Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

On the pixel selection criterion for the calculation of the Pearson's correlation coefficient in fluorescence microscopy.

Colocalisation microscopy analysis provides an intuitive and straightforward way of determining if two biomolecules occupy the same diffraction-limited volume. A popular colocalisation coefficient, the Pearson's correlation coefficient (PCC), can be calculated using different pixel selection criteria: PCCALL includes all image pixels, PCCOR only pixels exceeding the intensity thresholds for either one of the detection channels, and PCCAND only pixels exceeding the intensity thresholds for both detection channels. Our results show that PCCALL depends on the foreground to background ratio, producing values influenced by factors unrelated to biomolecular association. PCCAND focuses on areas with the highest intensities in both channels, which allows it to detect low levels of colocalisation, but makes it inappropriate for evaluating spatial cooccurrence between the signals. PCCOR produces values influenced both by signal proportionality and spatial cooccurrence but can sometimes overemphasise the lack of the latter. Overall, PCCAND excels at detecting low levels of colocalisation, PCCOR provides a balanced quantification of signal proportionality and spatial coincidence, and PCCALL risks misinterpretation yet avoids segmentation challenges. Awareness of their distinct properties should inform their appropriate application with the aim of accurately representing the underlying biology.

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.

Genome-wide identification of the SAUR gene family and screening for SmSAURs involved in root development in Salvia miltiorrhiza.

KEY MESSAGE: SmSAUR4, SmSAUR18, SmSAUR28, SmSAUR37, and SmSAUR38 were probably involved in the auxin-mediated root development in Salvia miltiorrhiza. Salvia miltiorrhiza is a widely utilized medicinal plant in China. Its roots and rhizomes are the main medicinal portions and are closely related to the quality of this herb. Previous studies have revealed that auxin plays pivotal roles in S. miltiorrhiza root development. Whether small auxin-up RNA genes (SAURs), which are crucial early auxin response genes, are involved in auxin-mediated root development in S. miltiorrhiza is worthy of investigation. In this study, 55 SmSAUR genes in S. miltiorrhiza were identified, and their physical and chemical properties, gene structure, cis-acting elements, and evolutionary relationships were analyzed. The expression levels of SmSAUR genes in different organs of S. miltiorrhiza were detected using RNA-seq combined with qRT‒PCR. The root development of S. miltiorrhiza seedlings was altered by the application of indole-3-acetic acid (IAA), and Pearson correlation coefficient analysis was conducted to screen SmSAURs that potentially participate in this physiological process. The diameter of primary lateral roots was positively correlated with SmSAUR4. The secondary lateral root number was positively correlated with SmSAUR18 and negatively correlated with SmSAUR4. The root length showed a positive correlation with SmSAUR28 and SmSAUR37 and a negative correlation with SmSAUR38. The fresh root biomass exhibited a positive correlation with SmSAUR38 and a negative correlation with SmSAUR28. The aforementioned SmSAURs were likely involved in auxin-mediated root development in S. miltiorrhiza. Our study provides a comprehensive overview of SmSAURs and provides the groundwork for elucidating the molecular mechanism underlying root morphogenesis in this species.

The Diagnostic Value of miR-124a Expression in Peripheral Blood and Synovial Fluid of Patients with Rheumatoid Arthritis.

