Scatter Plots Research Articles

Sensitivity analysis of NO2 differential slant column density according to spatial resolution using GCAS data from the SIJAQ 2022 campaign

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument made observations over the South Korea during Satellite Integrated Joint Monitoring for Air Quality (SIJAQ) 2022 (July–August 2022) campaign aimed at collecting high-resolution remote sensing data. To investigate the impact of spatial resolution on nitrogen dioxide (NO2) retrieval from remote sensing data, the NO2 differential slant column density (dSCD) was examined using high-resolution GCAS data, which are suitable for upscaling, to identify differences related to spatial resolution. Through the GCAS NO2 dSCD, the structure at the original resolution (100 m × 200 m) was maintained even following a shift to a coarser resolution; however, it was difficult to distinguish boundary areas with large concentration differences at resolutions coarser than 1.5 km × 3 km. In the comparison of coadded NO2 dSCD and merged NO2 dSCD, the standard deviations from the merged data increased when NO2 dSCD was >3 × 1016 molecules/cm2, such as in the high NO2 concentration cases. The slope of the scatter plot obtained using the top 25% of the data determined from the regression line tended to increase as the resolution became coarser. In addition, the average standard deviation within merged pixels at a resolution of 3 km × 6 km was twice that obtained at 400 m × 800 m resolution. Accordingly, the standard deviation of the information contained in the pixels increased when the spatial resolution became coarser.

Maintaining Delivery of Evidence-Based Interventions to Reduce Under-5 Mortality During COVID-19 in Rwanda: Lessons Learned through Implementation Research.

Background: The COVID-19 pandemic resulted in drops in access to and availability of a number of evidence-based interventions (EBIs) known to reduce under-5 mortality (U5M) across a wide range of countries, including Rwanda. We aimed to understand the strategies and contextual factors associated with preventing or mitigating drops nationally and subnationally, and the extent to which previous efforts to reduce U5M supported the maintenance of healthcare delivery. Methods: We used a convergent mixed methods implementation science approach, guided by hybrid implementation research and resiliency frameworks. We triangulated data from three sources: desk review of available documents, existing routine data from the health management information system, and key informant interviews (KIIs). We analyzed quantitative data through scatter plots using interrupted time series analysis to describe changes in EBI access, uptake, and delivery. We used a Poisson regression model to estimate the impact of COVID-19 on health management information system indicators, adjusting for seasonality. We used thematic analysis of coded interviews to identify emerging patterns and themes. Results: We found moderate 4% (IRR = 0.96; 95%CI: 0.93, 1.00) and 5% (IRR = 0.95; 95%CI: 0.92, 0.99) drops in pentavalent and rotavirus 2 doses vaccines administered, respectively. Nationally, there was a 5% drop in facility-based delivery (IRR = 0.95; 95%CI: 0.92, 0.99). Lockdown and movement restrictions and community and health-worker fear of COVID-19 were barriers to service delivery early in the pandemic. Key implementation strategies to prevent or respond to EBI drops included leveraging community-based healthcare delivery, data use for decision-making, mentorship and supervision, and use of digital platform. Conclusions: While Rwanda had drops in some EBIs early in the pandemic, especially during the initial lockdown, this was rapidly identified, and response implemented. The resiliency of the health system was associated with the Rwandan health system's ability to learn and adapt, encouraging a flexible response to fit the situation.

Modeling and analysis of crypto-backed over-collateralized stable derivatives in DeFi

In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, focusing on mechanisms for price stabilization and exemplified by the well-known stablecoin DAI from MakerDAO. For simplicity, we consider a single-collateral setting. We introduce a belief parameter to the simulation model of DAI in a previous work (DAISIM), reflecting market sentiments about the value and stability of DAI, and show that it better matches the expected behavior when this parameter is set within a particular range of values. Our methods include comparing simulated data with real-world data, focusing on monthly correlations between ETH and DAI prices and scatter plots illustrating the relationship of their price trends over time. We also propose a simple mathematical model of DAI price to explain its stability and dependency on ETH price. Finally, we analyze possible risk factors associated with these stable derivatives to provide valuable insights for stakeholders in the DeFi ecosystem.

