Analysis of carotid vulnerable plaque MRI high-risk features and clinical risk factors associated with concomitant acute cerebral infarction.

BACKGROUND: This study aimed to investigate the correlation between the high-risk characteristics of high-resolution MRI carotid vulnerable plaques and the clinical risk factors and concomitant acute cerebral infarction (ACI). METHODS: Forty-five patients diagnosed with a single vulnerable carotid plaque by MRI were divided into two groups based on whether they had ipsilateral ACI. The clinical risk factors and the observation values or frequency of occurrence of high-risk MRI phenotypes of plaque volume, LRNC, IPH and ulcer were statistically compared between the two groups. RESULTS: A total of 45 vulnerable carotid artery plaques were found in 45 patients, 23 patients with ACI and 22 patients without ACI. There were no significant differences in age, sex, smoking, serum TC, TG and LDL between the two groups (all P > 0.05), but the ACI group had significantly more patients with hypertension (P < 0.05) and the without ACI group coronary heart disease (P < 0.05). The volume of vulnerable carotid plaque in the group with ACI (1004.19 ± 663.57 mm3) was significantly larger than that in the group without ACI (487.21 ± 238.64 mm3) (P < 0.05). The phenotype of vulnerable carotid artery plaque was 13 cases of LRNC, 8 cases of LRNC + IPH, 5 cases of LRNC + Ulcer, and 19 cases of LRNC + IPH + Ulcer. There was no significant difference in this distribution between the two groups (all P > 0.05) with the exception of LRNC + IPH + Ulcer. The 14 cases of LRNC + IPH + LRNC + IPH + Ulcer (60.87%) in the group with ACI and was significantly greater than the 5 (22.73%) in patients without ACI (P < 0.05). CONCLUSION: It is preliminarily thought that hypertension is the main clinical risk factor for vulnerable carotid plaques with ACI and the combination of plaque volume with vulnerable carotid plaque and LRNC + IPH + Ulcer is a high-risk factor for complicated ACI. It has high clinical therapeutic value due to the accurate diagnosis of responsible vessels and plaques with high-resolution MRI.

The potential effects of climate change on amphibian distribution, range fragmentation and turnover in China.

Many studies predict that climate change will cause species movement and turnover, but few have considered the effect of climate change on range fragmentation for current species and/or populations. We used MaxEnt to predict suitable habitat, fragmentation and turnover for 134 amphibian species in China under 40 future climate change scenarios spanning four pathways (RCP2.6, RCP4.5, RCP6 and RCP8.5) and two time periods (the 2050s and 2070s). Our results show that climate change may cause a major shift in spatial patterns of amphibian diversity. Amphibians in China would lose 20% of their original ranges on average; the distribution outside current ranges would increase by 15%. Suitable habitats for over 90% of species will be located in the north of their current range, for over 95% of species in higher altitudes (from currently 137-4,124 m to 286-4,396 m in the 2050s or 314-4,448 m in the 2070s), and for over 75% of species in the west of their current range. Also, our results predict two different general responses to the climate change: some species contract their ranges while moving westwards, southwards and to higher altitudes, while others expand their ranges. Finally, our analyses indicate that range dynamics and fragmentation are related, which means that the effects of climate change on Chinese amphibians might be two-folded.

The complete mitochondrial genome of Amolops wuyiensis (Anura: Ranidae).

In the organisms, mitochondrial DNA plays an important role in the evolutionary studies, which can serve as a powerful molecular marker. Amolops wuyiensis, belongs to the family Ranidae that is known to occur in southeast China. The complete mitochondrial genome of A. wuyiensis was sequenced (17,797 bp in length, GenBank accession number KM386618). Similar to the typical mtDNA of amphibians, the complete mtDNA sequence of A. wuyiensis contained 13 protein-coding genes, 2 rRNA genes (12S rRNA and 16S rRNA), 22 tRNA genes and 1 control region. The nucleotide composition was 25.9% A, 33.8% T, 28.0% C and 12.3% G.

SDMdata: A Web-Based Software Tool for Collecting Species Occurrence Records.

It is important to easily and efficiently obtain high quality species distribution data for predicting the potential distribution of species using species distribution models (SDMs). There is a need for a powerful software tool to automatically or semi-automatically assist in identifying and correcting errors. Here, we use Python to develop a web-based software tool (SDMdata) to easily collect occurrence data from the Global Biodiversity Information Facility (GBIF) and check species names and the accuracy of coordinates (latitude and longitude). It is an open source software (GNU Affero General Public License/AGPL licensed) allowing anyone to access and manipulate the source code. SDMdata is available online free of charge from .

Ecophysiological responses to different forest patch type of two codominant tree seedlings.

According to gap-phase dynamics theory, forests can be divided into four distinct patch types: gap patch (G), building patch (B), mature patch (M), and degeneration patch (D). Varying light conditions across patch types are one of the most important factors affecting the coexistence of vegetation. Mechanisms of coexistence can be understood through detailed knowledge of ecophysiological responses of codominant tree seedlings to patch types. The following study was conducted to determine ecophysiological responses of Cyclobalanopsis glauca (an evergreen broad-leaved species) and Bothrocaryum controversum (a deciduous broad-leaved species) to four different patch types. During the gap-phase dynamics, light intensity and the magnitude of change in the four different patches followed the order of: G > B > D > M. Both species had the greatest photosynthetic capacity in the G patch. Dry leaf mass per area (LMA), Chlorophyll a + b concentration (Chl), carotenoids (Car), and nitrogen content per area (N a ) all responded to changes in light across patch type, but B. controversum showed greater sensitivity and changes than C. glauca. From G to M patch, the maximal quantum efficiency of PSII (F v /F m ) had a larger variation magnitude for B. controversum than for C. glauca. From G to M patch, B. controversum showed significant changes in gas exchange, while C. glauca showed only small changes. Ecophysiological trait partitioning of response to light in different patches provides a possible explanation of a coexistence mechanism.

The predictive performance and stability of six species distribution models.

BACKGROUND: Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. METHODOLOGY: We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. RESULTS: The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). CONCLUSIONS: According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.