CDK5 positively regulates notch1 signaling in pancreatic cancer cells by phosphorylation

The marked overexpression of cyclin-dependent kinase 5 (CDK5) or Notch1 receptor, which plays critical roles in pancreatic ductal adenocarcinoma (PDAC) development, has been detected in numerous PDAC cell lines and tissues. Although, a previous study has demonstrated that CDK5 inhibition disrupts Notch1 functions in human umbilical vein endothelial cells, the mechanism underlying Notch1 activation regulated by CDK5 remains unclear. Herein, we identified a physical interaction between CDK5 and Notch1 in PDAC cells, with the Notch1 peptide phosphorylated by CDK5/p25 kinase. CDK5 blockade resulted in the profound inhibition of Notch signaling. Accordingly, CDK5 inhibition sensitized PDAC cell proliferation and migration following Notch inhibition. In conclusion, CDK5 positively regulates Notch1 function via phosphorylation, which in turn promotes cell proliferation and migration. The combinational inhibition of CDK5 and Notch signaling may be an effective strategy in the treatment of PDAC

DALNet: A Rail Detection Network Based on Dynamic Anchor Line

Rail detection is one of the key factors for intelligent train. In the paper, motivated by the anchor line-based lane detection methods, we propose a rail detection network called DALNet based on dynamic anchor line. Aiming to solve the problem that the predefined anchor line is image agnostic, we design a novel dynamic anchor line mechanism. It utilizes a dynamic anchor line generator to dynamically generate an appropriate anchor line for each rail instance based on the position and shape of the rails in the input image. These dynamically generated anchor lines can be considered as better position references to accurately localize the rails than the predefined anchor lines. In addition, we present a challenging urban rail detection dataset DL-Rail with high-quality annotations and scenario diversity. DL-Rail contains 7000 pairs of images and annotations along with scene tags, and it is expected to encourage the development of rail detection. We extensively compare DALNet with many competitive lane methods. The results show that our DALNet achieves state-of-the-art performance on our DL-Rail rail detection dataset and the popular Tusimple and LLAMAS lane detection benchmarks. The code will be released at https://github.com/Yzichen/mmLaneDet

Improvement on PDP Evaluation Performance Based on Neural Networks and SGDK-means Algorithm

With the purpose of improving the PDP (policy decision point) evaluation performance, a novel and efficient evaluation engine, namely XDNNEngine, based on neural networks and an SGDK-means (stochastic gradient descent K-means) algorithm is proposed. We divide a policy set into different clusters, distinguish different rules based on their own features and label them for the training of neural networks by using the K-means algorithm and an asynchronous SGDK-means algorithm. Then, we utilize neural networks to search for the applicable rule. A quantitative neural network is introduced to reduce a server’s computational cost. By simulating the arrival of requests, XDNNEngine is compared with the Sun PDP, XEngine and SBA-XACML. Experimental results show that 1) if the number of requests reaches 10,000, the evaluation time of XDNNEngine on the large-scale policy set with 10,000 rules is approximately 2.5 ms, and 2) in the same condition as 1), the evaluation time of XDNNEngine is reduced by 98.27%, 90.36% and 84.69%, respectively, over that of the Sun PDP, XEngine and SBA-XACML

Training-Free Instance Segmentation from Semantic Image Segmentation Masks

In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods (i.e., with image-level class labels or point labels) struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm for instance segmentation called training-free instance segmentation (TFISeg), which achieves instance segmentation results from image masks predicted using off-the-shelf semantic segmentation models. TFISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient. Specifically, we first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets and representative semantic segmentation baselines (including CNNs and Transformers) demonstrate that TFISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs. The code is available at: TFISegComment: 14 pages,5 figure

An improved method of searching inferior parathyroid gland for the patients with papillary thyroid carcinoma based on a retrospective study

