Ecological Suitability Assessment of Larimichthys crocea in Coastal Waters of the East China Sea and Yellow Sea Based on MaxEnt Modeling

In recent decades, China's large-scale stock enhancement programs to restore the collapsing large yellow croaker (Larimichthys crocea) fishery resources have not yielded the desired results, and a comprehensive analysis of the underlying reasons for this problem is required. Based on small yellow croaker (Larimichthys polyactis) catch survey data obtained from 15 fishing ports along the coast of the East China Sea, we examined the proportion of large yellow croakers mixed in the small yellow croaker catch and their biological parameters. In addition, we analyzed the differences in the intestinal microbiota and feeding ecology between these two species to explore the reason why the stock enhancement program failed to achieve the desired outcome. The results show that there is a high likelihood of the two species appearing in each other's ecological niches, and there is a significant overlap in their dietary ecology. They may cohabitate and form a guild. The fishing season targeting the small yellow croaker indirectly catches the large yellow croaker population, which puts huge fishing pressure on large yellow croaker resource and shows obvious overfishing. Therefore, it is necessary to optimize and adjust the fishing ban policy and stock enhancement strategies, appropriately reducing the fishing intensity after the fishing ban to facilitate the effective accumulation of resource replenishment effects during the fishing ban period, thus effectively restoring wild large yellow croaker resources.

The large yellow croaker, Larimichthys crocea, was once the most abundant and economically important marine fish in China. Thus far, it has also been the most successful mariculture fish species in China. However, its wild stock severely declined in the 1970s because of overexploitation, and therefore hatchery release has been carried out for stock enhancement since 2000. As a migratory fish, large yellow croaker was divided into three geographical stocks according to ambiguous morphological and biological characteristics in early documents. To investigate the identity of wild large yellow croaker populations and assess the influence of hatchery supplementation on wild populations, a total of 2,785 cultured individuals and 591 wild individuals were collected from 91 hatcheries and six wild populations along the coast of mainland China and analyzed using two mitochondrial genes [cytochrome oxidase I (COI) and cytochrome b (Cyt b)] and one nuclear gene (RyR3). The higher haplotype diversity and moderate nucleotide diversity of wild large yellow croaker indicated that overexploitation, which caused a sharp decrease in biomass, did not lead to a loss of genetic diversity. According to phylogenetic construction and network analysis, the absence of a significant population structure pattern revealed a single panmictic population of wild large yellow croaker with exception of a population collected from the Sansha Bay, which showed high genetic relatedness to the cultured population, suggesting significant genetic effects resulting from stock enhancement. Overall, our study suggests no genetic differentiation in the entire wild population of large yellow croaker, which means that we have great flexibility in mixing and matching farmed and wild populations. However, since the result showed that domestication, the relaxation of purifying selection, increased genetic loads, and maladapted farmed fish will be at a selective disadvantage when cultured juveniles are released in the wild, the effectiveness of stock enhancement and the negative impact of hatchery-wild fish hybridization on the wild population must be carefully evaluated in future.

Scientific prediction of suitable cultivation regions is an effective way for the assessment of habitat suitability and resource conservation to protect endangered medicinal plants. In recent years, the natural habitat of Scutellaria baicalensis Georgi has been degenerating and disappearing in China owing to excessive market demand of medicinal plant resource. This paper reports a new approach to predict potential suitable cultivation regions and to explore the key environmental factors affecting the content of active ingredients in S. baicalensis using integrated Maxent (maximum entropy) modeling and fuzzy logics. The modeling procedure used 275 occurrence records and baicalin contents of S. baicalensis collected through 2000–2014, and 16 Worldclim environmental factors as well as HWSD soil data. The result showed that six environmental factors (alt, prec7, prec1, bio4, bio1 and t_ph) were determined as key influential factors that mostly affect both the habitat distribution and baicalin content of S. baicalensis. The highly suitable cultivation regions of S. baicalensis mainly distribute (with probability ≥0.50) in the northeast, the north-central and the northwest of China (total 419,857 km2). The statistically significant AUC (area under the curve) value (0.952) of ROC (receiver operating characteristic) curve indicated that integrated Maxent modeling and fuzzy logics could be used to predict the potential suitable cultivation regions of medicinal plants. These results could pave the road for the habitat conservation and resource utilization of endangered medicinal plants.

This paper proposes a fault-prone prediction approach that combines a fault-prone prediction model and manual inspection. Manual inspection is conducted by a predefined checklist that consists of questions and scoring procedures. The questions capture the fault signs or indications that are difficult to be captured by source code metrics used as input by prediction models. Our approach consists of two steps. In the first, the modules are prioritized by a fault-prone prediction model. In the second step, an inspector inspects and scores α percent of the prioritized modules. We conducted a case study of source code modules in commercial software that had been maintained and evolved over ten years and compared AUC (Area Under the Curve) values of Alberg Diagram among three prediction models: (A) support vector machines, (B) lines of code, and (C) random predictor with four prioritization orders. Our results indicated that the maximum AUC values under appropriate α and the coefficient of the inspection score were larger than the AUC values of the prediction models without manual inspection in each of the four combinations and the three models in our context. In two combinations, our approach increased the AUC values to 0.860 from 0.774 and 0.724. Our results also indicated that one of the combinations monotonically increased the AUC values with the numbers of manually inspected modules. This might lead to flexible inspection; the number of manually inspected modules has not been preliminary determined, and the inspectors can inspect as many modules as possible, depending on the available effort.

