Purpose-To investigate correlation between ocular Demodex infestation and serum.Design-A prospective study to correlate clinical findings with laboratory data.Participants-We consecutively enrolled 59 patients: 34 men and 25 women with a mean age of 60.4±17.6 years (range, 17-93).Methods-Demodex counting was performed based on lash sampling. Serum immunoreactivity to two 62-kDa and 83-kDa proteins derived from B oleronius was determined by Western blot analysis. Facial rosacea, lid margin, and ocular surface inflammation were documented by photography and graded in a masked fashion.Main Outcome Measures-Statistical significance based on correlative analyses of clinical and laboratory data.Results-These 59 patients were age matched, but not gender matched, regarding serum immunoreactivity, ocular Demodex infestation, or facial rosacea. There was a significant correlation between serum immunoreactivity and facial rosacea (P = 0.009), lid margin inflammation (P = 0.040), and ocular Demodex infestation (P = 0.048), but not inferior bulbar conjunctival inflammation (P = 0.573). The Demodex count was significantly higher in patients with positive facial rosacea (6.6±9.0 vs. 1.9±2.2; P = 0.014). There was a significant correlation of facial rosacea with lid margin inflammation (P = 0.016), but not with inferior bulbar conjunctival inflammation (P = 0.728). Ocular Demodex infestation was less prevalent in patients with aqueous tear-deficiency dry eye than those without (7/38 vs. 12/21; P = 0.002). Conclusions-The strong correlation provides a better understanding of comorbidity betweenDemodex mites and their symbiotic B oleronius in facial rosacea and blepharitis. Treatments directed to both warrant future investigation.

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Purpose-To evaluate the efficacy of early sutureless amniotic membrane transplantation in the management of severe bacterial keratitis to reduce pain, inflammation, and haze, and to promote healing.Method-A noncomparative case series including 3 eyes of 3 consecutive patients with severe bacterial keratitis exhibiting persistent epithelial defect/ulcer, more than 5 mm in diameter, located within 3mm from the visual axis with infiltration occupying more than 50% of the corneal thickness. They were retrospectively reviewed following early (ie, within 96 hours) sutureless amniotic membrane transplantation via ProKera together with selective topical antibiotics and preservativefree steroid. Pain relief, inflammation, haze, and corneal epithelial healing were monitored.Results-ProKera was inserted once in 1 eye and twice in the other 2 eyes. Pain was significantly relieved and inflammation was markedly reduced in all cases. The corneal epithelial defect and stromal ulceration rapidly healed while visual acuity improved in 2 of the 3 eyes.Conclusion-Temporary sutureless amniotic membrane transplantation via ProKera allows easy insertion and replacement of the membrane in the office, as well as early intervention to promote epithelialization, reduce pain, haze and inflammation in cases with severe bacterial keratitis. This result justifies large series controlled studies in the future.Keywords amniotic membrane; bacterial keratitis; sutureless; wound healing Microbial keratitis (caused by bacteria, fungi, viruses, or parasites) is a serious ocular infection resulting in persistent epithelial defect/ulcer at the acute phase but corneal scarring and a permanent loss of vision at the chronic phase. The principal therapeutic goals for infectious keratitis are to eliminate the pathogens and to prevent irreversible corneal structural damage.

Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46-13.46%.

High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid. Meteorological features and seasonal information are strongly related to wind power prediction. This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information. The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD). The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity. With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction. Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM. The proposed method is used to predict 15-minute wind power. In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method. The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.

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Social computing, the direction of network development in the future, has exerted a profound influence on our life and work. This paper, based on the presentation of social computing and its impact on education, explores the models of teaching computer basic course at college in the social computing context, with its focus on teaching contents and activities, experimental teaching and network teaching.