Satellite estimation of suspended particle types using a backscattering efficiency-based model in the marginal seas.

The particle composition of suspended matter provides crucial information for a deeper understanding of marine biogeochemical processes and environmental changes. Particulate backscattering efficiency (Qbbe(λ)) is critical to understand particle composition, and a Qbbe(λ)-based model for classifying particle types was proposed. In this study, we evaluated the applicability of the Qbbe(λ)-based model to satellite observations in the shallow marginal Bohai and Yellow Seas. Spatiotemporal variations of the particle types and their potential driving factors were studied. The results showed that the Qbbe(λ) products generated from Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua agreed well with the in situ measured values, with determination coefficient, root mean square error, bias, and mean absolute percentage error of 0.76, 0.007, 16.5%, and 31.0%, respectively. This result verifies the satellite applicability of the Qbbe(λ)-based model. Based on long-term MODIS data, we observed evident spatiotemporal variations of the Qbbe(λ), from which distinct particle types were identified. Coastal waters were often dominated by minerals, with high Qbbe(λ) values, though their temporal changes were also observed. In contrast, waters in the offshore regions showed clear changes in particle types, which shifted from organic-dominated with low Qbbe(λ) levels in summer to mineral-dominated with high Qbbe(λ) values in winter. We also observed long-term increasing and decreasing trends in Qbbe(λ) in some regions, indicating a relative increase in the proportions of mineral and organic particles in the past decades, respectively. These spatiotemporal variations of Qbbe(λ) and particle types were probably attributed to sediment re-suspension related to water mixing driven by wind and tidal forcing, and to sediment load associated with river discharge. Overall, the findings of this study may provide valuable proxies for better studying marine biogeochemical processes, material exchanges, and sediment flux.

Remote sensing detection of seagrass distribution in a marine lagoon (Swan Lake), China.

Seagrass, a submerged flowering plant, is widely distributed in coastal shallow waters and plays a significant role in maintaining marine biodiversity and carbon cycles. However, the seagrass ecosystem is currently facing degradation, necessitating effective monitoring. Satellite remote sensing observations offer distinct advantages in spatial coverage and temporal frequency. In this study, we focused on a marine lagoon (Swan Lake), located in the Shandong Peninsula of China which is characterized by a large and typical seagrass population. We conducted an analysis of remote sensing reflectance of seagrass and other objectives using a comprehensive Landsat satellite dataset spanning from 2002 to 2022. Subsequently, we constructed Seagrass Index I (SSI-I) and Seagrass Index II (SSI-II), and used them to develop a stepwise model for seagrass detection from Landsat images. Validation was performed using in situ acoustic survey data and visual interpretation, revealing the good performance of our model with an overall accuracy exceeding 0.90 and a kappa coefficient around 0.80. The long-term analysis (2002-2022) of the seagrass distribution area in Swan Lake, generated from Landsat data using our model, indicated that the central area of Swan Lake sustains seagrass for the longest duration. Seagrass in Swan Lake exhibits a regular seasonal variation, including seeding in early spring, growth in spring-summer, maturation in the middle of summer, and shrinkage in autumn. Furthermore, we observed an overall decreasing trend in the seagrass area over the past 20 years, while occasional periods of seagrass restoration were also observed. These findings provide crucial information for seagrass protection, marine blue carbon studies, and related endeavors in Swan Lake. Moreover, our study offers a valuable alternative approach that can be implemented for seagrass monitoring using satellite observations in other coastal regions.

A global satellite observation of phytoplankton taxonomic groups over the past two decades.

