Seasonal variation of the pupil light reflex

Abstract

PurposeVariation of retinal light sensitivity between winter and summer seasons may be a marker for seasonal affective disorder and the basis for response to phototherapy. As the pupil is a marker of retinal light sensitivity and shows a 24‐hour variation that reflects circadian rhythm, we used the pupil as proxy to assess light sensitivity of rods, cones and melanopsin as a function of seasonal light in healthy adults.Methods37 adults were tested during the short (January‐February) and long (July‐August) annual photoperiods. All subjects completed standardized questionnaires of seasonality and sleep. Pupil responses to blue (470 nm) and red (622 nm) light were recorded under dark and light adapted conditions using an automated pupillometer. Main outcome parameters were maximal contraction amplitude and post‐stimulus contraction amplitude.ResultsAll but two subjects reported good sleep quality. One subject was an extreme morning type. Twelve subjects reported a global seasonal variation in sleep, mood and appetite but without dysfunction or disability. Baseline pupil size of light adapted eyes was greater in summer compared to winter. Maximal contraction amplitudes to scotopic and photopic light stimuli were greater in summer. The post‐stimulus pupil contraction was also greater in summer, but only after light adaptation.ConclusionsBaseline pupil size and pupil responses derived from rods, cones and melanopsin photoreception were greater in summer in healthy adults. This seemingly greater retinal sensitivity to light when the photoperiod (daylength) is long suggests a more central basis for seasonal regulation of the pupil light reflex.

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

“…(4) The global subspaces and the local subspaces are obtained according to Equations (9-13). (5) The statistics and control limits are calculated for monitoring according to Equations (15)(16)(17)(18)(19)(20)(21). (6) Most similar mode information of faulty data is determined according to Equation (14).…”

“…[16,17] However, it is noted that industrial processes have dynamic and multimode characteristics and the variable trajectories measured over the duration are nonlinear, which cannot be directly monitored by traditional MSPM methods. [8,10,14,18,19] Therefore, monitoring methods for multimode processes should be well developed.…”

“…[15,[20][21][22][23] Multiple models methods were developed by building different monitoring models for different modes, which may be effective for each mode. [18] However, the correlations between different modes were neglected, which may cause a high false alarm rate. [13,14] Adaptive local models for multimode online monitoring were presented to solve the multimode problem.…”

“…[32] The a priori knowledge can be used to weight the variables appropriately for multimode process modelling. [18] Recently, considering multimode information, a new subspace decomposition method was developed in which each mode could have unequal mode length. Nevertheless, it needs to achieve mode division for multimode processes, which could not overcome the missing a priori knowledge in most practical industrial processes.…”

Dynamic mutual information similarity based transient process identification and fault detection

“…Kourti proposed an algorithm to analyze the grade transitions in the sight of batch processes. [26] Time coincidence of batch process was taken into consideration in the transition modelling and fault detection. However, it is often invalid in the continuous process.…”