In Vivo Evaluation of the Nitroimidazole-Based Thioflavin-T Derivatives as Cerebral Ischemia Markers

Timely imaging and accurate interpretation of cerebral ischemia are required to identify patients who might benefit from more aggressive therapy, and nuclear medicine offers a noninvasive method for demonstrating cerebral ischemia. Three nitroimidazole-based thioflavin-T derivatives, N-[4-(benzothiazol-2-yl)phenyl]-3-(4-nitroimidazole-1-yl) propanamide (4NPBTA), N-[4-(benzothiazol-2-yl)phenyl]-3-(4-nitroimidazole-1-yl)-N-methylpropanamide (4NPBTA-1), and N-[4-(benzothiazol-2-yl)phenyl]-3-(2-nitroimidazole-1-yl) propanamide (2NPBTA), were radioiodinated and evaluated as possible cerebral ischemia markers. In normal mice, these compounds showed good permeation of the intact blood-brain barrier (BBB), high initial brain uptake, and rapid washout. In gerbil stroke models that had been subjected to right common carotid artery ligation to produce cerebral ischemia, [131I]2NPBTA, uptake in the right cerebral hemisphere decreased more slowly than that of the left, and the right/left hemisphere uptake ratios increased with time. Also, the right/left hemisphere uptake ratios correlated positively with the severity of the stroke. The results showed that [131I]2NPBTA had a specific location in the cerebral ischemic tissue. This represented a first step in finding new drugs and might provide a possible cerebral ischemic marker

3D Visibility-aware Generalizable Neural Radiance Fields for Interacting Hands

Neural radiance fields (NeRFs) are promising 3D representations for scenes, objects, and humans. However, most existing methods require multi-view inputs and per-scene training, which limits their real-life applications. Moreover, current methods focus on single-subject cases, leaving scenes of interacting hands that involve severe inter-hand occlusions and challenging view variations remain unsolved. To tackle these issues, this paper proposes a generalizable visibility-aware NeRF (VA-NeRF) framework for interacting hands. Specifically, given an image of interacting hands as input, our VA-NeRF first obtains a mesh-based representation of hands and extracts their corresponding geometric and textural features. Subsequently, a feature fusion module that exploits the visibility of query points and mesh vertices is introduced to adaptively merge features of both hands, enabling the recovery of features in unseen areas. Additionally, our VA-NeRF is optimized together with a novel discriminator within an adversarial learning paradigm. In contrast to conventional discriminators that predict a single real/fake label for the synthesized image, the proposed discriminator generates a pixel-wise visibility map, providing fine-grained supervision for unseen areas and encouraging the VA-NeRF to improve the visual quality of synthesized images. Experiments on the Interhand2.6M dataset demonstrate that our proposed VA-NeRF outperforms conventional NeRFs significantly. Project Page: \url{https://github.com/XuanHuang0/VANeRF}.Comment: Accepted by AAAI-2

SAGA: Summarization-Guided Assert Statement Generation

Generating meaningful assert statements is one of the key challenges in automated test case generation, which requires understanding the intended functionality of the tested code. Recently, deep learning-based models have shown promise in improving the performance of assert statement generation. However, existing models only rely on the test prefixes along with their corresponding focal methods, yet ignore the developer-written summarization. Based on our observations, the summarization contents usually express the intended program behavior or contain parameters that will appear directly in the assert statement. Such information will help existing models address their current inability to accurately predict assert statements. This paper presents a novel summarization-guided approach for automatically generating assert statements. To derive generic representations for natural language (i.e., summarization) and programming language (i.e., test prefixes and focal methods), we leverage a pre-trained language model as the reference architecture and fine-tune it on the task of assert statement generation. To the best of our knowledge, the proposed approach makes the first attempt to leverage the summarization of focal methods as the guidance for making the generated assert statements more accurate. We demonstrate the effectiveness of our approach on two real-world datasets when compared with state-of-the-art models.Comment: Preprint, to appear in the Journal of Computer Science and Technology (JCST

Improving associative memory in younger and older adults with unitization: evidence from meta-analysis and behavioral studies

IntroductionThe finding that familiarity can support associative memory by unitizing the to -be-learned items into a novel representation has been widely accepted, but its effects on overall performance of associative memory and recollection are still controversial.MethodsThe current study aims to elucidate these discrepancies by identifying potential moderating factors through a combined approach of meta-analysis and behavioral experiment.ResultsResults consistently showed that changes in the level of unitization and age groups were two important moderators. Specifically, unitization enhanced younger and older adults’ associative memory and its supporting processes (i.e., familiarity and recollection) when the level of unitization between studied and rearranged pairs was changed. However, when this level remained constant, unitization exhibited no impact on associative memory and familiarity in younger adults, but showed an enhanced effect in older adults. Furthermore, results revealed a marked group difference between younger and older adults in associative memory when the unitization level of noncompound words remained unaltered. Upon breaking this condition, the group difference was reduced by enhancing familiarity or recollection.DiscussionThese findings not only clarify some of the inconsistencies in the literature concerning the impact of unitization on associative memory, but also suggest that unitization is a beneficial strategy for reducing group difference in associative memory, with its effectiveness varying according to the level of unitization changes

Auroral event detection using spatiotemporal statistics of local motion vectors

The analysis and exploration of auroral dynamics are very significant for studying auroral mechanisms. This paper proposes a method based on auroral dynamic processes for detecting auroral events automatically. We first obtained the motion fields using the multiscale fluid flow estimator. Then, the auroral video frame sequence was represented by the spatiotemporal statistics of local motion vectors. Finally, automatic auroral event detection was achieved. The experimental results show that our methods could detect the required auroral events effectively and accurately, and that the detections were independent on any specific auroral event. The proposed method makes it feasible to statistically analyze a large number of continuous observations based on the auroral dynamic process