Coefficient Of Variation Analysis Research Articles

Temporal and Spatial Dynamics in Carbon Utilization Efficiency and Driving Mechanisms in Southeast Tibet from 2012 to 2022

Carbon utilization efficiency (CUE) in terrestrial ecosystems stands as a pivotal metric for assessing ecosystem functionality. Investigating the spatiotemporal dynamics of regional CUE within the context of global climate change not only provides a theoretical foundation for understanding terrestrial carbon cycling but also furnishes essential data support for formulating sustainable management strategies at a regional scale. This study focuses on the southeastern region of Tibet. Utilizing monthly and yearly MOD17A2HGF as primary sources, we employ Thiel–Sen estimation and Mann–Kendall trend analysis to scrutinize the spatiotemporal dynamics of CUE. Systematic analysis of the stability of CUE spatiotemporal changes in the Southeast Tibet region is conducted using the coefficient of variation analysis. The Hurst model is then applied to prognosticate future CUE changes in Southeast Tibet. Additionally, a comprehensive analysis of CUE is undertaken by integrating meteorological data and land-use data. The findings reveal the following: (1) At the monthly scale, regional CUE exhibits discernible variations synchronized with the growth season, with different vegetation types displaying diverse fluctuation patterns. The high-altitude forest area manifests the least annual CUE fluctuations, while evergreen needleleaf forests and evergreen broadleaf forests demonstrate larger variations. At the yearly scale, CUE reveals a non-significant upward trend overall, but there is an augmented fluctuation observed from 2019 to 2022. (2) CUE in Southeast Tibet demonstrates sensitivity to temperature and precipitation variations, with temperature exhibiting a more pronounced and strongly correlated impact, especially in Gongjo County and Qamdo Town. Temperature and precipitation exert opposing influences on CUE changes in the Southeast Tibet region. In the southern (below 28° N) and northern (above 31° N) regions of Southeast Tibet, the response of CUE to temperature and precipitation variations differs. Moreover, over 62.3% of the areas show no sustained trend of change. (3) Vegetation type emerges as a principal factor determining the scope and features of vegetation CUE changes. Grassland and sparse grassland areas exhibit markedly higher CUE values than evergreen broadleaf forests, deciduous broadleaf forests, evergreen needleleaf forests, and deciduous needleleaf forests. Notably, the CUE fluctuation in shrublands and areas with embedded farmland vegetation surpasses that of other vegetation types.

Evaluation method for psychological resilience of athletes in high-intensity sports training based on evolutionary neural network

To enhance the psychological resilience of athletes, a method for evaluating the psychological resilience of High-intensity Interval Training (HIIT) athletes based on evolutionary neural networks is studied. From the six criteria of frustration coping, personal characteristics, self-promotion, self-regulation, internal protection and external protection, the evaluation index of psychological resilience of athletes in sports High-intensity Interval Training is selected; the audition indicators are qualitatively analyzed according to the principle of indicator selection, and the indicators that do not meet the requirements are eliminated; Cluster analysis and coefficient of variation analysis are used to carry out quantitative analysis on the remaining evaluation indicators after qualitative analysis; the indicators after quantitative analysis are improved, to build the assessment index system of psychological resilience of athletes in high-intensity sports training. The Back Propagation (BP) neural network is optimized by a genetic algorithm, and the evolutionary neural network is constructed. The index data set is input into the evolutionary neural network as a sample, and the index weight value is output through training. The evaluation result and corresponding evaluation grade are determined based on the index weight value and membership degree. The experimental results show that when the number of hidden layers is 3, the calculation of evaluation index weights is the best; The weight of personal traits obtained from the evaluation results is the highest (0.206), while the weight of external protection is the lowest (0.151), and the evaluation results are basically consistent with the expert results. The above results show that this method can accurately evaluate the psychological resilience of athletes and significantly enhance their psychological resilience.

