Jagged2a-Notch Signaling Mediates Cell Fate Choice in the Zebrafish Pronephric Duct

Pronephros, a developmental model for adult mammalian kidneys (metanephros) and a functional kidney in early teleosts, consists of glomerulus, tubule, and duct. These structural and functional elements are responsible for different kidney functions, e.g., blood filtration, waste extraction, salt recovery, and water balance. During pronephros organogenesis, cell differentiation is a key step in generating different cell types in specific locations to accomplish designated functions. However, it is poorly understood what molecules regulate the differentiation of different cell types in different parts of the kidney. Two types of epithelial cells, multi-cilia cells and principal cells, are found in the epithelia of the zebrafish distal pronephric duct. While the former is characterized by at least 15 apically localized cilia and expresses centrin2 and rfx2, the latter is characterized by a single primary cilium and sodium pumps. Multi-cilia cells and principal cells differentiate from 17.5 hours post-fertilization onwards in a mosaic pattern. Jagged2a-Notch1a/Notch3-Her9 is responsible for specification and patterning of these two cell types through a lateral inhibition mechanism. Furthermore, multi-cilia cell hyperplasia was observed in mind bomb mutants and Mind bomb was shown to interact with Jagged2a and facilitate its internalization. Taken together, our findings add a new paradigm of Notch signaling in kidney development, namely, that Jagged2a-Notch signaling modulates cell fate choice in a nephric segment, the distal pronephric duct

Fractal Modeling of Pore Structure and Ionic Diffusivity for Cement Paste

Pore structure in cement based composites is of paramount importance to ionic diffusivity. In this paper, pore structure in cement paste is modeled by means of the recently proposed solid mass fractal model. Moreover, an enhanced Maxwell homogenization method that incorporates the solid mass fractal model is proposed to determine the associated ionic diffusivity. Experiments are performed to validate the modeling, that is, mercury intrusion porosimetry and rapid chloride migration. Results indicate that modeling agrees well with those obtained from experiments

INFLUENCE OF ENVIRONMENTAL CONDITIONS ON RELEASE RULES OF FERTILIZER FROM WOOD RESIDUE SLOW-RELEASE FERTILIZER SHELL

Slow/controlled-release fertilizer is a kind of fertilizer that controls or slows down the nutrient release rate according to a specific release rate or release period. The development and application of slow/controlled-release fertilizer is highly valued all over the world because of its benefits. However, the materials used for the fertilizer coating are mostly difficult to degrade, causing many negative effects to the environment. Wood is a porous material that could be used as a coating material through which fertilizers could infiltrate. In addition, a shell glued with adhesives could degrade in soil because of the loose structure, providing another channel for infiltration of fertilizer. As a kind of environmental friendly material, a wood residue fertilizer shell could be used to provide fertilizer for trees, flowers, and other plants. Toona sinensis wood residues were used as the raw material to manufacture a slow-release fertilizer shell using thesecondary molding method. The influence of external environmental conditions such as temperature andrainfall on the release rules of fertilizers from shells were studied through artificial rainfall simulation. Results showed that release rules were similar in three sets of rainfall. The release amount increased quickly at the early stages and then decreased gradually. Also, the release amount changed as rainfall increased. Temperature also had a major influence on release rate of fertilizer from the shell. Generally, the release rate of fertilizer in the shell increased with increase of environmental temperature. The release amount kept relatively stable at lower temperatures. This study indicated that the wood residue shell could slow down the release of fertilizer. Both rainfall and temperature had a great influence on the release rate of fertilizer from the shell

The chemokine Sdf-1 and its receptor Cxcr4 are required for formation of muscle in zebrafish

<p>Abstract</p> <p>Background</p> <p>During development cell migration takes place prior to differentiation of many cell types. The chemokine receptor Cxcr4 and its ligand Sdf1 are implicated in migration of several cell lineages, including appendicular muscles.</p> <p>Results</p> <p>We dissected the role of <it>sdf1</it>-<it>cxcr4 </it>during skeletal myogenesis. We demonstrated that the receptor <it>cxcr4a </it>is expressed in the medial-anterior part of somites, suggesting that chemokine signaling plays a role in this region of the somite. Previous reports emphasized co-operation of Sdf1a and Cxcr4b. We found that during early myogenesis Sdf1a co-operates with the second Cxcr4 of zebrafish – Cxcr4a resulting in the commitment of myoblast to form fast muscle. Disrupting this chemokine signal caused a reduction in <it>myoD </it>and <it>myf5 </it>expression and fast fiber formation. In addition, we showed that a dimerization partner of MyoD and Myf5, E12, positively regulates transcription of <it>cxcr4a </it>and <it>sdf1a </it>in contrast to that of Sonic hedgehog, which inhibited these genes through induction of expression of <it>id2</it>.</p> <p>Conclusion</p> <p>We revealed a regulatory feedback mechanism between <it>cxcr4a</it>-<it>sdf1a </it>and genes encoding myogenic regulatory factors, which is involved in differentiation of fast myofibers. This demonstrated a role of chemokine signaling during development of skeletal muscles.</p

Adversarial AutoMixup

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier, while the target classifier's aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on seven image benchmarks consistently prove that our approach outperforms the state of the art in various classification scenarios. The source code is available at https://github.com/JinXins/Adversarial-AutoMixup.Comment: ICLR 2024 Camera Ready.(19 pages) with the source code at https://github.com/JinXins/Adversarial-AutoMixu