An agricultural drought index for assessing droughts using a water balance method: a case study in Jilin Province, Northeast China

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed

Anatomical analysis of antebrachial cutaneous nerve distribution pattern and its clinical implications for sensory reconstruction.

This study aimed to reveal the distribution pattern of antebrachial cutaneous nerves and provide a morphological basis for sensory reconstruction during flap transplantation. Forearm specimens containing skin and subcutaneous fat were obtained from 24 upper extremities of 12 adult cadavers. Cutaneous nerves were visualized using modified Sihler's staining. Then the data was used to show the distribution pattern and innervation area of the forearm cutaneous nerve. The anterior branch of lateral antebrachial cutaneous nerve innervates 26% of the medial anterior forearm; the posterior branch innervates 38.21% of the lateral anterior forearm and 24.46% of the lateral posterior forearm. The anterior branch of medial antebrachial cutaneous nerve innervates the medial aspect of the forearm covering 27.67% of the anterior region; the posterior branch the lateral part of the forearm covering 7.67% and 34.75% of the anterior and posterior regions, respectively. The posterior antebrachial cutaneous nerve covers 41.04% of the posterior forearm. Coaptations were found between the branches of these cutaneous nerves. The relatively dense secondary nerve branches were found in the middle 1/3 of the lateral anterior forearm and the middle 1/3 of the medial posterior forearm. The relatively dense tertiary nerve branches were the middle 1/3 and lower 1/3 of the medial anterior forearm. The intradermal nerve branches were the relatively dense in the middle 1/3 of the medial anterior and lateral posterior forearm. The middle 1/3 of the medial and lateral forearm had the relatively dense total nerve branches. These results can be used sensory matching while designing forearm flaps for reconstruction surgeries to obtain improved recovery of sensory

Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features

The research of infrared image small-target detection is of great significance to security monitoring, satellite remote sensing, infrared early warning, and precision guidance systems. However, small infrared targets occupy few pixels and lack color and texture features, which make the detection of small infrared targets extremely challenging. This paper proposes an effective single-stage infrared small-target detection method based on improved FCOS (Fully Convolutional One-Stage Object Detection) and spatio-temporal features. In view of the simple features of infrared small targets and the requirement of real-time detection, based on the standard FCOS network, we propose a lightweight network model combined with traditional filtering methods, whose response for small infrared targets is enhanced, and the background response is suppressed. At the same time, in order to eliminate the influence of static noise points in the infrared image on the detection of small infrared targets, time domain features are added to the improved FCOS network in the form of image sequences, so that the network can learn the spatio-temporal correlation features in the image sequence. Finally, compared with current typical infrared small-target detection methods, the comparative experiments show that the improved FCOS method proposed in this paper had better detection accuracy and real-time performance for infrared small targets

Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features

The research of infrared image small-target detection is of great significance to security monitoring, satellite remote sensing, infrared early warning, and precision guidance systems. However, small infrared targets occupy few pixels and lack color and texture features, which make the detection of small infrared targets extremely challenging. This paper proposes an effective single-stage infrared small-target detection method based on improved FCOS (Fully Convolutional One-Stage Object Detection) and spatio-temporal features. In view of the simple features of infrared small targets and the requirement of real-time detection, based on the standard FCOS network, we propose a lightweight network model combined with traditional filtering methods, whose response for small infrared targets is enhanced, and the background response is suppressed. At the same time, in order to eliminate the influence of static noise points in the infrared image on the detection of small infrared targets, time domain features are added to the improved FCOS network in the form of image sequences, so that the network can learn the spatio-temporal correlation features in the image sequence. Finally, compared with current typical infrared small-target detection methods, the comparative experiments show that the improved FCOS method proposed in this paper had better detection accuracy and real-time performance for infrared small targets

A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields

Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R2 of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R2 of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale

Dynamic analysis of the microbial communities and metabolome of healthy banana rhizosphere soil during one growth cycle

