Noble Metal Nanoparticle-Based Photothermal Therapy: Development and Application in Effective Cancer Therapy.

Photothermal therapy (PTT) is a promising cancer therapy modality with significant advantages such as precise targeting, convenient drug delivery, better efficacy, and minimal adverse effects. Photothermal therapy effectively absorbs the photothermal transducers in the near-infrared region (NIR), which induces the photothermal effect to work. Although PTT has a better role in tumor therapy, it also suffers from low photothermal conversion efficiency, biosafety, and incomplete tumor elimination. Therefore, the use of nanomaterials themselves as photosensitizers, the targeted modification of nanomaterials to improve targeting efficiency, or the combined use of nanomaterials with other therapies can improve the therapeutic effects and reduce side effects. Notably, noble metal nanomaterials have attracted much attention in PTT because they have strong surface plasmon resonance and an effective absorbance light at specific near-infrared wavelengths. Therefore, they can be used as excellent photosensitizers to mediate photothermal conversion and improve its efficiency. This paper provides a comprehensive review of the key role played by noble metal nanomaterials in tumor photothermal therapy. It also describes the major challenges encountered during the implementation of photothermal therapy.

Application of Nanomaterial-Based Sonodynamic Therapy in Tumor Therapy.

Sonodynamic therapy (SDT) has attracted significant attention in recent years as it is an innovative approach to tumor treatment. It involves the utilization of sound waves or ultrasound (US) to activate acoustic sensitizers, enabling targeted drug release for precise tumor treatment. This review aims to provide a comprehensive overview of SDT, encompassing its underlying principles and therapeutic mechanisms, the applications of nanomaterials, and potential synergies with combination therapies. The review begins by introducing the fundamental principle of SDT and delving into the intricate mechanisms through which it facilitates tumor treatment. A detailed analysis is presented, outlining how SDT effectively destroys tumor cells by modulating drug release mechanisms. Subsequently, this review explores the diverse range of nanomaterials utilized in SDT applications and highlights their specific contributions to enhancing treatment outcomes. Furthermore, the potential to combine SDT with other therapeutic modalities such as photothermal therapy (PTT) and chemotherapy is discussed. These combined approaches aim to synergistically improve therapeutic efficacy while mitigating side effects. In conclusion, SDT emerges as a promising frontier in tumor treatment that offers personalized and effective treatment options with the potential to revolutionize patient care. As research progresses, SDT is poised to play a pivotal role in shaping the future landscape of oncology by providing patients with a broader spectrum of efficacious and tailored treatment options.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

The function of sphingolipids in different pathogenesis of Alzheimer's disease: A comprehensive review.

Sphingolipids (SPLs) represent a highly diverse and structurally complex lipid class. The discussion of SPL metabolism-related issues is of importance in understanding the neuropathological progression of Alzheimer's disease (AD). AD is characterized by the accumulation of extracellular deposits of the amyloid ß-peptide (Aß) and intraneuronal aggregates of the microtubule-associated protein tau. Critical roles of Aß oligomer deposited and ganglioside GM1 could be formed as "seed" from insoluble GAß polymer in initiating the pathogenic process, while tau might also mediate SPLs and their toxicity. The interaction between ceramide and α-Synuclein (α-Syn) accelerates the aggregation of ferroptosis and exacerbates the pathogenesis of AD. For instance, reducing the levels of SPLs can mitigate α-Syn accumulation and inhibit AD progression. Meanwhile, loss of SPLs may inhibit the expression of APOE4 and confer protection against AD, while the loss of APOE4 expression also disrupts SPLs homeostasis. Moreover, the heightened activation of sphingomyelinase promotes the ferroptosis signaling pathway, leading to exacerbated AD symptoms. Ferroptosis plays a vital role in the pathological progression of AD by influencing Aß, tau, APOE, and α-Syn. Conversely, the development of AD also exacerbates the manifestation of ferroptosis and SPLs. We are compiling the emerging techniques (Derivatization and IM-MS) of sphingolipidomics, to overcome the challenges of AD diagnosis and treatment. In this review, we examined the intricate neuro-mechanistic interactions between SPLs and Aß, tau, α-Syn, APOE, and ferroptosis, mediating the onset of AD. Furthermore, our findings highlight the potential of targeting SPLs as underexplored avenue for devising innovative therapeutic strategies against AD.

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery.

Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr-1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56-76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data.

Multifunctional Nanoplatform for NIR-II Imaging-Guided Synergistic Oncotherapy.

