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In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

Brown mustard (Brassica juncea (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four B. juncea varieties in Korea. The spectra from the leaves of four different growth stages of four B. juncea varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating B. juncea varieties in the field in all the growth stages.

The current study demonstrates an effective protocol for encapsulation of in vitro nodal segments of Solanum trilobatum L. for short-term storage. In a modified Murashige and Skoog medium supplemented with 8.8 µM 6-benzyl adenine (BA), 0.5 µM indole-3-acetic acid (IAA), and 25 mM ammonium nitrate, a gelling matrix of 3% sodium alginate and 80 mM CaCl2.2H2O promoted the highest shoot regeneration frequency (98.4%). The effect of storage temperature, sucrose concentration, and storage time interval on shoot regrowth efficiency was assessed. Synthetic seeds maintained at culture room temperature (25 °C) were shown to be more competent than low-temperature storage (8 °C and 4 °C), with better regeneration efficiency (82.2%). The alginate matrix added with 1.5% sucrose increased the storage potential of synthetic seeds up to 12 weeks with a regeneration frequency of 57.7%. Using start codon-targeted polymorphic (SCoT) markers, the genetic fidelity of plants regrown after 12 weeks of storage was assessed. The SCoT fingerprinting approach has confirmed the post-storage genetic stability of recovered plants with their in vitro source. The present investigation is the first report on the development of synthetic seeds of S. trilobatum L., which would greatly facilitate germplasm conservation and exchange of this invaluable medicinal plant.

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Interspecific hybridization between transgenic crops and their wild relatives is a major concern for transgene dispersal in the environment. Under controlled conditions, artificial hand pollination experiments were performed in order to assess the hybridization potential and the fitness of interspecific hybrids between Brassica rapa and genetically modified (GM) Brassica napus. Initially, six subspecies of B. rapa were hybridized with GM B. napus through hand pollination. In the resulting F1 hybrids, the combination of B. rapa ssp. narinosa (♀) × GM B. napus (♂) had the highest crossability index (16.9 ± 2.6). However, the F1 selfing progenies of B. rapa ssp. rapa (♀) × GM B. napus were found to be more effective in producing viable future generations with the highest crossability index (1.6 ± 0.69) compared to other subspecies. Consequently, they were used for the generation of F2 and F3 progenies. The 18 different morphological characteristics among the parental cross-combinations and F1 hybrid progenies were measured and visualized through hierarchical clustering. Different generations were found to be grouped based on their different morphological characteristics. The chromosome numbers among the interspecific hybrids ranged from 2n = 29 to 2n = 40. Furthermore, the SSR markers revealed the presence of genomic portions in the hybrids in comparison with their parental lines. There is a high possibility of transgene flow between GM B. napus and B. rapa. The study concluded that the interspecific hybrids between B. napus and B. rapa can be viable and can actively hybridize up to F3 generations and more. This suggests that the GM B. napus can disperse the transgene into B. rapa, and that it can pass through for several generations by hand pollination in a greenhouse environment.

In nature, interspecific hybridization occurs frequently and can contribute to the production of new species or the introgression of beneficial adaptive features between species. It has great potential in agricultural systems to boost the process of targeted crop improvement. In the advent of genetically modified (GM) crops, it has a disadvantage that it involves the transgene escaping to unintended plants, which could result in non-specific weedy crops. Several crop species in the

Microbial pathogens cause a quarter of all deaths worldwide annually due to deadly infectious diseases. Nevertheless, the fast and precise identification of pathogens remains one of the most challenging tasks in the medical sector. Early identification and characterization of microbes through medical diagnosis could pave the way for specific treatment strategies that could dramatically improve infection management, reduce healthcare costs, mitigate increasing antimicrobial resistance, and save numerous lives. To date, numerous traditional and molecular methods have been employed to diagnose illnesses with proven accuracy, reliability, and efficiency. Here, we have reviewed the most reliable tools that are prerequisites for the rapid detection of microbes. In particular, the remarkable roles of surface-enhanced Raman scattering, Fourier-transform infrared, electrochemical impedance, near-infrared, and MALDI-TOF/TOF in the identification and characterization of pathogenic microbes are discussed in detail. The approaches described herein cover broad ranges of biomedical applications, including the diagnosis of clinical infectious diseases, epidemiology, detection of vector-borne diseases, food security, phytosanitary monitoring, biosensing, and food- and waterborne pathogen detection. Considering the current pandemic outbreak, this review briefly emphasizes the importance of rapid detection and upgraded tools for early diagnosis to prevent the loss of lives.