INTRODUCTION: Rheumatoid arthritis (RA), a chronic autoimmune disorder, is currently a severe health threat. Previous studies have documented the altered expression of various miRNAs in RA patients. This study determined the expression of miR-124a in RA patients and estimated its diagnostic value for RA. METHODS: A total of 80 RA patients were enrolled as the study subjects, and 36 patients with osteoarthritis were included, with another 36 healthy people as the controls. miR-124a expression levels in peripheral blood plasma, peripheral blood mononuclear cells (PBMCs), and synovial fluid were measured using reverse transcription quantitative polymerase chain reaction, followed by Pearson correlation analysis. Additionally, the association between miR-124a and major clinical indicators was assessed, such as rheumatoid factor (RF), erythrocyte sedimentation rate (ESR), and disease activity score of 28 joints (DAS28). The diagnostic efficacy of miR-124a expression in plasma, PBMCs, and synovial fluid for RA was evaluated by the receiver operating characteristic curve, and the difference in the area under the curve (AUC) was analyzed. RESULTS: miR-124a was downregulated in RA patients, and the expression levels of miR-124a in plasma, PBMCs, and synovial fluid showed a certain degree of positive correlation. miR-124a was inversely linked with RF, ESR, and DAS28. For the diagnosis of RA patients, the AUC of plasma miR-124a was 0.899 and the cut-off value was 0.800, with 68.75% sensitivity and 94.44% specificity; the AUC of miR-124a in PBMCs was 0.937 and the cut-off value was 0.805, with 82.50% sensitivity and 91.67% specificity; the AUC of miR-124a in plasma combined with PBMCs was 0.961, with a higher diagnostic value than independent plasma or PBMCs; the AUC of miR-124a in synovial fluid was 0.929 and the cut-off value was 0.835, with 80.00% sensitivity and 88.89% specificity. CONCLUSION: miR-124a expression is downregulated in the plasma, PBMCs, and synovial fluid of RA patients and has a high diagnostic value for RA.

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis.

BACKGROUND: The assessment of the effectiveness of teaching interventions in enhancing students' understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students' understanding of the PCNE Classification System in pharmaceutical education. METHODS: Undergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires. RESULTS: The paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire's validity. CONCLUSION: The implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students' comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Multi-model exploration of groundwater quality and potential health risk assessment in Jajpur district, Eastern India.

The presence of fluoride and nitrate is a serious groundwater quality issue in India impacting human health. In the present study, 14 different hydrochemical parameters for 76 groundwater samples collected from the Jajpur district of Odisha, India, were evaluated. Entropy-weighted water quality index (EWQI), fixed-weight groundwater quality index (GWQI), principal component analysis (PCA), and rotated factor loading-based water quality index (PCWQI) were employed to assess groundwater quality. About 65.79 ± 4.68%, 33.55 ± 3.95%, and 0.66 ± 0.76% of the samples were rated as "excellent," "good," or "medium" quality, respectively, across the four different water quality indices, with a nominal rating discrepancy of 13.15%. Though 86% of samples consistently received excellent or good ratings across all WQI frameworks, concentrations of F- and NO3- in 36.8% and 11.84% of the samples exceeded the WHO permissible limit. In health risk assessment, about 38.15% of samples surpassed the F- hazard quotient (HQ > 1) posing non-carcinogenic health risks for children. The non-carcinogenic health risks due to NO3- were evident in 55.26% and 11.84% of samples for children and adults, respectively. The higher concentration of NO3- in some of the water samples, together with its positive correlation with HCO3-, may worsen groundwater pollution. The moderate correlation between Ca2+ and HCO3- (r = 0.410) and the insignificant correlation between Mg2+ and HCO3- (r = 0.234) suggests calcite dissolution is far more common than dolomite.

Eucalyptus Oils Phytochemical Composition in Correlation with Their Newly Explored Anti-SARS-CoV-2 Potential: in Vitro and in Silico Approaches.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the latest arisen contagious respiratory pathogen related to the global outbreak of atypical pneumonia pandemic (COVID-19). The essential oils (EOs) of Eucalyptus camaldulensis, E. ficifolia F. Muell., E. citriodora Hook, E. globulus Labill, E. sideroxylon Cunn. ex Woolls, and E. torquata Luehm. were investigated for its antiviral activity against SARS-CoV-2. The EOs phytochemical composition was determined using GC/MS analysis. Correlation with the explored antiviral activity was also studied using multi-variate data analysis and Pearson's correlation. The antiviral MTT and cytopathic effect inhibition assays revealed very potent and promising anti SARS-CoV-2 potential for E. citriodora EO (IC50 = 0.00019 µg/mL and SI = 26.27). The multivariate analysis revealed α-pinene, α-terpinyl acetate, globulol, γ -terpinene, and pinocarvone were the main biomarkers for E. citriodora oil. Pearson's correlation revealed that globulol is the top positively correlated compound in E. citriodora oil to its newly explored potent anti SARS-CoV-2 potential. A molecular simulation was performed on globulol via docking in the main active sites of both SARS-CoV-2 viral main protease (Mpro) and spike protein (S). In silico predictive ADMET study was also developed to investigate the pharmacokinetic profile and predict globulol toxicity. The obtained in silico, in vitro and Pearson's correlation results were aligned showing promising SARS-CoV-2 inhibitory activity of E. citriodora and globulol. This study is a first record for E. citriodora EO as a novel lead exhibiting potent in vitro, and in silico anti SARS-CoV-2 potential and suggesting its component globulol as a promising candidate for further extensive in silico, in vitro and in vivo anti-COVID studies.

Evolution and health risk of indicator microorganisms in landscape water replenished by reclaimed water.

As an important means to solve water shortage, reclaimed water has been widely used for landscape water supply. However, with the emergence of large-scale epidemic diseases such as SARS, avian influenza and COVID-19 in recent years, people are increasingly concerned about the public health safety of reclaimed water discharged into landscape water, especially the pathogenic microorganisms in it. In this study, the water quality and microorganisms of the Old Summer Palace, a landscape water body with reclaimed water as the only replenishment water source, were tracked through long-term dynamic monitoring. And the health risks of indicator microorganisms were analyzed using Quantitative Microbial Risk Assessment (QMRA). It was found that the concentration of indicator microorganisms Enterococcus (ENT), Escherichia coli (EC) and Fecal coliform (FC) generally showed an upward trend along the direction of water flow and increased by more than 0.6 log at the end of the flow. The concentrations of indicator microorganisms were higher in summer and autumn than those in spring. And there was a positive correlation between the concentration of indicator microorganisms and COD. Further research suggested that increased concentration of indicator microorganisms also led to increased health risks, which were more than 30% higher in other areas of the park than the water inlet area and required special attention. In addition, (water) surface operation exposure pathway had much higher health risks than other pathways and people in related occupations were advised to take precautions to reduce the risks.

GeCoNet-Tool: a software package for gene co-expression network construction and analysis.

BACKGROUND: Network analysis is a powerful tool for studying gene regulation and identifying biological processes associated with gene function. However, constructing gene co-expression networks can be a challenging task, particularly when dealing with a large number of missing values. RESULTS: We introduce GeCoNet-Tool, an integrated gene co-expression network construction and analysis tool. The tool comprises two main parts: network construction and network analysis. In the network construction part, GeCoNet-Tool offers users various options for processing gene co-expression data derived from diverse technologies. The output of the tool is an edge list with the option of weights associated with each link. In network analysis part, the user can produce a table that includes several network properties such as communities, cores, and centrality measures. With GeCoNet-Tool, users can explore and gain insights into the complex interactions between genes.

Integrated metabolomics and transcriptomics analyses reveal the key genes regulating differential metabolites of longissimus dorsi muscle in castrated South Sichuan black goats (Capra hircus).

The main aim of the current work was to explore the differential metabolites and differentially expressed genes of longissimus dorsi muscle (LDM) between castrated and uncastrated fattening male South Sichuan black goats (Capra hircus). Then, the key genes regulating important differential metabolites (DMs) in castrated male goats were observed by integrated metabolomics and transcriptomics analyses. In addition, we evaluated the effects of castration on blood constituents, dressing percentage, and water holding capacity of LDM in male black goats. The results showed that the concentrations of alkaline phosphatase (ALP), total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) were significantly increased and testosterone was significantly decreased in castrated male goats compared with the uncastrated male goats, while dressing percentage of black goats and water holding capacity of longissimus dorsi muscle were not significant differences. Through metabolomics and transcriptomics analyses, 23 important KEGG pathways, 13 important DMs, 32 important differentially expressed genes (DEGs), and 13 key genes related to the "Metabolism" and "Organismal systems" pathways were screened. Lipid accumulation may be elevated in the blood of fattening South Sichuan black goats after castration. Castration might play a positive role in energy provision, intercellular signaling, muscle function, softening of meat, disease reduction, and anti-oxidation of LDM. P4HA2, AKR1B1, GPT2, L2HGDH, ENSCHIG00000021660, ENSCHIG00000023861, DGAT2, ULK1, SLC38A3, PLA2G4A, SLC6A1, ENSCHIG00000026624, and ND2 might be the key genes regulating important DMs in the KEGG pathways related to "Metabolism" and "Organismal systems" of castrated male goats compared with the uncastrated male goats.