Empirical analysis of the influence of vegetation photosynthetic productivity on negative air ions in forest ecosystems driven by solar radiation

Air pollution stands as the single largest environmental risk to human health globally, and negative air ions (NAIs) serve as essential indicators for measuring regional air quality. They play a crucial role in alleviating the pressure and risks posed by air pollution. NAIs are notably abundant in forests, where the photosynthesis of vegetation serves as a significant factor influencing their presence. However, a dearth of quantitative research exists regarding the relationship between vegetation photosynthesis and NAIs. Current literature only offers qualitative insights, suggesting that vegetation photosynthesis may foster NAIs production. The mechanisms through which the photosynthetic process influences NAIs and the potential contribution of forest vegetation to NAIs remain unclear, lacking quantitative investigation. In this study, Quercus variabilis, a typical tree species in the warm temperate zone, was chosen as the research subject. We examined the dynamic changes in NAIs and net ecosystem productivity (NEP) from 2019 to 2021. The results demonstrated a strong correlation between NAIs and NEP at the ecosystem scale during the primary vegetation growing season. NEP effectively tracked daily changes in NAIs and exhibited a good fit. Notably, the scatter plots of NAIs and NEP exhibited clear stratification under varying photosynthetically active radiation (PAR) intensities. The correlation between NAIs and NEP was significantly higher under high PAR conditions compared to low PAR conditions, with R2 values of 0.458 and 0.367, respectively. Consequently, NEP emerges as a superior indicator for tracking NAIs variation characteristics at the ecosystem scale compared to PAR. This finding offers a rapid and accurate evaluation method for discerning the spatial and temporal distribution patterns of NAIs within forest ecosystems. Such insights provide a theoretical foundation for further assessing the potential contribution of forest vegetation to NAIs.

Assessment of PERSIANN Satellite Products over the Tulijá River Basin, Mexico

Precipitation is a fundamental component of the Earth’s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulijá River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error.

Exploring PCSWMM for Large Mixed Land Use Watershed by Establishing Monitoring Sites to Evaluate Stream Water Quality

Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD5), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R2) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R2 values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R2 values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy.

The contribution of commonly consumed edible insects to nutrition security in the Eastern D.R. Congo

Edible insects are perceived as an incredible opportunity to mitigate the major challenge of sustainably producing healthy foods for a growing world population in the face of climate change uncertainties over the coming decade. In this study, we assessed the nutrient composition and sensory properties of Acheta domesticus, Apis mellifera, Gnathocera trivittata, Gryllotalpa africana, Imbrasia epimethea, Imbrasia oyemensis, Locusta migratoria, Macrotermes subhylanus, Nomadacris septemfasciata, Rhyncophorus phoenicis, Ruspolia differens and Rhynchophorus ferrugineus consumed in Eastern D. R. Congo. The investigated edible insects are highly appreciated and nutritious, with proteins (20.67–43.93 g/100 g) and fats (14.53–36.02 g/100 g) being the major macro-nutrients, proving their potential to improve diets through food enrichment. The high potassium (24–386.67 mg/100 g), sodium (152–257.82 mg/100 g), magnesium (32–64 mg/100 g), iron (5.3–16.13 mg/100 g), calcium (25–156.67 mg/100 g) and zinc (11–19.67 mg/100 g) content make the assessed edible insects a useful mineral-containing ingredient for preventing undernutrition in countries which are plagued by micronutrient deficiencies. A scatter plot of matrices and Pearson’s correlations between sensory attributes and nutritional composition showed a negative correlation (r = − 0.45) between protein and appearance. While no strong correlation was observed between nutritional attributes and sensory acceptance, a positive correlation was observed between potassium and aroma (r = 0.50), after-taste (r = 0.50) and acceptability (r = 0.52). Principal component analysis results indicated that the two axes accounted for up to 97.4% of the observed variability in the nutrient composition and sensory attributes of commonly consumed edible insects in the Eastern D. R. Congo. Given the significant delicacy and nutritional potential of edible insects highlighted in this paper, households can rely on the latter to meet their nutritional needs rather than conventional livestock, thus contributing to environmental and financial security through local business opportunities.

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

Modelling the fundamental diagram of traffic flow mixed with connected vehicles based on the risk potential field

AbstractThe fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non‐connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car‐following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car‐following behaviour and the macroscopic traffic flow under steady‐state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure.

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

Genetically predicted 1091 blood metabolites and 309 metabolite ratios in relation to risk of type 2 diabetes: a Mendelian randomization study.

Metabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM. Data encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran's Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0. The IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899-0.962, p = 2.16 × 10-5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917-0.968, p = 1.64 × 10-5), Mannose (IVW: OR:1.133, 95% CI: 1.072-1.197, p = 1.02 × 10-5), X-21829 (IVW: OR:1.036, 95% CI: 1.036-1.122, p = 9.44 × 10-5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818-0.926, p = 1.29 × 10-5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism. This study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.

An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms

With the rapid advancement of science and technology, chemical and physical research has entered an era of complexity and high dimensionality, where traditional research paradigms struggle to optimize vast chemical parameter searches. The Machine Chemist platform was developed in this context, leveraging big data and intelligent models to automate chemical synthesis, characterization, and testing processes. This study aims to predict the y1, y2 and y3 properties and classes of 2,580 molecules based on their physicochemical properties and improve model accuracy through data analysis and modeling. Data preprocessing involved removing missing values, duplicates, and outliers using the quartile method, resulting in an analyzable dataset. A scatter plot of y2 and id suggested a univariate polynomial function relationship, leading to the construction of a univariate nonlinear regression model. The model achieved a high prediction accuracy, with a low root mean square error and a high coefficient of determination. Additionally, the relationship between class and y1~y3 , x1~x100 indicators was examined, revealing mostly nonlinear relationships. A random forest model was established to classify 2,580 molecules into 1 to 4 classes based on the properties of 200,000 chemical molecules. The model's performance was evaluated using decision trees, confusion matrices, precision, and recall metrics. Finally, the SHAP method assessed the impact of feature indicators on classification outcomes, contributing to the development of a reliable molecular category prediction model.

Cellular Senescence-Related Long Non-coding RNA Signatures Predict Prognosis in Juvenile Osteosarcoma

AbstractOsteosarcoma is the most common malignant bone tumor and is frequently diagnosed in juvenile. Cellular senescence is a fundamental hallmark of osteosarcoma and plays a vital role in the initiation and progression of aging and tumorigenesis. Long non-coding RNAs (lncRNAs) are implicated in tumorigenesis. In this study, six cellular senescence-related lncRNAs with independent prognostic significance in juvenile osteosarcoma patients were identified through univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis. Prognostic significance was further confirmed by Kaplan–Meier (KM) survival curves, co-expression interaction networks, and sankey diagrams. A prognostic model of cellular senescence-related genes in juvenile osteosarcoma patients was then constructed using multivariate Cox regression analysis based on these six genes. High- and low-risk groups were identified according to the median risk score calculated by the prognostic model. The favorable prognostic significance of this model was demonstrated through survival curves, receiver operating characteristic (ROC) curves, distribution scatter plots and lncRNA expression heatmaps. Furthermore, cellular senescence-related lncRNAs were validated by enrichment analysis, immunological correlation analysis, m6A correlation analysis, and drug sensitivity correlation analysis. These findings are important for improving the prognosis of juvenile osteosarcoma patients and understanding the mechanisms underlying cellular senescence in juvenile osteosarcoma development.

Prediction of grain size distribution using ordinary kriging and compositional kriging methods

Grain size analysis plays a crucial role in understanding the geological characteristics of the coastal environments that influence the optimizations for oil and gas production operations. This paper aims to explore a sophisticated geostatistical approach using the ordinary kriging and compositional kriging techniques, to forecast the grain size fluctuations of sediments in the Long Island region located in the United States. In addition, utilizing a comprehensive dataset collected from the same region about an integrated seventeen compositional components for investigation using the spatial model of the grain size distribution. Moreover, a variogram and the scatter plot predicted a distinctive spatial dependency was achieved. The compositional kriging method used to predict the grain size distribution in the coastal areas presented an accurate result based on the shape of the histogram, Root Mean Square Error (RMSE), and the Mean Squared Error (MSE). In conclusion, the geo-statistics assisted in the integration of the sedimentological analysis in the coastal settings and showed an effective configuration for the decision-making in the oil and gas industry business.