Spatial convergence characteristics of low carbon economy and economic growth quality: based on Guangdong urban data

<abstract> <p>As China's economy transitions from a stage of high-speed growth to a stage of high-quality development, the concept of low-carbon and green economic development has gained increasing popularity. Mastering the regional differences and changing patterns of low-carbon economy and economic growth quality is an important prerequisite for further promoting low-carbon economic development and improving the quality of economic growth. Taking the data of 21 prefecture-level cities in Guangdong Province from 2008 to 2019 as examples, we calculated the low-carbon economy and the quality index of economic growth, and analyzed the convergences between them through coefficient of variation analysis and a panel data convergence model with fixed effects. The results showed that: First, the convergence of low-carbon economy was better than the convergence of economic growth quality. Second, the low-carbon economy of Guangdong Province had σ convergence, and the imbalance between regions of low-carbon economy was alleviated, but the quality of economic growth of Guangdong Province did not have σ convergence. Third, there was absolute and conditional <italic>β</italic> convergence in the quality of low-carbon economy and economic growth in Guangdong Province. Fourth, the convergence rate of low-carbon economy in Guangdong Province showed "club difference"; the same was true of σ convergence, absolute <italic>β</italic> convergence, conditional <italic>β</italic> convergence, and dimensional convergence of economic growth quality in various regions of Guangdong Province. The exploration conducted in this article was conducive to better grasping the changing patterns of low-carbon economy and economic growth quality, enriching relevant research. The conclusions of this paper can provide decision-making basis for China to formulate urban and regional economic policies, achieve high-quality economic development, and "double carbon goal".</p> </abstract>

Effects of coal mining and climate-environment factors on the evolution of a typical Eurasian grassland

Coal mining can significantly impact vegetation evolution, yet the limited information on its patterns and driving factors hampers efforts to mitigate these effects and reclaim abandoned mines. This study aimed to 1) examine vegetation evolution in a semiarid steppe watershed in northeast China; and 2) characterize the driving factors behind this evolution. We analyzed the impact of twelve selected driving factors on fractional vegetation coverage (FVC) from 2000 to 2021 using a dimidiate pixel model, Sen's slope analysis, Mann-Kendall trend test, coefficient of variation analysis, and Geodetector model. At a significance level of α = 0.05, our findings revealed a south-to-north decline pattern in FVC, a significant decrease trend in proximity to coal mines, and a notable increase trend adjacent to river channels. Approximately 37% of the watershed exhibited low FVC, while the overall temporal trend across the watershed was deemed insignificant. Areas surrounding the mines experienced a substantial reduction in FVC due to coal mining activities, while FVC variations across the watershed were linked to precipitation, temperature, and soil type. FVC predictions improved notably when interactions between multiple two-way factors were considered. Each driving factors displayed an optimal range (e.g., precipitation = 63–71 mm) for maximizing FVC. Given the study watershed's status as a national energy base, understanding vegetation responses to coal mining and climate-environment changes is crucial for sustaining fragile terrestrial ecosystems and socioeconomic development. Achieving a long-time balance between coal extraction and ecological protection is essential. The study outcomes hold significant promise for advancing ecological conservation, vegetation restoration, and mitigation of environmental degradation in semiarid regions affected by extensive coal mining and climate fluctuations. These findings contribute to the strategic management of such areas, promoting sustainable practices amidst evolving environmental challenges.

