The Effects of Lesson Study Concept on the Online Engineering Course Delivery

Short Abstract: Introducing the “Learning Progress Tracking System (LPTS)” that allows the instructor to effectively monitor students’ Lesson Study outcomes for the online delivered engineering courses. Full Abstract: One of the biggest challenges of use Lesson Study pedagogy for online delivery course lies in the “live observation”. “Live observation” is critical for the Lesson Study because it allows the instructor to track the student learning during the class period. It is hard to “observe and track” learning process for the online students. In order to overcome this challenge, the research team developed a “Learning Progress Tracking System (LPTS)”. The function of LPTS is to reflect students’ learning outcomes at different stages. This LPTS system includes learning progress, learning outcome, time intervals between each learning stage, and other parameters

Relationship between hyperlipidemia and the risk of death in aneurysm: a cohort study on patients of different ages, genders, and aneurysm locations

Aims: The study aimed to assess the association of hyperlipidemia and the risk of death in the aneurysm population, focusing on age, gender, and aneurysm location differences.Methods: All patients’ data on this retrospective cohort study were obtained from the Medical Information Mart for Intensive Care (MIMIC-III) database, and the baseline characteristics and laboratory parameters of all patients were collected. The COX regression model was established to explore the association of hyperlipidemia and the risk of death for patients with aneurysms. More importantly, subgroup analyses based on the age, gender, and aneurysm location differences were performed.Results: A total of 1,645 eligible patients were enrolled in this study. These patients were divided into the survival group (n = 1,098) and the death group (n = 547), with a total mortality rate of approximately 33.25%. The result displayed that hyperlipidemia was associated with a decreased death risk in aneurysm patients. In addition, we also found that hyperlipidemia was associated with a lower death risk of abdominal aortic aneurysm and thoracic aortic arch aneurysm among aneurysm patients aged ≥60 years; hyperlipidemia was only a protective factor for the death risk of male patients diagnosed with abdominal aortic aneurysm. For female patients diagnosed with abdominal aortic aneurysm and thoracic aortic arch aneurysm, hyperlipidemia was associated with a decreased death risk.Conclusion: The relationship of hyperlipidemia, hypercholesterolemia, and the risk of death for patients diagnosed with aneurysms was significantly associated with age, gender, and aneurysm location

Parameter-Efficient Tuning Makes a Good Classification Head

In recent years, pretrained models revolutionized the paradigm of natural language understanding (NLU), where we append a randomly initialized classification head after the pretrained backbone, e.g. BERT, and finetune the whole model. As the pretrained backbone makes a major contribution to the improvement, we naturally expect a good pretrained classification head can also benefit the training. However, the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning, making the usual head-only pretraining (LP-FT) ineffective. In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain. Our experiments demonstrate that the classification head jointly pretrained with parameter-efficient tuning consistently improves the performance on 9 tasks in GLUE and SuperGLUE.Comment: Accepted as a long paper to EMNLP 2022 Main Conferenc

First Total Synthesis of a Naturally Occurring Iodinated 5′-Deoxyxylofuranosyl Marine Nucleoside

4-Amino-7-(5′-deoxy-β-D-xylofuranosyl)-5-iodo-pyrrolo[2,3-d]pyrimidine 1, an unusual naturally occurring marine nucleoside isolated from an ascidan, Diplosoma sp., was synthesized from D-xylose in seven steps with 28% overall yield on 10 g scale. The key step was Vorbrüggen glycosylation of 5-iodo-pyrrolo[2,3-d]pyrimidine with 5-deoxy-1,2-O-diacetyl-3-O-benzoyl-D-xylofuranose. Its absolute configuration was confirmed

Volatility distribution in the S&P500 Stock Index

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent $\alpha\cong0.9$.Comment: 6 pages, 5 figure

Correlations in Economic Time Series

The correlation function of a financial index of the New York stock exchange, the S&P 500, is analyzed at 1 min intervals over the 13-year period, Jan 84 -- Dec 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with a crossover time t_\times\approx 600 min. Detrended fluctuation analysis gives exponents $\alpha_1=0.66$ and $\alpha_2=0.93$ for $t<t_\times$ and $t>t_\times$ respectively. Power spectrum analysis gives corresponding exponents $\beta_1=0.31$ and $\beta_2=0.90$ for $f>f_\times$ and $f< f_\times$ respectively.Comment: 6 pages, 2 figure

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii

Cloning and Characterization of a Putative TAC1 Ortholog Associated with Leaf Angle in Maize (Zea mays L.)

BACKGROUND: Modifying plant architecture to increase photosynthesis efficiency and reduce shade avoidance response is very important for further yield improvement when crops are grown in high density. Identification of alleles controlling leaf angle in maize is needed to provide insight into molecular mechanism of leaf development and achieving ideal plant architecture to improve grain yield. METHODOLOGY/PRINCIPAL FINDINGS: The gene cloning was done by using comparative genomics, and then performing real-time polymerase chain reaction (RT-PCR) analysis to assay gene expression. The gene function was validated by sequence dissimilarity analysis and QTL mapping using a functional cleaved amplified polymorphism (CAP). CONCLUSIONS: The leaf angle is controlled by a major quantitative trait locus, ZmTAC1 (Zea mays L. Leaf Angle Control 1). ZmTAC1 has 4 exons encoding a protein with 263 amino acids, and its domains are the same as those of the rice OsTAC1 protein. ZmTAC1 was found to be located in the region of qLA2 by using the CAP marker and the F(2:3) families from the cross between Yu82 and Shen137. Real-time PCR analysis revealed ZmTAC1 expression was the highest in the leaf-sheath pulvinus, less in the leaf and shoot apical meristem, and the lowest in the root. A nucleotide difference in the 5'-untranslated region (UTR) between the compact inbred line Yu82 ("CTCC") and the expanded inbred line Shen137 ("CCCC") influences the expression level of ZmTAC1, further controlling the size of the leaf angle. Sequence verification of the change in the 5'-UTR revealed ZmTAC1 with "CTCC" was present in 13 compact inbred lines and ZmTAC1 with "CCCC" was present in 18 expanded inbred lines, indicating ZmTAC1 had been extensively utilized in breeding with regard to the improvement of the maize plant architecture

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value &lt; 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value &lt; 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values &lt; 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value &lt; 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis