An Integrated Plasmo‐Photoelectronic Nanostructure Biosensor Detects an Infection Biomarker Accompanying Cell Death in Neutrophils

Bacterial infections leading to sepsis are a major cause of deaths in the intensive care unit. Unfortunately, no effective methods are available to capture the early onset of infectious sepsis near the patient with both speed and sensitivity required for timely clinical treatment. To fill the gap, the authors develop a highly miniaturized (2.5 × 2.5 µm2) plasmo‐photoelectronic nanostructure device that detected citrullinated histone H3 (CitH3), a biomarker released to the blood circulatory system by neutrophils. Rapidly detecting CitH3 with high sensitivity has the great potential to prevent infections from developing life‐threatening septic shock. To this end, the author’s device incorporates structurally engineered arrayed hemispherical gold nanoparticles that are functionalized with high‐affinity antibodies. A nanoplasmonic resonance shift induces a photoconduction increase in a few‐layer molybdenum disulfide (MoS2) channel, and it provides the sensor signal. The device achieves label‐free detection of serum CitH3 with a 5‐log dynamic range from 10−4 to 101 ng mL and a sample‐to‐answer time <20 min. Using this biosensor, the authors longitudinally measure the dynamic CitH3 profiles of individual living mice in a sepsis model at high resolution over 12 hours. The developed biosensor may be poised for future translation to personalized management of systemic bacterial infections.The lack of an appropriate biosensing technology to detect the early onset of bacterial infections has prohibited timely clinical treatment of sepitc shock. This article presents a highly miniaturized plasmo‐photoelectronic device incorporating high‐affinity antibody‐conjugated hemispherical gold nanoparticles and a few‐layer molybdenum disulfide (MoS2) photoconductive channel to detect a blood biomarker released by neutrophils with high speed and sensitivity.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152883/1/smll201905611-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152883/2/smll201905611_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152883/3/smll201905611.pd

LncRNA SENCR suppresses abdominal aortic aneurysm formation by inhibiting smooth muscle cells apoptosis and extracellular matrix degradation

Abdominal aortic aneurysm (AAA) is a progressive chronic dilatation of the abdominal aorta without effective medical treatment. This study aims to clarify the potential of long non-coding RNA SENCR as a treatment target in AAA. Angiotensin II (Ang-II) was used to establish AAA model in vitro and in vivo. Reverse transcription quantitative PCR and western blot were performed to measure the expression of SENCR and proteins, respectively. Annexin V-FITC/PI double staining was carried out to detect the apoptotic rate in vascular smooth muscle cells (VSMCs), and cell apoptosis in aortic tissues was determined by TUNEL staining. Besides, hematoxylin and eosin and Elastica van Gieson staining were performed for histological analysis of aortic tissues. SENCR was downregulated in AAA tissues and Ang-II-stimulated VSMCs. Overexpression of SENCR could inhibit Ang-II-induced VSMC apoptosis, while inhibition of SENCR facilitated Ang-II-induced VSMC apoptosis. Moreover, the expression of matrix metalloproteinase (MMP)-2 and MMP-9 in Ang-II-induced VSMCs was reduced following SENCR overexpression, while tissue inhibitor of metalloproteinases 1 (TIMP-1) expression was increased. In vivo, overexpression of SENCR improved the pathological change in aortic tissues and the damage in arterial wall elastic fibers induced by Ang-II, as well as suppressed Ang-II-induced cell apoptosis and extracellular matrix degradation. Overall, SENCR was decreased in AAA. Overexpression of SENCR inhibited AAA formation via inhibition of VSMC apoptosis and extracellular matrix degradation. We provided a reliable evidence for SENCR acting as a potential target for AAA treatment

Development of a Phenology-Based Method for Identifying Sugarcane Plantation Areas in China Using High-Resolution Satellite Datasets

Sugarcane is an important sugar and biofuel crop with high socio-economic importance, and its planted area has increased rapidly in recent years. China is the world&rsquo;s third or fourth sugarcane producer. However, to our knowledge, no study has investigated the mapping of sugarcane cultivation areas across entire China. In this study, we developed a phenology-based method to identify sugarcane plantations in China at 30-m spatial resolution from 2016&ndash;2020 using the time-series of Landsat and Sentinel-1/2 images derived from Google Earth Engine (GEE) platform. The method worked by comparing the phenological similarity in normalized difference vegetation index (NDVI) series between unknown pixels and sugarcane samples. The phenological similarity was assessed using the time-weighted dynamic time warping method (TWDTW), which has less sensitivity to training samples than machine learning methods and therefore can be easily applied to large areas with limited samples. More importantly, our method introduced multiple and moving time standard phenological curves of sugarcane to the TWDTW by fully considering the variable crop life-cycle of sugarcane, particularly its long harvest season spanning from December to March of the following year. Validations showed the method performed well in 2019, with overall accuracies of 93.47% and 92.74% for surface reflectance (SR) and top of atmosphere reflectance (TOA) data, respectively. The sugarcane maps agreed well with the agricultural statistical areas from 2016&ndash;2020. The mapping accuracies using TOA data were comparable to SR data in 2019&ndash;2020, but outperformed SR data in 2016&ndash;2018 when SR data had lower availability on GEE. The sugarcane maps produced in this study can be used to monitor growing conditions and production of sugarcane and, therefore, can benefit sugarcane management, sustainable sugarcane production, and national food security

