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Image-based gauging stations offer the potential for substantial enhancement in the monitoring networks of river water levels. Nonetheless, the majority of camera gauges fall short in delivering reliable and precise measurements because of the fluctuating appearance of water in the rivers over the course of the year. In this study, we introduce a method for measuring water levels in rivers using both the traditional continuous image subtraction (CIS) approach and a SegNet neural network based on deep learning computer vision. The historical images collected from on-site investigations were employed to train three neural networks (SegNet, U-Net, and FCN) in order to evaluate their effectiveness, overall performance, and reliability. The research findings demonstrated that the SegNet neural network outperformed the CIS method in accurately measuring water levels. The root mean square error (RMSE) between the water level measurements obtained by the SegNet neural network and the gauge station's readings ranged from 0.013 m to 0.066 m, with a high correlation coefficient of 0.998. Furthermore, the study revealed that the performance of the SegNet neural network in analyzing water levels in rivers improved with the inclusion of a larger number of images, diverse image categories, and higher image resolutions in the training dataset. These promising results emphasize the potential of deep learning computer vision technology, particularly the SegNet neural network, to enhance water level measurement in rivers. Notably, the quality and diversity of the training dataset play a crucial role in optimizing the network's performance. Overall, the application of this advanced technology holds great promise for advancing water level monitoring and management in river systems.

This study presents the meteotsunami behavior in response to different storm types in the coasts of southern Asia-Pacific Ocean from 16 years water level records. Through the size- frequency analysis, the dangerous meteotsunami, wave height exceeding 0.3 m, can occur up to 44 events per year. Notably, during the extreme waves of the 2007 event, wave heights reached approximately 0.9-1.5 m in the Taiwan Strait and the western coastal areas of Taiwan. We have classified storms into six types by radar reflectivity images and satellite-derived precipitation. Findings indicate that predicted wave heights caused by bows and typhoons could reach hazardous magnitude of exceeding 2 m in a 100-yr interval. Spatial and temporal analysis reveals that meteotsunami occurrences are most frequent in the western regions during the winter to early spring months (December to April). Of all meteotsunami occurrences, cluster storms are identified as the most prevalent atmospheric forcing, accounting for 60% of meteostunamis. Typhoons have a 20% association with meteotsunamis along the east coasts of Taiwan during late summer to autumn. On the east coasts, typhoon type-induced events may be attributed to the combination effect of meteotsunamis and infra-gravity waves. Overall, this study provides the first comprehensive examination of meteotsunami-storm characteristics and their associated hazard risks in the coastal areas of the southern Asia-Pacific Ocean.

Background: Coronary artery calcification (CAC) is a well-validated parameter reflecting the extent of subclinical atherosclerosis. Atherosclerosis manifestations are commonly presented in atrial fibrillation (AF) patients. Nevertheless, the long-term cardiovascular risks in AF patients with concomitant CAC are limited. Aim: The aim of this study is to identify the prognostic impact of CAC in patients with AF. Methods: A total of 646 eligible patients who underwent noncontrast coronary computed tomography (nCCT) from January 2012 to December 2018 were evaluated and retrospectively followed up for 2 years. The patients were assessed for cardiovascular outcomes, including nonfatal myocardial infarction, nonfatal stroke, late coronary revascularization, major adverse cardiovascular event (MACE), and total coronary and total composite events, by a multivariable Cox regression hazards model with adjusting for significant confounding factors. Results: AF patients with severe CAC (CAC score [CACS] >400 Agatston units) had significantly higher risks of composite cardiovascular outcomes, including MACEs (adjusted hazard ratio [HR]: 57.18, 95% confidence interval [CI]: 2.28–1434.41, P = 0.014), total coronary events (adjusted HR: 16.48, 95% CI: 1.21–224.15, P = 0.035), and total composite events (adjusted HR: 26.35, 95% CI: 2.45–283.69, P = 0.007), than sinus rhythm patients without CAC. Moreover, severe CAC in AF patients was a significant predictor of total composite events (adjusted HR: 59.1, 95% CI: 2.16–1616.33, P = 0.016). Conclusion: Severe CAC in AF patients may cause significantly higher cardiovascular risks, highlighting the role of nCCT in determining CACs for early risk evaluation to facilitate aggressive risk modification and thereby to prevent subsequent cardiovascular events. Further, large, prospective studies are needed to validate the impact of CAC in patients with AF.

There has been over half a century since the discovery of hepatitis B virus (HBV) to now, but approximately 300 million patients with chronic hepatitis B (CHB) still live in the world, resulting in about one million deaths every year. Although currently approved antivirals (e.g., nucleoside analogues) are effective at reducing HBV replication, they have almost no impact on the existing HBV covalently closed circular DNA (cccDNA) reservoir. HBV cccDNA is a critical obstacle to the complete elimination of the virus via antiviral therapy. The true cure of HBV infection requires the eradication of viral cccDNA from HBV-infected cells; thus, the development of new agents directly or indirectly targeting HBV cccDNA is urgently needed due to the limitations of current available drugs against HBV infection. In this regard, it is the major focus of current anti-HBV research worldwide via different mechanisms to either inactivate/inhibit (functional cure) or eliminate (complete cure) HBV cccDNA. Therefore, this review discussed and summarized recent advances and challenges in efforts to inactivate/silence or eliminate viral cccDNA using anti-HBV agents from different sources, such as small molecules (including epigenetic drugs) and polypeptides/proteins, and siRNA or gene-editing approaches targeting/attenuating HBV cccDNA via different mechanisms, as well as future directions that may be considered in efforts to truly cure chronic HBV infection. In conclusion, no breakthrough has been made yet in attenuating HBV cccDNA, although a number of candidates have advanced into the phase of clinical trials. Furthermore, the overwhelming majority of the candidates function to indirectly target HBV cccDNA. No outstanding candidate directly targets HBV cccDNA. Relatively speaking, CCC_R08 and nitazoxanide may be some of the most promising agents to clear HBV infection in small molecule compounds. Additionally, CRISPR-Cas9 systems can directly target HBV cccDNA for decay and demonstrate significant anti-HBV activity. Consequently, gene-editing approaches targeting HBV cccDNA may be one of the most promising means to achieve the core goal of anti-HBV therapeutic strategies. In short, more basic studies on HBV infection need to be carried out to overcome these challenges.

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

IntroductionThe aim of this study was to develop and validate a new diabetes distress scale suitable for Chinese and Taiwanese culture.MethodsThis study collected the current diabetes distress measurement tools, re-organized current definitions about the domains of diabetes distress, and then developed a new tool. Three hundred and ninety-five participants from four hospitals in northern Taiwan were recruited by cluster randomized sampling for validity test.ResultsWe found the new diabetes distress scale had appropriate reliability and validity, including an acceptable model fit for the 12-item scale.ConclusionsThis new diabetes distress scale might be more directly related to emotional distress issues blood glucose control, improve the clinical conspicuity of diabetes distress, and even benefit the overall care of diabetic patients in Taiwan. Further studies about the validity and reliability of this new tool in a nationwide setting are needed.

Large-scale particle image velocimetry (LSPIV) provides a cost-effective, rapid, and secure monitoring tool for streamflow measurements. However, surveys of ground control points (GCPs) might affect the camera parameters through the solution of collinearity equations and then impose uncertainty on the measurement results. In this paper, we explore and present an uncertainty analysis for image-based streamflow measurements with the main focus on the ground control points. The study area was Yufeng Creek, which is upstream of the Shimen Reservoir in Northern Taiwan. A monitoring system with dual cameras was set up on the platform of a gauge station to measure the surface velocity. To evaluate the feasibility and accuracy of image-based LSPIV, a comparison with the conventional measurement using a flow meter was conducted. Furthermore, the degree of uncertainty in LSPIV streamflow measurements influenced by the ground control points was quantified using Monte Carlo simulation (MCS). Different operations (with survey times from one to nine) and standard errors (30 mm, 10 mm, and 3 mm) during GCP measurements were considered. Overall, the impacts in the case of single GCP measurement are apparent, i.e., a shifted and wider confidence interval. This uncertainty can be alleviated if the coordinates of the control points are measured and averaged with three repetitions. In terms of the standard errors, the degrees of uncertainty (i.e., normalized confidence intervals) in the streamflow measurement were 20.7%, 12.8%, and 10.7%. Given a smaller SE in GCPs, less uncertain estimations of the river surface velocity and streamflow from LSPIV could be obtained.

Abstract Background ABC1K (Activity of BC1 complex Kinase) is an evolutionarily primitive atypical kinase family widely distributed among prokaryotes and eukaryotes. The ABC1K protein kinases in Arabidopsis are predicted to localize either to the mitochondria or chloroplasts, in which plastid-located ABC1K proteins are involved in the response against photo-oxidative stress and cadmium-induced oxidative stress. Results Here, we report that the mitochondria-localized ABC1K10a functions in plant salt stress tolerance by regulating reactive oxygen species (ROS). Our results show that the ABC1K10a expression is induced by salt stress, and the mutations in this gene result in overaccumulation of ROS and hypersensitivity to salt stress. Exogenous application of the ROS-scavenger GSH significantly represses ROS accumulation and rescues the salt hypersensitive phenotype of abc1k10a. ROS overaccumulation in abc1k10a mutants under salt stress is likely due to the defect in mitochondria electron transport chain. Furthermore, defects of several other mitochondria-localized ABC1K genes also result in salt hypersensitivity. Conclusions Taken together, our results reveal that the mitochondria-located ABC1K10a regulates mitochondrial ROS production and is a positive regulator of salt tolerance in Arabidopsis.

High salinity causes ionic, osmotic, and oxidative stresses to plants, and the antioxidant enzyme Catalase2 (CAT2) plays a vital role in this process, while how CAT2 expression is regulated during plant response to high salinity remains elusive. Here, we report that phytohormone jasmonic acid (JA) impairs plant salt stress tolerance by repressing CAT2 expression in an MYC2-dependent manner. Exogenous JA application decreased plant salt stress tolerance while the jar1 mutant with reduced bioactive JA-Ile accumulation showed enhanced salt stress tolerance. JA enhanced salt-induced hydrogen peroxide (H2O2) accumulation, while treatment with H2O2-scavenger glutathione compromised such effects of JA on plant H2O2 accumulation and salt stress tolerance. In addition, JA repressed CAT2 expression in salt-stressed wild-type plant but not in myc2, a mutant of the master transcriptional factor MYC2 in JA signaling, therefore, the myc2 mutant exhibited increased salt stress tolerance. Further study showed that mutation of CAT2 largely reverted lower reactive oxygen species (ROS) accumulation, higher CAT activity, and enhanced salt stress tolerance of the myc2 mutant in myc2 cat2-1 double mutant, revealing that CAT2 functions downstream JA-MYC2 module in plant response to high salinity. Together, our study reveals that JA impairs Arabidopsis seedling salt stress tolerance through MYC2-mediated repression of CAT2 expression.

