Expression pattern and activity of six glutelin gene promoters in transgenic rice*

The shortage of strong endosperm-specific expression promoters for driving the expression of recombinant protein genes in cereal endosperm is a major limitation in obtaining the required level and pattern of expression. Six promoters of seed storage glutelin genes (GluA-1, GluA-2, GluA-3, GluB-3, GluB-5, and GluC) were isolated from rice (Oryza sativa L.) genomic DNA by PCR. Their spatial and temporal expression patterns and expression potential in stable transgenic rice plants were examined with β-glucuronidase (GUS) used as a reporter gene. All the promoters showed the expected spatial expression within the endosperm. The GluA-1, GluA-2, and GluA-3 promoters directed GUS expression mainly in the outer portion (peripheral region) of the endosperm. The GluB-5 and GluC promoters directed GUS expression in the whole endosperm, with the latter expressed almost evenly throughout the whole endosperm, a feature different from that of other rice glutelin gene promoters. The GluB-3 promoter directed GUS expression solely in aleurone and subaleurone layers. Promoter activities examined during seed maturation showed that the GluC promoter had much higher activity than the other promoters. These promoters are ideal candidates for achieving gene expression for multiple purposes in monocot endosperm but avoid promoter homology-based gene silencing. The GluC promoter did not contain the endosperm specificity-determining motifs GCN4, AACA, and the prolamin-box, which suggests the existence of additional regulatory mechanism in determining endosperm specificity

ULIP: Learning Unified Representation of Language, Image and Point Cloud for 3D Understanding

The understanding capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. Inspired by this, leveraging multimodal information for 3D modality could be promising to improve 3D understanding under the restricted data regime, but this line of research is not well studied. Therefore, we introduce ULIP to learn a unified representation of image, text, and 3D point cloud by pre-training with object triplets from the three modalities. To overcome the shortage of training triplets, ULIP leverages a pre-trained vision-language model that has already learned a common visual and textual space by training with massive image-text pairs. Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets. ULIP is agnostic to 3D backbone networks and can easily be integrated into any 3D architecture. Experiments show that ULIP effectively improves the performance of multiple recent 3D backbones by simply pre-training them on ShapeNet55 using our framework, achieving state-of-the-art performance in both standard 3D classification and zero-shot 3D classification on ModelNet40 and ScanObjectNN. ULIP also improves the performance of PointMLP by around 3% in 3D classification on ScanObjectNN, and outperforms PointCLIP by 28.8% on top-1 accuracy for zero-shot 3D classification on ModelNet40. Our code and pre-trained models will be released

(Z)-Methyl 3-(2,4-dichloro­phen­yl)-3-hy­droxy­acrylate

The mol­ecular structure of the title compound, C10H8Cl2O3, exists in a cis-enol form, which is stabilized by a strong intra­molecular O—H⋯O hydrogen bond. In the crystal, C—H⋯O inter­actions generate zigzag chains along the c axis which are, in turn, linked by further C—H⋯O inter­actions into sheets parallel to (100)

XSearch: A domain-specific cross-language relevant question retrieval tool

NSF

Offline Pre-trained Multi-agent Decision Transformer

Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the combinatorially increased interactions among agents and with the environment. However, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor even datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of MARL. We investigate the generalization of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to online fine tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages the transformer’s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios. On the StarCraft II offline dataset, MADT outperforms the state-of-the-art offline reinforcement learning (RL) baselines, including BCQ and CQL. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot cases. To the best of our knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL

P-coumaric acid reverses depression-like behavior and memory deficit via inhibiting AGE-RAGE-Mediated neuroinflammation

Depression, a mood disorder, affects one in fifteen adults, has multiple risk factors and is associated with complicated underlying pathological mechanisms. P-coumaric acid (p-CA), a phenolic acid, is widely distributed in vegetables, fruits and mushrooms. P-CA has demonstrated a protective role against oxidative stress and inflammation in various diseases, including cardiovascular disease, diabetes and cancer. In the current study, we investigated the protection of p-CA against depression and memory impairment in a corticosterone (CORT)-induced chronic depressive mouse model. CORT administration resulted in depression-like behaviors and memory impairment. P-CA treatment alleviated CORT-induced depression-related behaviors and memory impairment. Network pharmacology predicted that p-CA had multiple targets and mediated various signaling pathways, of which inflammation-associated targets and signaling pathways are predominant. Western blotting showed CORT-induced activation of the advanced glycation end product (AGE)-receptor of AGE (RAGE) (AGE-RAGE) signaling and increased expression of the proinflammatory cytokines interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNFα) in the hippocampus, while p-CA treatment inactivated AGE-RAGE signaling and decreased the levels of IL-1β and TNFα, suggesting that protection against depression and memory impairment by p-CA is mediated by the inhibition of inflammation, mainly via the AGE-RAGE signaling pathway. Our data suggest that p-CA treatment will benefit patients with depression

Transfer-free, lithography-free and fast growth of patterned CVD graphene directly on insulators by using sacrificial metal catalyst

Chemical vapor deposited graphene suffers from two problems: transfer from metal catalysts to insulators, and photoresist induced degradation during patterning. Both result in macroscopic and microscopic damages such as holes, tears, doping, and contamination, translated into property and yield dropping. We attempt to solve the problems simultaneously. A nickel thin film is evaporated on SiO2 as a sacrificial catalyst, on which surface graphene is grown. A polymer (PMMA) support is spin-coated on the graphene. During the Ni wet etching process, the etchant can permeate the polymer, making the etching efficient. The PMMA/graphene layer is fixed on the substrate by controlling the surface morphology of Ni film during the graphene growth. After etching, the graphene naturally adheres to the insulating substrate. By using this method, transfer-free, lithography-free and fast growth of graphene realized. The whole experiment has good repeatability and controllability. Compared with graphene transfer between substrates, here, no mechanical manipulation is required, leading to minimal damage. Due to the presence of Ni, the graphene quality is intrinsically better than catalyst-free growth. The Ni thickness and growth temperature are controlled to limit the number of layers of graphene. The technology can be extended to grow other two-dimensional materials with other catalysts