Gamma-Ray Burst Jet Breaks Revisited

Gamma-ray Burst (GRB) collimation has been inferred with the observations of achromatic steepening in GRB light curves, known as jet breaks. Identifying a jet break from a GRB afterglow light curve allows a measurement of the jet opening angle and true energetics of GRBs. In this paper, we re-investigate this problem using a large sample of GRBs that have an optical jet break that is consistent with being achromatic in the X-ray band. Our sample includes 99 GRBs from 1997 February to 2015 March that have optical and, for Swift GRBs, X-ray light curves that are consistent with the jet break interpretation. Out of the 99 GRBs we have studied, 55 GRBs are found to have temporal and spectral behaviors both before and after the break, consistent with the theoretical predictions of the jet break models, respectively. These include 53 long/soft (Type II) and 2 short/hard (Type I) GRBs. Only 1 GRB is classified as the candidate of a jet break with energy injection. Another 41 and 3 GRBs are classified as the candidates with the lower and upper limits of the jet break time, respectively. Most jet breaks occur at 90 ks, with a typical opening angle θj = (2.5 ± 1.0)°. This gives a typical beaming correction factor ${f}_{b}^{-1}\sim 1000$ for Type II GRBs, suggesting an even higher total GRB event rate density in the universe. Both isotropic and jet-corrected energies have a wide span in their distributions: log(Eγ,iso/erg) = 53.11 with σ = 0.84; log(EK,iso/erg) = 54.82 with σ = 0.56; log(Eγ/erg) = 49.54 with σ = 1.29; and log(EK/erg) = 51.33 with σ = 0.58. We also investigate several empirical correlations (Amati, Frail, Ghirlanda, and Liang–Zhang) previously discussed in the literature. We find that in general most of these relations are less tight than before. The existence of early jet breaks and hence small opening angle jets, which were detected in the Swfit era, is most likely the source of scatter. If one limits the sample to jet breaks later than 104 s, the Liang–Zhang relation remains tight and the Ghirlanda relation still exists. These relations are derived from Type II GRBs, and Type I GRBs usually deviate from them

High spatial-resolution imaging of label-free in vivo protein aggregates by VISTA

Amyloid aggregation, formed by aberrant proteins, is a pathological hallmark for neurodegenerative diseases, including Alzheimer's disease and Huntington's disease. High-resolution holistic mapping of the fine structures from these aggregates should facilitate our understanding of their pathological roles. Here, we achieved label-free high-resolution imaging of the polyQ and the amyloid-beta (Aβ) aggregates in cells and tissues utilizing a sample-expansion stimulated Raman strategy. We further focused on characterizing the Aβ plaques in 5XFAD mouse brain tissues. 3D volumetric imaging enabled visualization of the whole plaques, resolving both the fine protein filaments and the surrounding components. Coupling our expanded label-free Raman imaging with machine learning, we obtained specific segmentation of aggregate cores, peripheral filaments together with cell nuclei and blood vessels by pre-trained convolutional neural network models. Combining with 2-channel fluorescence imaging, we achieved a 6-color holistic view of the same sample. This ability for precise and multiplex high-resolution imaging of the protein aggregates and their micro-environment without the requirement of labeling would open new biomedical applications

Motor neuron-derived Thsd7a is essential for zebrafish vascular development via the Notch-dll4 signaling pathway.

BackgroundDevelopment of neural and vascular systems displays astonishing similarities among vertebrates. This parallelism is under a precise control of complex guidance signals and neurovascular interactions. Previously, our group identified a highly conserved neural protein called thrombospondin type I domain containing 7A (THSD7A). Soluble THSD7A promoted and guided endothelial cell migration, tube formation and sprouting. In addition, we showed that thsd7a could be detected in the nervous system and was required for intersegmental vessels (ISV) patterning during zebrafish development. However, the exact origin of THSD7A and its effect on neurovascular interaction remains unclear.ResultsIn this study, we discovered that zebrafish thsd7a was expressed in the primary motor neurons. Knockdown of Thsd7a disrupted normal primary motor neuron formation and ISV sprouting in the Tg(kdr:EGFP/mnx1:TagRFP) double transgenic zebrafish. Interestingly, we found that Thsd7a morphants displayed distinct phenotypes that are very similar to the loss of Notch-delta like 4 (dll4) signaling. Transcript profiling further revealed that expression levels of notch1b and its downstream targets, vegfr2/3 and nrarpb, were down-regulated in the Thsd7a morphants. These data supported that zebrafish Thsd7a could regulate angiogenic sprouting via Notch-dll4 signaling during development.ConclusionsOur results suggested that motor neuron-derived Thsd7a plays a significant role in neurovascular interactions. Thsd7a could regulate ISV angiogenesis via Notch-dll4 signaling. Thus, Thsd7a is a potent angioneurin involved in the development of both neural and vascular systems

GRB 120729A: External Shock Origin for Both the Prompt Gamma-Ray Emission and Afterglow

Gamma-ray burst (GRB) 120729A was detected by Swift/BAT and Fermi/GBM, and then rapidly observed by Swift/XRT, Swift/UVOT, and ground-based telescopes. It had a single long and smooth \gamma-ray emission pulse, which extends continuously to the X-rays. We report Lick/KAIT observations of the source, and make temporal and spectral joint fits of the multiwavelength light curves of GRB 120729A. It exhibits achromatic light-curve behavior, consistent with the predictions of the external shock model. The light curves are decomposed into four typical phases: onset bump (Phase I), normal decay (Phase II), shallow decay (Phase III), and post-jet break (Phase IV). The spectral energy distribution (SED) evolves from prompt \gamma-ray emission to the afterglow with photon index from Γγ=1.36 to Γ≈1.75. There is no obvious evolution of the SED during the afterglow. ...(Please see article full tet for complete abstract.

UniSA: Unified Generative Framework for Sentiment Analysis

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.Comment: Accepted to ACM MM 202

Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models

Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts. In this study, we propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts, demonstrating its potential as a powerful tool in enhancing LLMs' comprehension in complex dialogue tasks