Journal: bioRxiv

Article Title: High speed functional imaging with a microfluidics-compatible open-top light-sheet microscope enabled by model predictive control of a tunable lens

doi: 10.1101/2025.07.23.666439

Figure Lengend Snippet: Open-top light-sheet microscope with electrically tunable lens (ETL) remote focusing under model predictive control. (a) Scan methods for volumetric light-sheet imaging. (i) In the simplest case, a sample is scanned through a static sheet. (ii) An actuated objective lens can follow a scanning light-sheet, which is faster than (i) but inertia limited and can cause problems with water immersion. (iii) Remote focusing optically shifts the focal plane using an active element like an ETL (b). This setup has less inertia and no moving parts at the sample. (c) Long-working distance open-top imaging is achieved using an asymmetric pair of objective lenses coupled with a water immersion fitting. This enables unobstructed imaging across a water-matched barrier such as FEP (above). (d) Maximum intensity projections along the Z (top) and Y (bottom) axes of a worm expressing a pan-neuronal nuclear-localized fluorescent protein positioned in a microfluidic channel as shown in (c). Scale bar 20 µ m. (e) Fast actuation of an ETL induces high frequency oscillation which slows response time. Performance is improved by using model predictive control to optimize drive signals. The controller iteratively optimizes the input signal to the ETL by minimizing simulated output error while obeying system constraints.

Article Snippet: Finally, we used the generated model to construct a model predictive controller using the MATLAB Model Predictive Control toolkit.

Techniques: Microscopy, Control, Imaging, Expressing

Journal: PLoS ONE

Article Title: Wheat rust epidemics damage Ethiopian wheat production: A decade of field disease surveillance reveals national-scale trends in past outbreaks

doi: 10.1371/journal.pone.0245697

Figure Lengend Snippet: Blue: FAO data; grey: wheat stem rust; yellow: wheat stripe rust; red/brown: wheat leaf rust. ( A-D ) show national wheat production statistics of Ethiopia obtained from FAOSTAT . ( E-H ) illustrate our estimates of the damage caused by wheat rusts during years 2010–2019. ( E ) shows the estimated area infected with wheat rusts; ( F ) shows the estimated fraction of yield lost due to wheat rusts; ( G ) shows the approximate total financial loss caused by wheat rusts; and ( H ) shows the approximate loss relative to the national total financial value of wheat produce at market price per year. As no FAO statistics were available for year 2019 at the time of this study (last checked on the 20 th of June 2020), we use the mean of years 2010–2018 as input for our estimates of yield losses in year 2019.

Article Snippet: The methods for detailed analysis of past outbreak patterns include: calculation of the Morans-I statistic for testing spatial autocorrelation and a ‘hotspot’ analysis based on the Getis-Ord Gi* statistic (calculated using the R package spdep [ ]) ; Chi-Square tests, regression analyses and a Receiver Operating Characteristic (ROC) analysis for testing the performance of simple empirical models for predicting wheat rusts in Ethiopia (using the statistics and machine learning toolbox in MATLAB [ ]).

Techniques: Infection

Journal: PLoS ONE

Article Title: Wheat rust epidemics damage Ethiopian wheat production: A decade of field disease surveillance reveals national-scale trends in past outbreaks

doi: 10.1371/journal.pone.0245697

Figure Lengend Snippet: (A-B) wheat stripe rust; (C-D) wheat stem rust; (E-F) wheat leaf rust. Two simple logistic models were used to predict wheat rust occurrence: a temporal model (model 1, see ) predicting wheat rust occurrence as a function of the time since the start of the main wheat season and a spatiotemporal model (model 2, see ), predicting wheat rust occurrence as a function of the time since the start of the main season and the location in Ethiopia (latitude, longitude, and altitude). Model performance was tested by fitting the models to training data from all but 1 year of surveys and then conducting a ROC analysis for testing the performance of the fitted model against the data from the year not used for fitting (repeated for every year). The upper row shows the resulting AUC score of both models for each year and all rusts. The bottom row shows the corresponding ROC curves of one exemplar year. For the analysis illustrated here all survey entries with non-zero disease incidence were classified as “diseased” and all surveys with zero incidence were classified as “healthy”. The testing procedure was also conducted using an alternative dichotomization scheme classifying all surveys with moderate or high incidence values as “diseased” and all surveys with zero or low incidence as “healthy” (see ).

Article Snippet: The methods for detailed analysis of past outbreak patterns include: calculation of the Morans-I statistic for testing spatial autocorrelation and a ‘hotspot’ analysis based on the Getis-Ord Gi* statistic (calculated using the R package spdep [ ]) ; Chi-Square tests, regression analyses and a Receiver Operating Characteristic (ROC) analysis for testing the performance of simple empirical models for predicting wheat rusts in Ethiopia (using the statistics and machine learning toolbox in MATLAB [ ]).

Techniques: