A review of fine-grained sketch image retrieval based on deep learning.

Sketch image retrieval is an important branch of the image retrieval field, mainly relying on sketch images as queries for content search. The acquisition process of sketch images is relatively simple and in some scenarios, such as when it is impossible to obtain photos of real objects, it demonstrates its unique practical application value, attracting the attention of many researchers. Furthermore, traditional generalized sketch image retrieval has its limitations when it comes to practical applications; merely retrieving images from the same category may not adequately identify the specific target that the user desires. Consequently, fine-grained sketch image retrieval merits further exploration and study. This approach offers the potential for more precise and targeted image retrieval, making it a valuable area of investigation compared to traditional sketch image retrieval. Therefore, we comprehensively review the fine-grained sketch image retrieval technology based on deep learning and its applications and conduct an in-depth analysis and summary of research literature in recent years. We also provide a detailed introduction to three fine-grained sketch image retrieval datasets: Queen Mary University of London (QMUL) ShoeV2, ChairV2 and PKU Sketch Re-ID, and list common evaluation metrics in the sketch image retrieval field, while showcasing the best performance achieved for these datasets. Finally, we discuss the existing challenges, unresolved issues and potential research directions in this field, aiming to provide guidance and inspiration for future research.

Striatal D1 Dopamine Neuronal Population Dynamics in a Rat Model of Levodopa-Induced Dyskinesia.

BACKGROUND: The pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) is not well understood. Experimental data from numerous investigations support the idea that aberrant activity of D1 dopamine receptor-positive medium spiny neurons in the striatal direct pathway is associated with LID. However, a direct link between the real-time activity of these striatal neurons and dyskinetic symptoms remains to be established. METHODS: We examined the effect of acute levodopa treatment on striatal c-Fos expression in LID using D1-Cre PD rats with dyskinetic symptoms induced by chronic levodopa administration. We studied the real-time dynamics of striatal D1 + neurons during dyskinetic behavior using GCaMP6-based in vivo fiber photometry. We also examined the effects of striatal D1 + neuronal deactivation on dyskinesia in LID rats using optogenetics and chemogenetic methods. RESULTS: Striatal D1 + neurons in LID rats showed increased expression of c-Fos, a widely used marker for neuronal activation, following levodopa injection. Fiber photometry revealed synchronized overactivity of striatal D1 + neurons during dyskinetic behavior in LID rats following levodopa administration. Consistent with these observations, optogenetic deactivation of striatal D1 + neurons was sufficient to inhibit most of the dyskinetic behaviors of LID animals. Moreover, chemogenetic inhibition of striatal D1 + neurons delayed the onset of dyskinetic behavior after levodopa administration. CONCLUSION: Our data demonstrated that aberrant activity of striatal D1 + neuronal population was causally linked with real-time dyskinetic symptoms in LID rats.

A computational method for predicting nucleocapsid protein in retroviruses.

Nucleocapsid protein (NC) in the group-specific antigen (gag) of retrovirus is essential in the interactions of most retroviral gag proteins with RNAs. Computational method to predict NCs would benefit subsequent structure analysis and functional study on them. However, no computational method to predict the exact locations of NCs in retroviruses has been proposed yet. The wide range of length variation of NCs also increases the difficulties. In this paper, a computational method to identify NCs in retroviruses is proposed. All available retrovirus sequences with NC annotations were collected from NCBI. Models based on random forest (RF) and weighted support vector machine (WSVM) were built to predict initiation and termination sites of NCs. Factor analysis scales of generalized amino acid information along with position weight matrix were utilized to generate the feature space. Homology based gene prediction methods were also compared and integrated to bring out better predicting performance. Candidate initiation and termination sites predicted were then combined and screened according to their intervals, decision values and alignment scores. All available gag sequences without NC annotations were scanned with the model to detect putative NCs. Geometric means of sensitivity and specificity generated from prediction of initiation and termination sites under fivefold cross-validation are 0.9900 and 0.9548 respectively. 90.91% of all the collected retrovirus sequences with NC annotations could be predicted totally correct by the model combining WSVM, RF and simple alignment. The composite model performs better than the simplex ones. 235 putative NCs in unannotated gags were detected by the model. Our prediction method performs well on NC recognition and could also be expanded to solve other gene prediction problems, especially those whose training samples have large length variations.

Inhibition of striatal dopamine D5 receptor attenuates levodopa-induced dyskinesia in a rat model of Parkinson's disease.

Levodopa-induced dyskinesia (LID) is experienced by most patients of Parkinson's disease (PD) upon the long-term use of the dopamine precursor levodopa. Striatal dopaminergic signaling plays a critical role in the pathogenesis of LID through its interactions with dopamine receptors. The specific roles of striatal dopaminergic D5 receptors in the pathophysiological process of LID are still poorly established. In the study, we investigated the role of striatal dopamine D5 receptor in LID by using PD rats with or without dyskinetic symptoms after chronic levodopa administration. The experimental results showed that the expression level of D5 receptors in the sensorimotor striatum of dyskinetic rats is significantly higher than that of the non-dyskinetic controls. The administration of levodopa increased c-Fos expression in a subpopulation of sensorimotor striatum neurons of dyskinetic rats, but not in non-dyskinetic rats. The majority of the c-Fos+ neurons activated by levodopa in the striatum are positive for D5 receptor staining. Intrastriatal injection of D1-like (D1 and D5) dopamine receptor antagonist, SCH-23390, significantly inhibited dyskinetic behavior in dyskinetic rats after the injection of levodopa, meanwhile, intrastriatal administration of SKF-83959, a partial D5 receptor agonist, yielded significant dyskinetic movements in dyskinetic rats without levodopa. In contrast, intrastriatal perfusion of small interfering RNA directed against DRD5 downregulated D5 receptors expression and moderately inhibited dyskinetic behavior of dyskinetic animals. Our data suggested that the striatal dopamine D5 receptor might play a novel role in the pathophysiology of LID.