BoxSnake: Polygonal Instance Segmentation with Box Supervision

Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations. However, existing box-supervised instance segmentation models mainly focus on mask-based frameworks. We propose a new end-to-end training technique, termed BoxSnake, to achieve effective polygonal instance segmentation using only box annotations for the first time. Our method consists of two loss functions: (1) a point-based unary loss that constrains the bounding box of predicted polygons to achieve coarse-grained segmentation; and (2) a distance-aware pairwise loss that encourages the predicted polygons to fit the object boundaries. Compared with the mask-based weakly-supervised methods, BoxSnake further reduces the performance gap between the predicted segmentation and the bounding box, and shows significant superiority on the Cityscapes dataset. The code has been available publicly.Comment: ICCV 202

Molecular docking studies on rocaglamide, a traditional Chinese medicine for periodontitis

Purpose: To undertake an in silico assessment of rocaglamide as a potential drug therapy forperiodontitis (dental arthritis).Method: Lamarckian algorithm-based automated docking approach using AutoDock4.2 tool wasapplied for calculating the best possible binding mode of rocaglamide to IL-23p19 and IL-17, the targets of anti-inflammatory drugs in periodontal disease.Results: The top two interactions of rocaglamide with IL-17 (ΔG = -5.45 and -4.83 kcal/mol) were more spontaneous, and the physical interactions (two hydrogen bonds and one π-πbond) generated in the two IL-17- rocaglamide complexes were higher in number than in IL-23p14-rocaglamide complexes.Conclusion: In silico analysis of rocaglamide, a known antimicrobial and anti-inflammatory agent, is a promising natural candidate for periodontitis therapy, and should be further subjected to in vitro and in vivo anti-periodontitis investigations.Keywords: Periodontitis, Inflammation, Rocaglamide, Molecular docking, Lamarckian algorithm, IL- 23p19, IL-1

Deep Descriptor Transforming for Image Co-Localization

Reusable model design becomes desirable with the rapid expansion of machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detector for the common object in the image co-localization problem. We propose a simple but effective method, named Deep Descriptor Transforming (DDT), for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of images. Empirical studies validate the effectiveness of the proposed DDT method. On benchmark image co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. Moreover, DDT also demonstrates good generalization ability for unseen categories and robustness for dealing with noisy data.Comment: Accepted by IJCAI 201

Molecular and Functional Characterization of Odorant Binding Protein 7 From the Oriental Fruit Moth Grapholita molesta (Busck) (Lepidoptera: Tortricidae)

Odorant-binding proteins (OBPs) are widely and abundantly distributed in the insect sensillar lymph and are essential for insect olfactory processes. The OBPs can capture and transfer odor molecules across the sensillum lymph to odorant receptors and trigger the signal transduction pathway. In this study, a putative OBP gene, GmolOBP7, was cloned using specific-primers, based on the annotated unigene which forms the antennal transcriptome of Grapholita molesta. Real-time PCR (qRT-PCR) analysis revealed that GmolOBP7 was highly expressed in the wings of males and the antennae of both male and female adult moths, while low levels were expressed in other tissues. The recombinant GmolOBP7 (rGmolOBP7) was successfully expressed and purified via Ni-ion affinity chromatography. The results of binding assays revealed that rGmolOBP7 exhibited a high binding affinity to the minor sex pheromone 1-dodecanol containing Ki of 7.48 μM and had high binding capacities to the host-plant volatiles, such as pear ester, lauraldehyde and α-ocimene. RNA-interference experiments were performed to further assess the function of GmolOBP7. qRT-PCR showed that the levels of mRNA transcripts significantly declined in 1 and 2 day old male and female moths, treated with GmolOBP7 dsRNA, compared with non-injection controls. The EAG responses of dsRNA-injected males and females to pear ester, as well as the EAG responses of dsRNA-injected males to 1-dodecanol, were significantly reduced compared to the GFP-dsRNA-injected and non-injected controls. We therefore infer that GmolOBP7 has a dual function in the perception and recognition of the host-plant volatiles and sex pheromones

GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction

This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools

Sesquiterpenes from the marine red alga Laurencia composita.

Four new chamigrane derivatives, laurecomin A (1). laurecomin B (2), laurecomin C (3), and laurecomin D (4), one new naturally occurring sesquiterpene, 2,10-dibromo-3-chloro-7-chamigren-9-ol acetate (5), and three known halogenated structures, deoxyprepacifenol (6), 1-bromoselin-4(14),11-diene (7), and 9-bromoselin-4(14).11-diene (8), were isolated from the marine red alga Laurencia cornposita collected from Pingtan Island, China. The structures of these compounds were unambiguously established by 1D, 2D NMR and mass spectroscopic techniques. The bioassay results showed that 2 was active against both brine shrimp and fungus Colletotrichum lagenarium. (C) 2012 Elsevier B.V. All rights reserved.Four new chamigrane derivatives, laurecomin A (1). laurecomin B (2), laurecomin C (3), and laurecomin D (4), one new naturally occurring sesquiterpene, 2,10-dibromo-3-chloro-7-chamigren-9-ol acetate (5), and three known halogenated structures, deoxyprepacifenol (6), 1-bromoselin-4(14),11-diene (7), and 9-bromoselin-4(14).11-diene (8), were isolated from the marine red alga Laurencia cornposita collected from Pingtan Island, China. The structures of these compounds were unambiguously established by 1D, 2D NMR and mass spectroscopic techniques. The bioassay results showed that 2 was active against both brine shrimp and fungus Colletotrichum lagenarium. (C) 2012 Elsevier B.V. All rights reserved