The Silk Road pattern (SR) is a leading mode of atmospheric circulation over midlatitude Eurasia in boreal summer. Its temporal phase is known to be unpredictable in many models. Previous studies have not reached a clear consensus on the role of sea surface temperature (SST) associated with SR. By comparing seasonal hindcasts from the Pusan National University (PNU) coupled general circulation model with reanalysis, we investigate if there are any sources of predictability originating from the SST. It was found that the PNU model cannot predict SR temporally. In fact, SR is associated with El Niño–Southern Oscillation (ENSO) in the model hindcasts, in contrast to reanalysis results in which SR is more associated with North Atlantic SST anomalies. The PNU system, however, shows potential predictability in SR associated with tropical Pacific SST. Bias in stationary Rossby waveguides is proposed as an explanation for the SR–ENSO relationship in hindcast runs. Model upper-level wind bias in the North Atlantic results in a less continuous waveguide connecting the North Atlantic to Asia, and may hinder wave propagations induced by North Atlantic SST to trigger SR. On the other hand, model upper-level wind bias in the subtropical western Pacific may favor westward propagation of zonally elongated waves from the ENSO region to trigger SR. This study implies that the role of SST with regard to SR can be substantially changed depending on the fidelity of model upper-level background winds.

The occurrence of exotic weeds and their influx into farmlands due to climate change poses many problems. Therefore, it is necessary to generate a prediction model for the occurrence pattern of these exotic weeds based on scientific evidence and devise prevention measures. The photosynthetic apparatus is known as the most temperaturesensitive component of a plant cell and its initial response to temperature stress is to inhibit the activation of photosystem II. This study investigated the potential of OJIP transients in assessing temperature stress in exotic weeds. The four exotic weeds currently flowing into Korean farmlands include Amaranthus spinosus, Conyza bonariensis, Crassocephalum crepidioides, and Amaranthus viridis. These weeds were treated at 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C, and 40°C and the OJIP curves and JIP parameters were measured and analyzed. The results showed that heat and chilling stress affected the photosystem II (PSII) electron transport of A. spinosus, whereas C. crepidioides and A. viridis were more affected by high-temperature stress than by low-temperature stress. Lastly, C. bonariensis showed resistance to both high and low-temperature stress. The results of this study suggest that OJIP transients and JIP parameters can be used to analyze damage to the photosynthetic apparatus by temperature stress and that they can serve as sensitive indicators for the occurrence pattern of exotic weeds.

The rapid advancement of genetically modified (GM) technology over the years has raised concerns about the safety of GM crops and foods for human health and the environment. Gene flow from GM crops may be a threat to the environment. Therefore, it is critical to develop reliable, rapid, and low-cost technologies for detecting and monitoring the presence of GM crops and crop products. Here, we used visible near-infrared (Vis-NIR) spectroscopy to distinguish between GM and non-GM Brassica napus, B. juncea, and F1 hybrids (B. juncea X GM B. napus). The Vis-NIR spectra were preprocessed with different preprocessing methods, namely normalization, standard normal variate, and Savitzky–Golay. Both raw and preprocessed spectra were used in combination with eight different chemometric methods for the effective discrimination of GM and non-GM plants. The standard normal variate and support vector machine combination was determined to be the most accurate model in the discrimination of GM, non-GM, and hybrid plants among the many combinations (99.4%). The use of deep learning in combination with Savitzky–Golay resulted in 99.1% classification accuracy. According to the findings, it is concluded that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used to distinguish between GM and non-GM B. napus, B. juncea, and F1 hybrids.

