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The large yellow croaker (Larimichthys crocea) is an economically important marine species with the highest annual production among the farmed marine fishes in China. However, the aquaculture industry of this species is suffering from severe problems that include weakened disease resistance, decreased growth rate, and reduced meat quality due to frequent inbreeding. Genome editing, which has a huge potential for solving those problems by introducing favorable genetic changes, is not yet available for the large yellow croaker. Here, we pioneered the techniques of embryo microinjection and genome editing using the CRISPR/Cas9 system in this species. Recombinant plasmids encoding green fluorescent protein (GFP) were introduced into the fertilized eggs of L. crocea by microinjection before the chorion had hardened. A high survival rate (40%) and GFP-positive larvae rate (81.8%) were achieved, indicating that the microinjection technique in L. crocea was successfully established. On this basis, Cas9 mRNA and sgRNA targeting the tyrosinase a gene in L. crocea (Lc-tyra) were co-injected into fertilized eggs of L. crocea. Mutant individuals with insertion and deletion mutations of Lc-tyra were detected. These results indicated that the CRISPR/Cas9-based genome editing technology established herein could efficiently introduce mutations at a specific site in the L. crocea genome. This method provides the potential for genetic improvement and functional genomic study in this species. This is the first report on effective CRISPR/Cas9-based genome editing in L. crocea.

Granulocyte colony-stimulating factor (GCSF) is a growth factor that drives the proliferation and differentiation of granulocytes and monocytes/macrophages. Currently, two copies of GCSF, named GCSFa and GCSFb, have been identified in teleost fish, but data on the functions and signal pathways of these fish GCSFs are still limited. In the present study, a GCSFa homologue (LcGCSFa) was identified from large yellow croaker (Larimichthys crocea). The open reading frame (ORF) of LcGCSFa is 636 bp long and encodes a protein of 211 amino acids (aa), with a 19-aa signal peptide and a typical IL-6 domain, conserved in fish GCSF sequences. The phylogenetic analysis showed that LcGCSFa clustered with other fish GCSFa homologues. LcGCSFa was constitutively expressed in all tissues tested and significantly up-regulated in head kidney and spleen by Vibrio alginolyticus or poly(I:C). LcGCSFa transcripts were also detected in primary head kidney leucocytes (PKL), primary head kidney macrophages (PKM), and primary head kidney granulocytes (PKG), and significantly up-regulated in PKL and PKG by LPS or poly(I:C). These data indicated that LcGCSFa may be involved in the immune responses induced by bacterium and virus. The recombinant LcGCSFa protein (rLcGCSFa) produced in Pichia pastoris promoted the proliferation of PKL both in vivo and in vitro. Furthermore, rLcGCSFa significantly increased both transcription and phosphorylation levels of the signal transducers and activators of transcription (STAT) proteins (LcSTAT3 and LcSTAT5) in PKL, which are required for the GCSF-dependent proliferation. These results showed that LcGCSFa may promote the proliferation of PKL via the activation of LcSTAT3 and LcSTAT5, suggesting a conserved role across vertebrate GCSFs.

Granulocyte colony-stimulating factor (GCSF) is a pleiotropic cytokine that plays a key role in regulation of hematopoiesis, innate and adaptive immune responses in mammals. However, bioactivity of GCSF in teleost fish remains largely unknown. In this study, a GCSFb homologue from large yellow croaker (Larimichthys crocea) (LcGCSFb) was cloned by RACE-PCR techniques. The open reading frame (ORF) of LcGCSFb is 603 bp long and encoded a protein precursor of 200 amino acids (aa), with a 19-aa signal peptide and a 181-aa mature peptide. In healthy fish, the LcGCSFb was constitutively expressed in all examined tissues, with the highest levels in mucous tissues, such as gills, intestine, and stomach. Its transcripts in head kidney, spleen, gills, intestine and stomach were significantly induced by Vibrio alginolyticus challenge. LcGCSFb transcripts were also detected in primary head kidney leukocytes (PKL), primary head kidney macrophages (PKM), primary head kidney granulocytes (PKG) and head kidney cell line (LYCK), and markedly upregulated by inactivated V. alginolyticus. These data suggested that LcGCSFb may play a role in immune response against bacterial infection. In vivo administration of recombinant LcGCSFb protein (rLcGCSFb) significantly upregulated the expression levels of the inflammatory cytokines (IL-6 and TNFα), and transcription factor C/EBPβ, which is required for proliferation of neutrophils. Furthermore, rLcGCSFb showed an ability to strengthen the phagocytosis of PKL in vitro. Taken together, LcGCSFb may be involved in antibacterial immunity via promoting the inflammatory response and the phagocytic activity of leukocytes. To our knowledge, this is the first report on immunoregulatory roles of GCSF in teleost.

