Pub Date : 2023-11-14DOI: 10.22541/au.169994410.04755017/v1
Yalan Peng, Zuming Lin, Lili Zhu, Shiqing Han, Sha-Hua Huang, Ran Hong
Valbenazine (Ingrezza), a potent and highly selective inhibitor of vesicular monoamine transporter type 2 (VMAT2) through the active metabolite hydrotetrabenazine (HTBZ), has been approved for the treatment of tardive dyskinesia and, very recently, for chorea, which is associated with Huntington’s disease. Despite numerous synthetic efforts dedicated to the synthesis of HTBZ, the industrial preparation of valbenazine uses dihydroisoquinoline as a starting material and the chiral resolution of racemic HTBZ derived from ketone reduction. Herein, we present a practical synthesis of HTBZ and valbenazine featuring a highly stereoselective 1,3-dipolar cycloaddition and enzymatic kinetic resolution. The cascade process includes cycloaddition, N˗O bond cleavage, and lactamization, which proved to be operationally facile. The allure of the enzymatic resolution developed in this work offers a rapid access toward affording tetrahydroi-soquinoline (THIQ)-fused piperidine to access key frameworks in the production of medically significant compounds, such as yohimbine and reserpine.
{"title":"Practical Synthesis of Valbenazine via 1,3-Dipolar Cycloaddition","authors":"Yalan Peng, Zuming Lin, Lili Zhu, Shiqing Han, Sha-Hua Huang, Ran Hong","doi":"10.22541/au.169994410.04755017/v1","DOIUrl":"https://doi.org/10.22541/au.169994410.04755017/v1","url":null,"abstract":"Valbenazine (Ingrezza), a potent and highly selective inhibitor of vesicular monoamine transporter type 2 (VMAT2) through the active metabolite hydrotetrabenazine (HTBZ), has been approved for the treatment of tardive dyskinesia and, very recently, for chorea, which is associated with Huntington’s disease. Despite numerous synthetic efforts dedicated to the synthesis of HTBZ, the industrial preparation of valbenazine uses dihydroisoquinoline as a starting material and the chiral resolution of racemic HTBZ derived from ketone reduction. Herein, we present a practical synthesis of HTBZ and valbenazine featuring a highly stereoselective 1,3-dipolar cycloaddition and enzymatic kinetic resolution. The cascade process includes cycloaddition, N˗O bond cleavage, and lactamization, which proved to be operationally facile. The allure of the enzymatic resolution developed in this work offers a rapid access toward affording tetrahydroi-soquinoline (THIQ)-fused piperidine to access key frameworks in the production of medically significant compounds, such as yohimbine and reserpine.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"17 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169993986.65715321/v1
shenglan xiao, Dina Wang, Yadi Wu, Xi Huang, Qing Zhang, Dayan Wang, Yuelong Shu
Influenza constitutes a critical respiratory infection that imposes significant public health burdens. The precise influence of these pollutants on influenza activity remains unclear. This study aimed to investigate the effects of different air pollutants on the incidence of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B) in China based on nationwide data on air pollution and the influenza data from 554 sentinel hospitals across 30 provinces and municipalities from 2014 to 2017. Distributed Lag Nonlinear Model (DLNM) was employed to discern the lagged effects amid the concentrations of six distinct air pollutants, namely PM2.5, PM10, O , CO, SO , and NO , and the incidence of ILI, Flu A, as well as Flu B. Our analysis indicated that there was generally no distinction in the effects of air pollutants on the incidence of ILI, Flu A, and Flu B, although variations existed in terms of the specific level of risk associated with each of these categories. Specifically, elevated levels of PM2.5, PM10, CO, SO , and NO were predominantly associated with an increased risk of influenza. In contrast, the effect of O concentration on influenza was bidirectional whereby it promoted influenza outbreaks at low and high levels.
{"title":"Impact of Ambient Air Pollutants on Influenza-like illness, Influenza A and Influenza B: A Nationwide Time-Series Study in China","authors":"shenglan xiao, Dina Wang, Yadi Wu, Xi Huang, Qing Zhang, Dayan Wang, Yuelong Shu","doi":"10.22541/au.169993986.65715321/v1","DOIUrl":"https://doi.org/10.22541/au.169993986.65715321/v1","url":null,"abstract":"Influenza constitutes a critical respiratory infection that imposes significant public health burdens. The precise influence of these pollutants on influenza activity remains unclear. This study aimed to investigate the effects of different air pollutants on the incidence of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B) in China based on nationwide data on air pollution and the influenza data from 554 sentinel hospitals across 30 provinces and municipalities from 2014 to 2017. Distributed Lag Nonlinear Model (DLNM) was employed to discern the lagged effects amid the concentrations of six distinct air pollutants, namely PM2.5, PM10, O , CO, SO , and NO , and the incidence of ILI, Flu A, as well as Flu B. Our analysis indicated that there was generally no distinction in the effects of air pollutants on the incidence of ILI, Flu A, and Flu B, although variations existed in terms of the specific level of risk associated with each of these categories. Specifically, elevated levels of PM2.5, PM10, CO, SO , and NO were predominantly associated with an increased risk of influenza. In contrast, the effect of O concentration on influenza was bidirectional whereby it promoted influenza outbreaks at low and high levels.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"23 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169997327.74961038/v1
Chenwen Zhang, Yugang Shan
In recent years, China has become more closely connected with other countries, and Internet technology has developed rapidly. The new situation has put forward new requirements and challenges to the study of oral English. The disadvantages of traditional oral English teaching are gradually exposed. It is difficult for traditional teaching methods to adapt to the new situation of English learning. This paper analyzes the disadvantages of traditional oral English teaching, analyzes the significance of Internet-based oral English learning, and come up with the basic implementation strategies of independent learning, hoping to improve the efficiency of oral English learning.
{"title":"Knowledge graph modeling of college students' independent learning style and application of knowledge-based reasoning","authors":"Chenwen Zhang, Yugang Shan","doi":"10.22541/au.169997327.74961038/v1","DOIUrl":"https://doi.org/10.22541/au.169997327.74961038/v1","url":null,"abstract":"In recent years, China has become more closely connected with other countries, and Internet technology has developed rapidly. The new situation has put forward new requirements and challenges to the study of oral English. The disadvantages of traditional oral English teaching are gradually exposed. It is difficult for traditional teaching methods to adapt to the new situation of English learning. This paper analyzes the disadvantages of traditional oral English teaching, analyzes the significance of Internet-based oral English learning, and come up with the basic implementation strategies of independent learning, hoping to improve the efficiency of oral English learning.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"34 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/essoar.170000021.14010690/v1
Amitesh Kumar Singam
The Key facts of Translations are meant to validated the Fuzzy Weights. Moreover, In our case Fuzzy weights are based on Logical Reasoning and Logical Thinking, even though fuzzy logics are not concerned with data rather it depends on thought process based on human brain Imitation. Generally, at some point mankind depends on his own creations and tries to understand its usage through Machine Language or so called Machine Teaching and this is were humans try to understand fuzzy concepts based on binary language, moreover this translations are meant to become complicated the more we go deeper but here comes our originality of introducing fuzzy weights or logic based on Switching Theory Logical design which produces binary keys instead of values, we concentrated on reducing complexity of Machine Teaching through fuzzy weights.
{"title":"Translations Of Neural Networks based on Fuzzy Weights for Binary Keys within Delayed and Actual Time","authors":"Amitesh Kumar Singam","doi":"10.22541/essoar.170000021.14010690/v1","DOIUrl":"https://doi.org/10.22541/essoar.170000021.14010690/v1","url":null,"abstract":"The Key facts of Translations are meant to validated the Fuzzy Weights. Moreover, In our case Fuzzy weights are based on Logical Reasoning and Logical Thinking, even though fuzzy logics are not concerned with data rather it depends on thought process based on human brain Imitation. Generally, at some point mankind depends on his own creations and tries to understand its usage through Machine Language or so called Machine Teaching and this is were humans try to understand fuzzy concepts based on binary language, moreover this translations are meant to become complicated the more we go deeper but here comes our originality of introducing fuzzy weights or logic based on Switching Theory Logical design which produces binary keys instead of values, we concentrated on reducing complexity of Machine Teaching through fuzzy weights.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169996506.63522482/v1
Dhrita Khatri, Ben Felmingham, Claire Moore, Smaro Lazarakis, Tayla Stenta, Lane Collier, David Elliott, David Metz, Rachel Conyers
Tacrolimus, a calcineurin inhibitor, is an effective immunosuppressant for solid organ transplants (SOT). However, its narrow therapeutic index and high variability in pharmacokinetics can lead to inefficacy, toxicities, and suboptimal outcomes. Genotyping for CYP3A5 gene prior to SOT can identify individuals at risk of high or low tacrolimus levels and guide first-dose dosing. Genotype-guided Bayesian dosing uses population pharmacokinetic data and individual patient characteristics to accurately predict the tacrolimus dose required to achieve a target concentration. This can help achieve target tacrolimus concentrations sooner and maintain them within range, reducing risk of organ rejection or tacrolimus toxicity. This review aims to assess the benefits of genotype-guided Bayesian dosing for tacrolimus and its ability to accurately predict tacrolimus dosing, leading to increased maintenance of therapeutic drug exposure in these individuals. This systematic review identified three studies that incorporated genotyping and Bayesian informed methods to predict tacrolimus dosing in the paediatric population post SOT. The studies included 369 kidney, 231 heart, 246 liver and 16 lung transplant individuals. The review found that combination of clinical, demographic, and genetic data has a significant influence on tacrolimus clearance. Combining these parameters allowed the prediction of first dose tacrolimus post SOT and ongoing therapeutic tacrolimus dosing to optimally maintain target tacrolimus levels. In conclusion, personalised tacrolimus dosing models in paediatric SOT can be developed using clinical, demographic, and genetic data to predict first dose and ongoing adjustments to meet therapeutic tacrolimus targets and reduce the risk of under- and over- exposure.