Temporal and Spatial Variations in the Normalized Difference Vegetation Index in Shanxi Section of the Yellow River Basin and Coal Mines and Their Response to Climatic Factors

Investigating the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Shanxi section of the Yellow River Basin and its coal mining areas holds significant importance for dynamic vegetation monitoring and mining area management. This study employs MODIS NDVI data and combines various analytical methods, including trend analysis and coefficient of variation analysis, to reveal the characteristics of NDVI spatiotemporal variations and their response to climatic factors in the study area. The results indicate the following: (1) The overall NDVI in the Shanxi section of the Yellow River Basin exhibits a growth trend with an annual growth rate of 1.82% and a 36% increase. Among the mining areas, the NDVI increase is most prominent in the Hebaopian mining area with a 100% growth, while the QinYuan mining area shows the lowest increase at 21%; (2) The NDVI in the Shanxi section of the Yellow River Basin displays high fluctuations, with areas of moderate and high fluctuations accounting for 54.39% of the total. The Hebaopian mining area has a substantial portion of high-fluctuation areas at 38.85%; (3) According to the Hurst index analysis, future vegetation changes in the Shanxi section of the Yellow River Basin are uncertain, with approximately 9.77% of areas expected to continue improving; (4) The variations in the NDVI and climatic factors across the Shanxi section of the Yellow River Basin display spatial heterogeneity. The NDVI exhibits a positive correlation with both temperature and precipitation, with the correlation with precipitation being more pronounced than that with temperature. Precipitation exerts a more significant influence on the NDVI than temperature. These findings not only provide scientific guidance for vegetation restoration and area management in the Shanxi section of the Yellow River Basin and its mining areas but also serve as a scientific basis for decision making regarding vegetation management under the influence of climate change and human activities.

Spatial Distribution and Influencing Factors of Traditional Villages in Inner Mongolia Autonomous Region

This paper takes 207 traditional villages in Inner Mongolia as the research object and uses the ArcGIS10.7 software platform, using the nearest neighbor index, coefficient of variation analysis, spatial autocorrelation analysis, imbalance index, kernel density estimation method, geographical detector, and other methods to explore the spatial distribution characteristics and influencing factors of traditional villages in Inner Mongolia. The research shows that: (1) the spatial distribution of traditional villages in Inner Mongolia is condensed; the distribution of cities is uneven; and the overall distribution pattern of ‘two main and two vice’ is presented. (2) The traditional villages are mainly distributed in the altitude area of 500–1500 m, and their spatial distribution characteristics are positively correlated with the annual average temperature, annual precipitation, total population, the proportion of the primary industry, and the number of intangible cultural heritage, and negatively correlated with the slope, river distance, highway density, per capita GDP, urbanization, and the proportion of the secondary industry. (3) The results of GeoDetector2018 software show that socio-economic factors are the primary factors affecting the spatial distribution of traditional villages in Inner Mongolia, followed by natural geographical factors. The interaction and synergy between the influencing factors have increased significantly, which jointly affects the spatial pattern of the distribution of traditional villages in Inner Mongolia. The purpose of the study is to provide reference for the protection and development of traditional villages in Inner Mongolia and the implementation of the national rural revitalization strategy.

A-161 Analytical Performance of Reagents for Determination of Activated Partial Thromboplastin Time: A Sigma Metric Perspective

Abstract Background Sigma Metric (SM) assesses the degree of deviation of lab. tests from established specifications, combining Total Acceptable Error (ETa), imprecision and analytical bias. Quality criteria, defined by models of desirable specification, are used to determine the analytical performance of lab. tests. The concepts of biological variation are used as the preferred approach for establishing specifications. Reagents (RE) for the determination of activated partial thromboplastin time (APTT) use thromboplastin obtained from different tissue extracts and activation factors, being standardized to reduce the variability of the tests, and ensure equivalence of clinical diagnosis. This work evaluated three RE for APTT and the fulfillment of the desirable specifications of analytical quality, using the SM as an analysis tool. Material & methods APTT determinations were performed on the Sysmex®CS-2500 (Siemens Healthineers) with Actin®FS (AFS), Actin®FSL (FSL), and Pathromtin®SL (PSL) RE and bi-level Control Plasma N (CN) and Ci-Trol®2 Control (CI). Two different lots were used for each RE and control (CT), and the results were analyzed in RE-CT combination. We determined (a)analytical bias (Bias), from intra-assay imprecision determined by 30 repetitions in a single analytical run and by comparison with manufacturer's data, and (b)analytical coefficient of variation (CVa), from inter-assay imprecision, by triplicate in 3 periods each 3 h, for the 2 CT levels. Analytical quality specifications for APTT were selected in the literature, adopting desirable ETa values of 4.5% and minimum of 6.7%, obtained from a database for biological variation and acceptable limit of 15%, based on CLIA-CAP. SM was calculated with formula: Sigma = ETa–Bias/CVa, with graphic representation adapted from the Westgard’s model. Results For CVa, AFS and FSL results with CN and CI were <1.0%. PSL presented results above 1.0% for CN and CI. All RE presented results lower than those recommended by the manufacturer, respectively 4.0% for AFS; 3.0% for FSL; and 2.8% for PSL. For bias, a greater variation was observed for the combinations of RE and CT, ranging from 1.16% to 4.07%, being related to the differences between the averages obtained and the values defined in the leaflets. For the desirable specification of ETa (4.5%), 3 combinations of RE and CT level AFS-CN, FSL-CN and PSL-CI presented Sigma values >3 (respectively, 3.61, 3.87 and 3.33). When using the minimum specification (6.7%), the PSL-CN combination presented a Sigma = 2.06. With the CLIA-CAP specification, all RE and CT combinations had Sigma >6. Discussion All RE showed similar analytical performance when compared by SM, demonstrating that different commercial formulations for APTT determination do not show differences in analytical Bias and CVa determinations. The graphical representation by SM allows you to visually compare analytical imprecision and inaccuracy values. Conclusion All RE showed no differences in analytical performance, with SM being a visual tool for analyzing and comparing RE performance.

Low-dose CT radiomics features-based neural networks predict lymphoma types

BackgroundFluorodeoxyglucose positron emission tomography (PET)–computed tomography (CT) is preferred for pretreatment staging and treatment planning in patients with lymphoma. This study aims to train and validate the neural networks (NN) for predicting lymphoma types using low-dose CT radiomics.ResultsFew radiomics features were stable in intraclass correlation coefficient and coefficient of variation analysis (n = 119). High collinear ones with variance inflation factor were eliminated (n = 56). Twenty-four features were selected with the least absolute shrinkage and selection operator regression for network training. NN had 75.76% predictive accuracy in the validation set and has 0.73 (95% CI 0.55–0.91) area under the curve (AUC) to differentiate Hodgkin lymphoma from non-Hodgkin lymphoma. NN which was used to differentiate B-cell lymphoma from T-cell lymphoma had 78.79% predictive accuracy and has 0.81 (95% CI 0.63–0.99) AUC.ConclusionsIn this study, in which we used low-dose CT images of PET–CT scans, predictions of the neural network were near acceptable lower bound for Hodgkin and non-Hodgkin lymphoma discrimination, and B-cell and T-cell lymphoma differentiation.

Direct Comparison of the Circadian Rhythm of Cardiac Myosin-Binding Protein C (cMyC) and Cardiac Troponin.

We have previously shown that significant circadian oscillations exist for cardiac troponin T (cTnT) but not for cardiac troponin I (cTnI) (1). Cardiac myosin-binding protein C (cMyC) is a novel protein biomarker of myocardial injury with a promising role in the diagnosis and risk stratification of acute myocardial injury. In this study, we examine and compare the diurnal variation of cMyC with cTnT/I. Study design, population and methods are described by Klinkenberg et al. (1). Briefly, 26 consecutive hourly blood samples were drawn between 8.30 Am and 09.30 Am (+ 1 day) from 24 individuals without active cardiac disease, uncontrolled hypertension, anemia, renal dysfunction, or a history of acute myocardial infarction or stroke within 12 months. Seven percent had type 2 diabetes (all on oral blood glucose lowering medications) and 42% were on antihypertensive medication. cMyC was measured by Millipore Sigma (2). Limit of detection 0.4 ng/L, limit of quantitation (20% analytical coefficient of variation) of 1.2 ng/L; intra-series precision (CV, 11 +/− 3%), and inter-series precision (CV, 13 +/− 3%), 99th percentile is 87 ng/L. cTnT (Roche hs-cTnT) and cTnI (Abbott hs-cTnI) were measured previously by Klinkenberg et al. (1), but data were reanalyzed and are represented here to enable direct comparisons with cMyC.

