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Campylobacter jejuni is the major cause of campylobacteriosis, one of the most common foodborne illnesses worldwide. Here, we report the development of RAA-exo-probe and RAA-CRIPSR/Cas12a assays for the detection of C. jejuni in food samples. The two assays were found to be highly specific to C. jejuni and highly sensitive, as they were one log more sensitive compared to the traditional culture method, with detection thresholds of 9 and 5 copies per reaction, respectively. These assays successfully detected C. jejuni in spiked chicken samples and natural meat samples (chicken, beef, mutton, etc.) and were overall less dependent on expensive equipment, only requiring a fluorescent reader. Their ease of use compared to other nucleic acid amplification-based methods indicates that these assays could be adapted for the rapid, routine surveillance of C. jejuni contamination in food samples, particularly for work done in the field or poorly equipped labs.

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The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset. CCS Concepts •Computing methodologies → Supervised learning by regression; •Applied computing → Environmental sciences;