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Recent studies have described active nitrogen fixation in high-latitude waters, but the ecological controls on the occurrence or activity of nitrogen-fixing organisms (diazotrophs) in such systems remain unknown. Turnover rates and top-down controls are also general knowledge gaps for marine diazotrophs. We detected abundant UCYN-A (endosymbiotic nitrogen-fixing cyanobacteria) in the Gulf of Anadyr, western Bering Sea, which correlated with high dissolved iron to dissolved inorganic nitrogen ratios (Fe : DIN) due to riverine input. Growth and grazing mortality of UCYN-A sublineages were almost balanced with higher biomass-turnover rates compared to the whole phytoplankton community, indicating selective grazing of UCYN-A in nitrogen-depleted waters. Grazing rates on UCYN-A1 (small cells) were higher than for UCYN-A2/3/4 (large cells), consistent with the general size dependence of phytoplankton growth and grazing mortality. We found that Fe : DIN is a major determinant of UCYN-A abundances in high-latitude waters, where UCYN-A could make substantial contributions to plankton food-web cycling.

Background: Video lectures which include the instructor's presence are becoming increasingly popular. Presenting a real human does, however, entail higher financial and time costs in making videos, and one innovative approach to reduce costs has been to generate a virtual speaking instructor. Objectives: The current study examined whether the use of a virtual instructor in video lectures would facilitate learning as well as a human instructor, and whether manipulating the virtual instructor's characteristics (i.e., voice and appearance) might optimize the effectiveness of the virtual instructor. Methods: Our study set four conditions. In the control condition, students watched a human instructor. In the experiment conditions, students watched one of (a) a virtual instructor which used the human instructor's voice and an AI image, (b) a virtual instructor which spoke in an AI voice with an AI image made to speak using text-to-speech and lip synthesis techniques, or (c) a virtual instructor with used an AI voice and an AI likable-image of an instructor. Results and Conclusions: The AI likable instructor condition had a significant positive effect on students' learning performance and motivation, without decreasing the attention students paid to the learning materials. Implications: Our findings suggest that instructional video designers can make use of AI voices and AI images of likable humans as instructors to motivate students and enhance their learning performance.

Objective To explore the effect of acupoint application of Xiefu prescription on prevention of functional constipation in long-term bedridden patients with pelvic fracture. Methods A total of 60 patients with pelvic fracture without abnormal defecation function and constipation main symptom score ≤3 were selected and randomly divided into two groups: 30 patients in the control group received routine prevention and nursing measures, and 30 patients in the treatment group received acupoint application of Xiefu prescription based on the routine nursing interventions. The main symptoms of constipation were scored on the day of admission, the 7th and 14th day of bed rest, and the intervention of various assisted defecation measures within 14 days of bed rest was recorded. Results The total score of Main Constipation Symptoms Scale in the treatment group was lower than that in the control group on the 7th and 14th day of bed rest, respectively(P＜0. 01); On the 14th day of bed rest, the scores of stool, difficulty in defecation, defecation duration, defecation frequency and abdominal distension in the treatment group were lower than those in the control group(P＜0. 01). During the 14 days of bed rest, the number of assisted defecation interventions in the treatment group was less than that of the control group (P＜0. 01) . Conclusion Acupoint application of Xiefu prescription and sticking therapy has a good preventive effect on functional constipation in long-term bedridden patients with pelvic fracture. It is an effective traditional Chinese medicine treatment technology, which is worthy of popularization and application. (目的 探讨泻腑贴方穴位贴敷对长期卧床单纯骨盆骨折患者功能性便秘的预防效果。方法 选取既往排便功能无异常且便秘主要症状评分≤3分的单纯骨盆骨折患者60例, 随机分为两组: 对照组(n=30)接受便秘常规预防护理, 治疗组(n=30)在对照组基础上增加泻腑贴方穴位贴敷。于入院当天、卧床第7、14天, 记录便秘主要症状评分, 并记录卧床14d内辅助排便措施介入情况。结果 卧床第7、14天治疗组便秘主要症状总分低于对照组, 差异有统计学意义(P＜0. 01); 卧床第14天治疗组粪便性状、排便费力、排便时间、下坠/不尽/胀感、排便频率、腹胀条目得分低于对照组, 差异有统计学意义(P

