[Effects of aridity on carbon, nitrogen, and phosphorus contents in soils and plants of mountain swamps, Zhejiang, China]. / 旱化对浙江山地沼泽湿地土壤与植物碳氮磷含量的影响.

Mountain swamps in Zhejiang Province have been suffered from severe drouhgt threats because of climate change and artificial interruption. Sphagnum bogs and swamps were gradually degraded into arid swamps. However, the effects of arid processes on the C, N, P contents of soils and their stoichiometry in mountain swamps are still unclear. We measured C, N and P concentrations, pH values, and bulk density in the upper 0-60 cm soil layers in the stands of five mountain swamps with the different arid levels. Moreover, the aboveground biomass and the C, N, P concentrations in the crushed plant mixture were also measured. The results showed that the soils of mountain swamps in Zhejiang Province were rich in soil organic carbon (SOC), total nitrogen (TN), but infertile in phosphorus (TP). Aboveground biomass, soil pH, bulk density increased, while SOC, TN, TP, C:N, C:P, N:P decreased with aridity. Soil pH and bulk density had significant negative correlations with SOC, TN, and TP in soils, respectively. The differences in the C, N, P accumulation in the soils were probably associated with litterfall production, the oxygen condition of wetlands, and the degree of plant decomposition at the different types of mountain swamps. In all, arid trends were obvious at the mountain swamps in Zhejiang Province. Soil nutrients, such as C, N, P, deceased, while plant community succeeded from the wet swamp to the mesophyte vegetation with the arid processes. The contents of C, N and P in the plants varied across species, and were not coupled with those in the soils.

Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network.

BACKGROUND: The purpose of this study was to explore the diagnostic value of convolutional neural networks (CNNs) in middle cerebral artery (MCA) stenosis by analyzing transcranial Doppler (TCD) images. METHODS: Overall, 278 patients who underwent cerebral vascular TCD and cerebral angiography were enrolled and classified into stenosis and non-stenosis groups based on cerebral angiography findings. Manual measurements were performed on TCD images. The patients were divided into a training set and a test set, and the CNN architecture was used to classify TCD images. The diagnostic accuracies of manual measurements, CNNs, and TCD parameters for MCA stenosis were calculated and compared. RESULTS: Overall, 203 patients without stenosis and 75 patients with stenosis were evaluated. The sensitivity, specificity, and area under the curve (AUC) for manual measurements of MCA stenosis were 0.80, 0.83, and 0.81, respectively. After 24 iterations of the running model in the training set, the sensitivity, specificity, and AUC of the CNNs in the test set were 0.84, 0.86, and 0.80, respectively. The diagnostic value of CNNs differed minimally from that of manual measurements. Two parameters of TCD, peak systolic velocity and mean flow velocity, were higher in patients with stenosis than in those without stenosis; however, their diagnostic values were significantly lower than those of CNNs (P < 0.05). CONCLUSIONS: The diagnostic value of CNNs for MCA stenosis based on TCD images paralleled that of manual measurements. CNNs could be used as an auxiliary diagnostic tool to improve the diagnosis of MCA stenosis.

[Correlation Between Water Purification Capacity and Bacterial Community Composition of Different Submerged Macrophytes].

Eight submerged macrophytes are commonly found in subtropical areas, including Vallisneria natans, Vallisneria denseserrulata, Hydrilla verticillata, Elodea canadensis, Ceratophyllum demersum, Potamogeton malaianus, Potamogeton pectinatus, and Potamogeton maackianus, and these eight macrophytes were selected as research objects. The absorption capacity of nitrogen and phosphorus and water purification ability of submerged macrophytes were compared under indoor static water conditions. Furthermore, combining the bacterial community composition of submerged macrophytes, which was determined by 16S rRNA gene sequencing, the correlation between the water purification ability and the bacterial community of submerged macrophytes was determined. The results showed that all of the submerged macrophytes had obvious purification effects on nitrogen and phosphorus in water. The removal of nitrogen and phosphorus by submerged macrophytes was mainly through plant synergism, and the removal rate of plant absorption and enrichment was low. Among them, the removal rate of nitrogen and phosphorus was the highest in Vallisneria denseserrulata, reaching 91.58% and 96.81%. The self-absorption ability of nitrogen and phosphorus from water of Elodea canadensis and Ceratophyllum demersum was higher than other groups. The plant synergistic purification ability of Vallisneria denseserrulata and Vallisneria natans was the highest. The absolute dominant phyla of eight submerged macrophyte-associated bacteria were Proteobacteria (abundance values were more than 40%). At the genus level, Cupriavidus, Rhodobacter, and Gemmatimonas were the dominant genera for different submerged macrophytes. Most of these bacterial groups were degradable, which may be the main reason for the strong ability of eight submerged macrophytes to purify nitrogen and phosphorus in the water. The LEfSe analysis showed that Vallisneria denseserrulata and Vallisneria natans had the highest number of bacteria with significant differences. Among them, Rhizobiales, Burkholderiales, Flavobacteriales, Alcaligenaceae, Cupriavidus, and Bacillales may be the dominant bacteria to enhance the efficiency of plant purification of the water by Vallisneria denseserrulata. The bacteria of Deinococci, Comamonadaceae, Saprospiraceae, and Hyphomicrobium may be the dominant bacteria to enhance the efficiency of plant purification of the water by Vallisneria natans.

[Spatial and Temporal Variability of CO2 Emissions from the Xin'anjiang Reservoir].

Xin'anjiang Reservoir is the largest reservoir in eastern China, with a surface area of 580 km2 and a mean depth of 30 m. It is in an oligotrophic or mesotrophic state at present. This study measured carbon dioxide (CO2) emissions from the upstream river, the reservoir's main body, and the river downstream of the Xin'anjiang Reservoir to investigate the spatial and seasonal variability of CO2 emissions from the water surface using static floating chambers and gas chromatography. Results showed that the downstream river had, significantly, the highest CO2 emission flux[(1535.00±1447.46) mg·(m2·h)-1], followed by the upstream river[(120.39±135.41) mg·(m2·h)-1]. The reservoir's main body had the lowest flux[(36.65-61.94) mg·(m2·h)-1]. The high CO2 emission flux in the downstream river was probably influenced by turbulence during the discharge periods, which would allow the dissolved CO2 in the hypolimnion before the dam to be released to the atmosphere in the watercourse of the downstream river. However, the CO2 emission flux decreased with distance to the dam, likely because of the drop in strength of the turbulence. Moreover, there was an obvious alternation between CO2 source and CO2 sink in the main body of the reservoir, with CO2 sources in autumn and winter and CO2 sinks in spring and summer. The maximum and minimum CO2 emission values occurred in winter and spring, respectively. Such variability in the CO2 emissions was probably influenced by the bloom of alga in spring and summer, because dissolved CO2 in the water was absorbed by the respiration of alga. However, hydrologic conditions were unstable in the upstream river due to a fast water flow, so alga was difficult to bloom there, and a CO2 source was observed throughout the year, except during April and August. The measurement of the flux from the upstream river, main body, and downstream river required a long period for the investigation of greenhouse gas emissions to avoid underestimating the total CO2 emission from a hydroelectric reservoir system.