Background The poultry red mite (PRM), Dermanyssus gallinae , is one of the most economically deleterious ectoparasites affecting egg-laying hens worldwide. It may be possible to control D. gallinae populations by manipulating lighting regimes within poultry units. However, no studies have clearly shown the effects of darkness on the population growth rate of D. gallinae . Methods The effect of darkness on the population growth rate of D. gallinae was investigated, together with the first description of the molecular identity of the mite from China. Mite variables under two lighting regimens (1:23 h L:D and 12:12 h L:D) were compared, including number of mites and eggs, survival and feeding rates, engorgement, oviposition, hatchability and the life-cycle of D. gallinae. Results The results showed that the number of mites (13,763 ± 956) and eggs (5424 ± 317) in the rearing system with prolonged darkness of 1:23 h L:D at 4th week were 2.4- and 3.6-fold higher than those under a conventional lighting regimen of 12:12 h L:D, respectively. The feeding rates of mites under prolonged darkness ranged from 36.7 ± 1.1% to 52.0 ± 7.0%, which were significantly higher than those under conventional lighting regimen (ranging from 22.6 ± 1.9% to 37.3 ± 1.6%). The mean weight of engorged females (0.26 ± 0.01 mg) and the mean number of eggs per female (on average 5.87 ± 0.36) under prolonged darkness were significantly higher than those under conventional lighting regimen (0.22 ± 0.01 mg and 3.62 ± 0.31, respectively). However, the survival rate ranging from 98.07 ± 0.10% to 98.93 ± 0.19%, hatchability of 97.93 ± 0.01% and the life-cycle of D. gallinae (9 days) was not affected by the lighting period. Conclusions Our findings demonstrated that prolonged darkness significantly promoted the proliferation levels of D. gallinae , resulting in increased number of mites and eggs in the rearing system. The promoted population growth of D. gallinae was found to be related to the increased feeding rate, engorgement level and oviposition level of mites under prolonged darkness. The egg hatchability, the survival rates and the duration of life-cycle of D. gallinae were not affected by the light regimes.

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A new software package, autoPX, for processing X-ray diffraction data from biomacromolecular crystals is reported. This processing software package is designed on the basis of novel methods such as the location of diffraction spots by an improved Canny operator, indexing by a modified Fourier transform, a novel definition of mosaicity that expresses the dispersion state of reciprocal diffraction spots, and the correction of predicted diffraction spot coordinates by homography transform. New programming of some traditional algorithms necessary for integration and scaling is also included. Several examples of crystal structure determination using data from the SSRF beamlines reduced using autoPX, HKL-2000, DIALS and XDS are also demonstrated, and indicate that autoPX is capable of processing diffraction data from biomacromolecular crystals and providing adequate solutions to problems encountered at the SSRF beamlines.

Corneal ulcer is a common leading cause of corneal blindness. It is difficult to accurately segment corneal ulcers due to the following problems: large differences in the pathological shapes between point-flaky and flaky corneal ulcers, blurred boundary, noise interference, and the lack of sufficient slit-lamp images with ground truth. To address these problems, in this paper, we proposed a novel semi-supervised multi-scale self-transformer generative adversarial network (Semi-MsST-GAN) that can leverage unlabeled images to improve the performance of corneal ulcer segmentation in fluorescein staining of slit-lamp images. Firstly, to improve the performance of segmenting the corneal ulcer regions with complex pathological features, we proposed a novel multi-scale self-transformer network (MsSTNet) as the MsST-GAN generator, which can guide the model to aggregate the low-level weak semantic features with the high-level strong semantic information and adaptively learn the spatial correlation in feature maps. Then, to further improve the segmentation performance by leveraging unlabeled data, the semi-supervised approach based on the proposed MsST-GAN was explored to solve the problem of the lack of slit-lamp images with corresponding ground truth. The proposed Semi-MsST-GAN was comprehensively evaluated on the public SUSTech-SYSU dataset, which contains 354 labeled and 358 unlabeled fluorescein staining slit-lamp images. The results showed that, compared with other state-of-the-art methods, our proposed method achieves better performance with comparable efficiency.

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