The effects of environmental factors on suspended sediment plumes in the continental shelf out of the Danshuei River estuary were numerically investigated using an unstructured-grid three-dimensional hydrodynamic model (SCHISM) together with a suspended sediment (SS) module. The coupled model (SCHISM-SS) was calibrated and validated against the in situ measurement data in 2016. Consistent with the observation results, the model simulations satisfactorily reproduced the water levels, velocities, salinities, and suspended sediment concentrations. The model was then applied to explore the role of various environmental factors in the dynamics of suspended sediment plumes from the estuary to the adjacent coastal seas. These factors include tidal forcing, salinity, river discharge, and wind stress. Analysis and comparisons of different scenario results indicated that the suspended sediment plume was greatly affected by tides, e.g., a longer plume distance resulted from a larger flux under tidal motions. A higher sediment concentration in the plume in the offshore area was also found during the neap tide, relative to that observed during the spring tide. In addition, salinity affects the movement of density currents and the spread of the sediment plume, i.e., the plume distance is longer due to the residual circulation when a salinity difference is present. Further, an extreme river flow could occur during typhoon periods and would discharge a greater water volume into the coastal region, causing the suspended sediment plume to expand from the near shore. Finally, the directions of prevailing winds can slightly influence the sediment plumes.

Almost all abiotic stresses induce reactive oxygen species (ROS) overaccumulation, causing oxidative damages to plant cells. Catalase (CAT) plays a vital role in plant oxidative stress tolerance by scavenging stress-induced excess H2O2; thus, the identification of factors regulating catalase function will shed light on the underlying regulatory mechanisms. Here, we identified leucine aminopeptidase 2 (LAP2) as a novel CAT2-interacting protein and showed a mutual promotion effect of the two proteins in plant stress responses. LAP2 has a physical interaction with CAT2 in plant cells. The loss-of-function mutant of LAP2, lap2-3, is hypersensitive to salt or osmotic stress with increased ROS accumulation and malondialdehyde content and decreased catalase activity. The lap2-3 mutant has less CAT2 protein levels as CAT2 protein stability is impaired in the mutant. Scavenging excess ROS by glutathione or overexpressing CAT2 in the lap2-3 mutant recovers its hypersensitive phenotype to salt or osmotic stress. Further study showed that CAT2 promotes LAP2 hydrolysis activity with leucine-4-methylcoumaryl-7-amides as a substrate in vivo and in vitro, and thus, similar to the lap2-3 mutant, the cat2-1 mutant also has lower γ-aminobutyric acid content than the wild type. Together, our study reveals mutual promotion effects of CAT2 and LAP2 in conferring plant salt and osmotic stress tolerance.

Estimates of extreme water level return periods in river systems are crucial for hydraulic engineering design and planning. Recorded historical water level data of Taiwan’s rivers are not long enough for traditional frequency analyses when predicting extreme water levels for different return periods. In this study, the integration of a one-dimensional flash flood routing hydrodynamic model with the Monte Carlo simulation was used to predict extreme water levels in the Danshuei River system of northern Taiwan. The numerical model was calibrated and verified with observed water levels using four typhoon events. The results indicated a reasonable agreement between the model simulation and observation data. Seven parameters, including the astronomical tide and surge height at the mouth of the Danshuei River and the river discharge at five gauge stations, were adopted to calculate the joint probability and generate stochastic scenarios via the Monte Carlo simulation. The validated hydrodynamic model driven by the stochastic scenarios was then used to simulate extreme water levels for further frequency analysis. The design water level was estimated using different probability distributions in the frequency analysis at five stations. The design high-water levels for a 200-year return period at Guandu Bridge, Taipei Bridge, Hsin-Hai Bridge, Da-Zhi Bridge, and Chung-Cheng Bridge were 2.90, 5.13, 6.38, 6.05, and 9.94 m, respectively. The estimated design water levels plus the freeboard are proposed and recommended for further engineering design and planning.

Abstract Background Most ABC transporters are engaged in transport of various compounds, but its subfamily F lacks transmembrane domain essential for chemical transportation. Thus the function of subfamily F remains further elusive. Results Here, we identified General Control Non-Repressible 20 (GCN20), a member of subfamily F, as new factor for DNA damage repair in root growth. While gcn20–1 mutant had a short primary root with reduced meristem size and cell number, similar primary root lengths were assayed in both wild-type and GCN20::GCN20 gcn20–1 plants, indicating the involvement of GCN20 in root elongation. Further experiments with EdU incorporation and comet assay demonstrated that gcn20–1 displays increased cell cycle arrest at G2/M checkpoint and accumulates more damaged DNA. This is possible due to impaired ability of DNA repair in gcn20–1 since gcn20–1 seedlings are hypersensitive to DNA damage inducers MMC and MMS compared with the wild type plants. This note was further supported by the observation that gcn20–1 is more sensitive than the wild type when subjected to UV treatment in term of changes of both fresh weight and survival rate. Conclusions Our study indicates that GCN20 functions in primary root growth by modulating DNA damage repair in Arabidopsis. Our study will be useful to understand the functions of non-transporter ABC proteins in plant growth.

Tidal estuaries provide crucial pathways for contaminant transport. The salinity levels in estuaries and coasts are conserved substances that function as natural tracers to easily understand the offshore transport of substances that are subject to environmental factors. A three-dimensional (3D) circulation and mass transport model were utilized to delineate the salinity plume in a tidal estuary and continental shelf. The numerical modeling results were compared with the tidal amplitudes and phases, velocities, and salinities at different gauging stations in 2017. Quantitatively, the simulation and measurement results are in reasonably good agreement. Furthermore, the validated model was adopted to estimate the recovery times in tidal estuaries that are subjected to extreme freshwater discharges that come from the upstream reaches during typhoon events and to analyze the influences of freshwater discharge and wind stress on the river plume around the continental shelf. The simulated results revealed that the salinity recovery time at the river mouth due to Typhoon Saola in 2012 was less than 8 days. Increased inputs from freshwater discharge resulted in changes in the distances and areas of the river plumes. Linear regression relationships between the plume distance/plume area and the total freshwater discharge inputs were established. Neap and high slack tides were associated with the maximum plume distances and areas. Excluding tidal forcing resulted in larger plume distances and areas compared to the case in which tidal forcing was considered. The southward-favorable and northward-favorable plumes were controlled by northeasterly winds and southwesterly winds, respectively. The relative importance of freshwater discharges and wind forcing was explored. The results indicate that freshwater discharges frequently dominated the river plume, except when strong southwesterly or northeasterly winds prevailed.

Background: In the era of drug-eluting stents, although bare-metal stent (BMS) remains an option for percutaneous coronary intervention (PCI), restenosis remains the Achilles' heel of BMS implantation. A recent study demonstrated several pleiotropic anti-inflammatory effects of dextromethorphan (DXM). This study aims to evaluate the effects of DXM on the outcome of PCI with BMS implantation.Methods: In this prospective, double-blind, randomized trial, we enrolled 55 patients who underwent PCI with BMS implantation from May 2006 to February 2009. The patients were divided into DXM (60 mg once daily) and placebo groups. We compared mortality rates, myocardial infarction (MI), target lesion revascularization (TLR), restenosis, stent thrombosis, and plasma levels of high-sensitivity C-reactive protein (hs-CRP) with repeated coronary angiography 6 months after the initial procedure.Results: During the 6-month follow-up period, no events of death, MI and stent thrombosis were reported in both groups. The TLR rate was 16.7% in patients receiving DXM compared to 24% receiving a placebo (P = 0.521). The restenosis rate was 30% in patients receiving DXM as compared to 40% receiving the placebo (P = 0.571). Although nonsignificant, the percentage of hs-CRP elevation was lower in the DXM group (20%) compared to the placebo group 32%; P = 0.363).Conclusions: DXM is safe to use in patients who underwent PCI. Although DXM therapy following BMS implantation did not significantly reduce the TLR and restenosis rates, it implied a trend toward a lower TLR and restenosis and reduced inflammation in the DXM group compared to the placebo group. Nonetheless, further extensive studies are warranted to elucidate the anti-restenosis effects of DXM.

Low temperature remarkably limits rubber tree (Hevea brasiliensis Muell. Arg.) growth, latex production, and geographical distribution, but the underlying mechanisms of Hevea brasiliensis cold stress response remain elusive. Here, we identified HbSnRK2.6 as a key component in ABA signaling functions in phytohormone abscisic acid (ABA)-regulated cold stress response in Hevea brasiliensis. Exogenous application of ABA enhances Hevea brasiliensis cold tolerance. Cold-regulated (COR) genes in the CBF pathway are upregulated by ABA. Transcript levels of all five HbSnRK2.6 members are significantly induced by cold, while HbSnRK2.6A, HbSnRK2.6B, and HbSnRK2.6C can be further activated by ABA under cold conditions. Additionally, HbSnRK2.6s are localized in the cytoplasm and nucleus, and can physically interact with HbICE2, a crucial positive regulator in the cold signaling pathway. Overexpression of HbSnRK2.6A or HbSnRK2.6B in Arabidopsis extensively enhances plant responses to ABA and expression of COR genes, leading to increased cold stress tolerance. Furthermore, HbSnRK2.6A and HbSnRK2.6B can promote transcriptional activity of HbICE2, thus, increasing the expression of HbCBF1. Taken together, we demonstrate that HbSnRK2.6s are involved in ABA-regulated cold stress response in Hevea brasiliensis by regulating transcriptional activity of HbICE2.

(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers.

