Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in ß2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

A qualitative pilot study exploring clients' and health-care professionals' experiences with aquatic therapy post-stroke in Ontario, Canada.

BACKGROUND: Aquatic therapy is beneficial for people post-stroke, as it improves their physical function, well-being, and quality of life. There is a lack of description of users' experiences and perspectives toward aquatic therapy that could elucidate contextual factors for aquatic therapy implementation. OBJECTIVES: To explore participants' experiences with aquatic therapy post-stroke as part of a participatory design project to develop an education tool-kit to address the users' needs for aquatic therapy post-stroke. METHODS: A qualitative descriptive study was employed using a purposive sampling. Letters were sent to stroke and aquatic therapy organizations. Individual interviews were conducted either by phone or Zoom with nine participants in the chronic phase of stroke and 14 health-care professionals. All transcripts were coded and analyzed independently by two researchers. Inductive thematic analysis was used to identify the main themes. RESULTS: Health-care professionals practiced aquatic therapy in rehabilitation hospitals (N = 7), community centers (N = 8) and private clinics (N = 3). From the interviews, two organizing themes were identified: (1) Importance of aquatic therapy (e.g. experiences, benefits, and program approaches); and (2) Aquatic therapy education (e.g. knowledge gaps, sources of learning and communication). CONCLUSIONS: Health-care professionals and clients reported numerous benefits of aquatic therapy post-stroke including, but not limited to, improvements in mobility, balance, wellbeing, and socialization. Lack of formal and informal education and communication as participants' transition from rehab to community were viewed as barriers to aquatic therapy use post-stroke. Developing education material and communication strategies may improve the uptake of aquatic therapy post-stroke.

Review of machine learning and deep learning models for toxicity prediction.

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Machine learning and deep learning for brain tumor MRI image segmentation.

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding.

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

Can carbon emission trading improve carbon emission performance? Evidence from a quasi-natural experiment in China.

Carbon emission trading policies play a key role in reducing carbon emissions through market-based mechanisms. In the context of China's carbon neutrality goals and carbon peaking targets, it is important to predict and evaluate the effectiveness of such policies. The combined impact of carbon trading policies on carbon emission reduction and economic output has not been well investigated in previous studies. In this study, the impact of carbon emission trading policies on regional carbon emission performance was assessed through mechanism analysis and empirical tests. The mechanism analysis showed that carbon emission intensity reduction relied on three mediating effects: technological innovation incentives, industrial structure optimization, and energy substitution. For the empirical test, the multi-time difference-in-differences (DID) method was adopted to study the impact using panel data from 30 provinces in China between 2005 and 2019. Moreover, the specific impact mechanism was further tested using mediating effects. The results showed that China's carbon trading policy has significantly affected the carbon emission performance of the pilot regions, and factors such as GDP per capita, urbanization level, and capital-labor ratio have notably contributed to the reduction of carbon emission intensity. The proportions of the three mediating effects in the total effect were estimated to be 60.98%, 23.17%, and 10.14%, respectively. This study provides an empirical approach to the study of the impact of carbon trading policy on carbon emission reduction and economic output and can serve as a reference for addressing climate change and alleviating conflicts between the environment and economic growth in similar regions.

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Scanxiety Conversations on Twitter: Observational Study.