Seasonal climate forecasts play a critical role in building a climate-resilient society in the Pacific Island Countries (PICs) that are highly exposed to high-impact climate events. To assist the PICs National Meteorological and Hydrological Services in generating reliable national climate outlooks, we developed a hybrid seasonal prediction system, the Pacific Island Countries Advanced Seasonal Outlook (PICASO), which has the strengths of both statistical and dynamical systems. PICASO is based on the APEC Climate Center Multi-Model Ensemble (APCC-MME), tailored to generate station-level rainfall forecasts for 49 stations in 13 countries by applying predictor optimization and the large-scale relationship-based Bayesian regression approaches. Overall, performance is improved and further stabilized temporally and spatially relative to not only APCC-MME but also other existing operational prediction systems in the Pacific. Gaps and challenges in operationalization of the PICASO system and its incorporation into operational climate services in the PICs are discussed.

Simple Summary With the advent of genetic engineering technology, the development and cultivation of genetically modified (GM) crops has increased. They were mainly developed for high yielding, herbicide resistance, and tolerance against different biotic and abiotic stresses. Rapeseed, also known as canola, was developed mainly for herbicide resistance and to increase the production of canola oil. Since it forms weedy, feral populations and has a proven ability to hybridize with its close relatives, it is important to manage the GM crops’ cultivation and spread, especially the rapeseed. Several studies have reported that the spread of GM rapeseed in non-GM fields and road verges is possible due to transport and agronomic practices, and it may become a weed. Hence, in this review, we summarized the cases of unintentional spread of feral GM rapeseed in the fields and road verges. In addition, we made recommendations for the effective management of feral GM and non-GM rapeseed in agricultural fields and along roads. Abstract Globally, the cultivation area of genetically modified (GM) crops is increasing dramatically. Despite their well-known benefits, they may also pose many risks to agriculture and the environment. Among the various GM crops, GM rapeseed (Brassica napus L.) is widely cultivated, mainly for oil production. At the same time, B. napus possesses a number of characteristics, including the ability to form feral populations and act as small-seeded weeds, and has a high potential for hybridization with other species. In this review, we provide an overview of the commercialization, approval status, and cultivation of GM rapeseed, as well as the status of the feral rapeseed populations. In addition, we highlight the case studies on the unintentional environmental release of GM rapeseed during transportation in several countries. Previous studies suggest that the main reason for the unintentional release is seed spillage during transport/importing of rapeseed in both GM rapeseed-cultivating and -non-cultivating countries. Despite the fact that incidents of unintentional release have been recorded often, there have been no reports of serious detrimental consequences. However, since rapeseed has a high potential for hybridization, the possibilities of gene flow within the genus, especially with B. rapa, are relatively significant, and considering their weedy properties, effective management methods are needed. Hence, we recommend that specific programs be used for the effective monitoring of environmental releases of GM rapeseed as well as management to avoid environmental and agricultural perturbations.

Seaweed extracts are considered effective therapeutic alternatives to synthetic anticancer, antioxidant, and antimicrobial agents, owing to their availability, low cost, greater efficacy, eco-friendliness, and non-toxic nature. Since the bioactive constituents of seaweed, in particular, phytosterols, possess plenty of medicinal benefits over other conventional pharmaceutical agents, they have been extensively evaluated for many years. Fortunately, recent advances in phytosterol-based research have begun to unravel the evidence concerning these important processes and to endow the field with the understanding and identification of the potential contributions of seaweed-steroidal molecules that can be used as chemotherapeutic drugs. Despite the myriad of research interests in phytosterols, there is an immense need to fill the void with an up-to-date literature survey elucidating their biosynthesis, pharmacological effects, and other biomedical applications. Hence, in the present review, we summarize studies dealing with several types of seaweed to provide a comprehensive overview of the structural determination of several phytosterol molecules, their properties, biosynthetic pathways, and mechanisms of action, along with their health benefits, which could significantly contribute to the development of novel drugs and functional foods.