Osmotic stress severely inhibits plant growth and development, causing huge loss of crop quality and quantity worldwide. Melatonin is an important signaling molecule that generally confers plant increased tolerance to various environmental stresses, however, whether and how melatonin participates in plant osmotic stress response remain elusive. Here, we report that melatonin enhances plant osmotic stress tolerance through increasing ROS-scavenging ability, and melatonin receptor CAND2 plays a key role in melatonin-mediated plant response to osmotic stress. Upon osmotic stress treatment, the expression of melatonin biosynthetic genes including SNAT1, COMT1, and ASMT1 and the accumulation of melatonin are increased in the wild-type plants. The snat1 mutant is defective in osmotic stress-induced melatonin accumulation and thus sensitive to osmotic stress, while exogenous melatonin enhances the tolerance of the wild-type plant and rescues the sensitivity of the snat1 mutant to osmotic stress by upregulating the expression and activity of catalase and superoxide dismutase to repress H2O2 accumulation. Further study showed that the melatonin receptor mutant cand2 exhibits reduced osmotic stress tolerance with increased ROS accumulation, but exogenous melatonin cannot revert its osmotic stress phenotype. Together, our study reveals that CADN2 functions necessarily in melatonin-conferred osmotic stress tolerance by activating ROS-scavenging ability in Arabidopsis.

Nitric oxide (NO) production via NO synthase (NOS) plays a vital role in plant tolerance to salt stress. However, the factor(s) regulating NOS-like activity in plant salt stress tolerance remains elusive. Here, we show that Arabidopsis SORTING NEXIN 1 (SNX1), which can restore H2O2-induced NO accumulation in yeast Δsnx4 mutant, functions in plant salt stress tolerance. Salt stress induced NO accumulation through promoted NOS-like activity in the wild type, but this induction was repressed in salt-stressed snx1-2 mutant with the mutation of SNX1 because NOS-like activity was inhibited in the mutant. Consistently, snx1-2 displayed reduced tolerance to high salinity with decreased survival rate compared with the wild type, and exogenous treatment with NO donor significantly rescued the hypersensitivity of the mutant to salt stress. In addition, the snx1-2 mutant with reduced NOS-like activity repressed the expression of stress-responsive genes, decreased proline accumulation and anti-oxidant ability compared with wild-type plants when subjected to salt stress. Taken together with our finding that salt induces the expression of SNX1, our results reveal that SNX1 plays a crucial role in plant salt stress tolerance by regulating NOS-like activity and thus NO accumulation.

Storm surge induced by severe typhoons has caused many catastrophic tragedies to coastal communities over past decades. Accurate and efficient prediction/assessment of storm surge is still an important task in order to achieve coastal disaster mitigation especially under the influence of climate change. This study revisits storm surge predictions using artificial neural networks (ANN) and effective typhoon parameters. Recent progress of storm surge modeling and some remaining unresolved issues are reviewed. In this paper, we chose the northeastern region of Taiwan as the study area, where the largest storm surge record (over 1.8 m) has been observed. To develop the ANN-based storm surge model for various lead-times (from 1 to 12 h), typhoon parameters are carefully examined and selected by analogy with the physical modeling approach. A knowledge extraction method (KEM) with backward tracking and forward exploration procedures is also proposed to analyze the roles of hidden neurons and typhoon parameters in storm surge prediction, as well as to reveal the abundant, useful information covered in the fully-trained artificial brain. Finally, the capability of ANN model for long-lead-time predictions and influences in controlling parameters are investigated. Overall, excellent agreement with observations (i.e., the coefficient of efficiency CE > 0.95 for training and CE > 0.90 for validation) is achieved in one-hour-ahead prediction. When the typhoon affects coastal waters, contributions of wind speed, central pressure deficit, and relative angle are clarified via influential hidden neurons. A general pattern of maximum storm surge under various scenarios is also obtained. Moreover, satisfactory accuracy is successfully extended to a much longer lead time (i.e., CE > 0.85 for training and CE > 0.75 for validation in 12-h-ahead prediction). Possible reasons for further accuracy improvement compared to earlier works are addressed.

Anthropogenic changes in tidal estuaries have significantly altered bathymetry and topography over the past half century. The geomorphic-driven changes in estuarine hydrodynamics and salt transport remain unclear. To explore this issue, a SELFE (Semi-implicit Eulaerian-Lagrangian Finite Element) -based model was developed and utilized in a case study in the Danshui River, Taiwan. The model was calibrated and validated using observed water level, current, and salinity data from 2015, 2016, and 2017. The performance of the SELFE model corresponded well to the measured data. Furthermore, the validated model was utilized to analyze the hydrodynamics, residual current, limit of salt intrusion, and residence time under the predevelopment (1981) and present (2015) conditions. The predicted results revealed that the time lag of water surface elevation at both high tide and low tide under the present condition was approximately 0.5–2 h shorter under the predevelopment condition. The residual circulation under the predevelopment condition was stronger than under the present condition for low flow, causing the limit of salt intrusion to extend further upstream under the predevelopment condition compared to the limit of salt intrusion under the present condition. The calculated residence time under the predevelopment condition was longer than the residence time under the present condition. The freshwater discharge input is a dominating factor affecting the salt intrusion and residence time in a tidal estuary. A regression correlation between the maximum distance of salt intrusion and freshwater discharge and a correlation between residence time and freshwater discharge were established to predict the limit of salt intrusion and residence time under the predevelopment and present conditions with different scenarios of freshwater discharge input.