BACKGROUND: Scan-associated anxiety (or "scanxiety") is commonly experienced by people having cancer-related scans. Social media platforms such as Twitter provide a novel source of data for observational research. OBJECTIVE: We aimed to identify posts on Twitter (or "tweets") related to scanxiety, describe the volume and content of these tweets, and describe the demographics of users posting about scanxiety. METHODS: We manually searched for "scanxiety" and associated keywords in cancer-related, publicly available, English-language tweets posted between January 2018 and December 2020. We defined "conversations" as a primary tweet (the first tweet about scanxiety) and subsequent tweets (interactions stemming from the primary tweet). User demographics and the volume of primary tweets were assessed. Conversations underwent inductive thematic and content analysis. RESULTS: A total of 2031 unique Twitter users initiated a conversation about scanxiety from cancer-related scans. Most were patients (n=1306, 64%), female (n=1343, 66%), from North America (n=1130, 56%), and had breast cancer (449/1306, 34%). There were 3623 Twitter conversations, with a mean of 101 per month (range 40-180). Five themes were identified. The first theme was experiences of scanxiety, identified in 60% (2184/3623) of primary tweets, which captured the personal account of scanxiety by patients or their support person. Scanxiety was often described with negative adjectives or similes, despite being experienced differently by users. Scanxiety had psychological, physical, and functional impacts. Contributing factors to scanxiety included the presence and duration of uncertainty, which was exacerbated during the COVID-19 pandemic. The second theme (643/3623, 18%) was the acknowledgment of scanxiety, where users summarized or labeled an experience as scanxiety without providing emotive clarification, and advocacy of scanxiety, where users raised awareness of scanxiety without describing personal experiences. The third theme was messages of support (427/3623, 12%), where users expressed well wishes and encouraged positivity for people experiencing scanxiety. The fourth theme was strategies to reduce scanxiety (319/3623, 9%), which included general and specific strategies for patients and strategies that required improvements in clinical practice by clinicians or health care systems. The final theme was research about scanxiety (50/3623, 1%), which included tweets about the epidemiology, impact, and contributing factors of scanxiety as well as novel strategies to reduce scanxiety. CONCLUSIONS: Scanxiety was often a negative experience described by patients having cancer-related scans. Social media platforms like Twitter enable individuals to share their experiences and offer support while providing researchers with unique data to improve their understanding of a problem. Acknowledging scanxiety as a term and increasing awareness of scanxiety is an important first step in reducing scanxiety. Research is needed to guide evidence-based approaches to reduce scanxiety, though some low-cost, low-resource practical strategies identified in this study could be rapidly introduced into clinical care.

Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor.

The United States is experiencing the most profound and devastating opioid crisis in history, with the number of deaths involving opioids, including prescription and illegal opioids, continuing to climb over the past two decades. This severe public health issue is difficult to combat as opioids remain a crucial treatment for pain, and at the same time, they are also highly addictive. Opioids act on the opioid receptor, which in turn activates its downstream signaling pathway that eventually leads to an analgesic effect. Among the four types of opioid receptors, the µ subtype is primarily responsible for the analgesic cascade. This review describes available 3D structures of the µ opioid receptor in the protein data bank and provides structural insights for the binding of agonists and antagonists to the receptor. Comparative analysis on the atomic details of the binding site in these structures was conducted and distinct binding interactions for agonists, partial agonists, and antagonists were observed. The findings in this article deepen our understanding of the ligand binding activity and shed some light on the development of novel opioid analgesics which may improve the risk benefit balance of existing opioids.

Molecular Dynamics Refinement of Open State Serotonin 5-HT3A Receptor Structures.

Pentameric ligand-gated ion channels play an important role in mediating fast neurotransmissions. As a member of this receptor family, cation-selective 5-HT3 receptors are a clinical target for treating nausea and vomiting associated with chemotherapy and radiation therapy (Thompson and Lummis, 2006). Multiple cryo-electron microscopy (cryo-EM) structures of 5-HT3 receptors have been determined in distinct functional states (e.g., open, closed, etc.) (Basak et al., 2018; Basak et al., 2018; Polovinkin et al., 2018; Zhang et al., 2015). However, recent work has shown that the transmembrane pores of the open 5-HT3 receptor structures rapidly collapse and become artificially asymmetric in molecular dynamics (MD) simulations. To avoid this hydrophobic collapse, Dämgen and Biggin developed an equilibration protocol that led to a stable open state structure of the glycine receptor in MD simulations (Dämgen and Biggin, 2020). However, the protocol failed to yield open-like structures of the 5-HT3 receptor in our simulations. Here, we present a refined equilibration protocol that involves the rearrangement of the transmembrane helices to achieve stable open state structures of the 5-HT3 receptor that allow both water and ion permeation through the channel. Notably, channel gating is mediated through collective movement of the transmembrane helices, involving not only pore lining M2 helices but also their cross-talk with the adjacent M1 and M3 helices. Thus, the successful application of our refined equilibration protocol underscores the importance of the conformational coupling between the transmembrane helices in stabilizing open-like structures of the 5-HT3 receptor.

A Systematic Review on Navigation Programs for Persons Living With Dementia and Their Caregivers.