Background The banana-growing rhizosphere soil ecosystem is very complex and consists of an entangled network of interactions between banana plants, microbes and soil, so identifying key components in banana production is difficult. Most of the previous studies on these interactions ignore the role of the banana plant. At present, there is no research on the the micro-ecological environment of the banana planting growth cycle. Methods Based on high-throughput sequencing technology and metabolomics technology, this study analyzed the rhizosphere soil microbial community and metabolic dynamics of healthy banana plants during one growth cycle. Results Assessing the microbial community composition of healthy banana rhizosphere soil, we found that the bacteria with the highest levels were Proteobacteria, Chloroflexi, and Acidobacteria, and the dominant fungi were Ascomycota, Basidiomycota, and Mortierellomycota. The metabolite profile of healthy banana rhizosphere soil showed that sugars, lipids and organic acids were the most abundant, accounting for about 50% of the total metabolites. The correlation network between fungi and metabolites was more complex than that of bacteria and metabolites. In a soil environment with acidic pH, bacterial genera showed a significant negative correlation with pH value, while fungal genera showed no significant negative correlation with pH value. The network interactions between bacteria, between fungi, and between bacteria and fungi were all positively correlated. Conclusions Healthy banana rhizosphere soil not only has a stable micro-ecology, but also has stable metabolic characteristics. The microorganisms in healthy banana rhizosphere soil have mutually beneficial rather than competitive relationships

Roles of TLR3 and RIG-I in Mediating the Inflammatory Response in Mouse Microglia following Japanese Encephalitis Virus Infection

Japanese encephalitis virus (JEV) infection can cause central nervous system disease with irreversible neurological damage in humans and animals. Evidence suggests that overactivation of microglia leads to greatly increased neuronal damage during JEV infection. However, the mechanism by which JEV induces the activation of microglia remains unclear. Toll-like receptor 3 (TLR3) and retinoic acid-inducible gene I (RIG-I) can recognize double-stranded RNA, and their downstream signaling results in production of proinflammatory mediators. In this study, we investigated the roles of TLR3 and RIG-I in the inflammatory response caused by JEV infection in the mouse microglial cell line. JEV infection induced the expression of TLR3 and RIG-I and the activation of extracellular signal-regulated kinase (ERK) and p38 mitogen-activated protein kinase (p38MAPK). Knockdown of TLR3 and RIG-I attenuated activation of ERK, p38MAPK, activator protein 1 (AP-1), and nuclear factor κB (NF-κB). Secretion of TNF-α, IL-6, and CCL-2, which was induced by JEV, was reduced by TLR3 and RIG-I knockdown and inhibitors of phosphorylated ERK and p38MAPK. Furthermore, viral proliferation was increased following knockdown of TLR3 and RIG-I. Our findings suggest that the signaling pathways of TLR3 and RIG-I play important roles in the JEV-induced inflammatory response of microglia

Sesquiterpenes and Monoterpenes from the Leaves and Stems of Illicium simonsii and Their Antibacterial Activity

Two undescribed ether derivatives of sesquiterpenes, 1-ethoxycaryolane-1, 9β-diol (1) and 2-ethoxyclovane-2β, 9α-diol (3), and one new monoterpene glycoside, p-menthane-1α,2α,8-triol-4-O-β-D-glucoside (5), were obtained, together with eight known compounds from the stems and leaves of I. simonsii. Their structures were elucidated by spectroscopic methods. Compounds 1–11 were evaluated for their potency against Staphylococcus aureus and clinical methicillin-resistant S. aureus (MRSA). Among them, compound 3 was weakly active against S. aureus (MIC = 128 μg/mL), and compounds 6 and 7 exhibited good antibacterial activity against S. aureus and MRSA (MICs = 2–8 µg/mL). A primary mechanism study revealed that compounds 6 and 7 could kill bacteria by destroying bacterial cell membranes. Moreover, compounds 6 and 7 were not susceptible to drug resistance development

A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields