A non-image-forming visual circuit mediates the innate fear of heights in male mice.

The neural basis of fear of heights remains largely unknown. In this study, we investigated the fear response to heights in male mice and observed characteristic aversive behaviors resembling human height vertigo. We identified visual input as a critical factor in mouse reactions to heights, while peripheral vestibular input was found to be nonessential for fear of heights. Unexpectedly, we found that fear of heights in naïve mice does not rely on image-forming visual processing by the primary visual cortex. Instead, a subset of neurons in the ventral lateral geniculate nucleus (vLGN), which connects to the lateral/ventrolateral periaqueductal gray (l/vlPAG), drives the expression of fear associated with heights. Additionally, we observed that a subcortical visual pathway linking the superior colliculus to the lateral posterior thalamic nucleus inhibits the defensive response to height threats. These findings highlight a rapid fear response to height threats through a subcortical visual and defensive pathway from the vLGN to the l/vlPAG.

Effects of cell deformability and adhesion strength on dynamic cell seeding: Cell-scale investigation via mesoscopic modeling.

The flow of cell suspension through a porous scaffold is a common process in dynamic cell seeding, which determines the initial distribution of cells for constructing tissue-engineered grafts. Physical insights into the transport and adhesion behaviors of cells in this process are of great significance to the precise control of cell density and its distribution in the scaffold. Revealing of dynamic mechanisms underlying these cell behaviors through experiments is still difficult. The numerical approach therefore plays an important role in such studies. However, existing studies have mostly focused on external factors (e.g., flow conditions and scaffold architecture) but ignored the intrinsic biomechanical properties of cells as well as their associated effects. The present work utilized a well-established mesoscopic model to simulate the dynamic cell seeding within a porous scaffold, based on which a thorough investigation of the effects of cell deformability and cell-scaffold adhesion strength on the seeding process was carried out. The results show that the increase in either the stiffness or the bond strength of cells would augment the firm-adhesion rate and thus enhance seeding efficiency. In comparison to cell deformability, bond strength seems to play a more dominant role. Especially in the cases with weak bond strength, remarkable losses of seeding efficiency and distribution uniformity are observed. Noteworthily, it is found that both the firm-adhesion rate and the seeding efficiency are quantiatively related to the adhesion strength which is measured as the detachment force, suggesting a straightforward way to estimate the seeding outcome.

Cervical lesion image enhancement based on conditional entropy generative adversarial network framework.

Early screening and diagnosis of cervical precancerous lesions are very important to prevent cervical cancer. High-quality colposcopy images will help doctors make faster and more accurate diagnoses. To tackle the problem of low image quality caused by complex interference during colposcopy operation, this paper proposed a conditional entropy generative adversarial networks framework for image enhancement. A decomposition network based on Retinex theory is constructed to obtain the reflection images of the low-quality images, then the conditional generative adversarial network is used as the enhancement network. The low-quality images and the decomposed reflection images are both input the enhancement network for training, and the conditional entropy distance is used as a part of the loss of the conditional generative adversarial network to alleviate the over-fitting problem during the training process. The test results show that compared with published methods, the proposed method of this paper can significantly improve the image quality, and can enhance the colposcopy image while retaining image details.

Subjective and Objective Quality Assessment of Swimming Pool Images.

As the research basis of image processing and computer vision research, image quality evaluation (IQA) has been widely used in different visual task fields. As far as we know, limited efforts have been made to date to gather swimming pool image databases and benchmark reliable objective quality models, so far. To filled this gap, in this paper we reported a new database of underwater swimming pool images for the first time, which is composed of 1500 images and associated subjective ratings recorded by 16 inexperienced observers. In addition, we proposed a main target area extraction and multi-feature fusion image quality assessment (MM-IQA) for a swimming pool environment, which performs pixel-level fusion for multiple features of the image on the premise of highlighting important detection objects. Meanwhile, a variety of well-established full-reference (FR) quality evaluation methods and partial no-reference (NR) quality evaluation algorithms are selected to verify the database we created. Extensive experimental results show that the proposed algorithm is superior to the most advanced image quality models in performance evaluation and the outcomes of subjective and objective quality assessment of most methods involved in the comparison have good correlation and consistency, which further indicating indicates that the establishment of a large-scale pool image quality assessment database is of wide applicability and importance.