A dual 13C and 15N long-term labelling technique to investigate uptake and translocation of C and N in beech (Fagus sylvatica L.)

A continuous dual 13CO2 and 15NH4(15)NO3 labelling experimental set-up is presented that was used to investigate the C and N uptake and allocation within 3-year old beech (Fagus sylvatica L.) during one growing season. The C and N allocation pattern was determined after six, twelve and eighteen weeks of growth. The carbon uptake was distinctly different in the three phases examined: The first six weeks after budbreak were dedicated to leaf growth with a R/S (root to shoot) ratio of 0.14 for the new carbon. The second growth phase showed a balanced R/S ratio of C allocation and after week 13, the root compartment was the main carbon sink (R/S = 6.97). Nitrogen allocation was more basipetal as compared to carbon. In the second growth phase, R/S of Nnew was 5.57 but fell to 3.54 for the third growth phase probably due to formation of reserves in buds and stem.

Cerebrospinal fluid-contacting neurons: a promising source for adult neural stem cell transplantation in spinal cord injury treatment

Transplantation of adult neural stem cells (NSCs) is regarded as one of the most promising approaches for treating spinal cord injury (SCI). However, securing a sufficient and reliable source of adult NSCs remains one of the primary challenges in applying this method for SCI treatment. Cerebrospinal fluid-contacting neurons (CSF-cNs) act as adult NSCs and can be substantially expanded in vitro while maintaining their NSC characteristics even after 60 passages. When CSF-cNs are transplanted into the injury sites of SCI mice, they demonstrate high survival rates along with the ability to proliferate and differentiate into neurons, astrocytes, and oligodendrocytes. Additionally, significant improvements in motor function have been observed in SCI mice following the transplantation of CSF-cNs. These results suggest that CSF-cNs may represent a promising source of adult NSCs for transplantation therapy in SCI.

Vegfr3 activation of Pkd2l1+ CSF-cNs triggers the neural stem cell response in spinal cord injury

Activating adult neural stem cells (NSCs) located within the spinal cord niche is considered a promising therapeutic approach for treating spinal cord injury (SCI). Cerebrospinal fluid (CSF)-contacting neurons expressing Pkd2l1 exhibit phenotypic and molecular traits similar to those of adult NSCs. However, the mechanism responsible for regulating the activation of Pkd2l1+ CSF-cNs still needs to be discovered. This research demonstrated that Pkd2l1+ CSF-cNs have a high concentration of vascular endothelial growth factor receptor 3 (Vegfr3) and that SCI results in elevated Vegfr3 levels. The overexpression of Vegfr3 in Pkd2l1+CSF-cNs induced potential NSC activation. Blocking Vegfr3 led to a significant reduction in the percentage of active Pkd2l1+ CSF-cNs, suggesting that Vegfr3 is involved in controlling the shift from dormancy to activation in these cells. In vivo, the downregulation of Vegfr3 by SAR131475 inhibited Pkd2l1+CSF-cN proliferation and maintained self-renewal. Injection of vascular endothelial growth factor C (Vegf-C) into the lateral ventricle of adult mice confirmed the involvement of Vegfr3 in activating Pkd2l1+ CSF-cNs. Vegf-C administration significantly increased the number of activated Pkd2l1+ CSF-cNs. Mechanistically, Vegfr3 primed quiescent Pkd2l1+ CSF-cNs for cell cycle reentry by enabling the activation of PI3K/Akt signaling. The activation of Vegfr3 may enhance SCI outcomes by promoting neuronal survival and facilitating the recovery of motor function in mice. Together, our findings highlight that Vegfr3 is a crucial functional regulator of Pkd2l1+ CSF-cNs, governing the transition from NSC quiescence to activation.

Improved region proposal network for enhanced few-shot object detection

Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images across all classes. Data annotation is an arduous and time-consuming endeavor, particularly when dealing with infrequent objects. Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches based on deep learning. FSOD methods demonstrate remarkable performance by achieving robust object detection using a significantly smaller amount of training data. A challenge for FSOD is that instances from novel classes that do not belong to the fixed set of training classes appear in the background and the base model may pick them up as potential objects. These objects behave similarly to label noise because they are classified as one of the training dataset classes, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during the FSOD training stage to improve FSOD performance. Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. We test our approach and COCO and PASCAL VOC baselines that are commonly used in FSOD literature. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods. Our implementation is provided as a supplement to support reproducibility of the results https://github.com/zshanggu/HTRPN.1.