PurposeTo investigate if apparent diffusion coefficient (ADC) values within primary central nervous system lymphoma correlate with cellularity and proliferative activity in corresponding histological samples.Materials and MethodsEcho-planar diffusion-weighted magnetic resonance images obtained from 21 patients with primary central nervous system lymphoma were reviewed retrospectively. Regions of interest were drawn on ADC maps corresponding to the contrast enhancing parts of the tumors. Biopsies from all 21 patients were histologically analyzed. Nuclei count, total nuclei area and average nuclei area were measured. The proliferation index was estimated as Ki-67 positive nuclei divided by total number of nuclei. Correlations of ADC values and histopathologic parameters were determined statistically.ResultsKi-67 staining revealed a statistically significant correlation with ADCmin (r = -0.454, p = 0.038), ADCmean (r = -0.546, p = 0.010) and ADCmax (r = -0.515, p = 0.017). Furthermore, ADCmean correlated in a statistically significant manner with total nucleic area (r = -0.500, p = 0.021).ConclusionLow ADCmin, ADCmean and ADCmax values reflect a high proliferative activity of primary cental nervous system lymphoma. Low ADCmean values—in concordance with several previously published studies—indicate an increased cellularity within the tumor.

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for 3 months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than 1 year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first 3 months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. For most of the O3 sensors a decrease in sensitivity was encountered over time, clearly impacting the data quality. The NO2 low-cost sensors in our configuration exhibited better performance but did not reach the accuracy level of NO2 diffusion tubes (∼ 2 ppb for uncorrected 14-day average concentrations). Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly to changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models also include time-dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high-quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.

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Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for three months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than one year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first three months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. Hence, the low-cost sensors in our configuration do not reach the accuracy level of NO2 diffusion tubes. Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly as changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models include also time dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.

Abstract. More than 300 non-dispersive infrared (NDIR) CO2 low-cost sensors labelled as LP8 were integrated into sensor units and evaluated for the purpose of long-term operation in the Carbosense CO2 sensor network in Switzerland. Prior to deployment, all sensors were calibrated in a pressure and climate chamber and in ambient conditions co-located with a reference instrument. To investigate their long-term performance and to test different data processing strategies, 18 sensors were deployed at five locations equipped with a reference instrument after calibration. Their accuracy during 19 to 25 months deployment was between 8 and 12 ppm. This level of accuracy requires careful sensor calibration prior to deployment, continuous monitoring of the sensors, efficient data filtering, and a procedure to correct drifts and jumps in the sensor signal during operation. High relative humidity (> ∼85 %) impairs the LP8 measurements, and corresponding data filtering results in a significant loss during humid conditions. The LP8 sensors are not suitable for the detection of small regional gradients and long-term trends. However, with careful data processing, the sensors are able to resolve CO2 changes and differences with a magnitude larger than about 30 ppm. Thereby, the sensor can resolve the site-specific CO2 signal at most locations in Switzerland. A low-power network (LPN) using LoRaWAN allowed for reliable data transmission with low energy consumption and proved to be a key element of the Carbosense low-cost sensor network.