Evolutionary conservation of microRNA regulatory programs in plant flower development

AbstractMicroRNAs (miRNAs) are post-transcriptional regulators of growth and development in both plants and animals. Flowering is critical for the reproduction of angiosperms. Flower development entails the transition from vegetative growth to reproductive growth, floral organ initiation, and the development of floral organs. These developmental processes are genetically regulated by miRNAs, which participate in complex genetic networks of flower development. A survey of the literature shows that miRNAs, their specific targets, and the regulatory programs in which they participate are conserved throughout the plant kingdom. This review summarizes the role of miRNAs and their targets in the regulation of gene expression during the floral developmental phase, which includes the floral transition stage, followed by floral patterning, and then the development of floral organs. The conservation patterns observed in each component of the miRNA regulatory system suggest that these miRNAs play important roles in the evolution of flower development

Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement

Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge. This paper proposes a novel approach to improve point cloud few-shot segmentation (PC-FSS) models. Unlike existing PC-FSS methods that directly utilize categorical information from support prototypes to recognize novel classes in query samples, our method identifies two critical aspects that substantially enhance model performance by reducing contextual gaps between support prototypes and query features. Specifically, we (1) adapt support background prototypes to match query context while removing extraneous cues that may obscure foreground and background in query samples, and (2) holistically rectify support prototypes under the guidance of query features to emulate the latter having no semantic gap to the query targets. Our proposed designs are agnostic to the feature extractor, rendering them readily applicable to any prototype-based methods. The experimental results on S3DIS and ScanNet demonstrate notable practical benefits, as our approach achieves significant improvements while still maintaining high efficiency. The code for our approach is available at https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-EnhancementComment: Accepted to ACM MM 202

How to select patients and timing for rectal indomethacin to prevent post-ERCP pancreatitis: a systematic review and meta-analysis

Egger’s publication bias plot. (TIF 998 kb

Using Experience Classification for Training Non-Markovian Tasks

Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis, can be quite challenging. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic LTL$_f$ (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTL$_f$ into MDPs (Markov Decision Processes) is introduced to take advantage of advanced RL algorithms. Then, a prioritized experience replay technique based on the automata structure (semantics equivalent to LTL$_f$ specification) is utilized to improve the training process. We empirically evaluate several benchmark problems augmented with non-Markovian tasks to demonstrate the feasibility and effectiveness of our approach

Strong Photoluminescence Enhancement of MoS2 through Defect Engineering and Oxygen Bonding

We report on a strong photoluminescence (PL) enhancement of monolayer MoS2 through defect engineering and oxygen bonding. Micro- PL and Raman images clearly reveal that the PL enhancement occurs at cracks/defects formed during high temperature vacuum annealing. The PL enhancement at crack/defect sites could be as high as thousands of times after considering the laser spot size. The main reasons of such huge PL enhancement include: (1) the oxygen chemical adsorption induced heavy p doping and the conversion from trion to exciton; (2) the suppression of non-radiative recombination of excitons at defect sites as verified by low temperature PL measurements. First principle calculations reveal a strong binding energy of ~2.395 eV for oxygen molecule adsorbed on an S vacancy of MoS2. The chemical adsorbed oxygen also provides a much more effective charge transfer (0.997 electrons per O2) compared to physical adsorbed oxygen on ideal MoS2 surface. We also demonstrate that the defect engineering and oxygen bonding could be easily realized by oxygen plasma irradiation. X-ray photoelectron spectroscopy further confirms the formation of Mo-O bonding. Our results provide a new route for modulating the optical properties of two dimensional semiconductors. The strong and stable PL from defects sites of MoS2 may have promising applications in optoelectronic devices.Comment: 23 pages, 9 figures, to appear in ACS Nan