Marine phytoplankton fuel the oceanic biotic chain, determine the carbon sequestration levels, and are crucial for the global carbon cycle and climate change. In the present study, we show a near-two-decadal (2002-2022) spatiotemporal distribution of global phytoplankton abundance, proxy as dominant phytoplankton taxonomic groups (PTGs), with a newly developed remote sensing model. Globally, six chief PTGs, namely chlorophytes (~26%), diatoms (~24%), haptophytes (~15%), cryptophytes (~10%), cyanobacteria (~8%), and dinoflagellates (~3%), explain most of the variation (~86%) in phytoplankton assemblages. Spatially, diatoms generally dominate high latitudes, marginal seas, and coastal upwelling zones, whereas chlorophytes and haptophytes control the open oceans. Satellite observations reveal a gentle multi-annual trend of the PTGs in the major oceans, indicative of roughly "unchanged" conditions on the total biomass or compositions of the phytoplankton community. Jointly, "changed" status applies to a short-term (seasonal) timescale: (1) Fluctuations of PTGs exhibit different amplitudes among different subregions, together with a general rule-more intense vibration in the Northern Hemisphere and polar oceans than other zones; (2) diatoms and haptophytes vary more dramatically than other PTGs in a global-scale scope. These findings provide a clear picture of the global phytoplankton community composition and can improve our understanding of their state and further analysis of marine biological processes.

Altered Brain Function in Pediatric Patients With Complete Spinal Cord Injury: A Resting-State Functional MRI Study.

BACKGROUND: Injury to the spinal cord of children may cause potential brain reorganizations, affecting their rehabilitation. However, the specific functional alterations of children after complete spinal cord injury (CSCI) remain unclear. PURPOSE: To explore the specific functional changes in local brain and the relationship with clinical characteristics in pediatric CSCI patients, clarifying the impact of CSCI on brain function in developing children. STUDY TYPE: Prospective. SUBJECTS: Thirty pediatric CSCI patients (7.83 ± 1.206 years) and 30 age-, gender-matched healthy children as controls (HCs) (8.77 ± 2.079 years). FIELD STRENGTH/SEQUENCE: 3.0 T/Resting-state functional MRI (rs-fMRI) using echo-planar-imaging (EPI) sequence. ASSESSMENT: Amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were used to characterize regional neural function. STATISTICAL TESTS: Two-sample t-tests were used to compare the ALFF, fALFF, ReHo values of the brain between pediatric CSCI and HCs (voxel-level FWE correction, P < 0.05). Spearman correlation analyses were performed to analyze the associations between the ALFF, fALFF, ReHo values in altered regions and the injury duration, sensory motor scores of pediatric CSCI patients (P < 0.05). Then receiver operating characteristic (ROC) analysis was conducted to identify possible sensitive imaging indicators for clinical therapy. RESULTS: Compared with HCs, pediatric CSCI showed significantly decreased ALFF in the right postcentral gyrus (S1), orbitofrontal cortex, and left superior temporal gyrus (STG), increased ALFF in bilateral caudate nucleus, thalamus, middle cingulate gyrus, and cerebellar lobules IV-VI, and increased ReHo in left cerebellum Crus II and Brodmann area 21. The ALFF value in the right S1 negatively correlated with the pinprick and light touch sensory scores of pediatric CSCI. When the left STG was used as an imaging biomarker for pediatric CSCI, it achieved the highest area under the curve of 0.989. CONCLUSIONS: These findings may provide potential neural mechanisms for sensory motor and cognitive-emotional deficits in children after CSCI. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 5.

A multiple-step screening protocol to identify norepinephrine and dopamine reuptake inhibitors for depression.

