Soil moisture-atmosphere feedback dominates land N2 O nitrification emissions and denitrification reduction.

Soil moisture (SM) is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that SM-atmosphere feedbacks dominate the changes in land carbon fluxes. However, the influence of SM-atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. To fill this gap, we used in situ observation coupled with gridded and remote sensing data to analyze N2 O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N2 O fluxes, as the result of SM-atmosphere feedback impact on N fluxes. We found that SM-temperature feedback dominates land N2 O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces SM, which increases the dominant N2 O production nitrifier (containing amoA AOB gene) and decreases the N2 O consumption denitrifier (containing the nosZ gene), consequently will potential increasing N2 O emissions. However, we find that the spatial variations of soil-water availability as a result of the nonlinear response of SM to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N2 O emissions. Our data-driven assessment deepens the understanding of the impact of SM-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between SM and atmospheric temperature when estimating the response of the N2 O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.

A super-resolution scanning algorithm for lensless microfluidic imaging using the dual-line array image sensor.

The lensless optical fluid microscopy is of great significance to the miniaturization, portability and low cost development of cell detection instruments. However, the resolution of the cell image collected directly is low, because the physical pixel size of the image sensor is the same order of magnitude as the cell size. To solve this problem, this paper proposes a super-resolution scanning algorithm using a dual-line array sensor and a microfluidic chip. For dual-line array sensor images, the multi-group velocity and acceleration of cells flowing through the line array sensor are calculated. Then the reconstruction model of the super-resolution image is constructed with variable acceleration. By changing the angle between the line array image sensor and the direction of cell flow, the super-resolution image scanning and reconstruction are achieved in both horizontal and vertical directions. In addition, it is necessary to study the row by row extraction algorithm for cell foreground image. In this paper, the dual-line array sensor is implemented by adjusting the acquisition window of the image sensor with a pixel size of 2.2µm. When the tilt angle is 21 degrees, the equivalent pixel size is 0.79µm, improved 2.8 times, and after de-diffraction its average size error was 3.249%. As the angle decreases, the image resolution is higher, but the amount of information is less. This super-resolution scanning algorithm can be integrated on the chip and used with a microfluidic chip to realize on-chip instrument.

A Quantized CNN-Based Microfluidic Lensless-Sensing Mobile Blood-Acquisition and Analysis System.

This paper proposes a microfluidic lensless-sensing mobile blood-acquisition and analysis system. For a better tradeoff between accuracy and hardware cost, an integer-only quantization algorithm is proposed. Compared with floating-point inference, the proposed quantization algorithm makes a tradeoff that enables miniaturization while maintaining high accuracy. The quantization algorithm allows the convolutional neural network (CNN) inference to be carried out using integer arithmetic and facilitates hardware implementation with area and power savings. A dual configuration register group structure is also proposed to reduce the interval idle time between every neural network layer in order to improve the CNN processing efficiency. We designed a CNN accelerator architecture for the integer-only quantization algorithm and the dual configuration register group and implemented them in field-programmable gate arrays (FPGA). A microfluidic chip and mobile lensless sensing cell image acquisition device were also developed, then combined with the CNN accelerator to build the mobile lensless microfluidic blood image-acquisition and analysis prototype system. We applied the cell segmentation and cell classification CNN in the system and the classification accuracy reached 98.44%. Compared with the floating-point method, the accuracy dropped by only 0.56%, but the area decreased by 45%. When the system is implemented with the maximum frequency of 100 MHz in the FPGA, a classification speed of 17.9 frames per second (fps) can be obtained. The results show that the quantized CNN microfluidic lensless-sensing blood-acquisition and analysis system fully meets the needs of current portable medical devices, and is conducive to promoting the transformation of artificial intelligence (AI)-based blood cell acquisition and analysis work from large servers to portable cell analysis devices, facilitating rapid early analysis of diseases.