Behavior analysis of juvenile steelhead trout under blue and red light color conditions based on multiple object tracking

IntroductionThe lighting environment significantly influences fish behavior. This study explores the impact of diverse lighting conditions on the behavior of steelhead trout (Oncorhynchus mykiss) to illuminate the mechanisms underlying their behavioral responses.MethodsThis experiment was set up with six treatments at a constant light intensity of 150 lx: 12h white light + 12h dark (12 W), 12h blue light + 12h dark (12B), 12h red light + 12h dark (12 R), 1.5h blue light + 9h red light + 1.5h blue light + 12h dark (3B9R), 3h blue light + 6h red light + 3h blue light + 12h dark (6B6R), total 12h of blue and red light + 12h dark (T12BR). A multiple object tracking method, YOLOv5 with SORT, was employed to capture the movement trajectory of each fish, quantifying three motion metrics: swimming velocity, swimming angular velocity, and generalized intersection over union.ResultsThe results revealed that fish exposed to 12R light environment showed significantly higher activity levels than other groups. The mixed light environments (3B9R, 6B6R) formed significant differences in behavioral metrics with 12R earlier than pure light environments (12B, 12W, T12BR), indicating sudden light color changes should be avoided. Fish in the 3B9R environment exhibited the lowest activity level but highest growth performance, with the highest specific growth rate of 1.91±0.12 d-1, a value significantly surpassing the lowest recorded rate, supported by a p-value of 0.0054, indicating it is suitable for steelhead trout cultivation.DiscussBehavioral significant differences were observed as early as week eight, much earlier than physiological differences, which became apparent by week 16. Overall, this paper employs computer vision methods to study the impact of different light colors on fish behavior, found that 3B9R is the optimal lighting condition tested and sudden light color changes should be avoided, offering a new perspective on light conditions and behavior in steelhead trout cultivation

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot semantic segmentation methods first pre-train the models on `seen' classes, and then evaluate their generalization performance on `unseen' classes. However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes. To tackle these issues, we propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and a further training-based variant, TFS3D-T. Without any learnable parameters, TFS3D extracts dense representations by trigonometric positional encodings, and achieves comparable performance to previous training-based methods. Due to the elimination of pre-training, TFS3D can alleviate the domain gap issue and save a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to train a lightweight query-support transferring attention (QUEST), which enhances the interaction between the few-shot query and support data. Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by +6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the training time by -90%, indicating superior effectiveness and efficiency.Comment: Code is available at https://github.com/yangyangyang127/TFS3

Bioinspired cilia arrays with programmable nonreciprocal motion and metachronal coordination

Coordinated nonreciprocal dynamics in biological cilia is essential to many living systems, where the emergentmetachronal waves of cilia have been hypothesized to enhance net fluid flows at low Reynolds numbers (Re). Experimental investigation of this hypothesis is critical but remains challenging. Here, we report soft miniature devices with both ciliary nonreciprocal motion and metachronal coordination and use them to investigate the quantitative relationship between metachronal coordination and the induced fluid flow. We found that only antiplectic metachronal waves with specific wave vectors could enhance fluid flows compared with the synchronized case. These findings further enable various bioinspired cilia arrays with unique functionalities of pumping and mixing viscous synthetic and biological complex fluids at low Re. Our design method and developed soft miniature devices provide unprecedented opportunities for studying ciliary biomechanics and creating cilia-inspired wireless microfluidic pumping, object manipulation and lab- and organ-on-a-chip devices, mobile microrobots, and bioengineering systems.ISSN:2375-254

The Facile and Additive-Free Synthesis of a Cell-Friendly Iron(III)-Glutathione Complex

The straightfoward creation of an unreported glutathione-stabilised iron(III) complex is disclosed. In contrast to previous reports, glutathione was shown to coordinate and stabilise iron directly under physiological conditions in the absence of additional sulfur containing molecules, such as sodium sulfide. The complex was extensively characterised; the molecular geometry was determined as two inequivalent octahedra, approximately 2/3 of which is slightly distorted towards more tetrahedral in character, with the remaining 1/3 more regularly octahedral. The dispersion of the iron(III)-glutathione complex in aqueous solution yielded particles of 255±4 nm in diameter that enhanced the growth and proliferation of L929 fibroblast cells over 7 days, and inhibited the activity of matrix metalloproteinase-13. Consequently, the unprecedented glutathione-stabilised iron(III) complex disclosed has potential use as a simple-to-prepare growth factor for inclusion within cell culture media, and is an excellent candidate as a therapeutic for the treatment of metalloproteinase-13-associated diseases

Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation

Recently, video object segmentation (VOS) referred by multi-modal signals, e.g., language and audio, has evoked increasing attention in both industry and academia. It is challenging for exploring the semantic alignment within modalities and the visual correspondence across frames. However, existing methods adopt separate network architectures for different modalities, and neglect the inter-frame temporal interaction with references. In this paper, we propose MUTR, a Multi-modal Unified Temporal transformer for Referring video object segmentation. With a unified framework for the first time, MUTR adopts a DETR-style transformer and is capable of segmenting video objects designated by either text or audio reference. Specifically, we introduce two strategies to fully explore the temporal relations between videos and multi-modal signals. Firstly, for low-level temporal aggregation before the transformer, we enable the multi-modal references to capture multi-scale visual cues from consecutive video frames. This effectively endows the text or audio signals with temporal knowledge and boosts the semantic alignment between modalities. Secondly, for high-level temporal interaction after the transformer, we conduct inter-frame feature communication for different object embeddings, contributing to better object-wise correspondence for tracking along the video. On Ref-YouTube-VOS and AVSBench datasets with respective text and audio references, MUTR achieves +4.2% and +8.7% J&F improvements to state-of-the-art methods, demonstrating our significance for unified multi-modal VOS. Code is released at https://github.com/OpenGVLab/MUTR.Comment: Accepted by AAAI 2024. Code is released at https://github.com/OpenGVLab/MUT

Data-augmented matched subspace detector for hyperspectral subpixel target detection

The performance of subspace-based methods such as matched subspace detector (MSD) and MSD with interaction effects (MSDinter) heavily depends on the background subspace and the target subspace. Nonetheless, constructing a representative target subspace is challenging due to the limited availability of target spectra in a collected hyperspectral image. In this paper, we propose two new hyperspectral target detection methods termed data-augmented MSD (DAMSD) and data-augmented MSDinter (DAMSDI) that can effectively solve the scarcity problem of target spectra and from which a representative target-background mixed subspace can be learned. We first synthesise target-background mixed spectra based on classical hyperspectral mixing models and then learn a target-background mixed subspace via principal component analysis. Compared with MSD and MSDinter, the learned mixed subspace is more representative as spectral variability of target spectra is explained to the largest extent and it leads to an improvement in computational speed and numerical stability. We demonstrate the efficacy of DAMSD and DAMSDI for subpixel target detection on two public hyperspectral image datasets

Probing Quantum Confinement of Single-Walled Carbon Nanotubes by Resonant Soft-X-Ray Emission Spectroscopy

[[abstract]]We report the band-structure changes near Fermi level for single-walled carbon nanotubes (SWNTs) with diameters down to 1 nm from the study of soft-x-ray absorption and resonant emission spectroscopy. The observed quantum confinement of SWNTs affects both pi and sigma bands and bandgap through the rehybridization of pi and sigma orbitals. The significant changes of electronic structure are proved to be a measure for the mean diameter of the macroscopic amounts of SWNTs. (C) 2008 American Institute of Physics.[[notice]]補正完畢[[booktype]]紙