Depression severely impairs the health of people all over the world. Cognitive dysfunction due to depression has resulted in a severe economic burden to family and society induced by the reduction of social functioning of patients. Norepinephrine-dopamine reuptake inhibitors (NDRIs) targeted with the human norepinephrine transporter (hNET) and distributed with the human dopamine transporter (hDAT) simultaneously treat depression and improve cognitive function, and they effectively prevent sexual dysfunction and other side effects. Because many patients continue to poorly respond to NDRIs, it is urgent to discover novel NDRI antidepressants that do not interfere with cognitive function. The aim of this work was to selectively identify novel NDRI candidates acting against hNET and hDAT from large compound libraries by a comprehensive strategy integrating support vector machine (SVM) models, ADMET, molecular docking, in vitro binding assays, molecular dynamics simulation, and binding energy calculation. First, 6522 compounds that do not inhibit the human serotonin transporter (hSERT) were obtained by SVM models of hNET, hDAT, and non-target hSERT with similarity analyses from compound libraries. ADMET and molecular docking were then used to identify compounds that could potently bind to the hNET and hDAT with satisfactory ADMET, and 4 compounds were successfully identified. According to their docking scores and ADMET information, 3719810 was advanced for profiling by in vitro assays as a novel NDRI lead compound due to its strongest druggability and balancing activities. Encouragingly, 3719810 performed comparative activities on two targets, with Ki values of 7.32 µM for hNET and 5.23 µM for hDAT. To obtain candidates with additional activities and balance the activities of 2 targets, 5 analogs were optimized, and 2 novel scaffold compounds were successively designed. By assessment of molecular docking, molecular dynamics simulations, and binding energy calculations, 5 compounds were validated as NDRI candidates with high activities, and 4 of them performed acceptable balancing activities acting on hNET and hDAT. This work supplied promising novel NDRIs for treatment of depression with cognitive dysfunction or other related neurodegenerative disorders, and also provided a strategy for highly efficient and cost-effective identification of inhibitors for dual targets with homologous non-targets.

Long-term spatial variation of algal blooms extracted using the U-net model from 10 years of GOCI imagery in the East China Sea.

Long-term satellite missions could help to provide insights into spatial and temporal variations in algal blooms. However, the traditional reflectance-based method has limitations in regards to determining the available threshold for algal bloom detection among the time-varying observation conditions. In terms of extracting useful information from long-term data series precisely and efficiently, the deep learning method has shown its superiority over traditional algorithms in batch data processing. In this study, a U-net model for algal bloom extraction along the coast of the East China Sea was developed using GOCI images. The U-net model was trained with two different datasets that were constructed with six-band channels (all visible bands from GOCI imagery) and RGB-band channels (bands of 443, 555, and 680 nm from GOCI imagery). The quantitative assessment from the U-net models suggests that the U-net model trained with the six-band channel datasets outperformed the RGB-band channel datasets, with increases of 23.6%, 18.1%, and 12.5% in terms of accuracy, precision, and F-score, respectively. The validation map derived from the U-net model trained with six-band channel datasets also showed considerable matching with the ground-truth maps. By using the U-net model, the occurrence of algal blooms was automatically extracted from GOCI images. A 10-year time series of GOCI data collected between 2011 and 2020 was derived using an output-trained U-net model to explore spatial variation along the coast of the ECS. It was found that the most affected areas of the algal blooms varied by year, but were mainly located in the Zhoushan and Zhejiang coasts. Additionally, by performing principal component analysis on the daily meteorological data during April and August 2011-2020, factors related to algal bloom occurrence were discussed.

Selective synthesis of sulfoxides and sulfones via controllable oxidation of sulfides with N-fluorobenzenesulfonimide.

A practical and mild method for the switchable synthesis of sulfoxides or sulfones via selective oxidation of sulfides using cheap N-fluorobenzenesulfonimide (NFSI) as the oxidant has been developed. These highly chemoselective transformations were simply achieved by varying the NFSI loading with H2O as the green solvent and oxygen source without any additives. The good functional group tolerance makes the strategy valuable.

The knockdown of MALAT1 inhibits the proliferation, invasion and migration of hemangioma endothelial cells by regulating MiR-206 / VEGFA axis.