Taiwan usually suffers severe inundation disasters during typhoons and strong rainstorms, and therefore flood mitigation is considered an important issue. To assess the effect of the Yuansantze flood diversion tunnel (YFDT) on flood mitigation at the upstream reaches of the Keelung River, a three-dimensional, unstructured grid, Finite-Volume, primitive equation Community Ocean Model (FVCOM) was used. The model was validated with observed data for water levels and inundation extent during different typhoon events. The simulated results show a good agreement with field measurements of water level with three historical typhoon events but underestimated the measured inundation extent with Typhoon Nari. The validated model was then applied to assess the flood mitigation and economic loss with the YFDT. The results demonstrated that the river level decreases approximately 3 m with the YFDT and that the inundation extent decreases by more than 50% in the Ruifang District with YFDT. The YDFT aims to not only mitigate hazards but also reduce economic losses. The average annual expected benefit after construction of the YFDT is approximately 184 million NTD in the Ruifang District.

The change of nonpoint source and freshwater discharge as a result of land use change might affect the water quality in a river. In the current study, a coupled three-dimensional hydrodynamic and water quality model was created and applied to the Danshuei River estuarine system and its adjacent coastal ocean. The hydrodynamic and water quality models were validated using observations of water surface elevation, salinity distribution and water quality state variables. The predictions of hydrodynamics, salinity, dissolved oxygen and nutrients from the model simulation quantitatively agreed with the observational data. The validated model was then used to investigate the possible effects of the nonpoint source and freshwater discharge changes at the upstream reaches on water quality conditions in the Danshuei River estuarine system. Three scenarios were investigated to predict the dissolved oxygen using model simulations. The simulated results indicated that increasing nonpoint sources at the upstream reaches degraded the dissolved oxygen under low flow conditions. However, increasing freshwater discharges at the upstream reaches would overcome the loadings of the nonpoint source, which would result in increasing the dissolved oxygen in the tidal river estuary. The model can provide a useful tool for developing management practices for nonpoint sources to protect the water quality in the estuarine system.

Low-lying coastal regions and their populations are at risk during storm surge events and high freshwater discharges from upriver. An integrated storm surge and flood inundation modeling system was used to simulate storm surge and inundation in the Tsengwen River basin and the adjacent coastal area in southern Taiwan. A three-dimensional hydrodynamic model with an unstructured grid was used, which was driven by the tidal elevation at the open boundaries and freshwater discharge at the upriver boundary. The model was validated against the observed water levels for three typhoon events. The simulation results for the model were in reasonable agreement with the observational data. The model was then applied to investigate the effects of a storm surge, freshwater discharge, and a storm surge combined with freshwater discharge during an extreme typhoon event. The super Typhoon Haiyan (2013) was artificially shifted to hit Taiwan: the modeling results showed that the inundation area and depth would cause severe overbank flow and coastal flooding for a 200 year return period flow. A high-resolution grid model is essential for the accurate simulation of storm surges and inundation.

Accurate simulations of river stages during typhoon events are critically important for flood control and are necessary for disaster prevention and water resources management in Taiwan. This study applies two artificial neural network (ANN) models, including the back propagation neural network (BPNN) and genetic algorithm neural network (GANN) techniques, to improve predictions from a one-dimensional flood routing hydrodynamic model regarding the water stages during typhoon events in the Danshuei River system in northern Taiwan. The hydrodynamic model is driven by freshwater discharges at the upstream boundary conditions and by the water levels at the downstream boundary condition. The model provides a sound physical basis for simulating water stages along the river. The simulated results of the hydrodynamic model show that the model cannot reproduce the water stages at different stations during typhoon events for the model calibration and verification phases. The BPNN and GANN models can improve the simulated water stages compared with the performance of the hydrodynamic model. The GANN model satisfactorily predicts water stages during the training and verification phases and exhibits the lowest values of mean absolute error, root-mean-square error and peak error compared with the simulated results at different stations using the hydrodynamic model and the BPNN model. Comparison of the simulated results shows that the GANN model can be successfully applied to predict the water stages of the Danshuei River system during typhoon events.

Global climate change has resulted in a gradual sea level rise. Sea level rise can cause saline water to migrate upstream in estuaries and rivers, thereby threatening freshwater habitat and drinking-water supplies. In the present study, a three-dimensional hydrodynamic model was established to simulate salinity distributions and transport time scales in the Wu River estuary of central Taiwan. The model was calibrated and verified using tidal amplitudes and phases, time-series water surface elevation and salinity distributions in 2011. The results show that the model simulation and measured data are in good agreement. The validated model was then applied to calculate the salinity distribution, flushing time and residence time in response to a sea level rise of 38.27 cm. We found that the flushing time for high flow under the present condition was lower compared to the sea level rise scenario and that the flushing time for low flow under the present condition was higher compared to the sea level rise scenario. The residence time for the present condition and the sea level rise scenario was between 10.51 and 34.23 h and between 17.11 and 38.92 h, respectively. The simulated results reveal that the residence time of the Wu River estuary will increase when the sea level rises. The distance of salinity intrusion in the Wu River estuary will increase and move further upstream when the sea level rises, resulting in the limited availability of water of suitable quality for municipal and industrial uses.