Pre-harvest sprouting is a critical phenomenon involving the germination of seeds in the mother plant before harvest under relative humid conditions and reduced dormancy. As it results in reduced grain yield and quality, it is a common problem for the farmers who have cultivated the rice and wheat across the globe. Crop yields need to be steadily increased to improve the people’s ability to adapt to risks as the world’s population grows and natural disasters become more frequent. To improve the quality of grain and to avoid pre-harvest sprouting, a clear understanding of the crops should be known with the use of molecular omics approaches. Meanwhile, pre-harvest sprouting is a complicated phenomenon, especially in rice, and physiological, hormonal, and genetic changes should be monitored, which can be modified by high-throughput metabolic engineering techniques. The integration of these data allows the creation of tailored breeding lines suitable for various demands and regions, and it is crucial for increasing the crop yields and economic benefits. In this review, we have provided an overview of seed dormancy and its regulation, the major causes of pre-harvest sprouting, and also unraveled the novel avenues to battle pre-harvest sprouting in cereals with special reference to rice using genomics and transcriptomic approaches.

Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.

Kohlrabi is considered an important dietary vegetable worldwide. In this study, we investigated the growth and accumulation of phenolic compounds (PCs) and glucosinolates in sprouts of pale green and purple kohlrabi (Brassica oleracea var. gongylodes) in response to light and dark conditions. Pale green kohlrabi presented high fresh weight and root length irrespective of light treatment, whereas under dark conditions, it presented higher fresh weight and shoot length than purple kohlrabi. In contrast, the root length of both kohlrabies increased markedly under light conditions compared to that under dark conditions. Thirteen PCs and eight glucosinolates were detected and quantified in 10-day-old pale green and purple kohlrabies. In both kohlrabies, the individual and total phenolic levels were much higher under the light treatment than under the dark treatment. Under light and dark conditions, the total phenolic content was 6362.13 and 5475.04 µg/g dry weight in the pale green kohlrabi, respectively, whereas in the purple kohlrabi, it was 10,115.76 and 9361.74 µg/g dry weight, respectively. Dark conditions favored higher accumulation of glucosinolates than light conditions. Progoitrin, neoglucobrassicin, glucoerucin, and 4-methoxyglucobrassicin were the predominant glucosinolates in both kohlrabies and were present in much higher amounts in the pale green kohlrabi. In pale green kohlrabi under dark conditions, the total glucosinolates content was 4.75 and 2.62 times higher than that of the purple kohlrabi under light and dark conditions, respectively. Among individual glucosinolates, in the pale green kohlrabi under the dark condition, progoitrin was found to have the highest content, which was 90.28 and 54.51 times higher than that in the purple kohlrabi under light and dark conditions, respectively. These results show that the phenolic and glucosinolates levels varied widely, and these variations between the two types of kohlrabi under both light and dark conditions were significant. Our findings suggest that light and dark conditions enhance the accumulation of PCs and glucosinolates, respectively, during the development of kohlrabi seedlings.

BACKGROUND: This study investigated the effects of rice genetically modified to be resistant against rice blast and rice bacterial blight on the soil microbial community. A comparative analysis of the effects of rice genetically modified rice choline kinase (OsCK1) gene for disease resistance (GM rice) and the Nakdong parental cultivar (non-GM rice) on the soil microbial community at each stage was conducted using rhizosphere soil of the OsCK1 and Nakdong rice. METHODS AND RESULTS: The soil chemistry at each growth stage and the bacterial and fungal population densities were analyzed. Soil DNA was extracted from the samples, and the microbial community structures of the two soils were analyzed by pyrosequencing. No significant differences were observed in the soil chemistry and microbial population density between the two soils. The taxonomic analysis showed that Chloroflexi, Proteobacteria, Firmicutes, Actinobacteria, and Acidobacteria were present in all soils as the major phyla. Although the source tracking analysis per phylogenetic rank revealed that there were differences in the bacteria between the GM and non-GM soil as well as among the cultivation stages, the GM and non-GM soil were grouped according to the growth stages in the UPGMA dendrogram analysis. CONCLUSION: The difference in bacterial distributions between Nakdong and OsCK1 rice soils at each phylogenetic level detected in microbial community analysis by pyrosequencing may be due to the genetic modification done on GM rice or due to heterogeneity of the soil environment. In order to clarify this, it is necessary to analyze changes in root exudates along with the expression of transgene. A more detailed study involving additional multilateral soil analyses is required.