Neurochemical correlations in short echo time proton magnetic resonance spectroscopy.

Neurochemical concentrations determined by magnetic resonance spectroscopy (MRS) have been treated as statistically independent measurements in various clinical MRS studies. However, spectral overlap, independent of any biological effects, could lead to significant correlations between neurochemical concentrations extracted from spectral fitting of MRS data, confounding determination of correlations of biological origin. Short echo time (TE) proton MRS spectra are very crowded because of the comparatively narrow chemical shift dispersion of proton nuclear spins. In this study, the complex neurochemical correlations of spectral origin in short-TE MRS spectra were quantified. The effects of macromolecules and the background spectral baseline on metabolite-metabolite correlations were also analyzed. Our results demonstrate the importance of factoring in spectral correlations when correlating overlapping metabolite signals in short-TE spectra with clinical parameters.

A robust and fast two-sample test of equal correlations with an application to differential co-expression.

A robust and fast two-sample test for equal Pearson correlation coefficients (PCCs) is important in solving many biological problems, including, for example, analysis of differential co-expression. However, few existing methods for this test can achieve robustness against deviation from normal distributions, accuracy under small sample sizes, and computational efficiency simultaneously. Here, we propose a new method for testing differential correlation using a saddlepoint approximation of the residual bootstrap (DICOSAR). To achieve robustness, accuracy, and efficiency, DICOSAR combines the ideas underlying the pooled residual bootstrap, the signed root of a likelihood ratio statistic, and a multivariate saddlepoint approximation. Through a comprehensive simulation study and a real data analysis of gene co-expression, we demonstrate that DICOSAR is accurate and robust in controlling the type I error rate for detecting differential correlation and provides a faster alternative to the bootstrap and permutation methods. We further show that DICOSAR can also be used for testing differential correlation matrices. These results suggest that DICOSAR provides an analytical approach to facilitate rapid testing for the equality of PCCs in large-scale analysis.

Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network.

Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable structural abnormalities. In other words, MRI-negative TLE has normal MRI scans on visual inspection. Thus, MRI-negative TLE is a diagnostic and therapeutic challenge. In this study, we investigate the cortical morphological brain network to identify MRI-negative TLE. The 210 cortical ROIs based on the Brainnetome atlas were used to define the network nodes. The least absolute shrinkage and selection operator (LASSO) algorithm and Pearson correlation methods were used to calculate the inter-regional morphometric features vector correlation respectively. As a result, two types of networks were constructed. The topological characteristics of networks were calculated by graph theory. Then after, a two-stage feature selection strategy, including a two-sample t-test and support vector machine-based recursive feature elimination (SVM-RFE), was performed in feature selection. Finally, classification with support vector machine (SVM) and leave-one-out cross-validation (LOOCV) was employed for the training and evaluation of the classifiers. The performance of two constructed brain networks was compared in MRI-negative TLE classification. The results indicated that the LASSO algorithm achieved better performance than the Pearson pairwise correlation method. The LASSO algorithm provides a robust method of individual morphological network construction for distinguishing patients with MRI-negative TLE from normal controls.

Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition.

The accurate and reliable prediction of chlorophyll-a (Chl-a) concentration is of great significance in reservoir environment management and pollution control. To improve the accuracy of Chl-a index prediction, a novel hybrid water quality prediction method was proposed for gated recurrent unit (GRU) neural network based on particle swarm algorithm optimized variational modal decomposition (PV-GRU). The results showed that the variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) in this study effectively reduced the non-smooth of water quality data. In addition, the GRU neural network reduced the risk of overfitting the deep-learning model with small sample data. Overall, the PV-GRU prediction model exhibited significant superiority in predicting non-smooth and non-linear Chl-a sequences with a relatively small sample size. The prediction errors of PV-GRU model were all less than those of other comparative models, and the fitting determination coefficient R2 was 94.21%. These results indicated that the proposed PV-GRU model can effectively predict the content of Chl-a in reservoirs, which provides an alternative new method for water quality prediction to prevent and control eutrophication in reservoirs.

Hetero-pentamerization determines mobility and conductance of Glycine receptor α3 splice variants.