The maximum entropy (Maxent) model was used to predict the distribution of Persian leopards and wild sheep in the Tang-e-Sayad protected area in Iran. For this purpose, eight variables, as well as 30 occurrence points of leopard and 98 points of wild sheep, were used. Two techniques, density-based occurrence points thinning and performance-based predictor variables selection were used to improve the results of the model. The model results were analyzed based on four threshold limit-based statistics (sensitivity, specificity, kappa and true skill statistics) and area under the curve (AUC), followed by determining the relative importance of variables based on the jackknife procedure. The results of threshold limit-based statistics revealed that the success of the model for distribution prediction of leopard and wild sheep were good and relatively good, respectively. According to the jackknife procedure, for wild sheep and for leopard, slope and distance to road, respectively, were the most important predictor variables. The results also indicated that the efficiency of the model did not improve by reducing the density of occurrence points for the wild sheep (AUC=0.784–0.773). However, the selection of predictor variables slightly improved the performance of the model (AUC=0.794–0.819). The results of the study also showed overlapping habitat for two species due to both human and ecological reasons for which we proposed some conservation actions such as excluding domestic grazing, controlling illegal poaching and restoration of old migratory corridors.

Introduction: Cerebral computed tomography perfusion (CTP) imaging has been well-established for identifying candidates for endovascular therapy (EVT) in acute ischemic stroke. This study investigates the association between CTP parameters and functional independence post-EVT using data from the SELECT study. Methods: We analyzed baseline CTP images from SELECT patients, focusing on those with available cerebral blood volume (CBV), cerebral blood flow (CBF), and time to maximum perfusion (Tmax) maps. Patients who received EVT and medical management only (MM) were included. Logistic regression models were created with age, national institutes of health stroke scale (NIHSS), time to arrival, occlusion location, transfer status, and CT ASPECTS as covariates, and functional independence at 90 days (modified Rankin score 0-2) as the outcome. Receiver operating characteristic (ROC) curves were generated, and area under the curve (AUC) values were calculated and compared using DeLong’s test. Results: Among 361 patients, 171 (139 EVT, 32 MM) had volumetric estimates for CTP parameters with pre-defined thresholds. Median (IQR) age and NIHSS were 68 (56-78) and 16 (12-20), and 48% were females. Estimates of different thresholds within a given CTP parameter showed high correlation (>0.8), and a moderate to high correlation (>0.4-0.6) was observed in estimates across different parameters (all p<0.05, figure 1). ROC curves revealed AUC values >0.7 for all CTP parameters in both EVT (figure 2) and combined (EVT + MM, figure 3) subgroups, with the highest values for CBF thresholds: <30% [EVT – AUC 0.7507, combined – AUC 0.8028], <34% [EVT – AUC 0.737, combined – AUC 0.7857] and <38% [EVT - AUC 0.7351, combined – AUC 0.7831], followed by CBV parameters and then Tmax parameters. No significant differences were found between AUC values of different CTP parameters (EVT: p=0.88; combined: p=0.46). Conclusion: All CTP parameters demonstrated high AUC values (>0.7) for functional independence at 90 days in acute stroke patients, particularly CBF <30%, indicating their potential as prognostic markers.

BackgroundSchistosomiasis is a snail-borne disease endemic in sub-Saharan Africa transmitted by freshwater snails. The distribution of schistosomiasis coincides with that of the intermediate hosts as determined by climatic and environmental factors. The aim of this paper was to model the spatial and seasonal distribution of suitable habitats for Bulinus globosus and Biomphalaria pfeifferi snail species (intermediate hosts for Schistosoma haematobium and Schistosoma mansoni, respectively) in the Ndumo area of uMkhanyakude district, South Africa.MethodsMaximum Entropy (Maxent) modelling technique was used to predict the distribution of suitable habitats for B. globosus and B. pfeifferi using presence-only datasets with ≥ 5 and ≤ 12 sampling points in different seasons. Precipitation, maximum and minimum temperatures, Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), pH, slope and Enhanced Vegetation Index (EVI) were the background variables in the Maxent models. The models were validated using the area under the curve (AUC) and omission rate.ResultsThe predicted suitable habitats for intermediate snail hosts varied with seasons. The AUC for models in all seasons ranged from 0.71 to 1 and the prediction rates were between 0.8 and 0.9. Although B. globosus was found at more localities in the Ndumo area, there was also evidence of cohabiting with B. pfiefferi at some of the locations. NDWI had significant contribution to the models in all seasons.ConclusionThe Maxent model is robust in snail habitat suitability modelling even with small dataset of presence-only sampling sites. Application of the methods and design used in this study may be useful in developing a control and management programme for schistosomiasis in the Ndumo area.