Tumors are a major public health issue of concern to humans, seriously threatening the safety of people's lives and property. With the increasing demand for early and accurate diagnosis and efficient treatment of tumors, noninvasive optical imaging (including fluorescence imaging and photoacoustic imaging) and tumor synergistic therapies (phototherapy synergistic with chemotherapy, phototherapy synergistic with immunotherapy, etc.) have received increasing attention. In particular, light in the near-infrared second region (NIR-II) has triggered great research interest due to its penetration depth, minimal tissue autofluorescence, and reduced tissue absorption and scattering. Nanomaterials with many advantages, such as high brightness, great photostability, tunable photophysical properties, and excellent biosafety offer unlimited possibilities and are being investigated for NIR-II tumor imaging-guided synergistic oncotherapy. In recent years, many researchers have tried various approaches to investigate nanomaterials, including gold nanomaterials, two-dimensional materials, metal sulfide oxides, polymers, carbon nanomaterials, NIR-II dyes, and other nanomaterials for tumor diagnostic and therapeutic integrated nanoplatform construction. In this paper, the application of multifunctional nanomaterials in tumor NIR-II imaging and collaborative therapy in the past three years is briefly reviewed, and the current research status is summarized and prospected, with a view to contributing to future tumor therapy.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Effect of virtual reality-based exercise and physical exercise on adolescents with overweight and obesity: study protocol for a randomised controlled trial.

INTRODUCTION: Obesity is a complex and multifactorial disease that has affected many adolescents in recent decades. Clinical practice guidelines recommend exercise as the key treatment option for adolescents with overweight and obesity. However, the effects of virtual reality (VR) exercise on the physical and brain health of adolescents with overweight and obese remain unclear. This study aims to evaluate the effects of physical and VR exercises on physical and brain outcomes and explore the differences in benefits between them. Moreover, we will apply a multiomics analysis to investigate the mechanism underlying the effects of physical and VR exercises on adolescents with overweight and obesity. METHODS AND ANALYSIS: This randomised controlled clinical trial will include 220 adolescents with overweight and obesity aged between 11 and 17 years. The participants will be randomised into five groups after screening. Participants in the exercise groups will perform an exercise programme by adding physical or VR table tennis or soccer classes to routine physical education classes in schools three times a week for 8 weeks. Participants in the control group will maintain their usual physical activity. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include changes in other physical health-related parameters, brain health-related parameters and multiomics variables. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital and registered in the Chinese Clinical Trial Registry. Dissemination of the findings will include peer-reviewed publications, conference presentations and media releases. TRIAL REGISTRATION NUMBER: ChiCTR2300068786.

Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003-2019.

The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) together form one of the largest marginal sea systems in the world, including enclosed and semi-enclosed ocean margins and a wide continental shelf influenced by the Changjiang River and the strong western boundary current (Kuroshio). Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML-ECS). MeSAA-ML-ECS introduced semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO2 achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE = 20 µatm) and mean absolute percentage deviation (APD = 4.12 %) for independent in situ validation dataset. During 2003-2019, the annual average CO2 sinks in the BS, YS, ECS, and entire study area were 0.16 ± 0.26, 3.85 ± 0.68, 14.80 ± 3.09, and 18.81 ± 3.81 Tg C/yr, respectively. Under continuously increasing atmospheric CO2 concentration, the BS changed from a weak source to a weak sink, the YS experienced interannual fluctuations but did not show significant trend, while the ECS acted as a strong sink with CO2 absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO2 uptake in the entire study area increased by 85 % in 17 years. For the first time, we present the most refined variation in the satellite-derived pCO2 and air-sea CO2 flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.

Human and natural activities regulate organic matter transport in Chinese rivers.

Rivers connect terrestrial and aquatic ecosystems and export approximately 55.47 % of the net terrestrial carbon fixation. However, due to unavailable high-frequency monitoring data, litter is known about diurnal variation in riverine carbon transport on a national scale. Based on daily measurements between March 2021 and February 2022 at 1491 stations across China, this study clarified the spatiotemporal variations in riverine organic matter indicated by chemical oxygen demand (COD). Spatially, COD content showed a spatial pattern with high values in the northwest (p < 0.05), and COD flux was determined by water discharge (84.01 %). Human activities explained 73.20 % of the spatial variations in riverine COD content; in particular, agricultural planting significantly elevated riverine COD (r = 0.73, p < 0.01). Seasonally, 95.53 % of stations showed significant seasonal variations in COD contents (p < 0.05); 69.72 % (25.81 %) were identified as Type II (III) typically had the maximum (minimum) COD in summer (autumn). Moreover, except for human activities (41.08 ± 22.94 %), natural factors also contributed 47.41 ± 24.04 % to the seasonal variations. In summer, high temperatures increased COD by promoting algal proliferation at Type II stations; however, heavy precipitation diluted COD contents at Type III stations. In these cases, seasonal measurements were essential for estimating riverine organic matter transport, especially the values measured in spring and winter. This study has significant implications for managing the aquatic environment, estimating riverine organic matter transport, and balancing the global carbon budget.