High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016&ndash;2020

Winter cereals, including wheat, rye, barley, and triticale, are important food crops, and it is crucial to identify the distribution of winter cereals for monitoring crop growth and predicting yield. The production and plating area of winter cereals in Europe both contribute 12.57% to the total global cereal production and plating area in 2020. However, the distribution maps of winter cereals with high spatial resolution are scarce in Europe. Here, we first used synthetic aperture radar (SAR) data from Sentinel-1 A/B, in the Interferometric Wide (IW) swath mode, to distinguish rapeseed and winter cereals; we then used a time-weighted dynamic time warping (TWDTW) method to discriminate winter cereals from other crops by comparing the similarity of seasonal changes in the Normalized Difference Vegetation Index (NDVI) from Landsat and Sentinel-2 images. We generated winter cereal maps for 2016&ndash;2020 that cover 32 European countries with 30 m spatial resolution. Validation using field samples obtained from the Google Earth Engine (GEE) platform show that the producer&rsquo;s and user&rsquo;s accuracies are 91% &plusmn; 7.8% and 89% &plusmn; 10.3%, respectively, averaged over 32 countries in Europe. The winter cereal map agrees well with agricultural census data for planted winter cereal areas at municipal and country levels, with the averaged coefficient of determination R2 as 0.77 &plusmn; 0.15 for 2016&ndash;2019. In addition, our method can identify the distribution of winter cereals two months before harvest, with an overall accuracy of 88.4%, indicating that TWDTW is an effective method for timely crop growth monitoring and identification at the continent level. The winter cereal maps in Europe are available via an open-data repository

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield

Estimating Yield and Economic Losses Induced by Ozone Exposure in South China Based on Full-Coverage Surface Ozone Reanalysis Data and High-Resolution Rice Maps

Surface ozone (O3) pollution is an emerging environmental abiotic stress that poses substantial risks to crop yield losses and food security worldwide, and especially in China. However, the O3-induced detrimental effects on double-season rice have rarely been investigated at large scales and over relatively long temporal spans. In this study, we estimated the crop production reductions and associated economic losses for double-season rice across southern China during 2013–2019, using a high spatial resolution surface ozone reanalysis dataset and rice distribution maps, and county-level production data, in combination with a locally derived exposure-response function for rice. Results show that AOT40 (cumulative hourly O3 exposure above 40 ppb) presented generally increasing trends over growing seasons in 2013–2019, spanning from 4.0 to 7.1 ppm h and 6.1 to 10.5 ppm h for double-early rice and double-late rice, respectively. Moreover, O3-induced relative yield losses ranged from 4.0% to 6.6% for double-early rice and 6.3% to 11.1% for double-late rice. Over the seven years, ambient O3 exposure resulted in crop production losses of 1951.5 × 104 tons and economic losses of 8,081.03 million USD in total. To combat the O3-induced agricultural risks, measures such as stringent precursors emission reductions and breeding O3-resistant cultivars should be continuously implemented in the future

High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016–2020

Winter cereals, including wheat, rye, barley, and triticale, are important food crops, and it is crucial to identify the distribution of winter cereals for monitoring crop growth and predicting yield. The production and plating area of winter cereals in Europe both contribute 12.57% to the total global cereal production and plating area in 2020. However, the distribution maps of winter cereals with high spatial resolution are scarce in Europe. Here, we first used synthetic aperture radar (SAR) data from Sentinel-1 A/B, in the Interferometric Wide (IW) swath mode, to distinguish rapeseed and winter cereals; we then used a time-weighted dynamic time warping (TWDTW) method to discriminate winter cereals from other crops by comparing the similarity of seasonal changes in the Normalized Difference Vegetation Index (NDVI) from Landsat and Sentinel-2 images. We generated winter cereal maps for 2016–2020 that cover 32 European countries with 30 m spatial resolution. Validation using field samples obtained from the Google Earth Engine (GEE) platform show that the producer’s and user’s accuracies are 91% ± 7.8% and 89% ± 10.3%, respectively, averaged over 32 countries in Europe. The winter cereal map agrees well with agricultural census data for planted winter cereal areas at municipal and country levels, with the averaged coefficient of determination R2 as 0.77 ± 0.15 for 2016–2019. In addition, our method can identify the distribution of winter cereals two months before harvest, with an overall accuracy of 88.4%, indicating that TWDTW is an effective method for timely crop growth monitoring and identification at the continent level. The winter cereal maps in Europe are available via an open-data repository