AIM: LncRNA MALAT1 is involved in regulation of angiogenesis, however, its expression and mechanism in infantile hemangioma (IH) are less reported. The study aimed to investigate MALAT1 in IH and to reveal the potential mechanism of MALAT1 acting on IH. METHODS: Isolated form IH tissue, human CD31+ hemangioma endothelial cells (HemECs) were cultured and sorted by magnetic-activated cell sorting (MACS). Quantitative real-time (qRT)-PCR was performed to detect the expressions of MALAT1, miR-206 and VEGFA. The correlations among MALAT1, miR-206 and VEGFA were confirmed by bioinformatics analysis and dual-luciferase reporter assay. The effects of MALAT1, miR-206 and VEGFA on cell proliferation were detected by cell counting kit-8 (CCK-8) and cell colony formation assay. Flow cytometry, wound scratch, Transwell and Tube formation assay were performed to determine cell apoptosis, migration, invasion and vasoformation, respectively. Apoptosis-related proteins were determined by Western blot. RESULTS: The results showed that MALAT1 and VEGFA were high-expressed and miR-206 was low-expressed in IH tissues. SiMALAT1 negatively affected the cell proliferation, migration, invasion and vasoformation of HemECs and promoted apoptosis of HemECs. Moreover, Bcl-2 expression was significantly inhibited and the expressions of Bax and c cleaved-3 were greatly promoted. MALAT1 directly targeted and inhibited the expression of miR-206, and VEGFA was predicted to be the target gene for miR-206. SiMALAT1 suppressed the cell proliferation, migration, invasion and vasoformation of HemECs through modulating miR-206/VEGFA axis. CONCLUSION: Knock-down of MALAT1 inhibits the growth of HemECs through regulating miR-206/VEGFA axis, indicating that MALAT1 is a potential therapeutic mechanism for the treatment of IH.

Synoptic relationships to estimate phytoplankton communities specific to sizes and species from satellite observations in coastal waters.

Knowing variations of phytoplankton community characteristics is of great significance to many marine ecological and biogeochemical processes in oceanography and related fields research. Satellite remote sensing provides the only viable path for continuously detecting phytoplankton community characteristics in the large-scale spatial areas. However, remote sensing approaches are currently hindered by limited understanding on reflectance responses to variations from phytoplankton community compositions and further do not achieve a true application by satellite observations. Here we analyze in situ observation data sets from three cruises in a dynamic marine environment covering those coastal water areas in the marginal seas of the Pacific Northwest (Bohai Sea, Yellow Sea, and East China Sea). The size/species-specific phytoplankton assemblages can be quantitatively defined by the high-performance liquid chromatography (HPLC)-derived phytoplankton pigments and customized diagnostic pigment analysis, as well as a matrix factorization "CHEMTAX" program. Therein, note that a suit of updated weight values for diagnostic pigments are proposed with better performance than others. The above-mentioned size/species-specific phytoplankton assemblages include three size classes, i.e., micro-, nano-, and picoplankton, and eight species typically existing in the investigated water areas. Relationship analysis illustrates us that relatively close and robust models can be established to associate three size-specific and four dominant species-specific phytoplankton biomasses with the total chlorophyll a. Those models are then applied to the Geostationary Ocean Color Imager (GOCI) images for the whole 2015 year, which generated annual mean distributions of size/species-specific phytoplankton biomasses. The current study represents a meaningful attempt to achieve the satellite remote-sensing retrievals on the phytoplankton community composition, especially the species-specific phytoplankton biomass in the study region.

Variability of particulate backscattering ratio and its relations to particle intrinsic features in the Bohai Sea, Yellow Sea, and East China Sea.