A novel indicator of anthropogenic influence on the fluctuability and stability of phytoplankton community composition

Marine community composition is expected to be relatively stable in a natural environment over time but shift under increasing anthropogenic disturbances. In coastal waters, diatoms and dinoflagellates are two dominant phytoplankton functional groups. In this study, we developed an areal phytoplankton community composition index (APCI) that is based on the area of a scatter plot of dinoflagellate abundance (y-axis) vs diatom abundance (x-axis) using a time window of 1 year, 2 years or 3 years data. An APCI allows an ecological interpretation: it represents the fluctuability of a community composition within a time window and a temporal change between two neighbouring APCIs in a time series represents the stability of the composition. We used a 28-yr time series of monthly data on diatom and dinoflagellate abundance at four stations in Tolo Harbour and Channel (Tolo), Hong Kong to test the hypothesis that temporal changes in APCIs indicate environmental disturbances and to examine the applicability of APCI to indicate changes in nutrient conditions. We calculated the area (APCI) of a scatter plot of monthly data for 1-year, 2-year and 3-year windows, referred to as APCI-1y, −2y and -3y, respectively. The results show that, the fluctuability, is larger in APCI-3y than in APCI-1y, while the stability is stronger as temporal changes between neighbouring APCI-3y are smaller than between APCI-1ys. Temporal trends of APCIs are significantly correlated with those of dissolved inorganic nitrogen and phosphate concentration, which have declined after the implementation of a sewage diversion management plan in 1998. Hence, the APCI method is likely a robust indicator to assess a response of the phytoplankton community composition in a water body to environmental disturbances.

XGBoost performance in predicting corrosion inhibition efficiency of Benzimidazole Compounds

In this study, we compare the performance of the XGBoost model with a Support Vector Machine (SVM) model from the literature in predicting a given task. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were utilized to evaluate and compare the models. The XGBoost model achieved an R² of 0.99, an RMSE of 2.54, and an MAE of 1.96, significantly outperforming the SVM model, which recorded an R² of 0.96 and an RMSE of 6.79. The scatter plot for the XGBoost model further illustrated its superior performance, showing a tight clustering of points around the ideal line (y = x), indicating high accuracy and low prediction errors. These findings suggest that the XGBoost model is highly effective for the given prediction task, likely due to its ability to capture complex patterns and interactions within the data.

Traffic flow prediction based on nonlinear weight decreasing PSO-SVR univariate time series prediction algorithm

The aim of this paper is to investigate the application of car flow prediction in the field of transport in order to solve the problem of urban traffic congestion. For this purpose, we adopt the nonlinear weight decreasing PSO-SVR univariate time series prediction algorithm to predict the car flow, and divide the data set into training set and test set according to the ratio of 7:3. By analysing the scatterplot and line graph between the predicted and actual values of the training and test sets, we find that the prediction effect is better, but there is a certain deviation. Specifically, the scatter plot shows a Y=X distribution, but the actual values have a wide range of variation relative to the predicted values. Meanwhile, the line graph shows that the actual traffic flow and the predicted traffic flow have the same trend, but the actual traffic flow value changes more, while the predicted traffic flow value changes less. This may be due to the long time span resulting in too many cycles, and shortening the time span can reduce the number of cycles and thus improve the prediction accuracy. Further analysis of the MAE values for the training and test sets shows that the MAE values for both the training and test sets are relatively small, 1.8437 and 2.6408, respectively, where the MAE for the test set is on the large side, but the overall prediction results of the model are better. Therefore, the nonlinear weight decreasing PSO-SVR univariate time-series prediction algorithm used in this paper can provide powerful decision support for traffic management departments and help them to better formulate traffic planning and management strategies.

Exploring morphological diversity in carrot (Daucus carota l.) germplasm: A multivariate analysis approach

This study looks at how quantitative parameter variation changes over two years in different carrot accessions and the genetic diversity that exists in a group of accessions that come from different parts of the world. An analysis of variance (ANOVA) was conducted to assess the significance of different factors, including the block effect, treatment effect, and check effect. The result indicated a significant mean sum of squares across various sources of variation. Both adjusted and unadjusted block and treatment effects were significant for all traits, while the check effect was significant for more. During both years, there was moderate to high variation in quantitative parameters for numerous quantitative traits among carrot accessions. Mature leaf length showed the highest variation, followed by mature leaf width, leaf area, root weight, leaf weight, and plant biomass. Principal component analysis revealed distinct patterns in the contributions of parameters during both years. Plotting carrot accessions on the scatter plot using the first three principal components also revealed an informative spread of accessions. A notable increase in variation during the second year was observed. Moreover, accessions from different continents displayed varying degrees of genetic diversity, with Asian accessions exhibiting the highest levels. These findings underscore the importance of global germplasm collection in breeding programs aimed at enhancing crop resilience and productivity. Leveraging this genetic diversity through advanced breeding strategies holds promise for developing cultivars capable of adapting to changing environmental conditions and meeting the demands of sustainable agriculture.