Nowadays, machine learning (ML) methods have gained much attention and have been applied in some important related applications in earth science field, including observation data mining, geoscience image recognition, remote sensing image classification and so on. These ML-based applications play important roles in our daily life. However, in meteorological and oceanographic forecast, numerical is still the most popular method. Although researchers have proposed some ML-based prediction methods to overcome the shortcomings of numerical weather forecast methods, the explainability for the forecast result of artificial intelligence (AI) technology is still not as good as numerical weather forecast methods. Therefore, in this paper, we propose a random forest based adjusting method, which introduces AI technology to correct wind prediction results of numerical model. The proposed adjusting method greatly improves the accuracy of forecast results. Furthermore, the physical meanings of parameters in the numerical model are retained in adjusting results. From experimental evaluations, it is obvious that the root mean square error (RMSE) of each feature is reduced greatly. In detail, the average RMSE of 10m wind decreased by more than 45%, and the average RMSE of sea level pressure decreased by more than 50%. It is worth noting that the improvement here is the average of all forecasts for whole region within 7 days.

Transcription factor c-Jun is a member of AP-1 transcription complex that can be induced by various pathogens and plays a broad regulatory role in vertebrate immune response. In this study, the complete c-Jun cDNA of large yellow croaker Larimichthys crocea (Lcc-Jun) was cloned, whose open reading frame (ORF) is 984 bp long and encodes a protein of 327 amino acids (aa). The deduced Lcc-Jun protein contains three highly conserved domains, a transactivation domain (TAD, Met1-His118), a DNA binding domain (DBD, Lys218-Arg243), and a Leucine zipper domain (LZD, Leu271-Leu299), as found in other specie c-Jun. Lcc-Jun was constitutively expressed in all examined tissues, with the higher levels in blood, heart, and head kidney. Its transcripts were not only induced in spleen and head kidney by poly (I: C) or LPS, but also up-regulated in primary head kidney leukocytes (PKL), macrophages (PKM), and granulocytes (PKG), suggesting that Lcc-Jun may be involved in immune responses induced by poly (I: C), a viral mimic, and LPS, a Gram-negative bacterial component. Overexpression of Lcc-Jun in PKL increased the expression of cytokines and transcription factors involved in T helper 1 (Th1: TNF-α, IFN-γ, and T-bet) and Th2 (IL-4/13 A/B, IL-6, and GATA3) cell development and differentiation, suggesting that Lcc-Jun may play a role in regulation of Th1/Th2 cell response. These results therefore led us to suggest that the c-Jun-mediated signaling pathways may have an important immune-modulatory function in teleost fish.

To explore the spatial variation of source-specific ecological risks and identify critical sources of heavy metals in road dust, 36 road dust samples collected in Beijing in March 2017 were analyzed for heavy metals. A new method that takes into consideration the heavy-metal toxic response and is flexible to changes in the number of calculated heavy metals, called the Nemerow integrated risk index (NIRI), was developed for ecological risk assessment. The NIRI indicated that heavy metals posed considerable to high risks at the majority of sites, and 22 % of the sites suffered extreme risk in spring (NIRI > 320). Four main sources were identified based on positive matrix factorization (PMF): traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Owing to the lower toxic response factors of representative heavy metals of fuel combustion than those of other sources, although fuel combustion had the highest contribution (34.21 %) to heavy metals in spring, it only contributed 5.57 % to ecological risks. Critical sources and critical source areas were determined by considering the contributions to both heavy metals and ecological risks. The use of pesticide and fertilizer and traffic-related exhaust were identified as critical sources of heavy metals in spring. Source-specific ecological risks and critical sources of heavy metals changed with the changing seasons, which suggests that different strategies should be adopted in different seasons.