BACKGROUND AND OBJECTIVES: System navigation programs are becoming more available to meet the needs of patients with complex care needs. The aim of this review was to systematically assess the outcomes of navigation programs for persons with dementia and their family caregivers. RESEARCH DESIGN AND METHODS: A systematic review methodology was employed. Ten databases were searched for all relevant articles published until October 30, 2021. English-language full-text articles were included if they focused on implemented navigation program(s) that primarily supported persons with dementia who were aged 50 or older. Methodological quality was assessed by 2 independent raters using the Physiotherapy Evidence Database Scale, the STrengthening the Reporting of OBservational studies in Epidemiology checklist, and the Mixed Methods Appraisal Tool. RESULTS: Fourteen articles were included in the review. There was Level 1 evidence for the benefits of system navigation programs on delaying institutionalization, wherein benefits appeared to be specific to interventions that had an in-person component. There was Level 1 (n = 4) and Level 3 (n = 1) evidence on service use from time of diagnosis to continued management of dementia. Finally, Level 1 to Level 5 evidence indicated a number of benefits on caregiver outcomes. DISCUSSION AND IMPLICATIONS: There is strong evidence on the benefits of system navigation for people with dementia on delaying institutionalization and caregiver outcomes, but outcomes across other domains (i.e., functional independence) are less clear, which may be due to the varied approaches within system navigation models of care.

Electrostatic Contributions to the Binding Free Energy of Nicotine to the Acetylcholine Binding Protein.

Biomolecular binding relies on specific attractive interactions between two partner molecules, including electrostatics, dispersion, hydrophobicity, and solvation. Assessing the contributions of electrostatic interactions to binding is key to the understanding of ligand binding mechanisms and the design of improved biomolecular binders. For example, nicotine is a well-known agonist of nicotinic acetylcholine receptors (nAChRs), but the molecular mechanisms for the differential action of nicotine on brain and muscle nAChRs remain elusive. In this work, we have chosen the acetylcholine binding protein (AChBP) in complex with nicotine as a model system to interrogate the electrostatic contributions to nicotine binding. Our absolute binding free energy simulations confirm that nicotine binds AChBP predominantly in its protonated (charged) form. By comparing energetic contributions from decomposed interactions for either neutral or charged nicotine, our calculations shed light on the nature of the binding of nicotine to the AChBP. The preferred binding of charged nicotine over neutral nicotine originates from its stronger electrostatic interactions with AChBP, a cation-π interaction to a tryptophan residue and a hydrogen bond between nicotine and the backbone carbonyl of the tryptophan, whereas the major force driving the binding process appears to be van der Waals interactions. The various nonelectrostatic terms can also indirectly modulate the electrostatic interactions through fine-tuning the binding pose of the ligand in the binding site, providing an explanation of why the binding specificity of nicotine to the brain versus muscle nAChRs is driven by electrostatic interaction, given that the immediate binding site residues, including the key tryptophan residue, are identical in the two receptors.

Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China.

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds.

Ammonia stimulates SCAP/Insig dissociation and SREBP-1 activation to promote lipogenesis and tumour growth.

Tumorigenesis is associated with elevated glucose and glutamine consumption, but how cancer cells can sense their levels to activate lipid synthesis is unknown. Here, we reveal that ammonia, released from glutamine, promotes lipogenesis via activation of sterol regulatory element-binding proteins (SREBPs), endoplasmic reticulum-bound transcription factors that play a central role in lipid metabolism. Ammonia activates the dissociation of glucose-regulated, N-glycosylated SREBP-cleavage-activating protein (SCAP) from insulin-inducible gene protein (Insig), an endoplasmic reticulum-retention protein, leading to SREBP translocation and lipogenic gene expression. Notably, 25-hydroxycholesterol blocks ammonia to access its binding site on SCAP. Mutating aspartate D428 to alanine prevents ammonia binding to SCAP, abolishes SREBP-1 activation and suppresses tumour growth. Our study characterizes the unknown role, opposite to sterols, of ammonia as a key activator that stimulates SCAP-Insig dissociation and SREBP-1 activation to promote tumour growth and demonstrates that SCAP is a critical sensor of glutamine, glucose and sterol levels to precisely control lipid synthesis.

Rapid redesign and effect on clinical workload of a supra-regional burns and plastic surgery service during the COVID-19 pandemic.