Bone turnover markers for assessment of anti-resorptive effect in clinical practice: A good idea meets the problem of measurement uncertainty

ObjectivesBone turnover markers (BTM) are measures for understanding the effect of anti-resorptives upon osteoclast activity. Post-hoc trial data suggests reduction in BTM of 40% may represent a target for defining appropriate response to therapy. We modeled clinical application of this target threshold in an individual patient setting where assay measurement uncertainty and biological variation are included. DesignUsing serum C-telo-peptide (ß-CTX), we constructed hypothetical scenarios of ß-CTX measurement pre and post bisphosphonate therapy. Using typical ß-CTX assay characteristics (analytical coefficient of variation, CV 5.0%) and published intra-individual ß-CTX data for post-menopausal women (CV 18.0%), we calculated the post-therapy ß-CTX that must be seen on single repeat measure for 95% confidence that the observed result was ≥40% below baseline. Sensitivity analyses considered greater and lesser variations in the combined sources of variation. ResultsThe one-tailed 95% reference change value for any detectable therapeutic decrease in ß-CTX was 22%. However, to have 95% confidence of having achieved a reduction ≥40%, an observed ß-CTX decrease of ≥56% is required. Larger decreases are needed for scenarios of greater analytical or intra-individual variation. ConclusionsAlthough population data suggest a ß-CTX decrease of 40% is commensurate with adequate therapeutic response to anti-resorptives, application to an individual patient where measurement and natural variation are present is problematic. ß-CTX decreases much >40% are required to be confident of having achieved the optimal treatment response. It is uncertain whether this is a legitimate change to be expected in all individual patients and therefore clinical application of this threshold is uncertain.

Optimal modeling of anti breast cancer drug candidates

As breast cancer is one of the most common cancers with high mortality rate in the world, this paper studies the optimal screening of anti-breast cancer candidate drugs. First, the data of 729 compound molecular descriptors are preprocessed, rough cleaned and filtered to 253, and then the cluster feature tree and correlation analysis are used to further reduce the dimension of data redundancy information, and 29 representative molecular descriptors are screened. In order to determine the importance and significance variables affecting the activity of compounds, the preliminary results were obtained by Pearson correlation analysis, and the results were further verified by Spearman correlation analysis. The stability of molecular descriptors was determined by coefficient of variation analysis, and the most representative 20 variables were selected from 29 molecular descriptors by principal component analysis. Projection pursuit model (PP) can reduce the difficulty of storage and calculation of high-dimensional data. 253 variable information reflecting biological activity are extracted with this model, and then the projection direction is optimized by using accelerated genetic algorithm (RAGA). Finally, 20 projection function values in the optimal projection direction are calculated to complete the prediction of compound activity index.

Spatio-Temporal Variation of Carbon Emission Intensity and Spatial Heterogeneity of Influencing Factors in the Yangtze River Delta

