysis was built on 70 samples out of the total of 129 samples fully positive or fully negative through these two orthogonal methods for tainted prediction. Then, the model was applied to the 59 remaining samples. Finally, 7 samples were classified as tainted.

Evaluation of the scaling and corrosive potential of the cooling water supply system of a nuclear power plant based on the physicochemical control dataset

The data of physicochemical control for the cooling water supply system of the Rivne Nuclear Power Plant (Ukraine), where water samples were monitored three times a day during 2022-2023. The pH, temperature, total dissolved salts, total hardness and total alkalinity were measured using standard methods. The differences in ϕ and ψ, Langelier saturation index (LSI) and Ryznar stability index (RSI), which characterise scaling and corrosive potential, were calculated The calculated values are ϕ - ψ: 0.29 (± 0.62), LSI: 1.51 (± 0.39), and RSI 5.74 (± 0.69). According to the scaling and corrosive classification, the water is characterised as susceptible to scale formation. Moreover, to the Pearson correlation coefficient (ρ), there is a very strong relationship ρ = -0.9635 between LSI and RSI, a weak relationship ρ = -0.2370 between ϕ - ψ and RSI, and ρ = -0.2997 between ϕ - ψ and LSI.

Modeling of liquid-liquid hollow fiber supported membrane equilibrium including the non-ideality of the aqueous and organic phases in the recovery systems of radionuclides in sulphate-halide/aliquat-336 in O-xylene

Study of modeling of L/LHFSLM equilibrium based on the Non-ideality of the Aqueous and Organic Phases in the Recovery of 152+154Eu in H2SO4-Halides/Aliquat-336 in 20% kerosene as feeding phase at pH 3.78-4.55, by the ratio 89.7%,while separation of 90Sr and 134Cs there was a problem between them by using hollow HFSLM only, the reason for that the organic solvents affect the rate of reaction in the Diamino-1,2-cyclohexane/tetraacetic acid (DCTA) as stripping phase concentration from 0.15 to 0.55 mol/L. The system has been developed; this led to the extraction of elements in the same time. The Matlab software program was introduced to obtain some mathematical parameters like, standard deviation (SD), equilibrium constant Kex and standard deviation coefficient (SDC).

An evaluation of treatment response and remission definitions in adult obsessive-compulsive disorder: A systematic review and individual-patient data meta-analysis

INTRODUCTION

Expert consensus operationalized treatment response and remission in obsessive-compulsive disorder (OCD) as a Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) reduction ≥35% and score ≤12 with ≤2 on Clinical Global Impressions Improvement (CGI-I) and Severity (CGI-S) scales, respectively. However, there has been scant empirical evidence supporting these definitions.

METHODS

We conducted a systematic review and an individual participant data meta-analysis of randomized-controlled trials (RCTs) in adults with OCD to determine optimal Y-BOCS thresholds for response and remission. We estimated pooled sensitivity/specificity for each percent reduction threshold (response) or posttreatment score (remission) to determine response and remission defined by a CGI-I and CGI-S ≤ 2, respectively.

RESULTS

Individual participant data from 25 of 94 eligible RCTs (1235 participants) were included. The optimal threshold for response was ≥30% Y-BOCS reduction and for remission was ≤15 posttreatment Y-BOCS. However, differences in sensitivity and specificity between the optimal and nearby thresholds for response and remission were small with some uncertainty demonstrated by the confidence ellipses.

CONCLUSION

While the empirically derived Y-BOCS thresholds in our meta-analysis differ from expert consensus, given the predominance of data from more recent trials of OCD, which involved more refractory participants and novel treatment modalities as opposed to first-line therapies, we recommend the continued use of the consensus definitions.

Triticale field phenotyping using RGB camera for ear counting and yield estimation

Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018-2019 and 2019-2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Spatiotemporal dynamics of forest fires in the context of climate change: a review

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about -8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (-0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

Prevalence and factors associated with sarcopenia among Brazilian older adults: An exploratory network analysis

OBJECTIVES

This study aimed to verify the prevalence of sarcopenia and its associations with sociodemographic, clinical and psychological factors in community-dwelling older adults.

STUDY DESIGN

A randomized cross-sectional study was extracted from a probabilistic cluster conducted on individuals aged 65 years or older residing in the community.

METHODS

Sarcopenia was defined according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP2). Body composition was assessed using dual-energy X-ray absorptiometry (DXA). Associations were analyzed using networks based on mixed graphical models. Predictability indices of the estimated networks were assessed using the proportion of explained variance for numerical variables and the proportion of correct classification for categorical variables.

RESULTS

The study included 278 participants, with a majority being female (61 %). The prevalence of sarcopenia was 39.57 %. Among those with sarcopenia, 67 % were women and 33 % were men. In the network model, age, race, education, family income, bone mass, depression, cardiovascular disease, diabetes, total cholesterol levels and rheumatism were associated with sarcopenia. The covariates demonstrated a high accuracy (62.9 %) in predicting sarcopenia categories.

CONCLUSION

The prevalence of sarcopenia was high, especially in women. In addition, network analysis proved useful in visualizing complex relationships between sociodemographic and clinical factors with sarcopenia. The results suggest early screening of sarcopenia for appropriate treatment of this common geriatric syndrome in older adults in Brazil.

Pneumococcal conjugate vaccination schedules in infants-acquisition, immunogenicity, and pneumococcal conjugate and yellow fever vaccine co-administration study: statistical analysis plan

RATIONALE

The effectiveness of immunisation with pneumococcal conjugate vaccine (PCV) has been demonstrated in many countries. However, the global impact of PCV is limited by its cost, which has prevented its introduction in some countries. Reducing the cost of PCV programmes will facilitate further vaccine introductions and improve the sustainability of PCV in low-income countries when they transition from subsidised vaccine supply. We are conducting a large, population-level, cluster-randomised field trial (PVS) of an alternative reduced-dose schedule of PCV compared to the standard schedule. We are also conducting a nested sub-study at the individual level to investigate the immunogenicity of the two schedules and their effects on pneumococcal carriage acquisition (PVS-AcqImm).

METHODS AND DESIGN

PVS-AcqImm is a prospective, cluster-randomised trial of an alternative schedule of one dose of PCV scheduled at age 6 weeks with a booster dose at age 9 months compared to the standard of three primary doses scheduled at 6, 10, and 14 weeks of age. Sub-groups within the alternative schedule group receive yellow fever vaccine separately or co-administered with PCV at 9 months of age. The primary endpoints are (a) concentrations of vaccine-type anti-pneumococcal IgG at 18 months of age, (b) proportions with yellow fever neutralising antibody titre ≥ 1:8 4 weeks after separate or co-administration of PCV and yellow fever vaccines, and (c) rate of nasopharyngeal vaccine-type pneumococcal acquisition from 10-14 months of age. Participants and field staff are not masked to group allocation while measurement of the laboratory endpoints is masked. Approximately equal numbers of participants are resident in each of 28 randomly allocated geographic clusters (14 clusters in each group); 784 enrolled for acquisition measurements and 336 for immunogenicity measurements.

