For the last 20 years, a great amount of evidence has accumulated through epidemiological studies that most of the dry eye disease encountered in daily life, especially in video display terminal (VDT) workers, involves short tear film breakup time (TFBUT) type dry eye, a category characterized by severe symptoms but minimal clinical signs other than short TFBUT. An unstable tear film also affects the visual function, possibly due to the increase of higher order aberrations. Based on the change in the understanding of the types, symptoms, and signs of dry eye disease, the Asia Dry Eye Society agreed to the following definition of dry eye: "Dry eye is a multifactorial disease characterized by unstable tear film causing a variety of symptoms and/or visual impairment, potentially accompanied by ocular surface damage." The definition stresses instability of the tear film as well as the importance of visual impairment, highlighting an essential role for TFBUT assessment. This paper discusses the concept of Tear Film Oriented Therapy (TFOT), which evolved from the definition of dry eye, emphasizing the importance of a stable tear film.

Specific patterns of Th-1 and -2 chemokines and their receptors are induced in the mouse ocular surface by desiccating stress in a strain-related fashion. Desiccating stress potently stimulates the expression of Th-1 cell-attracting chemokines and chemokine receptors on the ocular surface of C57BL/6 mice.

The detection of curvilinear structures in medical images, e.g., blood vessels or nerve fibers, is important in aiding management of many diseases. In this work, we propose a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography (OCT-A), color fundus image, and corneal confocal microscopy (CCM). Instead of the U-Net based convolutional neural network, we propose a novel network (CS-Net) which includes a self-attention mechanism in the encoder and decoder. Two types of attention modules are utilized-spatial attention and channel attention, to further integrate local features with their global dependencies adaptively. The proposed network has been validated on five datasets: two color fundus datasets, two corneal nerve datasets and one OCT-A dataset. Experimental results show that our method outperforms state-of-the-art methods, for example, sensitivities of corneal nerve fiber segmentation were at least 2% higher than the competitors. As a complementary output, we made manual annotations of two corneal nerve datasets which have been released for public access.

Society's techno-social systems are becoming ever faster and more computer-orientated. However, far from simply generating faster versions of existing behaviour, we show that this speed-up can generate a new behavioural regime as humans lose the ability to intervene in real time. Analyzing millisecond-scale data for the world's largest and most powerful techno-social system, the global financial market, we uncover an abrupt transition to a new all-machine phase characterized by large numbers of subsecond extreme events. The proliferation of these subsecond events shows an intriguing correlation with the onset of the system-wide financial collapse in 2008. Our findings are consistent with an emerging ecology of competitive machines featuring ‘crowds' of predatory algorithms, and highlight the need for a new scientific theory of subsecond financial phenomena.