The Yangtze River Delta plays an important strategic role in China’s economic development pattern, and its carbon emission intensity, which reflects the development of a low-carbon economy, has attracted much attention. From the perspective of the city-level, this study uses the coefficient of variation, spatial autocorrelation and the Multi-scale Geographically Weighted Regression (MGWR) model to study the spatio-temporal characteristics of carbon emission intensity in the Yangtze River Delta from 1997 to 2017 and the spatial heterogeneity of its influencing factors. The results indicated that: (1) the carbon emission intensity in the Yangtze River Delta increased first and then decreased during the sample period, and the number of low-carbon emission intensity zones decreased first and then increased. (2) Through the coefficient of variation analysis, it is known that the ratio of nugget value to base value is much less than 25%, indicating that the correlation between the cities in the Yangtze River Delta is becoming more and more obvious, the spatial difference is becoming smaller, and the integration level is growing higher and higher. (3) The carbon emission intensity of the Yangtze River Delta has a strong positive spatial correlation, and the carbon emission intensity of the Yangtze River Delta decreases from the north to the south. (4) The effect of population size on carbon emission intensity is bidirectional, but the inhibition effect is greater than the promotion effect, and the average regression coefficient is −0.0796; the average regression coefficient of economic development level is 0.3674, and the average regression coefficient of industrial structure is 0.1702, both of which have a positive impact on carbon emission intensity. The degree of urbanization has a bidirectional effect, and the regression coefficient ranges from −0.920 to 0.091, and the negative effect is quite strong. Additionally, each factor has spatial heterogeneity.

IMPROVING ACCURACY AND REPEATABILITY OF T2 MAPPING IN THE OAI DATA THROUGH EXTEND PHASE GRAPH MODELING

IMPROVING ACCURACY AND REPEATABILITY OF T2 MAPPING IN THE OAI DATA THROUGH EXTEND PHASE GRAPH MODELING

Influence of Hankel matrix dimension on system identification of structures using stochastic subspace algorithms

In stochastic subspace methods, the modal analysis results are highly dependent on the dimensions of the Hankel matrix. Increasing the dimensions of the Hankel matrix (especially the number of rows) improves the estimation of the modal features by decreasing the impact of noise effects. Due to processing time and memory use, it is impossible to adjust the size of the Hankel matrix to the maximum compatible value. Therefore, this study uses a sensitivity analysis of the dimensions of the Hankel matrix to pick models with the slightest estimate error in the data-driven (DD-SSI) method. First, using the condition number criteria, the desirable models in which the effects of system errors are not predominating are determined. Second, the modal properties of desired models are validated by clustering the damping ratios and modal frequencies using the Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and assessing the complexity of shape modes with the Modal Complexity Factor (MCF). Finally, the optimum models are identified by analyzing the estimated error of modal properties (particularly damping ratio) and the coefficient of variation (CV) of damping of validated clusters. The proposed method was investigated for a two-dimensional simulated concrete building frame, a three-dimensional experimental model, and the Namin city Overpass Bridge ambient vibration tests. Sensitivity analysis was performed for canonical correlation analysis (SSI-CCA) and canonical variate analysis (SSI-CVA) methods. Analyses of computational and laboratory models revealed that the likelihood of non-physical modes arising outside the system's maximum order is relatively high. Also, the MCF is valuable for identifying computational and noise modes. These criteria accurately detected the flexural mode of the foundation set, which was clustered as the stable mode of the slab deck in the practical bridge. Furthermore, the CV analysis revealed that the desired dimension obtained from the condition number could be a reasonable estimate of the optimal system dimension.

Responses of NDVI to Climate Change and LUCC along Large-Scale Transportation Projects in Fragile Karst Areas, SW China

The fragile karst habitat is extremely sensitive to human activities such as large-scale engineering construction. To explore the influence of the construction and operation of the GH (Guiyang-Huangguoshu) highway on the vegetation within a certain range and the response of NDVI to climate factors, Landsat data were used to synthesize annual NDVI maps using the maximum value compositing method. Trend, correlation, and coefficient of variation analyses were performed. The results demonstrate that: (1) During the construction and operation periods, NDVI showed an overall upward trend, and the NDVI value and growth rate in the contrast area were greater than those in the core area; (2) the correlation between temperature and vegetation cover along the GH highway was stronger than that between precipitation and vegetation; (3) construction of the GH highway has had a significant impact on the surrounding vegetation, with the impact on vegetation ecology along the road mainly concentrated within the 2 km range. The increase of artificial surfaces along the road has had a great impact on the NDVI, and the vegetation cover change in the core area is more significant than that in the contrast area; and (4) the overall disturbance of the GH highway project to the surrounding ecology was mainly observed in the form of low and medium fluctuations. This study aims to provide a reference for environmental assessment and management in karst areas.