ObjectiveMany surgeons knew the importance of parathyroid gland (PG) in the thyroid surgery, but it was even more difficult to be protected. This study aimed at evaluating the effectiveness of the improved method of searching inferior parathyroid gland (IPG).Methods213 patients were enrolled and divided into test and control groups according to different methods of searching IPG in the surgery. Consequently, we compared the surgical outcome parameters between the two groups, including the operative time, numbers of PG identifying (PG protection in situ, PG auto-transplantation, and PG accidental removal), numbers of the total lymph node (LN) and metastatic LN, parathyroid hormone (PTH), transient hypoparathyroidism, transient recurrent laryngeal nerve palsy, and postoperative bleeding.ResultsWe identified 194 (194/196, 98.98%) and 215 (215/230, 93.48%) PGs in the test group and control group, respectively, and there was a significant difference (P = 0.005), and this result was due to IPG identification differences (96/98, 97.96% vs. 100/115, 86.96%, P = 0.004). Meanwhile, there was a lower ratio of IPG auto-transplantation in the test group compared with that in the control group (46.94% vs. 64.35%, P = 0.013). Serum PTH one day after the operation was 3.65 ± 1.86 vs. 2.96 ± 1.64 (P = 0.043) but with no difference at 6 months. There were no differences in metastatic LN and recurrent laryngeal nerve palsy between two groups.ConclusionThe improved method of searching IPG was simple, efficient, and safe, which was easy to be implemented for searching IPG and protecting it well

An association of a simultaneous nuclear and cytoplasmic localization of Fra-1 with breast malignancy

BACKGROUND: Overexpression of Fra-1 in fibroblasts causes anchorage-independent cell growth and oncogenic transformation. A high level of Fra-1 expression is found in various tumors and tumorigenic cell lines, suggesting that Fra-1 may be involved in malignant progression. This study aimed to investigate the significance of Fra-1 expression in breast carcinogenesis. METHODS: The expression of Fra-1 was investigated by immunohistochemistry in neoplastic breast diseases ranging from benign fibroadenoma to very aggressive undifferentiated carcinoma. The correlations of Fra-1 expression with other indicators of breast carcinoma prognosis (ER, PR and ErbB2 receptors) were analyzed. RESULTS: All neoplastic breast tissues, either benign or malignant breast tissues, were nuclear immunoreactive for Fra-1-recognizing antibody. The pattern of Fra-1 expression by benign neoplastic cells was predominantly nuclear. However, the nuclear/cytoplasmic concomitant immunoreactivity was observed in all types of breast carcinomas. A clear shift in Fra-1 immunoreactivity, from an exclusively nuclear to a simultaneous nuclear and cytoplasmic localization was noticed in ~90% of breast carcinomas. CONCLUSION: The overall expression, pattern and intensity of Fra-1 proteins were correlated with breast oncogenesis. Overexpression of Fra-1, leading to a persistent high cytoplasmic accumulation, may play a role in the process of breast carcinogenesis

Thirty-six months recurrence after acute ischemic stroke among patients with comorbid type 2 diabetes: A nested case-control study

Background: Stroke patients have to face a high risk of recurrence, especially for those with comorbid T2DM, which usually lead to much more serious neurologic damage and an increased likelihood of death. This study aimed to explore determinants of stroke relapse among patients with comorbid T2DM. Materials and methods: We conducted this case-control study nested a prospective cohort of ischemic stroke (IS) with comorbid T2DM. During 36-month follow-up, the second stroke occurred in 84 diabetic IS patients who were allocated into the case group, while 613 patients without recurrence were the controls. We collected the demographic data, behaviors and habits, therapies, and family history at baseline, and measured the variables during follow-up. LASSO and Logistic regression analyses were carried out to develop a prediction model of stroke recurrence. The receiver operator characteristic (ROC) curve was employed to evaluate the performance of the prediction model. Results: Compared to participants without recurrence, the higher levels of pulse rate (78.29 ± 12.79 vs. 74.88 ± 10.93) and hypertension (72.6 vs. 61.2 %) were recorded at baseline. Moreover, a lower level of physical activity (77.4 vs. 90.4 %), as well as a higher proportion of hypoglycemic therapy (36.9 vs. 23.3 %) was also observed during 36-month follow-up. Multivariate logistic regression revealed that higher pulse rate at admission (OR = 1.027, 95 % CI = 1.005 – 1.049), lacking physical activity (OR = 2.838, 9 5 % CI = 1.418 – 5.620) and not receiving hypoglycemic therapy (OR = 1.697, 95 % CI = 1.013 – 2.843) during follow-up increased the risk of stroke recurrence. We developed a prediction model using baseline pulse rate, hypoglycemic therapy, and physical activity, which produced an area under ROC curve (AUC) of 0.689. Conclusion: Physical activity and hypoglycemic therapy play a protective role for IS patients with comorbid diabetes. In addition to targeted therapeutics, the improvement of daily-life habit contributes to slowing the progress of the IS