PURPOSE

This statistical analysis plan (SAP) describes the PVS-AcqImm cohort and follow-up criteria to be used in different analyses. The SAP defines the endpoints and describes how adherence to the interventions will be presented. We describe the approach to analyses and how we will account for the effect of clustering. Defining the SAP prior to the conduct of analysis will avoid bias in analyses that may arise from prior knowledge of trial findings.

TRIAL REGISTRATION

ISRCTN, ISRCTN7282161328. Registered on 28 November 2019. https://www.isrctn.com/ISRCTN72821613 .

PROTOCOL

MRCG SCC number 1670, LSHTM Ref 17683. Current protocol version: 6.0, 24 May 2021. Version: 1.0 (5 April 2023); SAP revisions-none.

Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion

Seawater intrusion in coastal aquifers is a significant problem that can be addressed through the construction of subsurface dams or physical cut-off barriers. An alternative method is the use of microbially induced carbonate precipitation (MICP) to reduce the hydraulic conductivity of the porous medium and create a physical barrier. However, the effectiveness of this method depends on various factors, and the scientific literature presents conflicting results, making it challenging to generalise the findings. To overcome this challenge, a statistical and machine learning (ML) approach is employed to infer the causes for the reduction in hydraulic conductivity and identify the optimum MICP parameters for preventing seawater intrusion. The study involves data curation, exploratory analysis, and the development of various models to fit the input data (k-Nearest Neighbours - kNN, Support Vector Regression - SVR, Random Forests - RF, Gradient Boosting - XgBoost, Linear model with interaction terms, Ensemble learning algorithms with weighted averages - EnL-WA and stacking - EnL-Stack). The models performed reasonably well in the region where permeability reduction is sensitive to carbonate increase capturing the permeability reduction profile with respect to cementation level while demonstrating that they can be used in initial assessments of the specific conditions (e.g., soil properties). The best performing algorithms were the EnL-Stack and RF followed by XgBoost and SVR. The MICP method is effective in reducing hydraulic conductivity provided that the various biochemical parameters are optimised. Critical biochemical parameters for successful MICP formulations are the bacterial optical density, the urease activity, calcium chloride concentration and flow rate as well as the interaction terms across the properties of the porous media and the biochemical parameters. The models were used to identify the optimum MICP formulation for various porous media properties and the maximum permeability reduction profiles across cementation levels have been derived.

Geochemical processes, salinity sources and utility characterization of groundwater in a semi-arid region of Iraq through geostatistical and isotopic techniques

Identifying factors contributing to water salinity is paramount in efficiently managing limited water resources in arid environments. The primary objective of this study is to enhance understanding regarding the hydrochemistry, source, and mechanism of water salinity, as well as to assess the suitability of water for various uses in southern Iraq. The groundwater samples were collected from water wells and springs and analyzed for major cations and anions along with stable isotopes (δ18O and δ2H) to accomplish the objective. The analysis of major ion chemistry, hydrochemical techniques, principal component analysis (PCA), and isotope signatures were adopted to determine the primary factors contributing to water mineralization. The study inferred that evaporation and geological processes encompassing water-rock interactions, such as dissolution precipitation and ion exchange, were key processes. The stable isotope analysis revealed that the water originated from meteoric sources and underwent significant evaporation during or before infiltration. The utility assessment of water samples indicates that most samples are not appropriate for consumption and are significantly below the established standards for potable water. In contrast, a significant portion of the groundwater samples were found to meet the criteria for irrigation suitability by adopting Wilcox and the US Salinity Laboratory criteria. The groundwater could be considered for irrigation with proper salinity control management. Overall, this study has significantly improved the understanding of the hydrogeochemical regimes and acts as a first step toward the sustainable utilization of water resources.

Elemental analysis using portable X-ray fluorescence: Guidelines for the study of dry human bone

OBJECTIVE

X-ray fluorescence (XRF) is a non-destructive technique that measures the elemental concentration of different materials, including human bone. Recently, it began to be applied to paleopathological studies due to the development of portable devices and their relative ease of use. However, the lack of uniform procedures hampers comparability and reproducibility. This paper aims to provide guidelines for an efficient and standardized evaluation of bone elemental composition with a portable XRF (pXRF) device.

MATERIALS

This technical note is based on the application of the Thermo Scientific Niton XL3t 900 GOLDD+.

METHODS

This work includes suggestions for the choice and preparation of human bone samples, both from archaeological context and documented collections, and methodological procedures in pXRF setup, such as choice of calibration, assessment of accuracy, and analysis run time. Additionally, recommendations for data validation and statistical analysis are also included.

CONCLUSIONS

This technique has great potential in paleopathology since bone chemical variations may be associated with different pathological conditions, environmental contamination (e.g., lead), and/or administered treatments, such as mercury. Following an expected increase in the number of studies, it is essential to establish good practices that allow results from different researchers to be comparable.

SIGNIFICANCE

X-ray fluorescence is a non-destructive technique that measures small concentrations (ppm) of elements from magnesium (12Mg) through bismuth (83Bi).

LIMITATIONS

pXRF does not detect elements lighter than Mg, and its lower energy excitation penetrates less than other techniques.

SUGGESTIONS FOR FURTHER RESEARCH

Other research groups should test these guidelines and comment on their usefulness and replicability.

Risk factors as causes of accidents: Criterion of causality, logical structure of relationship to accidents and completeness of explanations

The causes of accidents are studied in the belief that by finding causes, accidents can be prevented by removing or controlling their causes. It follows that the risk factors that have traditionally been regarded as contributing to accidents can only be regarded as causes if it is possible to alter them by means of one or more road safety measures. Risk factors are causes if their relationship to accidents can be changed by implementing one or more road safety measures influencing the risk factors. Hence, road safety measures that could have been implemented to change risk factors identified as contributing to an accident, but have not, are also causes of accidents. Many of the human factors that have traditionally been identified as risk factors for accidents, like age, gender, driving experience, expectations or involuntary inattention are not causes of accidents, because they cannot be changed by means of any realistic road safety measure. What cannot be changed (could not have been different) is not a cause. It is possible, both in case studies and in statistical analyses, to determine when a set of factors precipitating or contributing to accidents is complete. A list of road safety measures that could have been implemented is only limited by our creativity and imagination and will therefore never be complete.