Gate-Tunable Critical Current of the Three-Dimensional Niobium Nanobridge Josephson Junction.

Recent studies have shown that the critical currents of several metallic superconducting nanowires and Dayem bridges can be locally tuned by using a gate voltage (Vg). Here, we report a gate-tunable Josephson junction structure constructed from a three-dimensional (3D) niobium nanobridge junction (NBJ) with a voltage gate on top. Measurements up to 6 K showed that the critical current of this structure can be tuned to zero by increasing Vg. The critical gate voltage was reduced to 16 V and may possibly be reduced further by reducing the thickness of the insulation layer between the gate and the NBJ. Furthermore, the flux modulation generated by Josephson interference of two parallel 3D NBJs can also be tuned by using Vg in a similar manner. Therefore, we believe that this gate-tunable Josephson junction structure is promising for superconducting circuit fabrication at high integration levels.

Geometric Scaling of the Current-Phase Relation of Niobium Nanobridge Junctions.

The nanobridge junction (NBJ) is a type of Josephson junction that is advantageous for the miniaturization of superconducting circuits. However, the current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal function, which has been explained by a simplified model with correlation only to its effective length. Here, we investigated both measured and calculated CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank structure. From a sine-wave to a sawtooth-like form, we showed that deviated CPRs of NBJs can be described quantitatively by its skewness Δθ. Furthermore, the measured dependence of Δθ on the critical current I0 from 108 NBJs turned out to be consistent with the calculated dependence derived from the change in geometric dimensions. This suggested that the CPRs of NBJs can be tuned by their geometric dimensions. In addition, the calculated scaling behavior of Δθ versus I0 in 3D space was provided for the future design of superconducting circuits of a high integration level by using niobium NBJs.

Sewage treatment decreased organic carbon resources in Hong Kong waters during 1986-2020.

Riverine organic carbon (OC) transport plays a role in regulating terrestrial and marine carbon pools and deteriorating coastal water quality. However, long-term OC transport in Asian rivers and its diffusion in marginal seas have remained unreported. This study reported the spatiotemporal variations in OC resources for Hong Kong waters, China, based on monthly monitoring data collected at 82 river stations and 94 ocean sites during 1986-2020. The station-based riverine OC varied spatially and was generally high, with a mean value of 1.4-52.0 mg/L. Moreover, along with improving water quality, OC at 97.6% of the river stations decreased during 1986-2020; overall, sewage treatment accounted for 83.4% of the exponential decrease in riverine OC (R2 = 0.68, p < 0.01). However, the reduction in riverine OC accounted for only 10.4% of the reduction in the marine five-day biochemical oxygen demand (BOD5), which occurred at 70.2% of the ocean sites, especially those closest to the shore. The linear reduction in the marine BOD5 (R2 = 0.24, p < 0.01) was mainly attributed to reduced OC input from the adjacent Pearl River (61.9%) and decreases in phytoplankton growth (19.0%). These results indicated that sewage treatment improved water quality and decreased OC resources in Hong Kong waters, which can serve as a sustainable development model for other coastal cities. This study has important implications for mitigating organic pollution in the context of human efforts to manage the water environment.

The carbon sink of the Coral Sea, the world's second largest marginal sea, weakened during 2006-2018.