Aldosterone, Renin, and Aldosterone-to-Renin Ratio Variability in Screening for Primary Aldosteronism.

The plasma aldosterone concentration (PAC), renin, and aldosterone-to-renin ratio (ARR) are used to screen for primary aldosteronism (PA). Substantial intra-individual variability of PAC and ARR using plasma renin activity in the context of usual antihypertensive therapy has been described, but there is no data on ARR variability calculated using direct renin concentration (DRC). To describe the intra-individual variability of PAC, DRC, and ARR in the absence of interfering medications in patients with and without PA. Retrospective cohort study. Hypertensive patients referred for investigation of PA, with at least 2 ARR measurements while off interfering medications. Endocrine hypertension service of a tertiary center, from May 2017 to July 2021. PAC, DRC, and ARR variability was calculated as coefficient of variation (CV) and percent difference (PD). Analysis of 223 patients (55% female, median age 52 years), including 162 with confirmed PA, demonstrated high variability with a sample CV of 22-25% in the PAC and sample CV of 41% to 42% in the DRC and ARR in both the PA and non-PA groups. The degree of variability was substantially higher than the assays' analytical CV. Sixty-two patients (38%) with PA had at least one ARR below 70 pmol/L:mU/L (2.4 ng/dL:mU/L), a cut-off for first-line screening of PA. Significant intra-individual variability in PAC, DRC, and hence ARR occurs in a large proportion of patients being investigated for PA. These findings support the need for at least 2 ARR before PA is excluded or further investigated.

Lot-to-lot reagent verification: Effect of sample size and replicate measurement on linear regression approaches

BackgroundWe investigate the simulated impact of varying sample size and replicate number using ordinary least squares (OLS) and Deming regression (DR) in both weighted and unweighted forms, when applied to paired measurements in lot-to-lot verification. MethodsSimulation parameter investigated in this study were: range ratio, analytical coefficient of variation, sample size, replicates, alpha (level of significance) and constant and proportional biases. For each simulation scenario, 10,000 iterations were performed, and the average probability of bias detection was determined. ResultsGenerally, the weighted forms of regression significantly outperformed the unweighted forms for bias detection. At the low range ratio (1:10), for both weighted OLS and DR, improved bias detection was observed with greater number of replicates, than increasing the number of comparison samples. At the high range ratio (1:1000), for both weighted OLS and DR, increasing the number of replicates above two is only slightly more advantageous in the scenarios examined. Increasing the numbers of comparison samples resulted in better detection of smaller biases between reagent lots. ConclusionsThe results of this study allow laboratories to determine a tailored approach to lot-to-lot verification studies, balancing the number of replicates and comparison samples with the analytical performance of measurement procedures involved.

Adenosine and Astrocytes Determine the Developmental Dynamics of Spike Timing-Dependent Plasticity in the Somatosensory Cortex.