Optimization of 3D printing parameters in polylactic acid bio-metamaterial under cyclic bending loading considering fracture features

3D printing has become a crucial additive manufacturing technique with the applications in various industries. Fused deposition modeling (FDM) is a common additive manufacturing process that offers considerable flexibility in the component fabrication through multiple parameters, which strongly influence the properties of the produced parts. This study focused on the impact of different printing parameters on the fatigue behavior of polylactic acid (PLA). The standard samples were 3D-printed with varying speed (5, 10, and 15 mm/s), print temperature (180, 210, and 240 °C), and nozzle diameter (0.2, 0.4, and 0.6 mm). The fatigue properties were evaluated through rotating bending fatigue tests, and a model was developed based on the results with a statistical analysis. The model accuracy was validated and the interactions between the parameters were analyzed. The optimization study found that a print speed of 5 mm/s, print temperature of 210 °C, and nozzle diameter of 0.2 mm were optimal. The fracture surfaces were also examined using a scanning electron microscopy, revealing the presence of crazing despite the brittle behavior of PLA.

Designing and evaluating tasks to measure individual differences in experimental psychology: a tutorial

Experimental psychology is witnessing an increase in research on individual differences, which requires the development of new tasks that can reliably assess variations among participants. To do this, cognitive researchers need statistical methods that many researchers have not learned during their training. The lack of expertise can pose challenges not only in designing good, new tasks but also in evaluating tasks developed by others. To bridge the gap, this article provides an overview of test psychology applied to performance tasks, covering fundamental concepts such as standardization, reliability, norming and validity. It provides practical guidelines for developing and evaluating experimental tasks, as well as for combining tasks to better understand individual differences. To further address common misconceptions, the article lists 11 prevailing myths. The purpose of this guide is to provide experimental psychologists with the knowledge and tools needed to conduct rigorous and insightful studies of individual differences.

Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)

Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.

Modeling the behavior of monoclonal antibodies on hydrophobic interaction chromatography resins

Monoclonal antibodies (mAbs) require a high level of purity for regulatory approval and safe administration. High-molecular weight (HMW) species are a common impurity associated with mAb therapies. Hydrophobic interaction chromatography (HIC) resins are often used to remove these HMW impurities. Determination of a suitable HIC resin can be a time and resource-intensive process. In this study, we modeled the chromatographic behavior of seven mAbs across 13 HIC resins using measurements of surface hydrophobicity, surface charge, and thermal stability for mAbs, and hydrophobicity and zeta-potential for HIC resins with high fit quality (adjusted R2 > 0.80). We identified zeta-potential as a novel key modeling parameter. When using these models to select a HIC resin for HMW clearance of a test mAb, we were able to achieve 60% HMW clearance and 89% recovery. These models can be used to expedite the downstream process development for mAbs in an industry setting.

Radiological landscape of natural resources and mining: Unveiling the environmental impact of naturally occurring radioactive materials in Ghana's mining areas

This study provides a general observation of the status of naturally occurring radioactive materials (NORMs) distribution in mining and industrial areas of Ghana in order to establish regional and national data on NORMs. The study includes data on radioactivity concentrations of U-238, Th-232, and K-40 in soils and for water concentrations of Ra-226, Th-228, and K-40 from various mining, oil, and gas communities, as well as water sources used for crop farming and farmlands. The average activity concentrations of U-238, Th-232, and K-40 in the soil samples were found to be 59 ± 16 Bq/kg, 48 ± 15 Bq/kg, and 286 ± 57 Bq/kg, respectively. The average concentration of Ra-226, Th-228, and K-40 in the water samples were found to be 1.62 ± 0.33 Bq/L, 2.08 ± 0.53 Bq/L, and 22.36 ± 3.44 Bq/L, respectively. The estimated average annual effective doses from external and internal exposure pathways in soil and water samples were 0.09 mSv/y and 0.54 mSv/y, respectively. The total annual effective dose resulting from both exposure pathways was calculated to be 0.63 mSv/y, which is below the 1 mSv/y dose limit recommended by the International Commission on Radiological Protection (ICRP) for controlling public radiation exposure. Based on the radiological hazard indices, the majority of the soil samples were found to be suitable as building materials as their respective indices were below the limits except for two sample locations and the sludge and scale samples. The average Excess Lifetime Cancer Risk (ELCR) value of the water samples was 1.6 times greater than the recommended value of 1.16 × 10-3, presenting a relatively higher risk to the public of developing cancer. No significant regional differences in the levels of radioactive elements. The regression models demonstrate strong interrelationships between the studied elements, with high R-squared values suggesting a predictable nature of one element's concentration based on others.

Characterising microplastics in indoor air: Insights from Raman imaging analysis of air filter samples

We are directly exposed to microplastic contamination via indoor air that we breathe daily, for which the characterisation of microplastics is still a challenge. Herein, two typical air filter samples were collected, one from an air-conditioner and another from a personal computer, both of which have been working for around half a year to collect and accumulate microplastics in the indoor air, like microplastic banks. After the sample preparation to remove the mineral dusts, Raman imaging was employed to directly and simultaneously identify and visualise microplastics of polyethylene terephthalate (PET) fibres, distinguish them from other fibres such as cellulose and cross-check them with a scanning electron microscope (SEM). To count the microplastics and to avoid the quantification bias, several areas were randomly scanned and imaged to statistically estimate the percentage of microplastic fibres in the analysed samples. The microplastics amount, which has been estimated at 73-88,000 fibers per filter per half a year, varies and depends on the indoor environment so that the air filter can work as a good indicator to monitor the quality of the indoor air from the microplastic perspective. Overall, human are directly exposed to this emerging contamination every day, raising environmental concerns. Raman imaging characterisation and its corresponding statistical information can help pursue further research on microplastics.

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

BACKGROUND

The prevalence of depression is increasing in the elderly population, and growing evidence suggests that malnutrition impacts mental health. Despites, research on the factors that predict depression is limited.

METHODS

We included 2946 elderly individuals from National Health and Nutrition Examination Survey (NHANES) spanning the years 2011 through 2014. Depressive symptoms were assessed using the PHQ-9 scale. Multinomial logistic regression was performed to evaluate the independent association between Geriatric Nutritional Risk Index (GNRI) and depression prevalence and scores. Subgroup analysis was conducted to explore potential factors influencing the negative correlation between GNRI and depression. Restricted cubic spline graph was employed to examine the presence of a non-linear relationship between GNRI and depression.

RESULTS

The depression group had a significantly lower GNRI than the non-depression group, and multivariate logistic regression showed that GNRI was a significant predictor of depression (P < 0.001). Subgroup analysis revealed that certain demographic characteristics were associated with a lower incidence of depression in individuals affected by GNRIs. These characteristics included being female (P < 0.0001), non-Hispanic black (P = 0.0003), having a moderate BMI (P = 0.0005), having a college or associates (AA) degree (P = 0.0003), being married (P = 0.0001), having a PIR between 1.50 and 3.49 (P = 0.0002), being a former smoker (P = 0.0002), and having no history of cardiovascular disease (P < 0.0001), hypertension (P < 0.0001), and diabetes (P = 0.0027). Additionally, a non-linear negative correlation (non-linear P < 0.01) was found between GNRI and depression prevalence, with a threshold identified at GNRI = 104.17814.

CONCLUSION

The GNRI demonstrates efficacy as a reliable indicator for forecasting depression in the elderly population. It exhibits a negative nonlinear correlation with the prevalence of depression among geriatric individuals.

Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.

Association between vaccination rates and COVID-19 health outcomes in the United States: a population-level statistical analysis

BACKGROUND

Population-level vaccine efficacy is a critical component of understanding COVID-19 risk, informing public health policy, and mitigating disease impacts. Unlike individual-level clinical trials, population-level analysis characterizes how well vaccines worked in the face of real-world challenges like emerging variants, differing mobility patterns, and policy changes.

METHODS

In this study, we analyze the association between time-dependent vaccination rates and COVID-19 health outcomes for 48 U.S. states. We primarily focus on case-hospitalization risk (CHR) as the outcome of interest, using it as a population-level proxy for disease burden on healthcare systems. Performing the analysis using Generalized Additive Models (GAMs) allowed us to incorporate real-world nonlinearities and control for critical dynamic (time-changing) and static (temporally constant) factors. Dynamic factors include testing rates, activity-related engagement levels in the population, underlying population immunity, and policy. Static factors incorporate comorbidities, social vulnerability, race, and state healthcare expenditures. We used SARS-CoV-2 genomic surveillance data to model the different COVID-19 variant-driven waves separately, and evaluate if there is a changing role of the potential drivers of health outcomes across waves.

RESULTS

Our study revealed a strong and statistically significant negative association between vaccine uptake and COVID-19 CHR across each variant wave, with boosters providing additional protection during the Omicron wave. Higher underlying population immunity is shown to be associated with reduced COVID-19 CHR. Additionally, more stringent government policies are generally associated with decreased CHR. However, the impact of activity-related engagement levels on COVID-19 health outcomes varied across different waves. Regarding static variables, the social vulnerability index consistently exhibits positive associations with CHR, while Medicaid spending per person consistently shows a negative association. However, the impacts of other static factors vary in magnitude and significance across different waves.

CONCLUSIONS

This study concludes that despite the emergence of new variants, vaccines remain highly correlated with reduced COVID-19 harm. Therefore, given the ongoing threat posed by COVID-19, vaccines remain a critical line of defense for protecting the public and reducing the burden on healthcare systems.

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

A dynamic Eu(III)-macrocycle served as the turn-on fluorescent probe for distinguishing H2O from D2O

H2O and D2O are an important pair of analogues, and their high-efficient detections are closely related to fields of chemical industry, food processing, semiconductor, environmental monitoring, etc. Because of their extremely similar physical and chemical properties, H2O and D2O can be mutually soluble in any ratios, and it is generally thought that the discrimination of H2O and D2O is an enormous challenge. Herein, upon the fact that vibrational frequency of O-H is greater than O-D, we design a dynamic Eu(III)-macrocycle Eu-2a with two emitters which exhibits the imine bond breakage of macrocycle emitter H2L2a in H2O or D2O, resulting in the turn-on fluorescence of Eu(III) emitter. For their differential fluorescence sensing signals of Eu-2a on three emission bands (433, 500 and 615 nm), the statistical analysis method is employed to produce fully separated fingerprints and thus high-throughput discrimination of 13 common solvents, especially the H2O and D2O. Fluorescent titration experiments by instrumental or smartphone-based analysis method also prove the successful determination of proportional H2O/D2O mixtures together with the good sensitivity and wide linear response range. Moreover, this H2O-triggered fluorescent complex Eu-2a used as the fluorescence ink also shows its potential in information encryption application. This article must be a valuable reference for the areas of lanthanide-based luminescent material, multianalyte detection and information encryption.

A longitudinal analysis of the impact of the local tobacco retail availability and neighbourhood deprivation on male smoking behaviours in Shanghai, China

Some evidence from Western high-income countries suggests local tobacco retail availability and neighbourhood deprivation may influence smoking behaviours. However, this assertion has not been considered in China, where 44% of males continue to smoke. Data were analysed from Chinese males (n = 2054) who participated in Waves 3-5 (2009-2015) of the International Tobacco Control (ITC) China Survey by linking information on tobacco retail availability (estimated through population weighted Kernel Density of tobacco retailers in 2019) and neighbourhood deprivation (calculated as a composite score derived from the 2010 Chinese census) across Shanghai. Generalised Estimating Equation models were fitted to examine the impacts of local tobacco availability and neighbourhood deprivation on smoking behaviours (current smoking versus current non-smoking, quitting versus current smoking, longer durations of smoking abstinence versus current smoking) using the longitudinal data. Examining the impacts separately, participants living in neighbourhoods with greater availability and higher levels of deprivation were less likely to maintain longer durations of smoking abstinence in both unadjusted and adjusted models. Neighbourhood deprivation, but not availability, was found to be associated with higher odds of being a current smoker. Examining the impacts jointly, neighbourhood deprivation was still positively associated with current smoking and negatively associated with longer durations of smoking abstinence, but the negative association between availability and longer durations of smoking abstinence disappeared. The findings offer some evidence that greater tobacco retail availability and deprivation are obstacles on prolonged smoking cessation among males in Shanghai, China. Policymakers should consider small-area level place-based restrictions in China, such as reducing the availability of tobacco, as part of a comprehensive tobacco control strategy aimed at addressing the high prevalence of smoking.

Radon concentration in spring water as an indicator of seismic activity: a case study of the Muzaffarabad Fault in Pakistan

Radon and its progenies found in water indicate the existence of seismically active faults in the region. However, exposure to high levels of radon can also result in radiation-related health risks. This study focuses on radon-based active tectonic studies along the Muzaffarabad Fault in the core of the Hazara-Kashmir Syntaxis (HKS), NW Himalayas, Pakistan. In this study, spring water samples were collected along roadside of Jhelum Valley and in close proximity to the Muzaffarabad Fault in Pakistan using Radon Thoron Monitor (RTM1688-2). The results of the study showed that the radon concentrations in the water samples ranged from 1.895 to 17.097 Bq/l. The study found that the highest radon concentration was observed in the samples collected closest to the fault, while the lowest concentration was observed in the samples collected further away. The statistical analysis between the radon concentration and the distance from the fault showed a strong inverse relationship (R2=0.73). The study also found that 68% of the sampling sites had radon concentrations that exceeded the maximum contamination level (MCL) set by the US Environmental Protection Agency (EPA). The higher radon concentrations in the springs water suggest the probability of earthquake, which in turn poses potential health risks for the local population. The findings suggest that the measurement of radon concentration in water can be used as a tool for identifying seismically active faults in the region.