Association between Iron deficiency and hypothyroxinemia in first-trimester pregnant women: A study from a tertiary care hospital

ABSTRACT Background: Iron deficiency (ID) is a common nutritional concern worldwide. Hypothyroidism during pregnancy also has harmful effects on both mother and child, such as post-partum hemorrhage, pre-eclampsia, low-birth weight, pre-term labour and abruptio placenta. Few articles have shown an association between ID and hypothyroidism. Aims: To evaluate the association between hypothyroidism, hypotriiodothyroninemia and ID in pregnant women. Materials and Methods: This cross-sectional study was conducted between January 2017 and June 2019 at JSS Hospital, a Tertiary Care Teaching and Research Hospital attached to JSS Medical College in Mysuru City, South India. Five hundred consecutive pregnant women aged 18–45 years in the first trimester of pregnancy were recruited. A venous blood sample of approximately 5 mL was drawn and sent for analysis of haemoglobin, thyroid-stimulating hormone (TSH), T3, T4, anti-thyroid peroxidase (TPO) antibodies and serum ferritin. T3, T4, TSH, anti-TPO antibody and serum ferritin levels were measured by chemiluminescence for all pregnant women. Correlation and linear regression analyses were performed to determine the association between ID and T3, T4 and TSH levels. Results: Out of 500 women, nine were excluded because of pre-analytical errors; hence, there were 491 pregnant women in the first trimester. Mean values of T3, T4, TSH and ferritin in the study were 1.45 ± 0.72, 9.29 ± 2.53, 2.37 ± 3.17 and 31.54 ± 30.28. There was a positive correlation between serum ferritin and T4 levels, with a correlation coefficient of 0.135. The scatter plot also showed a linear relationship between serum ferritin and T4 levels. There was no significant correlation between serum ferritin, T3 and TSH levels. Linear regression analysis also showed an association between ferritin and T4 levels, with a significance of 0.003. Linear regression analysis was insignificant for TSH, T3 and serum ferritin levels. Conclusion: ID anaemia (IDA) is associated with hypothyroxinemia in pregnant women. Further extensive studies are needed to evaluate the causal association between hypothyroidism and IDA. Iron therapy might improve thyroid parameters in patients with subclinical hypothyroidism and ID without thyroxine, which requires further research.

Cerebellitis following COVID-19 infection: A case-based systematic review and pooled analysis

BackgroundThe COVID-19 pandemic has been linked to neurological complications, including Cerebellitis. This study aims to investigate the clinical features, and consequences of Cerebellitis following COVID-19 infection, informing medical management strategies. MethodsA systematic search was conducted through PubMed, Web of Science, Embase, ProQuest, and Cochrane databases from January 2018 to September 12, 2023, on cases post-COVID-19. Demographics, clinical characteristics, and diagnostic techniques were analyzed using descriptive statistics. Chi-Square tests assessed associations between diagnoses and treatments, with visualizations including heatmaps and scatter plots. ResultsAfter the final Screening, the analysis of 18 cases revealed Cerebellitis post-COVID-19 spanned 9 countries, predominantly from the USA (27.8 %), with a mean patient age of 40.1 years (±24.6). Males comprised 94.4 % of cases. Common underlying conditions included hypertension (22.2 %) and diabetes (11.1 %). Neurological symptoms presented on average 15.15 ± 12.7 days post-COVID-19 infection. A moderate negative correlation (r = −0.358) was observed between age and symptom onset. Blood and CSF biomarkers showed weak correlations with symptom onset intervals. Treatment efficacy varied, with most cases achieving symptom-free outcomes. The Chi-Square test for diagnosis-treatment associations yielded a p-value of 0.089, and for follow-up outcomes, a p-value of 0.283, indicating no significant statistical associations. ConclusionThis systematic review highlights increased reports of Cerebellitis in males in their fourth decade of life, with the highest comorbidities being vascular diseases. Marker assessments show a decrease in CSF protein in half of patients, along with complete recovery following combination treatment with antivirals and steroids in acute Cerebellitis.