Using two field cruise observations collected during September and December 2016 in the Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS), our study explores the variability of the particulate backscattering ratio (i.e., a ratio of particulate backscattering, bbp in m-1, to particulate scattering, bp in m-1, denoted as b˜bp, dimensionless). A large variation of b˜bp (using 550 nm as a delegate) in magnitude is observed in the study regions, ranging from 0.0004 to 0.043 (with an average of 0.015 ± 0.0082), which implies optically complex water conditions. Spectral variation in b˜bp is analyzed quantitatively by our proposed so-called "spectral dependence index," K, recommended as a standard way to determine quantitatively the spectral dependence of b˜bp in water bodies worldwide. The driving mechanism on the b˜bp variability in the study regions is researched for the first time, based on those synchronous data on particle intrinsic attributes, herein mainly referring to particle concentration (TSM, for the content of total suspended matter), composition (using a ratio of Chla/TSM as a surrogate, where Chla refers to the content of chlorophyll a), mean particle size (DA), and mean apparent density (ρa). The TSM, Chla/TSM, and DA cumulatively contribute most (97.8%) of the b˜bp variability, while other factors, such as the ρa, show a weak influence (0.04%). Meanwhile, we model b˜bp with direct linkages to TSM, Chla/TSM, and DA by using a linear regression method, with low estimation errors (such as mean absolute percentage error, MAPE, of about 14%). In short, our findings promote an understanding on the essence of the b˜bp in the BS, YS, and ECS, and are significantly beneficial to the comprehensive grasp of those complex features on suspended particles and those related to biogeochemical processes in marine waters.

A simple and effective method for monitoring floating green macroalgae blooms: a case study in the Yellow Sea.

Several algorithms have been proposed to detect floating macroalgae blooms in the global ocean. However, some of them are difficult or even impossible to routinely apply by non-experts because of performing a sophisticated atmospheric correction scheme or due to the mismatch in spectral bands from one sensor to another. Here, a generic, simple and effective method, referred to as the Floating Green Tide Index (FGTI), was proposed to detect floating green macroalgae blooms (GMB). The FGTI was defined as the difference between greenness and wetness features extracted from digital number (DN) observation through Tasseled Cap Transformation analysis, providing the advantage of bypassing the atmospheric correction procedure. Through cross-index and cross-sensor comparisons, the FGTI showed similar performance to the existing VB-FAH (Virtual-Baseline Floating macroAlgae Height) and FAI (Floating Algae Index) algorithms but proved more robust than the traditional NDVI (Normalized Difference Vegetation Index) in terms of response to perturbations by environmental conditions, viewing geometry, sun glint, and thin cloud contamination. Given the requirement for spectral bands in the current and planned satellite sensors, the FGTI design can easily be extended to any satellite sensor, and therefore provide an excellent data resource for studying GMB in any part of the global ocean.

Mass spectrometric determination of disulfide bonds and free cysteine in grass carp IgM isoforms.

Disulfide bonds are fundamental in establishing Ig structure and maintaining Ig biological function. Here, we analysed disulfide bonds and free cysteine in three grass carp IgM isoforms (monomeric, dimeric/trimeric, and tetrameric IgM) by liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). The results revealed that Cys574 residue status at the C-terminal tail differed substantially in monomeric IgM in comparison with polymeric IgM, Cys574 was found as free thiol in monomeric IgM, while it formed disulfide linkages in dimeric/trimeric and tetrameric IgM. Five intra-chain disulfide bonds in the CH1~CH4 and CL1 domains, as well as one H-H and one H-L inter-chain disulfide linkages, were also observed and shown identical connectivity in monomeric, dimeric/trimeric, and tetrameric IgM. These findings represent the first experimental assignments of disulfide linkages of grass carp IgM and reveal that grass carp IgM isoform formation is due to alternative disulfide bonds connecting the Cys574 residue at the C-terminal tail.

Propofol exposure during early gestation impairs learning and memory in rat offspring by inhibiting the acetylation of histone.