Statistically and visually analyzing the latest advancements and future trends of uranium removal

Uranium contamination and remediation is a very important environmental research area. Removing radioactive and toxic uranium from contaminated media requires fundamental knowledge of targets and materials. To explore the-State-of-the-Art in uranium contamination control, we employed a statistical tool called CiteSpace to visualize and statistically analyze 4203 peer-reviewed papers on uranium treatment published between 2008 and 2022. The primary content presentations of visual analysis were co-authorships, co-citations, keyword co-occurrence analysis with cluster analysis, which could offer purposeful information of research hots and trends in the field of uranium removal. The statistical analysis results indicated that studies on uranium removal have focused on adsorption of uranium from aqueous solution. From 2008 to 2022, biochar and biological treatment were firstly used to sequester uranium, then adsorption for uranium removal dominates with adsorbents of graphene oxide, primary nanofiber magnetic polymers and metal-organic frameworks (MOFs). In recent years, photocatalysts and metal-organic frameworks are expected to be two of the most popular research topics. In addition, we further highlighted the characteristics and applications of MOFs and GOs in uranium removal. Overall, a statistical review was proposed to visualize and summarize the knowledge and research trends regarding uranium treatment.

Establishing the original order of the poems in Harward's Almanac using paleography, codicology, X-ray fluorescence spectroscopy, and statistical analysis

This work presents the results of a transdisciplinary analysis performed on Harward's Almanac (Dublin, 1666), an extremely rare volume currently housed in the National Library of Ireland. The uniqueness and historical value of the Almanac is related to the presence of nineteen handwritten poems, entered by an anonymous scribe. These record textually important English clandestine satire circulating anonymously in Dublin in the late seventeenth and early eighteenth century. Following a comprehensive historical assessment, it appeared evident that the current order of leaves was incorrect. To reconstruct the correct order of the leaves, and hence the likely sequence in which the manuscript poems were inscribed, this study employed a codicological/paleographic analysis complemented by analytical (X-ray fluorescence, XRF) and statistical (Self Organizing Map, SOM) investigation. Specifically, point XRF analysis was carried out for each handwritten page of the Almanac, allowing identification of ink elemental compositions (iron-based ink) and successfully supporting the validity of historical hypotheses on the poems' order of inscription. The statistical organization of XRF data by SOMs allowed easy bi-dimensional visualization of the data set (54 points) and identification of ink similarities, once more validating the historical assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40494-023-01107-y.

The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout

BACKGROUND

Dual energy computed tomography (DECT) allows direct visualization of monosodium urate crystal deposition in gout. However, DECT urate volume data are often highly skewed (mostly small volumes with the remainder considerably larger), making statistical analyses challenging in longitudinal research. The aim of this study was to explore the ability of various analysis methods to normalise DECT urate volume data and determine change in DECT urate volumes over time.

METHODS

Simulated datasets containing baseline and year 1 DECT urate volumes for 100 people with gout were created from two randomised controlled trials. Five methods were used to transform the DECT urate volume data prior to analysis: log-transformation, Box-Cox transformation, log(X-(min(X)-1)) transformation; inverse hyperbolic sine transformation, and rank order. Linear regression analyses were undertaken to determine the change in DECT urate volume between baseline and year 1. Cohen's d were calculated as a measure of effect size for each data treatment method. These analyses were then tested in a validation clinical trial dataset containing baseline and year 1 DECT urate volumes from 91 people with gout.

RESULTS

No data treatment method successfully normalised the distribution of DECT urate volumes. For both simulated and validation data sets, significant reductions in DECT urate volumes were observed between baseline and Year 1 across all data treatment methods and there were no significant differences in Cohen's d effect sizes.

CONCLUSIONS

Normalising highly skewed DECT urate volume data is challenging. Adopting commonly used transformation techniques may not significantly improve the ability to determine differences in measures of central tendency when comparing the change in DECT urate volumes over time.

The chemical profile and seasonal variation of the composition of the phenolic acids in different plant parts of Centaurea sp

This study investigated the seasonal variation (over seven months) of phenolic acid (PHA) components in different parts of Centaurea sp. The primary objective was to determine the pattern of variation, while the secondary objective was to identify which month or growth stage provides a greater total PHA content or percentage of bioactive components. Different patterns of seasonal variations were highlighted for the different PHA components and their classes (hydroxybenzoic and -cinnamic acids) in different parts of the plant. The leaves exclusively provided the highest PHA contents, with maximum values reached in April (1368.06 μg/g). The major hydroxybenzoic acid derivatives (HBAs) identified in the leaves were vanillic acid (VaA) "154.18-374.06 μg/g" and protocatechuic acid (PA) "9.37-595.61 μg/g", while the major hydroxycinnamic acid derivatives (HCAs) were p-coumaric acid (p-CoA) "109.35-261.77 μg/g", m-coumaric acid (m-CoA) "10.22-70.57 μg/g", and ferulic acid (FeA) "35.54-109.13 μg/g". The maximum percentage of PA was obtained in April "595.61 μg/g", while the maximum p-CoA content was obtained in January "261.77 μg/g". Therefore, the leaves can be recommended as the optimal source of PHAs. If there is a specific interest in certain PHA components, we recommend collection in either January or April. Multivariate statistical analysis (PCA & AHC) showed the existence of two main clusters. The first cluster comprised the leaves, distinguished by the highest VaA, PA, and p-CoA contents. The second cluster comprised roots and the root bark samples. This study provides information on the development of PHAs in different parts of Centaurea sp. and explores potential applications. It will be of considerable interest for determining the optimal harvesting time of shrub species used for their medicinal properties and bio-active phenolic contents.

Assessment of seasonal water quality and land use land cover change in Subarnarekha watershed of Ranchi stretch in Jharkhand

In this paper, the assessment of seasonal water quality and land use land cover change in the Subarnarekha watershed in Ranchi stretch was analysed. Agricultural runoff along with climate change adds to the pollution risk to the Subarnarekha River of Ranchi stretch in Jharkhand. Water quality indicators, like acidity alkalinity (ALK), total dissolved solids (TDS), hardness (H), dissolved oxygen (DO), biochemical oxygen demand (BOD), chlorides (CL-), electrical conductivity (EC), salinity (SAL), resistivity (RES) and pH, were assessed as per the standard method. During monsoon season, acidity, alkalinity, hardness, chlorides, salinity, pH and DO decreased, whereas EC, TDS, BOD and resistivity increased in comparison to pre-monsoon season. In post-monsoon, chloride problem was observed very high. Hardness was least in monsoon and maximum in post-monsoon season. EC and BOD increased in monsoon season in comparison to other seasons. Statistical analysis like HCA (hierarchical cluster analysis) and PCA (principal component analysis) also confirmed the problem of TDS, EC, chloride and hardness in the area. WQI (water quality index) analysis showed that the water quality was poor to unsuitable on all the sampling points throughout the study area in all seasons. LULC (land use land cover) and NDWI (normalized difference water index) analysis had also concluded that due to high rate of urbanization, the area has undergone a massive change in terms of forest cover and water bodies. The need for afforestation, forest protection and wetland protection can be clearly seen from the result of this study.