Coffee sequential flowering events detection and flower density estimation is essential to predict the ripening time and yield of coffee. In this study, we detected coffee flowering events automatically based on estimated flower densities in high spatial time-series digital images, using a multi-scale region based flower segmentation method. The study area is a coffee plantation in Lujiangba in the Yunnan Province of China. There are 5 flowering events in the coffee flowering period. A digital camera obtained 24 RGB images at each shooting time of day (8:00, 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00, 16:00, 17:00, 17:30) by automatic adjusting the sensor with 3 depression angles and 8 azimuth angles during the coffee flowering period from March 1st to May 31st. For segment the flowers in one image, multi-scale regions were primarily generated by equally-sized superpixel segmentation and subsequent superpixel merging process. Next, the feature vectors of each region were extracted by color moments (CM) operator and local binary patterns (LBP) operator. Afterwards, the support vector machine (SVM) classifier trained on these features was applied to recognize the flower regions. Thus, the percentage of flower pixels referred as flower proportion (FP), which can estimate the image-based flower density, was calculated in preparation for detecting flowering events of time-series images. In this stage, coefficients of Recall, Precision and intersection over union (IoU) were employed to evaluate the performance of segmentation methods on 14 test images at threes depression angles and then the best flower segmentation algorithm and the optimal angle can be determined. In the stage of flowering events detection, the FPs of different shooting time multitemporal images under the optimal depression angle were calculated and plotted. Then, a threshold of FP, K, was selected to determine whether an image is on a flowering day. To determine the best shooting time for flowering events detection, Recall, Precision and IoU were also employed to evaluate the performance of time-series images shot at 11 time of day for flowering day detection. The results show that the images shot at the depression angle of 77.5 degree is the optimal depression angle for flower segmentation and meanwhile our proposed method achieves the best performance with Recall, Precision and IoU of 84.89%, 74.83% and 65.46% respectively. In the test of flowering day detection of 11 shooting time multitemporal images, the time-series images shot at 13:00 is superior to the time-series images shot at other time of day, with Recall of 65.00%, Precision of 100% and IoU of 65.00% when the K set as 0.4%. Meanwhile, all flowering events can be detected except the fifth event which has few flowers and the FPs of time-series images can correctly indicate the flower densities of each events. In conclusion, our approach can estimate the image-based flower density and detect the coffee sequential flowering events in small fields, so the results can be used for coffee fruit maturity prediction and yield estimation.

Sugarcane is one of the most important economic crops in south China. It's of practical value to quickly assess the leaf area index (LAI) of sugarcane and evaluate its growing state. Field-based or in situ measurements are very labor intensive and time consuming, while satellite images are relatively coarse and unsuitable for real-time crop monitoring, not to mention the cloud-prone climate in south China. In this study, an automatic image processing technique is proposed for the estimation of sugarcane LAI using digital images. The test site is a sugarcane plantation in Guangxi province, southern China. In our experiment, a digital video camera was fixed on a 6.0 m pole to acquire sugarcane images during the entire sugarcane growing season. Reference data were also collected through field-based LAI measurement using a portable handheld leaf area meter. The daily acquired sugarcane images were processed in a MATLAB environment and the different color vegetation indices were extracted from the time series digital images. The linear relationship between different color vegetation indices and field-measured sugarcane LAI values were established. The color vegetation indices with the highest correlation coefficients with field-measured LAI were chosen to fit and test the model against the ground-measured LAI values. Based on the root mean square error (RMSE) and coefficient of determination R2 between field-measured LAI and model-predicted LAI, we concluded that the most suitable color index for sugarcane LAI estimation during the complete growth period was G-B, and the most appropriate image shooting time for use in the construction of the estimation model for sugarcane LAI was confirmed and also proposed for further studies.