Propofol is widely used in clinical practice, including non-obstetric surgery in pregnant women. Previously, we found that propofol anaesthesia in maternal rats during the third trimester (E18) caused learning and memory impairment to the offspring rats, but how about the exposure during early pregnancy and the underlying mechanisms? Histone acetylation plays an important role in synaptic plasticity. In this study, propofol was administered to the pregnant rats in the early pregnancy (E7). The learning and memory function of the offspring were tested by Morris water maze (MWM) test on post-natal day 30. Two hours before each MWM trial, histone deacetylase 2 (HDAC2) inhibitor, suberoylanilide hydroxamic acid (SAHA), Senegenin (SEN, traditional Chinese medicine), hippyragranin (HGN) antisense oligonucleotide (HGNA) or vehicle were given to the offspring. The protein levels of HDAC2, acetylated histone 3 (H3) and 4 (H4), cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB), N-methyl-D-aspartate receptor (NMDAR) 2 subunit B (NR2B), HGN and synaptophysin in offspring's hippocampus were determined by Western blot or immunofluorescence test. It was discovered that infusion with propofol in maternal rats on E7 leads to impairment of learning and memory in offspring, increased the protein levels of HDAC2 and HGN, decreased the levels of acetylated H3 and H4 and phosphorylated CREB, NR2B and synaptophysin. HDAC2 inhibitor SAHA, Senegenin or HGN antisense oligonucleotide reversed all the changes. Thus, present results indicate exposure to propofol during the early gestation impairs offspring's learning and memory via inhibiting histone acetylation. SAHA, Senegenin and HGN antisense oligonucleotide might have therapeutic value for the adverse effect of propofol.

Variations of transparency derived from GOCI in the Bohai Sea and the Yellow Sea.

Secchi disk depth (Zsd), represents water transparency which is an intuitive indicator of water quality and can be used to derive inherent optical properties, chlorophyll-a concentrations, and primary productivity. In this study, the Zsd was derived from the Geostationary Ocean Color Imager (GOCI) data over the Bohai Sea (BHS) and the Yellow Sea (YS) using a regional tuned model. To validate the GOCI derived Zsd observations, in situ data, were collected for the BHS and YS regions. Results showed a good agreement between the GOCI derived Zsd observations and in situ measurements with a determination coefficient of 0.90, root mean square error of 2.17 m and mean absolute percent error of 24.56%. Results for diurnal variations showed an increasing trend of Zsd at the first and then decreasing, and all the maxima of Zsd in the central areas of the BHS and YS were found in the midday. For seasonal variations, higher values of Zsd, both in range and intensity, were observed in summer compared with those in winter. The reasons to explain the variations of Zsd have also been explored. Solar zenith angle (SOLZ) has an impact on the daily dynamics of Zsd, due to the influence of SOLZ on the attenuation of light radiation in water. The influence level of SOLZ on Zsd is largely determined by the water bodies' composition. The significant seasonal variations are mainly controlled by the stability of the water column stratification, because it can lead to the sediment resuspension and influence the growth and distribution of phytoplankton. Runoff and sediment discharge are not the main factors that impact the seasonal dynamics of Zsd. Tidal currents and mean currents may have influences on the variations of Zsd. However, due to the lack of in situ measurements to support, further studies are still needed.

Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images.

Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain economic damage. Plenty of threshold-based index methods have been developed to detect surface algae pixels in various ocean color data with different manners; however, these methods cannot be used for every satellite sensor because of the spectral band configuration. Also, these traditional methods generally require other reliable indicators, and even visual inspection, in order to achieve an acceptable mapping of MAB that appears under diverse environmental conditions (cloud, aerosol, and sun glint). To overcome these drawbacks, a machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery. The method consists of two MLP models, which consider both spectral and spatial features of Rayleigh-corrected reflectance (Rrc) maps. Accuracy assessment and performance comparison showed that the proposed method has the capability to provide prediction maps of MAB with high accuracy (F1-score approaching 90% or more), and with more robustness than the traditional methods. Most importantly, the model is practically adaptable for other ocean color instruments. This allows customized models to be built and used for monitoring MAB in any regional areas. With the development of machine learning models, long-term mapping of MAB in global ocean is conducive to promoting the associated studies.

Retrievals of phytoplankton community structures from in situ fluorescence measurements by HS-6P.