Due to the relatively high shooting altitude of unmanned aerial vehicles (UAV), the captured images often contain a multitude of small‐scale targets. To solve the problems of small target scale, lack of semantic information, and high miss detection in drone target detection, in this paper we proposed a more effective unmanned aerial vehicle small target detection algorithm(MEU‐YOLOv5) based on YOLOv5s. Firstly, an efficient global contextual module is proposed to enhance the algorithm's performance in feature extraction while reducing the excessive loss of shallow features. Secondly, a small‐scale target detector is added to enhance the algorithm's detection capability for smaller targets. Lastly, a recursive multi‐level feature fusion path is introduced to better fuse the shallow and deep features of the images, reducing overfitting and improving the algorithm's generalizability and robustness. Experimental results demonstrated that compared to YOLOv5s, MEU‐YOLOv5 achieves a 7.4% improvement in mAP@0.5 and a 4.9% improvement in mAP@0.5:0.95. Additionally, the overall performance of this algorithm surpassed various algorithms in the YOLO series, including YOLOv3, YOLOv5l, YOLOv5m, and YOLOv8s. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Introduction: This work was to explore the efficacy and safety of self-made WenyangJianpi-qushi Decoction plus mometasone furoate cream in atopic dermatitis (AD) of spleen deficiency and dampness accumulation type. Material and method: 120 patients with this kind of atopic dermatitis were grouped: The Observation group (disease health education + basic treatment + mometasone furoate cream + self-made Decoction) and The Control group (disease health education + basic treatment + mometasone furoate cream), 60 cases in each group. The SCORAD score, serum IgE level, peripheral blood eosinophils, adverse events, recurrence rate, and total effective rate after treatment were observed.Result: Through treatment, SCORAD score of the observation group (29.96 ± 2.88) was lower as against controls (36.04 ± 3.12), p < 0.05. Through treatment, the peripheral blood eosinophil count in the observation group was (311.26 ± 50.19) 106/L, which was lower than (582.71 ± 54.75) 106/L in controls; the serum lgE of the observation group was (712.44 ± 93.32) IU/mL, which was lower than the controls (890.12 ± 81.25) IU/mL, p < 0.05. The Observation group (56/60, 93.33%) demonstrated superior total effective rate to the controls (34/60, 56.67%); The recurrence rate of the observation group was 4/60 (6.67%), which was lower than the controls 16/60 (26.67%), p < 0.05.Conclusion: Self-made WenyangJianpi-qushi Decoction plus mometasone furoate cream to treat atopic dermatitis of spleen deficiency and dampness accumulation type has significant efficacy and good safety. [ABSTRACT FROM AUTHOR]

Pedestrian multiobject tracking is the major research branch in the field of computer vision. In complicated scenarios with frequent scale changes and occlusion, the existing multiobject tracking methods based on detection have unsatisfactory tracking accuracy because of the low robustness of reidentification. This article proposed a multiobject tracking method to improve the reidentification module in YOLOv5‐DeepSORT at a more fine‐grained level. The feature extraction network for the Re‐ID part of this algorithm is built based on Res2Net and group convolution. This network's hierarchical connection structure effectively improved the network's ability to extract multiscale features, and at the same time increased the receptive field of each network layer. The PCB network structure with evenly divided feature maps is used in the output part of the backbone network to enhance the influence of local features on the overall network performance. Based on this, the reidentification model is trained on the public datasets Market‐1501 and DukeMTMC‐reID using triplet loss. ER‐DeepSORT is an algorithm that combined the improved reidentification module of this article into DeepSORT. This article compared ER‐DeepSORT with YOLOv5‐DeepSORT under the original reidentification module to evaluate the tracking effect in MOT16 test sequence, the experimental results showed that ER‐DeepSORT improved MOTA by 5.4% and MOTP by 2.2% on the Market‐1501 datasets, and improved MOTA by 9.6% and MOTP by 2.7% on the DukeMTMC‐reID datasets. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]