The latest reports show that the ocean absorbs approximately 26 % of anthropogenic CO2 and that the carbon sink of the global ocean (air-sea CO2 flux) is continually increasing, while variations in different marginal seas are complicated. The Coral Sea, the second largest marginal sea in the world, is characterized by a generally oligotrophic basin and borders the biodiversity hotspot of Great Barrier Reef. In this study, we proposed a semianalytical method and reconstructed the first high-resolution satellite-based pCO2 and air-sea CO2 flux dataset from 2006 to 2018 for the Coral Sea. This dataset performed well in the basin (RMSE<10 µatm, R2 > 0.72) and coral reef areas (RMSE<12 µatm, R2 > 0.8) based on validation by a massive independent dataset. We found that sea surface pCO2 is increasing (1.8 to 2.7 µatm/year) under the forcing of increasing atmospheric CO2, and the pCO2 growth rate in water is faster than that in the atmosphere. The combination of increasing sea surface pCO2, high pCO2 seawater from coral reef areas, and the low depletion capacity of the oligotrophic basin led to a gradual weakening of the carbon sink in the Coral Sea, with the 2016 carbon sink being 52 % of that in 2006. This weakening was more pronounced after strong El Niño events (e.g., 2007, 2010, and 2016), with the corresponding high SST and low wind speed further weakening the carbon sink. This understanding of the long-term change in the Coral Sea provides new insight on the carbonate system variation and carbon sink capacity evolution in seawater under increasing atmospheric CO2.

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks.

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%.

Effect of ultra-early intervention of NDT therapy on nerve and motor development in infants at high risk of cerebral palsy.

INTRODUCTION: The aim of the study was to investigate the effect of ultra-early intervention of nerve and motor development in infants at high risk of cerebral palsy. MATERIAL AND METHODS: One hundred and twenty cases of infants born in The Affiliated Hospital of Harbin Medical University from January 2017 to January 2019 and diagnosed with high risk of cerebral palsy were included in the observation group. In addition, 120 cases of infants at high risk of cerebral palsy (three to five months old) who were admitted to this hospital during the same period were included in the control group, and 120 healthy infants born in the same hospital were included in the healthy group. Intervention was performed on the observation group after diagnosis (within seven days of birth), mainly using neurodevelopmental therapy (NDT). Children in the control group underwent intervention after diagnosis (at three to five months old) using the same measures. The healthy group underwent no intervention. Changes in various indicators were compared among the observation group, healthy group, and control group. RESULTS: At baseline and at three months, the developmental quotient (DQ) at all functional areas, total DQ, and GESELL development scale (GDS) scores were significantly lower in the observation and control groups than in the healthy group ( p < 0.05). At six months, 12 months, 18 months, and 24 months, the DQ at all functional areas, total DQ, and GDS (adaptability, gross motor, fine motor, language, personal social interaction) scores in the observation and control groups were significantly lower than those in the healthy group ( p < 0.05). However, the observation group scores were significantly higher than the control group scores ( p < 0.05). In the observation group, the normalisation rate was higher than in the control group, and the incidence rate of cerebral palsy and full developmental delay was lower than in the control group ( p < 0.05). CONCLUSIONS: Ultra-early diagnosis and NDT intervention can significantly accelerate the motor development of infants at high risk of cerebral palsy. The earlier, the better. Ultra-early intervention can promote the normalisation of infants at high risk of cerebral palsy and significantly reduce the risk of progression to cerebral palsy.

[Analysis of a child with mental retardation due to a de novo variant of the KAT6A gene].

OBJECTIVE: To explore the genetic etiology for a child featuring mental retardation and speech delay. METHODS: Clinical data of the child was collected. DNA was extracted from peripheral blood samples of the child and members of his pedigree. Whole exome sequencing was carried out for the child, and candidate variants were verified by Sanger sequencing. Prenatal diagnosis was provided for his mother upon her subsequent pregnancy. RESULTS: The child has mainly featured mental retardation, speech delay, ptosis, strabismus, photophobia, hyperactivity, and irritability. Whole exome sequencing revealed that he has harbored a pathogenic heterozygous variant of the KAT6A gene, namely c.5314dupA (p.Ser1772fs*20), which was not detected in either of his parents. The child was diagnosed with Arboleda-Tham syndrome. The child was also found to harbor a hemizygous c.56T>G (p.Leu19Trp) variant of the AIFM1 gene, for which his mother was heterozygous and his phenotypically normal maternal grandfather was hemizygous. Pathogenicity was excluded. Prenatal diagnosis has excluded the c.5314dupA variant of the KAT6A gene in the fetus. CONCLUSION: The heterozygous c.5314dupA (p.Ser1772fs*20) variant of the KAT6A gene probably underlay the Arboleda-Tham syndrome in this child. Above finding has enabled genetic counseling and prenatal diagnosis for this pedigree.

Human activities changed organic carbon transport in Chinese rivers during 2004-2018.