Research progress and challenges of preimplantation genetic testing for polygenic diseases

Preimplantation genetic testing is an important part in assisted reproductive technology, which can block the intergenerational inheritance of single gene or chromosomal diseases. Preimplantation genetic testing for polygenic disease risk (PGT-P) is the latest development in the field. It is known that polygenic diseases usually have the characteristics of high incidence, late onset, affecting the quality of life and mental health of patients. On the basis of the development of artificial intelligence and genetic detection technology, PGT-P can analyze genetic material, calculate polygenic risk score turning into incidence probability. Embryos with relatively low incidence probability can be screened for transfer, so as to reduce the possibility of offspring suffering from the disease in the future, which has significant clinical and social significance. At present, PGT-P has been applied clinically and made phased progress at home and abroad. At the same time, as a developing technology, PGT-P still has some technical defects, unstable results, environmental influences and racial differences cannot be ruled out. From the perspective of ethics, if the screening indications are not strictly regulated, it is likely to cause new social problems. In this paper, we review the technical composition and recent progress of PGT-P, and put forward the prospect of its future development, especially how to establish a complete and suitable screening model for Chinese population.

An explorative and confirmative factor analysis of the Leadership and Management Inventory-II among staff working in elderly care

PURPOSE

The Purpose of the study was to investigate the construct validity and internal consistency of the LaMI among staff in the context of elderly care in Sweden.

DESIGN/METHODOLOGY/APPROACH

Questionnaire data from a longitudinal study of staff working in elderly care were used. Data were collected using the Leadership and Management Inventory. First data collection was for explorative factor analysis (n = 1,149), and the second collection, one year later, was for confirmatory factor analysis (n = 1,061).

FINDINGS

The explorative factor analysis resulted in a two-factor solution that explained 70.2% of the total variance. Different models were tested in the confirmatory factor analysis. The final model, a two-factor solution where three items were omitted, showed acceptable results.

ORIGINALITY/VALUE

The instrument measures both leadership and management performance and can be used to continually measure managers' performances as perceived by staff to identify areas for development.

Enhancing study quality assessment: an in-depth review of risk of bias tools for meta-analysis-a comprehensive guide for anesthesiologists

BACKGROUND

Yearly, a multitude of randomized controlled trials are published, overwhelming clinicians with conflicting information; this data saturation leads to confusion and hinders clinicians' everyday decision-making. Hence, it is crucial to assess the quality and reliability of the evidence in order to consolidate it. Through this synthesis, clinicians can guarantee that their decisions are informed by solid evidence. Meta-analysis, a statistical technique, can effectively combine data from multiple studies to furnish accurate and dependable evidence for clinical practice and policy decisions. Nonetheless, the reliability of the obtained results depends on the use of high-quality evidence.

MAIN BODY

Risk of bias is an assessment mandatory while performing a meta-analysis and is used to have an overview of the quality of the studies from which data are extracted. Several tools have been developed and are used to perform the risk of bias assessment. In this statistical round, we will provide an overview of the most used tools for both the randomized (Cochrane Risk of Bias 2 and Jadad) and the nonrandomized (Risk Of Bias In Non-randomized Studies and Newcastle-Ottawa Scale) clinical trials.

CONCLUSION

We provided an overview of the most used risk of bias tools used in meta-analysis.

Particulate matter pollution and non-targeted analysis of polar compounds in three regions of Brazil

Atmospheric Particulate Matter (PM) is a pollutant with diverse origins, exhibiting varying chemical compositions, and undergoes several molecular transformations in the atmosphere. In this study, PM samples (PM2.5, PM10 and TSP) were collected in five Brazilian cities (Camboriú-SC; Catalão-GO; Florianópolis-SC; Limeira-SP and Novo Hamburgo-RS) during the four seasons of the year. Analysis of Variance (ANOVA) was used to evaluate the differences between each city and season in PM concentration. PM10 average concentrations were higher in the city of Limeira, compared to the other (ANOVA p-values and Tukey's test). Moreover, Tukey's test demonstrated differences between the average PM10 concentrations in summer and winter. Regarding TSP and PM2.5, Tukey's test showed differences between winter and warm seasons (spring and summer). Moreover, polar compounds from the samples collected in the summer (February) and winter (August) periods were analyzed (Ultra-High-Performance Liquid Chromatography coupled to a Quadrupole Time-of-Flight Mass Spectrometer) following a non-targeted approach and annotated. This is the first study to carry out this type of analysis in these five Brazilian cities. Despite the differences in PM concentrations, profiles of polar organic compounds, showed similarities between samples/and, in general, the same compounds were present, albeit with different intensities. The annotated compounds are associated with vehicle emissions and plastics, which are considered important global air polluters. Therefore, there is an urgent necessity for comprehensive studies aimed at investigating the non-targeted compounds existing in the atmosphere. Such research can provide invaluable insights to policymakers, enabling them to formulate effective guidelines and policies to mitigate particulate matter concentration and enhance overall air quality.

Spatial distribution of potentially toxic elements contaminations and risk indices of water and sediments in the Darband and Samana streams, Pakistan

This study examined potentially toxic elements (PTE) of water and sediments in the Darband and Samana streams of Hangu District. Darband and Samana streams are the main fluvial ecosystems of Hangu District, Pakistan, directly or indirectly affecting more than 0.52 Million people. Water and sediment samples were collected and analyzed for PTE utilizing inductively coupled plasma mass spectrometry (ICP-MS). Water characteristics of Darband and Samana streams were noted within the WHO drinking water guidelines, except for Turbidity and sulfate. Most water characteristics showed higher values in the Samana stream than in the Darband stream. Similarly, most of PTE showed higher concentrations in sediments collected from the Samana stream than the Darband stream. For ecological risk evaluation, several pollution indices were employed to assess the eco-toxicological consequences. The highest contamination factor (CF) value of 15 and 20 were exhibited by molybdenum (Mo) in the Samana and Darband streams showing very high contamination. Similarly, the pollution load index (PLI) showed that 24% of the sediment samples were polluted (PLI > 1). Furthermore, a high ecological risk in a range of 160 < ERI < 320 was observed for Mo, while a low ecological risk ERI < 40 was by As for the Darband and Samana streams sediments. Statistical techniques revealed that various anthropogenic sources primarily contaminated in water and sediment. Therefore, this study recommends regular monitoring PTE contaminations in the area to avoid any health hazards in the future.