Phytoplankton community is an important organism indicator of monitoring water quality, and accurately estimating its composition and biomass is crucial for understanding marine ecosystems and biogeochemical processes. Identifying phytoplankton species remains a challenging task in the field of oceanography. Phytoplankton fluorescence is an important biological property of phytoplankton, whose fluorescence emissions are closely related to its community. However, the existing estimation approaches for phytoplankton communities by fluorescence are inaccurate and complex. In the present study, a new, simple method was developed for determining the Chlorophytes, Chrysophytes, Cryptophytes, Diatoms, Dinoflagellates, and Prymnesiophytes based on the fluorescence emission spectra measured from the HOBI Labs Hydroscat-6P (HS-6P) in the Bohai Sea, Yellow Sea, and East China Sea. This study used single bands, band ratios, and band combinations of the fluorescence signals to test their correlations with the six dominant algal species. The optimal band forms were confirmed, i.e., X1 (i.e., fl(700), which means the fluorescence emission signal at 700 nm band) for Chlorophytes, Cryptophytes, Dinoflagellates, and Prymnesiophytes (R = 0.947, 0.862, 0.911, and 0.918, respectively) and X7 (i.e., [fl(700) + fl(550)]/[fl(550)/fl(700)], where fl(550) denotes the fluorescence emission signal at 550 nm band) for Chrysophytes and Diatoms (R = 0.893 and 0.963, respectively). These established models here show good performances, yielding low estimation errors (i.e., root mean square errors of 0.16, 0.02, 0.06, 0.36, 0.18, and 0.03 for Chlorophytes, Chrysophytes, Cryptophytes, Diatoms, Dinoflagellates, and Prymnesiophytes, respectively) between in situ and modeled phytoplankton communities. Meanwhile, the spatial distributions of phytoplankton communities observed from both in situ and fluorescence-derived results agreed well. These excellent outputs indicate that the proposed method is to a large extent feasible and robust for estimating those dominant algal species in marine waters. In addition, we have applied this method to three vertical sections, and the retrieved vertical spatial distributions by this method can fill the gap of the common optical remote sensing approach, which usually only detects the sea surface information. Overall, our findings indicate that the proposed method by the fluorescence emission spectra is a potentially promising way to estimate phytoplankton communities, in particular enlarging the profiling information.

The challenge of maintaining microscopist capacity at basic levels for malaria elimination in Jiangsu Province, China.

BACKGROUND: Local malaria transmission has decreased rapidly since the National Malaria Elimination Action Plan was launched in China in 2010. However, imported malaria cases from Africa and Southeast Asia still occur in China due to overseas laborers. Diagnosis by microscopy is the gold standard for malaria and is used in most hospitals in China. However, the current capacity of microscopists to manage malaria cases in hospitals and public health facilities to meet the surveillance needs to eliminate and prevent the reintroduction of malaria is unknown. METHODS: Malaria diagnoses were assessed by comparing the percentage of first visit and confirmed malaria diagnoses at Centers for Disease Control and Prevention (CDCs) and hospitals. The basic personnel information for public health departments and hospitals at different levels was investigated. The skills of microscopists for blood smear preparation and slide interpretation were also examined at the county and township levels. RESULTS: Inaccurate rate with 13.49% and 7.32%, respectively, in 2013 and 2014, from 341 and 355 reported cases from sub-provincial levels in Jiangsu province. Most of the 523 malaria cases reported in Nantong Prefecture from 2000 to 2014 involved patients who first visited county CDCs seeking treatment, however, none of these cases received confirmed diagnosis of malaria in townships or villages.The staff at county CDCs and hospitals with a higher education background performed better at making and interpreting blood smears than staff from townships. CONCLUSIONS: The network for malaria elimination in an entire province has been well established. However, an insufficient capacity for malaria diagnosis was observed, especially the preparing and reading the blood smears at the township and village levels, which is a challenge to achieving and maintaining malaria elimination.

Pharmacophore-Based Virtual Screening of Novel Inhibitors and Docking Analysis for CYP51A from Penicillium italicum.