Rivers serve as regulators of global climate by releasing greenhouse gases, burying particulate carbon, and connecting different ecosystem carbon pools. However, long-term organic carbon (OC) transport features across different Asian rivers are not well known due to unavailable data. Based on routinely monitored environmental and hydrological data during 2004-2018, this study investigated the spatiotemporal variations in dissolved (DOC) and particulate OC (POC) transport across 41 rivers in China. Across different rivers, both DOC (1.35 - 16.8 mg/L) and POC (0.27 - 4.48 mg/L) concentrations covered wide ranges. The DOC content was high in the north and low in the south, with significantly higher (t test, p < 0.01) values for rivers north of 30°N (5.39 ± 3.66 mg/L vs. 2.39 ± 1.14 mg/L). Human activities greatly influenced the riverine DOC and POC distributions. The riverine ammonia nitrogen (NH+ 4-N) content was positively correlated with DOC (r = 0.81 and p < 0.01) and explained 85.59% of its spatial variation. High vegetation coverage had significant effects on decreasing the riverine POC content, with r = -0.55 and p < 0.05. During 2004-2018, water pollution prevention and control strategies decreased DOC concentrations in 60.98% of rivers; meanwhile, anthropogenic vegetation restoration and dam construction led to POC content decreases in 90.48% of rivers. Importantly, along with DOC and POC changes, increasing DOC/POC ratios were found in 90.48% of the rivers, with 42.86% being significant, which indicated that Chinese rivers are losing their Asian features of low DOC/POC ratios due to artificial disturbance. This study is significant for accurately quantifying greenhouse gas emissions, carbon burial, and OC export to estuaries by Chinese rivers.

A Copula-based interval linear programming model for water resources allocation under uncertainty.

Water scarcity tends to be aggravated by increase in water demand with the trend of socio-economic development. Thus, non-stationary characteristics of water demand should be identified in water resources allocation (WRA) to alleviate the potential influences from water shortages. In this study, a Copula-based interval linear programming model was established for regional WRA. Through combining correlation analysis and an interval linear programming model, this model can: 1) identify interactions between water demand and socio-economic development levels based on Copula functions, 2) explore variations in water shortage with consideration of multiple risk tolerance levels of decision-makers based on Copula sampling, and 3) obtain desired strategies for WRA through an interval linear programming model. Also, Dalian City in China was selected as a case study area to verify the effectiveness of the model for WRA to five water users (i.e., agricultural sector, industrial sector, public service sector, domestic residents, and ecological environment). Considering multiple tolerance levels of decision-makers to water shortage risk, three scenarios (i.e., S1 to S3), indicating 20%, 40%, and 60% of their low, medium, and high tolerance levels, were proposed. The results showed that the correlation between the amount of water demand and indicators of socio-economic development can be described by Clayton and Gaussian Copula functions. The total water supply of Dalian in 2030 would increase by 2.06%-2.65%, compared with the one in 2025. The allocation of water resources across districts was influenced by varied water demand, energy consumption, and risk tolerance levels. Compared with the amount of water allocation in 2025, the contribution of transferred water sources would increase by 6.71% and 7.04% under S1 and S2 in 2030, respectively, and decrease by 14.31% under S3. With the increase of risk tolerance levels of decision-makers, the amount of water supply in Dalian City would gradually decrease.

Different storm responses of organic carbon transported to Lake Taihu by the eutrophic Tiaoxi River, China.

Low-frequency high-magnitude storms can flush disproportionate amounts of terrigenous dissolved organic carbon (DOC) and particulate organic carbon (POC) into rivers during a short period. However, previous studies focused on the impacts of storms on organic carbon transport in headwater streams that are minimally influenced by human activities and are far from lakes. To better estimate the lake carbon budget and manage lake water environments, we need to understand the transport of storm-induced organic carbon into lakes by eutrophic rivers. Based on daily and hourly time-series monitoring data, this paper systematically studied the influences of storm precipitation on DOC and POC transport in the eutrophic Tiaoxi River entering Lake Taihu, the 3rd largest freshwater lake in China. The results showed that seven storms transported 59% of the annual total organic carbon into Lake Taihu in 2019, and all storms resulted in transport peaks. During the storm period on August 9-16, 2019, DOC was negatively related to the water level (r = -0.44, p < 0.05), but POC responded positively (r = 0.52, p < 0.05); allochthonous organic carbon contents were elevated, but the autochthonous components were diluted. Moreover, the storm-induced input of riverine organic carbon influenced the lake water environment across a large region, and the impacts lasted more than 10 days. These findings have important implications for accurately estimating riverine organic carbon fluxes into lakes and making better-informed decisions about when to pump drinking water from lakes.