During development, critical periods of synaptic plasticity facilitate the reordering and refinement of neural connections, allowing the definitive synaptic circuits responsible for correct adult physiology to be established. The L4-L2/3 synapses in the somatosensory cortex (S1) exhibit a presynaptic form of spike timing-dependent long-term depression (t-LTD) that probably fulfills a role in synaptic refinement. This t-LTD persists until the fourth postnatal week in mice, disappearing thereafter. When we investigated the mechanisms underlying this maturation-related loss of t-LTD in either sex mouse slices, we found that it could be completely recovered by antagonizing adenosine type 1 receptors. By contrast, an agonist of A1R impeded the induction of t-LTD at P13-27. Furthermore, we found that the adenosine that mediated the loss of t-LTD at the end of the fourth week of development is most probably supplied by astrocytes. At more mature stages (P38-60), we found that the protocol used to induce t-LTD provokes t-LTP. We characterized the mechanisms underlying the induction of this form of LTP, and we found it to be expressed presynaptically, as witnessed by paired-pulse and coefficient of variation analysis. In addition, this form of presynaptic t-LTP requires the activation of NMDARs and mGlu1Rs, and the entry of Ca2+ into the postsynaptic neuron through L-type voltage-dependent Ca2+ channels. Nitric oxide is also required for t-LTP as a messenger in the postsynaptic neuron as are the adenosine and glutamate that are released in association with astrocyte signaling. These results provide direct evidence of the mechanisms that close the window of plasticity associated with t-LTD and that drive the switch in synaptic transmission from t-LTD to t-LTP at L4-L2/3 synapses, in which astrocytes play a central role.SIGNIFICANCE STATEMENT During development, critical periods of plasticity facilitate the reordering and refining of neural connections, allowing correct adult physiology to be established. The L4-L2/3 synapses in the somatosensory cortex exhibit a presynaptic form plasticity (LTD) that probably fulfills a role in synaptic refinement. It is present until the fourth postnatal week in mice, disappearing thereafter. The mechanisms that are responsible for this loss of plasticity are not clear. We describe here these mechanisms and those involved in the switch from LTD to LTP observed as the brain matures. Defining these events responsible for closing (and opening) plasticity windows may be important for brain repair, sensorial recovery, the treatment of neurodevelopmental disorders, and for educational policy.

ANALISIS RISIKO PRODUKSI DAN RISIKO PENDAPATAN PADA USAHATANI PADI DI KECAMATAN BAURENO KABUPATEN BOJONEGORO

Rice is one of the leading commodities for Baureno District, Bojonegoro Regency. Along with high revenue potential, farmers working in this area also have to face various agricultural risks. This study aims to analyze the production risk and income risk of rice farming. The study was conducted in Baureno District, Bojonegoro Regency, involving 55 farmers as respondents who were selected by simple random sampling. The level of production risk and farm income is calculated by the coefficient of variation analysis. The results of this study showed that rice farming in Baureno sub-district has high production risk and income risk, as indicated by the analysis of the coefficient of variation on production risk of 0.584 and income risk of 0.626. Keywords: Production risk, income, rice

Comparison of six regression-based lot-to-lot verification approaches.

Detection of between-lot reagent bias is clinically important and can be assessed by application of regression-based statistics on several paired measurements obtained from the existing and new candidate lot. Here, the bias detection capability of six regression-based lot-to-lot reagent verification assessments, including an extension of the Bland-Altman with regression approach are compared. Least squares and Deming regression (in both weighted and unweighted forms), confidence ellipses and Bland-Altman with regression (BA-R) approaches were investigated. The numerical simulation included permutations of the following parameters: differing result range ratios (upper:lower measurement limits), levels of significance (alpha), constant and proportional biases, analytical coefficients of variation (CV), and numbers of replicates and sample sizes. The sample concentrations simulated were drawn from a uniformly distributed concentration range. At a low range ratio (1:10, CV 3%), the BA-R performed the best, albeit with a higher false rejection rate and closely followed by weighted regression approaches. At larger range ratios (1:1,000, CV 3%), the BA-R performed poorly and weighted regression approaches performed the best. At higher assay imprecision (CV 10%), all six approaches performed poorly with bias detection rates <50%. A lower alpha reduced the false rejection rate, while greater sample numbers and replicates improved bias detection. When performing reagent lot verification, laboratories need to finely balance the false rejection rate (selecting an appropriate alpha) with the power of bias detection (appropriate statistical approach to match assay performance characteristics) and operational considerations (number of clinical samples and replicates, not having alternate reagent lot).