The prediction skill of tropical Pacific sea surface temperature (SST) and precipitation has been investigated, based on retrospective forecasts from 1983 to 2005 of the APEC Climate Center multimodel ensemble (MME) 6-month climate prediction, with a focus on El Nino-Southern Oscillation (ENSO) and its diversity. It was found that the MME prediction skill is related to both strength and flavor of ENSO. To better analyze the relationship between ENSO diversity and tropical climate predictions on a year-to-year basis, anomalous SST data were stratified into those due to typical ENSO and their residue. The former is defined as the reconstruction of data based on the leading empirical orthogonal function of the Pacific SST itself. It was found that typical ENSO and its impacts on tropical rainfall are well captured by dynamical seasonal prediction systems. Singular vector decomposition (SVD) analyses were further carried out, in order to identify climate modes related to the co-variability between residual SST signals and precipitation. Observational results show that the first SVD mode gives a SST singular vector that greatly resembles the east–west shift from typical El Nino features to El Nino diverse patterns. Dynamical models, on the other hand, have difficulties in reproducing the strength of this mode on the interannual scale. Their skill in predicting the temporal variation of this mode is also much lower than that for typical ENSO. Finally, the role of ENSO diversity on SST and precipitation potential predictability was also examined. Typical ENSO is the major predictability source of tropical Pacific SST. On the other hand, it was demonstrated that residual ENSO variability actually acts as a limit to tropical rainfall predictability. In other words, residual ENSO plays a rather crucial role in seasonal forecasting of tropical Pacific rainfall.

Isoflavones are ecophysiologically active secondary metabolites derived from the phenylpropanoid pathway. They were mostly found in leguminous plants, especially in the pea family. Isoflavones play a key role in plant–environment interactions and act as phytoalexins also having an array of health benefits to the humans. According to epidemiological studies, a high intake of isoflavones-rich diets linked to a lower risk of hormone-related cancers, osteoporosis, menopausal symptoms, and cardiovascular diseases. These characteristics lead to the significant advancement in the studies on genetic and metabolic engineering of isoflavones in plants. As a result, a number of structural and regulatory genes involved in isoflavone biosynthesis in plants have been identified and characterized. Subsequently, they were engineered in various crop plants for the increased production of isoflavones. Furthermore, with the advent of high-throughput technologies, the regulation of isoflavone biosynthesis gains attention to increase or decrease the level of isoflavones in the crop plants. In the review, we begin with the role of isoflavones in plants, environment, and its benefits in human health. Besides, the main theme is to discuss the updated research progress in metabolic engineering of isoflavones in other plants species and regulation of production of isoflavones in soybeans.

Bacterial communities in rhizosphere and root nodules have significant contributions to the growth and productivity of the soybean (Glycine max (L.) Merr.). In this report, we analyzed the physiological properties and dynamics of bacterial community structure in rhizosphere and root nodules at different growth stages using BioLog EcoPlate and high-throughput sequencing technology, respectively. The BioLog assay found that the metabolic capability of rhizosphere is in increasing trend in the growth of soybeans as compared to the bulk soil. As a result of the Illumina sequencing analysis, the microbial community structure of rhizosphere and root nodules was found to be influenced by the variety and growth stage of the soybean. At the phylum level, Actinobacteria were the most abundant in rhizosphere at all growth stages, followed by Alphaproteobacteria and Acidobacteria, and the phylum Bacteroidetes showed the greatest change. But, in the root nodules Alphaproteobacteria were dominant. The results of the OTU analysis exhibited the dominance of Bradyrhizobium during the entire stage of growth, but the ratio of non-rhizobial bacteria showed an increasing trend as the soybean growth progressed. These findings revealed that bacterial community in the rhizosphere and root nodules changed according to both the variety and growth stages of soybean in the field.