Glycine receptors (GlyRs) are ligand-gated pentameric chloride channels in the central nervous system. GlyR-α3 is a possible target for chronic pain treatment and temporal lobe epilepsy. Alternative splicing into K or L variants determines the subcellular fate and function of GlyR-α3, yet it remains to be shown whether its different splice variants can functionally co-assemble, and what the properties of such heteropentamers would be. Here, we subjected GlyR-α3 to a combined fluorescence microscopy and electrophysiology analysis. We employ masked Pearson's and dual-color spatiotemporal correlation analysis to prove that GlyR-α3 splice variants heteropentamerize, adopting the mobility of the K variant. Fluorescence-based single-subunit counting experiments revealed a variable and concentration ratio dependent hetero-stoichiometry. Via cell-attached single-channel electrophysiology we show that heteropentamers exhibit currents in between those of K and L variants. Our data are compatible with a model where α3 heteropentamerization fine-tunes mobility and activity of GlyR-α3 channels, which is important to understand and tackle α3 related diseases.

Comparison of bispectral index and patient state index as measures of sedation depth during surgeries using remimazolam tosilate.

BACKGROUND: The Bispectral Index (BIS) and the Patient State Index (PSI) are commonly used measures to assess intraoperative sedation depth. However, model differences lead to different results, which in turn interferes with clinicians' judgment on the depth of anesthesia. Remimazolam tosilate (RT) for injection is a new benzodiazepine used in sedation. In its clinical application, there are few effective indicators for sedation depth monitoring. To close this gap, this study aims to compare BIS and PSI in measuring the sensitivity and specificity of intraoperative RT and to explore the safety of RT for intraspinal anesthesia in elderly patients. METHODS: This study included 40 patients undergoing elective electro-prostatectomy with intraspinal anesthesia, who were monitored by BIS and PSI simultaneously during operation. Remimazolam tosylate 0.1 mg/kg was intravenously administered after the intraspinal anesthesia when patients were in a completely painless status. Then BIS, PSI, the Modified Observer's Assessment of Alertness and Sedation (MOAA/S) scores and vital signs were observed and recorded per minute for 10 min. Pearson's correlation analysis and linear regression model were used to compare BIS and PSI sedation scores, and to test their associations with the MOAA/S score, respectively. ROC curves were drawn to compare the sensitivity and specificity of BIS and PSI. Changes of vital signs were presented as mean ± standard deviation. Perioperative liver and kidney function indicators were analyzed using a paired t-test to evaluate the safety of RT for intraspinal anesthesia in the elderly patients. RESULTS: According to Pearson's correlation analysis, a significant (P < 0.01) correlation between BIS and PSI was found when used to monitor intraoperative sedation of RT (r = 0.796). Significant associations between BIS and MOAA/S (r = 0.568, P < 0.01), and between PSI and MOAA/S (r = 0.390, P < 0.01) were also found. The areas under the ROC curves of BIS and PSI were 0.801 ± 0.022 and 0.734 ± 0.026, respectively, suggesting that both measures may predict patients' state of consciousness and BIS was more accurate than PSI. Vital signs remained stable throughout the study. No abnormal changes of clinical significance were found based on laboratory test results of liver and kidney function. CONCLUSION: BIS and PSI are strongly associated for monitoring the sedation of RT intraoperatively. Both methods can accurately reflect sedation depth. According to correlation analyses with MOAA/S scale and ROC curves, BIS is more accurate than PSI during such intraoperative monitoring. In addition, RT can be safely used in elderly patients under intraspinal anesthesia for supportive sedation, with stable vital signs and sound kidney and liver safety profiles. TRIAL REGISTRATION: http://www.chictr.org.cn (ChiCTR2100051912).

NASA POWER satellite meteorological system is a good tool for obtaining estimates of the temperature-humidity index under Brazilian conditions compared to INMET weather stations data.