INTRODUCTION: In March 2020, South Wales experienced the most significant COVID-19 outbreak in the UK outside of London. We share our experience of the rapid redesign and subsequent change in activity in one of the busiest supra-regional burns and plastic surgery services in the UK. METHODS: A time-matched retrospective service evaluation was completed for a 7-week "COVID-19" study period and the equivalent weeks in 2018 and 2019. The primary aim of this study was to evaluate plastic surgery theatre use and the impact of service redesign. Comparison between study periods was tested for statistical significance using two-tailed t-tests. RESULTS: Operation numbers reduced by 64% and total operating time by 70%. General anaesthetic cases reduced from 41% to 7% (p<0.0001), and surgery was mainly carried out in ringfenced daycase theatres. Emergency surgery decreased by 84% and elective surgery by 46%. Cancer surgery as a proportion of total elective operating increased from 51% to 96% (p<0.0001). The absolute number of cancer-related surgeries undertaken was maintained despite the pandemic. CONCLUSION: Rapid development of COVID-19 SOPs minimised inpatient admissions. There was a significant decrease in operating while maintaining emergency and cancer surgery. Our ringfenced local anaesthetic Plastic Surgery Treatment Centre was essential in delivering a service. COVID-19 acted as a catalyst for service innovations and the uptake of activities such as telemedicine, virtual MDTs, and online webinars. Our experiences support the need for a core burns and plastic service during a pandemic, and show that the service can be effectively redesigned at speed.

A Dangerous Mimic: Chronic Pancreatitis Masquerading As Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAC) is the most common form of pancreatic cancer in adults, although extremely rare before the age of 40 years. It is known that the cytology of chronic pancreatitis can mimic pancreatic adenocarcinoma. We present a case of a 13-year-old male with chronic pancreatitis that was misdiagnosed as PAC. The patient subsequently underwent a Whipple procedure, highlighting the importance of a correct diagnosis prior to undergoing invasive surgical procedures.

Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review.

OBJECTIVES: To estimate COVID-19 infections and deaths in healthcare workers (HCWs) from a global perspective during the early phases of the pandemic. DESIGN: Systematic review. METHODS: Two parallel searches of academic bibliographic databases and grey literature were undertaken until 8 May 2020. Governments were also contacted for further information where possible. There were no restrictions on language, information sources used, publication status and types of sources of evidence. The AACODS checklist or the National Institutes of Health study quality assessment tools were used to appraise each source of evidence. OUTCOME MEASURES: Publication characteristics, country-specific data points, COVID-19-specific data, demographics of affected HCWs and public health measures employed. RESULTS: A total of 152 888 infections and 1413 deaths were reported. Infections were mainly in women (71.6%, n=14 058) and nurses (38.6%, n=10 706), but deaths were mainly in men (70.8%, n=550) and doctors (51.4%, n=525). Limited data suggested that general practitioners and mental health nurses were the highest risk specialities for deaths. There were 37.2 deaths reported per 100 infections for HCWs aged over 70 years. Europe had the highest absolute numbers of reported infections (119 628) and deaths (712), but the Eastern Mediterranean region had the highest number of reported deaths per 100 infections (5.7). CONCLUSIONS: COVID-19 infections and deaths among HCWs follow that of the general population around the world. The reasons for gender and specialty differences require further exploration, as do the low rates reported in Africa and India. Although physicians working in certain specialities may be considered high risk due to exposure to oronasal secretions, the risk to other specialities must not be underestimated. Elderly HCWs may require assigning to less risky settings such as telemedicine or administrative positions. Our pragmatic approach provides general trends, and highlights the need for universal guidelines for testing and reporting of infections in HCWs.

An approach for runoff and sediment nexus analysis under multi-flow conditions in a hyper-concentrated sediment river, Southwest China.

Nexus of runoff and sediment in watercourses is important for sustainable watershed management, especially in rivers with hyper-concentrated sediments. However, complex relationships between runoff and sediment were not fully understood. In this research, Mann-Kendall (MK) and moving t-test methods were used to confirm the suitable temporal range based on time series data of precipitation in the Anning River Basin from 1971 to 2017. At the same time, the month of typical precipitation (i.e., the largest annual average precipitation) was identified. A model of runoff and sediment over two stations from 2010 to 2015 were constructed respectively based on copula analysis method. Synchronous and asynchronous probabilities of runoff and sediment were analysed from the temporal and spatial aspects separately. The encounter probabilities of runoff and sediment over multiple periods and at two stations were analysed. The sediment distribution was illustrated under multiple runoff distribution conditions. The encounter probability of runoff and sediment distribution changed greatly, which was principally due to employment of reservoir operation, soil and water conservation measures. The variability of the joint probability was much obvious in tributary regions than that of the mainstream areas of Anning River basin. The results could be adopted to improve the understanding of interactive relationships between runoff and sediment and provide a significant references for regional management of soil and water.