Sterol 14α-demethylases from Cytochrome P450 family (CYP51s) are essential enzymes in sterol biosynthesis and well-known as the target of antifungal drugs. The 3D structure of CYP51A from Penicillium italicum (PiCYP51A) was constructed through homology modeling based on the crystal structure of human CYP51A (PDB: 3LD6). Molecular dynamics (MD) simulation was operated to relax the initial model and followed by quality assessment using PROCHECK program. On the basis of the docking information on the currently available CYP51s with the patent demethylase inhibitors (DMIs), pharmacophore-based virtual screening combined with docking analysis was performed to pick out twelve new compounds from ZINC database. Six hits revealed in the ligand database suggested potential ability to inhibit PiCYP51A. Compared to patent fungicide triazolone, the top three lead compounds had similar or higher affinity with the target enzyme, and accordingly, exhibited comparable or lower EC50 values to P. italicum isolates. The results could provide references for de novo antifungal drug design.

Propofol exposure during late stages of pregnancy impairs learning and memory in rat offspring via the BDNF-TrkB signalling pathway.

The brain-derived neurotrophic factor (BDNF)-tyrosine kinase B (TrkB) (BDNF-TrkB) signalling pathway plays a crucial role in regulating learning and memory. Synaptophysin provides the structural basis for synaptic plasticity and depends on BDNF processing and subsequent TrkB signalling. Our previous studies demonstrated that maternal exposure to propofol during late stages of pregnancy impaired learning and memory in rat offspring. The purpose of this study is to investigate whether the BDNF-TrkB signalling pathway is involved in propofol-induced learning and memory impairments. Propofol was intravenously infused into pregnant rats for 4 hrs on gestational day 18 (E18). Thirty days after birth, learning and memory of offspring was assessed by the Morris water maze (MWM) test. After the MWM test, BDNF and TrkB transcript and protein levels were measured in rat offspring hippocampus tissues using real-time PCR (RT-PCR) and immunohistochemistry (IHC), respectively. The levels of phosphorylated-TrkB (phospho-TrkB) and synaptophysin were measured by western blot. It was discovered that maternal exposure to propofol on day E18 impaired spatial learning and memory of rat offspring, decreased mRNA and protein levels of BDNF and TrkB, and decreased the levels of both phospho-TrkB and synaptophysin in the hippocampus. Furthermore, the TrkB agonist 7,8-dihydroxyflavone (7,8-DHF) reversed all of the observed changes. Treatment with 7,8-DHF had no significant effects on the offspring that were not exposed to propofol. The results herein indicate that maternal exposure to propofol during the late stages of pregnancy impairs spatial learning and memory of offspring by disturbing the BDNF-TrkB signalling pathway. The TrkB agonist 7,8-DHF might be a potential therapy for learning and memory impairments induced by maternal propofol exposure.

Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea.

In this paper, a new daytime sea fog detection algorithm has been developed by using Geostationary Ocean Color Imager (GOCI) data. Based on spectral analysis, differences in spectral characteristics were found over different underlying surfaces, which include land, sea, middle/high level clouds, stratus clouds and sea fog. Statistical analysis showed that the Rrc (412 nm) (Rayleigh Corrected Reflectance) of sea fog pixels is approximately 0.1-0.6. Similarly, various band combinations could be used to separate different surfaces. Therefore, three indices (SLDI, MCDI and BSI) were set to discern land/sea, middle/high level clouds and fog/stratus clouds, respectively, from which it was generally easy to extract fog pixels. The remote sensing algorithm was verified using coastal sounding data, which demonstrated that the algorithm had the ability to detect sea fog. The algorithm was then used to monitor an 8-hour sea fog event and the results were consistent with observational data from buoys data deployed near the Sheyang coast (121°E, 34°N). The goal of this study was to establish a daytime sea fog detection algorithm based on GOCI data, which shows promise for detecting fog separately from stratus.