Evaluation of rs10811661 polymorphism in CDKN2A / B in colon and gastric cancer

One of the causes of colon and gastric cancer is the dysregulation of carcinogenic genes, tumor inhibitors, and micro-RNA. The purpose of this study is to apply rs10811661 polymorphism in CDKN2A /B gene as an effective biomarker of colon cancer and early detection of gastric cancer. As a result,400 blood samples, inclusive of 200 samples from healthy individuals and 200 samples (100 samples from intestinal cancer,100 samples from stomach cancer) from the blood of someone with these cancers, to determine the genotype of genes in healthful and ill people through PCR-RFLP approach and Allelic and genotypic tests of SPSS software. To observe the connection between gastric cancer and bowel cancer risk and genotypes, the t-student test for quantitative variables and Pearson distribution for qualitative variables have been tested and the results have been evaluated using the Chi-square test. The effects confirmed that the highest frequency of TT genotypes is in affected individuals and CC genotype is in healthful individuals. In addition, it confirmed that women were more inclined than men to T3 tumor invasion and most grade II and III colon cancers, and in older sufferers with gastric cancer, the grade of tumor tended to be grade I. Among genetic variety and rs10811661, with invasiveness, there is a tumor size and degree in the affected person. In summary, our findings suggest that the rs10811661 polymorphism of the CDKN2A / B gene is strongly associated with the occurrence of intestinal cancer and stomach is linked to its potential role as a prognostic biomarker for the management of bowel cancer and stomach.

Effects of socio-economic status and daily variation on municipal solid waste composition: a preliminary analysis for sustainable planning and development in Aligarh City

In the context of developing countries such as India, with great differences in people's living standards and different communities, municipal solid waste (MSW) management is one of the most promising problems in front of municipal organizations. Unlike every city in India, Aligarh City also faces the same problem of municipal solid waste management. This problem not only affects the esthetic view but is also hazardous to people nearby health. Currently, solid waste collected is either dumped in landfill unscientifically or partially treated by A to Z waste management (limited) by composting. In the present study, an effort was made to know about the per capita waste generation and variations in the quantity of different components of the MSW in five different regions of the city with dissimilar living standards. Also, weekly variation was analyzed in the study. One-way ANOVA analysis using Statistical Package for the Social Sciences is performed to investigate the variations in the mean composition of different components. The per capita solid waste generation in Aligarh City was found to be 0.42 kg/person/day. From the analysis, we came to know that compostable component (35.4%) is the highest, then inert (24.6%), plastic (12.2%), paper (10%), textile (9.2%), and sand (8.6%). After analysis, the results can help sort out the problem of MSW management in the city by selecting appropriate units as per the composition of MSW.

The effect of multiple factors on water-soluble inorganic ions in diesel particulate matter emissions

The water-soluble inorganic ions (WSII) in diesel particulate matter (DPM) have a significant impact on ambient air quality and human health. In this study, the 12 groups of bench tests were conducted to analyze the emission characteristics of two diesel engines, taking into account the influence of engine parameters, test cycle, fuel types, and after-treatment measures. Compared to conventional diesel, a blend of diesel with 5 % biodiesel resulted in a reduction of the WSII emission factors by 23.7-48.0 %. The emission factors of WSII decreased by 8.4 % after installing selective catalytic reduction (SCR). Dummy variable regression analysis was used to analyze the relationship between WSII and influencing factors. The emission factors of Na+, K+, and Ca2+ were mostly affected by the engine, potentially due to the use of coolants and lubricants containing metal oxides in the engine. The emission factors of NO3- were mainly affected by the test cycle. Techniques for order preference by similarity to ideal solution (TOPSIS) were used to analyze the priority of emission reduction technologies. The results indicated that SCR, biodiesel, and low-sulfur diesel could effectively reduce WSII. This study aims to explore the influence of multiple factors on WSII, providing valuable insights for future research on WSII in DPM.

Foam fractionation and electrochemical oxidation for the treatment of per- and polyfluoroalkyl substances (PFAS) in environmental water samples

Treatment of waters contaminated by per- and polyfluoroalkyl substances (PFAS) in large volumes remains a challenge to date. Treatment trains comprising separation and destruction technologies are promising to manage PFAS contamination. Foam fractionation (FF) and electrochemical oxidation (EO) are two cost-effective technologies for PFAS separation and destruction, respectively. This work systematically explored the performance of a treatment train of FF followed by EO (FF-EO) for treating PFAS in environmental water samples. For each treatment step, the dependence of the treatment performance on operational factors and other variables were analyzed statistically. The statistical analysis revealed PFAS enrichment and removal depend significantly on PFAS carbon chain length, solution conductivity, and PFAS concentration. Whether FF-EO treatment costs less energy than direct EO without FF mainly relies upon PFAS carbon chain length and TOC content in the sample. Both correlations were found to be linear. For all environmental water samples in this study, FF-EO is more energy-efficient than EO alone.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

Development and application of novel risk indices for assessing heavy metal pollution in aquatic sediments

This study presents the development of a new sediment individual risk index (SIRI) and sediment complex risk index (SCRI) to assess heavy metal pollution in Anzali Wetland sediments. SIRI incorporates total metal concentrations, bioavailability, and sediment quality guidelines. SCRI, derived through principal component analysis (PCA), integrates SIRI for a comprehensive risk assessment. The newly developed indices were systematically classified. Results showed varying risk levels with SIRI values of Zn, Cr, Cu, Pb, Ni, As, Cd, and Hg as 0.82, 1.32, 0.98, 0.71, 1.41, 1.37, 0.79, and 0.79, respectively. Pb, Cd, and Hg posed very low risk, Cu and Zn posed low risk, and Cr, Ni, and As posed moderate risk. SCRI yielded an index value of 1.02, indicating a moderate level of risk for the studied stations. Pearson correlation analysis validated SCRI with a highly significant and strong correlation coefficient (0.923) with metal bioavailability, serving as a risk indicator.