Studying mixing processes in a stratified lake is important for understanding the biological, chemical and physical processes occurring there. Statistical analyses were performed of data from a small, shallow, stratified lake in a subtropical alpine region (Yuan-Yang Lake in Taiwan) to determine the predominant physical factors in heavy-rainfall-induced mixing. This study focused on both vertical mixing in the entire water column and surface-layer mixing extending to the upper thermocline. The effects of meteorological driving forces, such as wind, heating/cooling and inflow on vertical mixing and surface layer mixing, were evaluated using the relationships between each driving force and the change in thermal stability between the pre-mixing and mixing periods. For surface layer mixing, a comparison between penetrative convection related to heating/cooling and wind-related friction velocity was conducted for each heavy rainfall event. A heat content parameter measuring thermal potential energy was introduced to further investigate inflow effects (e.g. effects of changes in discharge volume and temperature) on vertical mixing during heavy rainfall events. Results show that wind input affected vertical mixing more significantly than did other meteorological forcing factors during storm-dominant events. Indeed, wind energy input in the surface layer was more pronounced than was energy of heating/cooling for surface layer mixing. Furthermore, inflow effect was shown to be crucial during large scale and extreme weather events (i.e. lower air pressure events) in the vertical mixing process. Forcing by heating/cooling likely contributes less to mixing because it is likely less dynamic than the wind and inflow inputs with respect to internal response of the lake. In addition, a principal component analysis (PCA) modified by partial correlation was performed to verify the results quantitatively. The first and second components, which accounted for more than 90% of the total variance in the PCA, showed that the intensity of vertical mixing was affected primarily by wind-induced turbulence and inflow intrusion and was only weakly associated with the effect of net heat balance. Considering the interactions between chemical and physical processes, inflow intrusion may have an effect on dissolved oxygen concentration in the lake.

Tidal stream speeds in straits are accelerated because of geographic and bathymetric features. For instance, narrow channels and shallows can cause high tidal stream energy. In this study, water level and tidal current were simulated using a three-dimensional semi-implicit Eulerian-Lagrangian finite-element model to investigate the complex tidal characteristics in the Taiwan Strait and to determine potential locations for harnessing tidal stream energy. The model was driven by nine tidal components (M2, S2, N2, K2, K1, O1, P1, Q1, and M4) at open boundaries. The modeling results were validated with the measured data, including water level and tidal current. Through the model simulations, we found that the highest tidal currents occurred at the Penghu Channel in the Taiwan Strait. The Penghu Channel is an appropriate location for the deployment of a tidal turbine array because of its deep and flat bathymetry. The impacts of energy extraction on hydrodynamics were assessed by considering the momentum sink approach. The simulated results indicate that only minimal impacts would occur on water level and tidal current in the Taiwan Strait if a turbine array (55 turbines) was installed in the Penghu Channel.

Lake water temperature is one of the key parameters in determining the ecological conditions within a lake, as it influences both chemical and biological processes. Therefore, accurate prediction of water temperature is crucially important for lake management. In this paper, the performance of soft computing techniques including gene expression programming (GEP), which is a variant of genetic programming (GP), adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) to predict hourly water temperature at a buoy station in the Yuan-Yang Lake (YYL) in north-central Taiwan at various measured depths was evaluated. To evaluate the performance of the soft computing techniques, three different statistical indicators were used, including the root mean squared error (RMSE), the mean absolute error (MAE), and the coefficient of correlation (R). Results showed that the GEP had the best performances among other studied methods in the prediction of hourly water temperature at 0, 2 and 3 meter depths below water surface, but there was a different trend in the 1 meter depth below water surface. In this depth, the ANN had better accuracy than the GEP and ANFIS. Despite the error (RMSE value) is smaller in ANN than GEP, there is an upper bound in scatter plot of ANN that imposes a constant value, which is not suitable for predictive purposes. As a conclusion, results from the current study demonstrated that GEP provided moderately reasonable trends for the prediction of hourly water temperature in different depths. Resumen La temperatura del agua es uno de los parámetros básicos para determinar las condiciones ecológicas de un lago, ya que está influenciada por procesos químicos y biológicos. Además, la exactitud en la predicción de la temperatura del agua es esencial para el manejo del lago. En este artículo se evalúa el desempeño de técnicas de soft computing como la Programación de Expresiones de Genes (PEG), que es una variante de la Programación Genética (PG), el Sistema Neuro-fuzzy de Inferencia Adaptativa (Anfis, en inglés) y las Redes Neuronales Artificiales (RNA) para predecir la temperatura del agua en diferentes niveles de una estación flotante del lago Yuan-Yang (YYL), en el centro-norte de Taiwán. Se utilizaron tres indicadores estadísticos, el Error Cuadrático Medio (ECM), el Error Absoluto Medio (MAE, en inglés) y el Coeficiente de Correlación (R) para evaluar el desempeño de las técnicas de computación. Los resultados muestran que la PEG es más exacta en la predicción de la temperatura del agua entre 0,2 y 3 metros de profundidad. Sin embargo, se evidencia una tendencia diferente a partir del metro de profundidad. A esta distancia de la superficie, las RNA son más exactas que la PEG y el Anfis. Los resultados de este estudio probaron claramente la usabilidad del PEG y las RNA en la predicción de la temperatura del agua a diferentes profundidades.