Heat stress negatively affects livestock, with undesirable effects on animals' production and reproduction. Temperature and humidity index (THI) is a climatic variable used worldwide to study the effect of heat stress on farm animals. Temperature and humidity data can be obtained in Brazil through the National Institute of Meteorology (INMET), but complete data may not be available due to temporary failures on weather stations. An alternative to obtaining meteorological data is the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) satellite-based weather system. We aimed to compare THI estimates obtained from INMET weather stations and NASA POWER meteorological information sources using Pearson correlation and linear regression. After quality check, data from 489 INMET weather stations were used. The hourly, average daily and maximum daily THI were evaluated. We found greater correlations and better regression evaluation metrics when average daily THI values were considered, followed by maximum daily THI, and hourly THI. NASA POWER satellite-based weather system is a suitable tool for obtaining the average and maximum THI values using information collected from Brazil, showing high correlations with THI estimates from INMET and good regression evaluation metrics, and can assist studies that aim to analyze the impact of heat stress on livestock production in Brazil, providing additional data to complement the existing information available in the INMET database.

A retrospective multi-center feasibility study of a new PTV margin estimation approach for moving targets using CyberKnife log files.

PURPOSE: This study investigates a new approach for estimating the planning target volume (PTV) margin for moving tumors treated with robotic stereotactic body radiotherapy (SBRT). METHODS: In this new approach, the covariance of modeling and prediction errors was estimated using error propagation and implemented in the Van Herk formula to form a Modified Van Herk formula (MVHF). To perform a retrospective multi-center analysis, the MVHF was studied using 163 patients treated with different system versions of robotic SBRT (G3 version 6.2.3, VSI version 8.5, and VSI version 9.5) and compared with two established PTV margins estimation methods: The original Van Herk Formula (VHF) and the Uncertainty Estimation Method (UEM). RESULTS: Overall, the PTV margins provided by the three formalisms are similar with 4-5 mm in the lung region and 4 mm in abdomen region to the PTV margins used in clinical. Furthermore, when analyzing individual patients, a difference of up to 1 mm was found between the PTV margin estimations using MVHF and VHF. The corresponding average discrepancies for the superior-inferior (SI) direction ranged between -0.19 mm to 0.38 mm in CK G3 version 6.2.3, -0.36 mm to 0.33 mm in CK VSI version 8.5, and -0.34 mm to 0.40 mm in CK VSI version 9.5. CONCLUSIONS: It was found that for the lower left lung, upper left lung, lower right lung, upper right lung, central liver, and upper liver, the effect of covariance between model and prediction errors in SI direction was around 20%, 30%, 25%, 25%, 25%, and 30%, respectively. Notable covariance effects between model and prediction errors can be considered in PTV margin estimation using a modified VHF, which allowed for more precise target localization in robotic SBRT for moving tumors. Overall, in each of the three directions, the difference between MVHF and utilized clinical margins is 0.65 mm in the lung and abdominal region. Therefore, to improve the clinical PTV margins with the new approach, it is suggested to use the adaptive PTV margins in the next fractions.

6-Gingerol contents of several ginger varieties of Northeast India and correlation of their antioxidant activity in respect to phenolics and flavonoids contents.

INTRODUCTION: Ginger constitutes the rhizome part of the plant Zingiber officinale from the Zingiberaceae family. A large number of ginger varieties with high sensorial and functional quality are found in Northeast India. Hence, phytopharmacological screening of different ginger varieties is essential that will serve as a guideline in applied research to develop high-end products and improve economical margins. OBJECTIVE: To determine the variation in total phenolics content (TPC), total flavonoids content (TFC), and antioxidant activities and correlate that with 6-gingerol contents of different ginger varieties collected from Northeast India using Pearson's correlation analysis. MATERIALS AND METHODS: The TPC and TFC values were determined using standard methods. Antioxidant activities were measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radical scavenging assays, while reversed-phase high-performance liquid chromatography (RP-HPLC) analysis was utilised for quantitative determination of 6-gingerol content. RESULTS: The result revealed that ginger variety 6 (GV6) contains the highest 6-gingerol content and TPC value showing maximum antioxidant activity, followed by GV5, GV4, GV9, GV3, GV2, GV8, GV1, and GV7. The findings also suggested that the antioxidant activity has much better correlations with TPC as compared with TFC values. Pearson's correlation analysis showed a significant correlation between 6-gingerol contents and TPC values. CONCLUSION: This work underlines the importance of ginger varieties from Northeast India as a source of natural antioxidants with health benefits.