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To explore the effects of daily mean temperature (°C), average daily air pressure (hPa), humidity (%), wind speed (m/s), particulate matter (PM) 2.5 (μg/m3) and PM10 (μg/m3) on the admission rate of chronic kidney disease (CKD) patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence.

The COVID-19 pandemic has forced teachers to implement fully online teaching. This study reviewed the popular technologies that are used in online learning, as well as the advantages and difficulties of applying fully online courses for formal education. Based on this research background, this study proposed a nested scaffolding design of an online course for 215 college students in China with the help of six technological tools, which effectively replaced face-to-face interactions and significantly improved the usage of the supporting learning platform. The inner-outer learning cycles supported by the technological tools improved the quality of the scaffolding conversations, reduced the scaffolding time cost that teachers had to expend and enhanced the effectiveness of the individualised scaffolding instructions.

Bethe Ansatz equations for spin-s Heisenberg spin chain with s≥1 are significantly more difficult to analyze than the spin-$\tfrac{1}{2}$ case, due to the presence of repeated roots. As a result, it is challenging to derive extra conditions for the Bethe roots to be physical and study the related completeness problem. In this paper, we propose the rational Q-system for the XXX$_s$ spin chain. Solutions of the proposed Q-system give all and only physical solutions of the Bethe Ansatz equations required by completeness. This is checked numerically and proved rigorously. The rational Q-system is equivalent to the requirement that the solution and the corresponding dual solution of the TQ-relation are both polynomials, which we prove rigorously. Based on this analysis, we propose the extra conditions for solutions of the XXX$_s$ Bethe Ansatz equations to be physical.

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ. However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components.

BackgroundCryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal-to-noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher-quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed.MethodsWe propose a simulated annealing-based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo-ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets.ResultsExperimented on simulated and experimental 2D Cryo-EM images of Ribosome complex datasets respectively with SNR = 10 and SNR = 0.5, our method achieved better accuracy in terms of F-measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases.ConclusionsSA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo-EM and 3D Cryo-ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo-EM and Cryo-ET.

Objective To study the mechanism of learning and memory impairment in aged female mice caused by chronic stress. Methods Twenty-month-old ICR mice were randomly divided into four groups: control females, control males, stressed females, and stressed males. Chronic stress was applied to the stress group for 30 days. The learning and memory ability was measured by novel object recognition test and Morris water maze test. Damage to hippocampal neurons was observed with Nissl staining, and dendrites of hippocampal neurons were observed with Golgi-Cox staining microscopy, the expression of the mammalian target of rapamycin (mTOR) and p-mTOR in hippocampal tissue was measured by Western blot, and the level of serum corticotropin-releasing hormone (CRH) was measured by ELISA. Results There was a significant decrease in the learning and memory ability only in the stressed female group after applied stress. In the Morris water maze, after a 6-day swimming training, the escape latency decreased in the control female group, the control male group and in the stressed male group(P＜0.001, P＜0.001, P＜0.05), but not in the stressed female group. The swimming speed was consistent across groups, but the number of platform crossings and the number of target quadrant crossings were significantly lower in the stressed female group than those in the control female and stressed male groups(P＜0.001).There was significant damage to neurons in the hippocampal CA3, CA1, and DG regions of mice in the stressed female group. The expression of hippocampal m-TOR and p-mTOR protein was significantly decreased in the stressed female group of mice(P＜0.05). In addition, chronic stress caused a significant increase in serum CRH levels in aged female mice(P＜0.05). Conclusions Chronic stress caused learning and memory impairment and pathological damage of the hippocampus in aged female mice, but not in aged male mice, which may be related to a fact that chronic stress elevates CRH and inhibits the hippocampal m-TOR signaling pathway in aged female mice.