The productivity of forests is often considered to be limited by the availability of phosphorus (P). Knowledge of the role of organic and inorganic P in humid subtropical forest soils is lacking. In this study, we used chemical fractionation and 31P nuclear magnetic resonance (NMR) spectroscopy to characterize the form of P and its distribution in undisturbed perhumid Taiwan false cypress (Chamaecyparis formosensis Matsum.) forest soils. The toposequence of transects was investigated for the humic layer from summit to footslope and lakeshore. The clay layer combined with a placic-like horizon in the subsoil may affect the distribution of soil P because both total P and organic P (Po) contents in all studied soils decreased with soil depth. In addition, Po content was negatively correlated with soil crystalline Fe oxide content, whereas inorganic P (Pi) content was positively correlated with soil crystalline Fe oxide content and slightly increased with soil depth. Thus, Pi may be mostly adsorbed by soil crystalline Fe oxides in the soils. Among all extractable P fractions, the NaOH-Po fraction appeared to be the major component, followed by NaHCO3-Po; the resin-P and HCl-Pi fractions were lowest. In addition, we found no typical trend for Pi and Po contents in soils with topographical change among the three sites. From the 31P-NMR spectra, the dominant Po form in soils from all study sites was monoesters with similar spectra. The 31P-NMR findings were basically consistent with those from chemical extraction. Soil formation processes may be the critical factor affecting the distribution of soil P. High precipitation and year-round high humidity may be important in the differentiation of the P species in this landscape.

An automated discharge imaging system (ADIS), which is a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV) technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship) and cross-section area. The ADIS was deployed at the Yu-Feng gauging station in Shimen Reservoir upper catchment, northern Taiwan. For a rigorous validation, surface velocity measurements were conducted using ADIS/LSPIV and other instruments. In terms of the averaged surface velocity, all of the measured results were in good agreement with small differences, i.e., 0.004 to 0.39 m/s and 0.023 to 0.345 m/s when compared to those from acoustic Doppler current profiler (ADCP) and surface velocity radar (SVR), respectively. The ADIS/LSPIV was further applied to measure surface velocities and discharges during typhoon events (i.e., Chan-Hom, Soudelor, Goni, and Dujuan) in 2015. The measured water level and surface velocity both showed rapid increases due to flash floods. The estimated discharges from ADIS/LSPIV and ADCP were compared, presenting good consistency with correlation coefficient R = 0.996 and normalized root mean square error NRMSE = 7.96%. The results of sensitivity analysis indicate that the components till (τ) and roll (θ) of the camera are most sensitive parameters to affect the surface velocity using ADIS/LSPIV. Overall, the ADIS based upon LSPIV technique effectively measures surface velocities for reliable estimations of river discharges during typhoon events.

A large amount of accurate river cross-section data is indispensable for predicting river stages. However, the measured river cross-section data are usually sparse in the transverse direction at each cross-section as well as in the longitudinal direction along the river channel. This study presents three algorithms to resample the river cross-section data points in both the transverse and longitudinal directions from the original data. A two-dimensional (2D) high-resolution unstructured-grid hydrodynamic model was used to assess the performance of the original and resampled cross-section data on a simulated river stage under low flow and high flow conditions. The simulated river stages are significantly improved using the resampled cross-section data based on the linear interpolation in the tidal river and non-tidal river segments. The resampled cross-section data based on the linear interpolation satisfactorily maintains the topographic and morphological features of the river channel, especially in the meandering river segment. Furthermore, the performance of the 2D and three-dimensional (3D) models on the simulated river stage was also evaluated using the resampled cross-section data. The results indicate that the 2D and 3D models reproduce similar river stages in both tidal and non-tidal river segments under the low flow condition. However, the 2D model overestimates the river stages in both the tidal and non-tidal river segments compared to the 3D model under the high flow condition. The model sensitivity was implemented to investigate the influence of bottom drag coefficient and vertical eddy viscosity on river stage using 2D and 3D models based on the linear interpolation method to resample river bed cross-section. The results reveal that the change of bottom drag coefficient has a minor impact on river stage, but the change of vertical eddy viscosity is insensitive to river stage.

Climate change is one of the key factors affecting the future quality and quantity of water in rivers and tidal estuaries. A coupled three-dimensional hydrodynamic and water quality model has been developed and applied to the Danshuei River estuarine system in northern Taiwan to predict the influences of climate change on water quality. The water quality model considers state variables including nitrogen, phosphorus, organic carbon, and phytoplankton as well as dissolved oxygen, and is driven by a three-dimensional hydrodynamic model. The hydrodynamic water quality model was validated with observational salinity distribution and water quality state variables. According to the analyses of statistical error, predictions of salinity, dissolved oxygen, and nutrients from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict water quality conditions as a result of projected climate change effects. The simulated results indicated that the dissolved oxygen concentration was projected to significantly decrease whereas nutrients will increase because of climate change. Moreover, the dissolved oxygen concentration was lower than 2 mg/L in the main stream of the Danshuei River estuary and failed to meet the water quality standard. An appropriate strategy for effective water quality management for tidal estuaries is needed given the projected persistent climate trends.