O21. First trimester serum placental growth factor and hyperglycosylated human chorionic gonadotropin are associated with later pre-eclampsia

气候变化对东北沼泽湿地潜在分布的影响

BackgroundThis study aimed to compare anthropometric indices to predict type 2 diabetes mellitus (T2DM) among first-degree relatives of diabetic patients in the Iranian community.MethodsIn this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC).ResultsThe overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes.ConclusionThe WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.

The area under the curve (AUC) of the receiver operator characteristic (ROC) graph is regarded as an objective measure of the discrimination accuracy of predictive models. AUC scores calculated from background values, or pseudo-absences, have been proposed as a method of model selection for species distribution models (SDMs) fitted to presence-only data. However, the utility of AUC as a measure of model performance when data on confirmed absence are unavailable has not been fully investigated. We fitted SDMs using informative climatic variables for 2000 species of Mesoamerican trees. As a reference, we also built ‘pseudo-models’ using Gaussian random fields with no biological meaning. AUC correctly selected SDMs fitted to single environmental variables over ‘pseudo-models’ fitted to single random fields in almost all cases. However, when all seven variables were included in the models, AUC erroneously selected complex pseudo-models over complex climate models in 17% of the cases. The spatial distribution patterns predicted by the pseudo-models differed from the results derived from climate-based models, even when overall AUC scores were similar. Both model and pseudo-model AUC values increased when presence points were few and spatially aggregated. The results show that AUC calculated from presence-only data can be an unreliable guide for model selection. Pseudo-absences have ill-defined properties that challenge the interpretation of AUC values. Inference on multidimensional niche spaces should not be supported by AUC values calculated using pseudo-absences.

Different methods to calculate interval area under the curve (AUC) data may produce substantial error. The purpose of this study was to compare methods of calculating etoposide AUC and determine the effect of these values on white blood cell (WBC) count nadir predictions calculated from a previously reported equation. Three AUC calculation methods were used: (1) the linear trapezoidal method, (2) a combination of the linear and logarithmic trapezoidal methods, and (3) the Lagrange method. Since none of the methods for determining the AUC could be considered the standard, the methods were evaluated by comparing differences between pairs of calculated AUC values by each method. The 95% CI for differences between all pairs of AUC values were greater than zero (no difference) indicating significance. Consistent with the smoother fitting function between data points, the Lagrange method tended to produce a larger AUC, lower clearance values, and lower WBC nadir count predictions than the other methods. The largest difference encountered was between the Lagrange and the linear-log AUC methods with a mean value of 16.9 micrograms h/ml (95% CI 9.4-24.3) This difference would account for approximately 11% of the total AUC. Using a previously published equation, where WBC nadir = -0.057 +0.048 x etoposide clearance, with clearance determined as dose/AUC, mean differences in calculated WBC nadir count values between the three AUC methods ranged from 80 to 220 cells/microliters, which would be expected to be of little clinical consequence. The precision of this equation, using data derived from linear trapezoidal AUC calculations, had a mean absolute error of 0.93 x 10(3)/microliters (95% CI 0.53-1.32). Our findings suggest that any of the three mathematical methods studied would produce similar etoposide AUC values and pharmacodynamic predictions. Further, these findings also suggest that the major limitation in predicting etoposide leukopenia lies with the imprecision of the pharmacodynamic model more so than the ability to accurately determine the AUC. However, our findings may not be applicable if other factors intervene which dramatically alter the shape of the etoposide concentration-time curve.

阿拉善马鹿(<i>Cervus alashanicus</i>)生境适宜性评价

Estimating systemic exposure to ethinyl estradiol from an oral contraceptive

To analyze the area under the curve (AUC) from 24-hour pharyngeal pH probes and evaluate this parameter as a predictor of disease severity for laryngopharyngeal reflux. Eighty patients met inclusion criteria of a completed 24-hour pharyngeal pH probe and Reflux Symptom Index (RSI) questionnaire. The AUC was calculated below a pH threshold of 5.5 using the trapezoidal rule. The RSI and RYAN scores were correlated with AUC values, and nonparametric tests were used for comparisons. The median AUC value was 18 007 pH-seconds with an interquartile range (IQR) of 63 156, the median RSI score was 21 (IQR = 16), and the median RYAN score was 15.3 (IQR = 78). There was a Spearman's correlation of .36 between the RSI scores and AUC values ( P = .001) and a moderate correlation between AUC values and RYAN scores (0.58, P < .001). An insignificant correlation of .19 between RYAN scores and RSI scores was observed ( P = .09). The AUC may be a useful objective value in establishing the diagnosis of laryngopharyngeal reflux. Prospective studies with larger patient populations are necessary to validate these findings and determine standardized thresholds for symptomatic patients.