Environmental hazard assessment and metal contamination in coastal sediments

In order to reduce contamination levels from diverse sources, it is important to understand the factors affecting the natural ecosystems that are impacted by coastal and marine pollution. In this study, we used GIS and remote sensing techniques to investigate and evaluate the distribution of heavy metals (Fe, Mn, Zn, Cr, Pb, Co, and Cu) in surface sediments along Tamil Nadu's East Coast (from Besant Nagar to Sathurangapattinam). The CF and Igeo of metals indicate that sediments contain no evidence of Fe, Mn, or Zn metal pollution in the sediments, with only mild contamination from Co, Cu, and Pb. In contrast, the sediment samples were found to be significantly contaminated with Cr. Heavy metal contamination occurs in the following order, according to our research: Cr > Pb > Cu > Co > Mn > Zn > Fe. Except for sites 8, 10, 11, and 13, where PLI>1 implies that there is no pollution in this area, the PLI values show that most of the locations are contaminated. The ecological risk index (ERI) values for five metals in the study areas are as follows: Cr > Pb > Cu > Mn > Zn. The sediment samples fall into the low-risk and highly polluted to dangerous sediment categories for SPI, according to the Risk index (RI). Based on the Mean Effect Range-Median Quotient (M-ERM-Q), Cu, Pb, Zn, and Cr metals in the research region have a 9-21% probability of being harmful. Statistical approaches show that the majority of heavy metals in sediments are of natural origin. The spatial distribution of heavy metals in surface sediments provides the conceptual framework for practical strategies to protect coastal areas. Many shreds of evidence indicate that anthropogenic inputs from the surrounding land area are primarily responsible for the deposition of these heavy metals in the coastal zone.

Factors associated with heterochronic gastric cancer development post-endoscopic mucosal dissection in early gastric cancer patients

BACKGROUND

Endoscopic mucosal resection is an innovative method for treating early gastric cancer and has been widely used in clinical practice.

AIM

To analyze the factors associated with the development of heterochronic gastric cancer in patients with early gastric cancer who had undergone endoscopic mucosal dissection (EMD).

METHODS

A cohort of patients with early gastric cancer treated using EMD was retrospectively analyzed, and patients who developed heterochronic gastric cancer after the surgery were compared with those who did not. The effects of patient age, sex, tumor size, pathological type, and surgical technique on the development of heterochronic gastric cancer were assessed using statistical analysis.

RESULTS

Of the 300 patients with early gastric cancer, 150 patients developed heterochronic gastric cancer after EMD. Statistical analysis revealed that patient age (P value = XX), sex (P value = XX), tumor size (P value = XX), pathological type (P value = XX), and surgical technique (P value = XX) were significantly associated with the occurrence of heterochronic gastric cancer.

CONCLUSION

Age, sex, tumor size, pathological type, and surgical technique are key factors influencing the occurrence of heterochronic gastric cancer after EMD in patients with early gastric cancer. To address these factors, postoperative follow-up and management should be strengthened to improve the prognosis and survival rate of patients.

Current challenges of university laboratory: Characteristics of human factors and safety management system deficiencies based on accident statistics

INTRODUCTION

In recent years, with the rapid development of university laboratory construction, frequent laboratory accidents have aroused widespread concern. There is an urgent need to improve laboratory safety management's effectiveness further, enhance laboratory accident prevention ability, and reduce the occurrence of accidents.

METHOD

Based on the accident causation theory, this paper uses the accident analysis path of 24Model and the logical idea of WBA (Why-Because-Analysis) to statistically analyze the causative factors of 64 typical college laboratory fire and explosion accidents and find out the defects of current college laboratory management.

RESULTS

The study showed that unsafe human actions at the individual level were the most critical factors affecting laboratory safety management, with a high frequency of violations of experimental procedures (105 times) and managers' failure to perform their supervisory duties (98 times); low safety awareness and insufficient safety knowledge among laboratory personnel were key factors triggering unsafe actions. At the organizational level, the lack of training programs (92 times) and the lack of systematic procedures (106 times) are the weaknesses of the laboratory safety management system in general in all universities; the lack of safety culture construction is the root cause of laboratory management deficiencies.

CONCLUSIONS

Based on the above statistical results, and taking into account the characteristics of university laboratories themselves, the root causes of poor safety are specifically analyzed and preventive measures are proposed in six areas to address the key causes of accidents.

PRACTICAL APPLICATIONS

The results of this study are essential for improving the ability to prevent accidents in flammable and explosive laboratories in universities.

Investigating heavy metal pollution in Anzali coastal wetland sediments: A statistical approach to source identification

In this study, the pollution and bioavailability of heavy metals in the sediments of Anzali Wetland were measured by analyzing data from sequential chemical extraction of sediments, risk assessment code (RAC), and sediment pollution indices. The average RAC results indicated that the risk from Zn, Cr, Cu, and Hg was low, while the risk from Pb, Ni, As, and Cd was moderate. To identify the sources of heavy metal pollution in the sediments of Anzali Wetland, multivariate statistical techniques such as Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were employed. The results of the statistical analyses at a high significance level revealed that Zn, Cr, Cu, Pb, Ni, and As were attributed to natural sources. Additionally, the statistical analyses demonstrated that the concentrations of Cd and Hg in the sediments of Anzali Wetland were influenced by non-oil organic sources and atmospheric deposition, respectively.

Characteristics of baseline frequency data in spinal RCTs do not suggest widespread non-random allocation

BACKGROUND

Recent signs of fraudulent behaviour in spine RCTs have queried the integrity of trials in the field. RCTs are particularly important due to the weight they are accorded in guiding treatment decisions, and thus, ensuring RCTs' reliability is crucial. This study investigates the presence of non-random baseline frequency data in purported RCTs published in spine journals.

METHODS

A PubMed search was performed to obtain all RCTs published in four spine journals (Spine, The Spine Journal, the Journal of Neurosurgery Spine, and European Spine Journal) between Jan-2016 and Dec-2020. Baseline frequency data were extracted, and variable-wise p values were calculated using the Pearson Chi-squared test. These p values were combined for each study into study-wise p values using the Stouffer method. Studies with p values below 0.01 and 0.05 and those above 0.95 and 0.99 were reviewed. Results were compared to Carlisle's 2017 survey of anaesthesia and critical care medicine RCTs.

RESULTS

One hundred sixty-seven of the 228 studies identified were included. Study-wise p values were largely consistent with expected genuine randomized experiments. Slightly more study-wise p values above 0.99 were observed than expected, but a number of these had good explanations to account for that excess. The distribution of observed study-wise p values was more closely matched to the expected distribution than those in a similar survey of the anaesthesia and critical care medicine literature.

CONCLUSION

The data surveyed do not show evidence of systemic fraudulent behaviour. Spine RCTs in major spine journals were found to be consistent with genuine random allocation and experimentally derived data.

Colour descriptors for plant-based milk alternatives discrimination

Colour characteristics of plant-based milk alternatives (PBMAs: almond, coconut, cashew, oat, soy) were monitored during long-term storage to select suitable descriptors for PBMAs differentiation. All colour descriptors evaluated varied depending on the raw material used in the plant-based milk alternative production. Long-term storage of plant-based beverages resulted in slightly noticeable (0.5-1.5) and noticeable (1.5-3.0) colour changes. Based on all colour descriptors, an absolute differentiation of PBMAs according to the type of raw material and storage time was achieved using canonical discriminant analysis. The results also indicate the possibility of using colour descriptors to detect the addition of honey to these products. Statistical analysis identified yellowness, browning index and lightness as the most discriminating parameters.