The Silk Road pattern (SRP) is a leading mode of atmospheric circulation over mid-latitude Eurasia during boreal summer. Its temporal phase is known to be unpredictable in many climate models. Previous studies have not reached a clear consensus on the role of sea surface temperature (SST) associated with SRP. To investigate role of SST on SRP formation, we begin by comparing reanalysis with seasonal hindcast experiments of the Pusan National University coupled climate model. Although SRP cannot be predicted temporally, the ensemble runs show potential predictability in SRP related to tropical Pacific SST. While reanalysis SRP is associated with North Atlantic SST anomalies, hindcast SRP is associated with tropical Pacific SST anomalies similar to El-Nino Southern Oscillation (ENSO). To explain the different SST associations, we propose two jet biases in the climate model which may affect Rossby wave propagation. Bias in North Atlantic jet exit results in a discontinuous waveguide from North Atlantic to Asia, which may hinder propagation of waves associated with North Atlantic SST to trigger SRP. In addition, bias in subtropical western Pacific westerlies reduces the evanescent region between subtropical western Pacific and Asian jet, which may favour westward dispersion of zonally elongated waves associated with ENSO SST to trigger SRP. Therefore, we propose that the role of SST on SRP can be substantially changed depending on fidelity of model upper-level background winds. To investigate more quantitatively the roles of waveguides and the Rossby wave sources (RWS), we perform wave-making experiments using an idealised barotropic model prescribed with two different upper-level background winds, namely from reanalysis and from climate model. By comparing with result using reanalysis background winds, the preferred forcing locations - RWS hotspots - of SRP are identified from all the RWS associated with SRP in reanalysis. In addition to previously identified hotspots from the literature, a new hotspot in central North Pacific is discovered which can force SRP by westward dispersion of zonally elongated Rossby wave. Wave-making result using climate model background winds reveals that the upper-level wind bias changes the RWS hotspots locations of SRP. Experimental result is consistent with theoretical analysis of waveguide bias, and support our conclusion that the relationship between SRP and SST can be substantially changed depending on model background winds bias. The impact of our study is that this sensitivity of SRP hotspots to background winds may reduce seasonal forecast skill of SRP in models with background winds bias.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the virus strain that causes coronavirus disease 2019 (COVID-19), was first identified in Wuhan, China in December 2019. It spread to several countries across continents and infected more than one million people within three months. While there is no consensus on the treatment of the disease yet, understanding the virus and its transmission is a cardinal priority. SARS-CoV-2 can be transmitted through bodily fluid. Upon inoculation, the surface enzyme angiotensin-converting enzyme 2 (ACE2) acts as a receptor protein for viral entry. The mean incubation period is 5.1 days, and infected individuals can exhibit a variety of symptoms from fever, cough, dyspnea, and respiratory failure to even multiorgan failure. Given the current situation, it is of paramount importance to understand the virus as thoroughly as possible. In this review, we discuss the background, epidemiology, possible pathophysiology, clinical presentation, and diagnostic studies related to SARS-CoV-2 infection. We also elaborate on the current research and evidence on treatment options and vaccine development based on the literature.

Background High concentrations of mercury intake from seafood are known to cause various side effects in humans, including on the nervous system. Various studies have reported the effects of mercury concentrations in humans; however, the association between dyslipidemia, a cardiovascular disease risk factor, and mercury remains controversial. Therefore, this study aimed to investigate the association between mercury accumulation and cholesterol concentrations in a Korean population. Methods We analyzed data of a sample of 3,228 respondents obtained from the Korean National Environmental Health Survey cycle 3, surveyed between 2015 and 2017, to determine how lipid profiles changed according to the blood mercury concentrations (BHg) and urine mercury concentrations (UHg). Multiple regression analysis was used to determine the effects of mercury concentrations among various factors affecting blood cholesterol levels. Results The arithmetic mean (AM) of BHg was 2.91 (2.81-3.02) μg/L, and the geometric mean (GM) was 2.71 (2.59-2.85) μg/L. The AM of UHg was 0.52 (0.48-0.56) μg/L, and the GM was 0.35 (0.33-0.38) μg/L. Lipid profiles were more related to the BHg than to the UHg. Total cholesterol (total-C), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels increased significantly as BHg increased in males, and total-C, triglyceride, and LDL-C levels increased significantly in females. Multiple regression analysis indicated that BHg were significantly associated with total-C, HDL-C, and LDL-C levels. Conclusions We found an association between mercury exposure and the risk of dyslipidemia; however, further studies are required to elucidate a causal association.