Comparison of Nasopharyngeal MR, 18 F-FDG PET/CT, and 18 F-FDG PET/MR for Local Detection of Natural Killer/T-Cell Lymphoma, Nasal Type.

Objectives The present study aims to compare the diagnostic efficacy of MR, 18F-FDG PET/CT, and 18F-FDG PET/MR for the local detection of early-stage extranodal natural killer/T-cell lymphoma, nasal type (ENKTL). Patients and Methods Thirty-six patients with histologically proven early-stage ENKTL were enrolled from a phase 2 study (Cohort A). Eight nasopharyngeal anatomical regions from each patient were imaged using 18F-FDG PET/CT and MR. A further nine patients were prospectively enrolled from a multicenter, phase 3 study; these patients underwent 18F-FDG PET/CT and PET/MR after a single 18F-FDG injection (Cohort B). Region-based sensitivity and specificity were calculated. The standardized uptake values (SUV) obtained from PET/CT and PET/MR were compared, and the relationship between the SUV and apparent diffusion coefficients (ADC) of PET/MR were analyzed. Results In Cohort A, of the 288 anatomic regions, 86 demonstrated lymphoma involvement. All lesions were detected by 18F-FDG PET/CT, while only 70 were detected by MR. 18F-FDG PET/CT exhibited a higher sensitivity than MR (100% vs. 81.4%, χ2 = 17.641, P < 0.001) for local detection of malignancies. The specificity of 18F-FDG PET/CT and MR were 98.5 and 97.5%, respectively (χ2 = 0.510, P = 0.475). The accuracy of 18F-FDG PET/CT was 99.0% and the accuracy of MR was 92.7% (χ2 = 14.087, P < 0.001). In Cohort B, 72 anatomical regions were analyzed. PET/CT and PET/MR have a sensitivity of 100% and a specificity of 92.5%. The two methods were consistent (κ = 0.833, P < 0.001). There was a significant correlation between PET/MR SUVmax and PET/CT SUVmax (r = 0.711, P < 0.001), and SUVmean (r = 0.685, P < 0.001). No correlation was observed between the SUV and the ADC. Conclusion In early-stage ENKTL, nasopharyngeal MR showed a lower sensitivity and a similar specificity when compared with 18F-FDG PET/CT. PET/MR showed similar performance compared with PET/CT

Multimodel uncertainty changes in simulated river flows induced by human impact parameterizations

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not only better performances of historical simulations but also more robust and confidential future projections of hydrological changes under a changing environment

Learning Performance Prediction and Alert Method in Hybrid Learning

In online learning, students&rsquo; learning data such as time and logs are commonly used to predict the student&rsquo;s learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a &ldquo;prediction and alert&rdquo; model for students&rsquo; learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students&rsquo; continuous online and offline learning more effectively

Learning Performance Prediction and Alert Method in Hybrid Learning

In online learning, students’ learning data such as time and logs are commonly used to predict the student’s learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a “prediction and alert” model for students’ learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students’ continuous online and offline learning more effectively

Sediment Distribution and Treatment in the Inflow Water-Level-Fluctuating Zone of the Biliuhe Reservoir

Most of the pollutants carried by runoff accumulate in the form of sediment, impacting the capacity and water quality of reservoirs. To study the sediment distribution in the water-level-fluctuating zone (WLFZ) of a reservoir in North China with a long drying period and to explore its treatment, the Biliuhe Reservoir in Dalian, Liaoning Province, China, was selected as the study area. The sediment thicknesses along the thalwegs of the three tributaries were surveyed, including a detailed survey on the sediment thickness and particle size in Dapu, the inflow bay of the main river. According to our findings, the sediment distribution along the thalwegs is similar to the delta sedimentation. The inflow WLFZ, especially the inflow bay, is the main gathering area for sediment. Furthermore, the variation in sediment thickness in the top-set region has river siltation characteristics, which are mainly affected by the scouring and deposition of floods during the dry period. From the convex bank to the inside of the bay in Dapu, the hydrodynamic force of the sediment gradually weakens, the thickness gradually increases, and the bay is the main sedimentary area of the suspended load. A method of sediment reuse for vegetation buffer platform construction is proposed. This method can reduce the amount of sediment entering downstream and enhance the ability to remove the pollution along the bank of the reservoir

The complete chloroplast genome of Rhododendron calophytum Franch (Ericaceae)

Rhododendron calophytum Franch is a famous ornamental plant, which belongs to the Rhododendron genus. Here, we report the complete chloroplast (cp) genome and the cp genomic features of R. calophytum. The complete cp genome of R. calophytum is a double stranded, circular DNA with 200,196 bp in length and has an large single-copy (LSC) region of 108,602 bp and a small single-copy (SSC) region of 2606 bp separated by a pair of inverted repeat (IR) regions of 44,494 bp each. Its 110 genes include four unique rRNAs, 29 tRNAs, and 77 protein-coding genes. Maximum-likelihood (ML) phylogenetic tree reconstructed with 16 species complete cp sequences reveals that R. calophytum is closely related to R. platypodum. The complete cp genome of R. calophytum will be useful for further investigations and researches concerning this economically important plant

Application of Additives in Platycladus orientalis (L.) Franco Tending Shreds Compost in Forest

This study aimed to explore the effects of different additives on tending shreds of Platycladus orientalis (L.) Franco. Two different additives (priming 0.2% and common compost 0.2%) combined with C, N, and P adjustment of raw material treatments were tested on the temperature, moisture, EC, pH, lignocellulose degradation rate, nutrient content, and toxicity of compost. Priming made the compost temperature rise rapidly, and the peak temperature of the composting group with priming reached 51 &deg;C. At the end of composting, the moisture in each treatment from high to low was in the order: common compost &gt; priming &gt; C/N, C/P adjustment only &gt; control group. The increase of EC in the treatments with additives was great, and the peak value of EC in the treatment of priming was 1.30 ms&middot;cm&minus;1, which was 3.9 times higher than that of the control group. At the end of composting, the decomposition rate of cellulose in priming compost was 1.7 times higher than that in the control group, and the hemicellulose decomposition rate in the common compost group was 3.2 times higher than that in the control group. By the end of composting, the pH value of the composts in additive treatments was above 7.0, and the pH value of the priming treatment was the highest (7.5). The highest content of organic matter was found in the priming treatment, which was 52%, 1.7 times higher than that in the control group. The total nutrient content (TN + K2O + P2O5) of additive treatments was higher than 5.0%, and the priming treatment was 2.7% higher than that of the control group. By the end of composting, the germination rate and germination index ranged from 88% to 91% and 60% to 81%. Except for the control group, the C/N ratio of other treatments decreased to below 25. Additives can accelerate the decomposition of raw materials, shorten the composting cycle, and improve the quality of composts, and the effect of adding priming is the most significant