{"title":"Genotype Informed Bayesian Dosing of Tacrolimus in Paediatric Solid Organ Transplant Individuals","authors":"Dhrita Khatri, Ben Felmingham, Claire Moore, Smaro Lazarakis, Tayla Stenta, Lane Collier, David Elliott, David Metz, Rachel Conyers","doi":"10.22541/au.169996506.63522482/v1","DOIUrl":"https://doi.org/10.22541/au.169996506.63522482/v1","url":null,"abstract":"Tacrolimus, a calcineurin inhibitor, is an effective immunosuppressant for solid organ transplants (SOT). However, its narrow therapeutic index and high variability in pharmacokinetics can lead to inefficacy, toxicities, and suboptimal outcomes. Genotyping for CYP3A5 gene prior to SOT can identify individuals at risk of high or low tacrolimus levels and guide first-dose dosing. Genotype-guided Bayesian dosing uses population pharmacokinetic data and individual patient characteristics to accurately predict the tacrolimus dose required to achieve a target concentration. This can help achieve target tacrolimus concentrations sooner and maintain them within range, reducing risk of organ rejection or tacrolimus toxicity. This review aims to assess the benefits of genotype-guided Bayesian dosing for tacrolimus and its ability to accurately predict tacrolimus dosing, leading to increased maintenance of therapeutic drug exposure in these individuals. This systematic review identified three studies that incorporated genotyping and Bayesian informed methods to predict tacrolimus dosing in the paediatric population post SOT. The studies included 369 kidney, 231 heart, 246 liver and 16 lung transplant individuals. The review found that combination of clinical, demographic, and genetic data has a significant influence on tacrolimus clearance. Combining these parameters allowed the prediction of first dose tacrolimus post SOT and ongoing therapeutic tacrolimus dosing to optimally maintain target tacrolimus levels. In conclusion, personalised tacrolimus dosing models in paediatric SOT can be developed using clinical, demographic, and genetic data to predict first dose and ongoing adjustments to meet therapeutic tacrolimus targets and reduce the risk of under- and over- exposure.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"51 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134901699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169996507.76550842/v1
Leonardo Xavier, Sandro Filho, Izabel Alves
Pharmacometrics is instrumental in drug development, guiding decisions on dose selection, study design, formulation optimization, biomarker identification and commercial viability. While traditional Pharmacokinetic-Pharmacodynamic (PK/PD) modeling is widely embraced, Kinetic-Pharmacodynamic (KPD) modeling remains relatively underutilized. This paper introduces KPD modeling as an alternative approach for understanding dose-effect relationships in scenarios where conventional PK data is limited. KPD models use dose as the primary input to predict key parameters, offering a valuable tool for clinical applications. To explore KPD modeling’s scope and potential benefits, we conducted a systematic review following PRISMA guidelines. The research question was “Where can KPD modeling be applied, and what are the main outcomes from KPD models?”. We searched databases, including PubMed, Web of Science, Cochrane and EMBASE, using specific terms. Eligible articles had to be in english and discuss KPD modeling applications or its role in model development. Our review covered 132 articles published from January 2004 to October 2023, identifying 51 meeting inclusion criteria. Data included publication year, country, institution, study type, studied compounds, software tools, KPD applications, and outcomes. This paper presents a comprehensive analysis of reviewed studies, highlighting diverse KPD modeling applications in clinical and preclinical settings. It outlines limitations and suggests avenues for rational KPD integration into research, clinical trials, and regulatory approvals. By harnessing KPD modeling’s power, pharmacometrics can enhance decision-making, addressing challenges posed by limited PK data, ultimately advancing drug development and patient care.
药物计量学在药物开发、指导剂量选择、研究设计、配方优化、生物标志物鉴定和商业可行性方面发挥着重要作用。虽然传统的药代动力学-药效学(PK/PD)模型被广泛接受，但动力学-药效学(KPD)模型仍然相对未得到充分利用。本文介绍了KPD建模作为在常规PK数据有限的情况下理解剂量效应关系的替代方法。KPD模型使用剂量作为预测关键参数的主要输入，为临床应用提供了有价值的工具。为了探索KPD建模的范围和潜在的好处，我们按照PRISMA指南进行了系统的回顾。研究的问题是“KPD模型可以应用在哪里，KPD模型的主要结果是什么?”我们搜索数据库，包括PubMed, Web of Science, Cochrane和EMBASE，使用特定的术语。合格的文章必须是英文的，并且讨论KPD建模应用程序或其在模型开发中的作用。我们的综述涵盖了2004年1月至2023年10月期间发表的132篇文章，确定了51篇符合纳入标准。数据包括出版年份、国家、机构、研究类型、研究化合物、软件工具、KPD应用和结果。本文介绍了综述研究的综合分析，突出了临床和临床前环境中不同的KPD建模应用。它概述了局限性，并提出了将KPD合理整合到研究、临床试验和监管批准中的途径。通过利用KPD建模的力量，药物计量学可以增强决策，解决有限的PK数据带来的挑战，最终推进药物开发和患者护理。
{"title":"kinetic-pharmacodynamic models: applications, limitations and perspectives: A systematic review","authors":"Leonardo Xavier, Sandro Filho, Izabel Alves","doi":"10.22541/au.169996507.76550842/v1","DOIUrl":"https://doi.org/10.22541/au.169996507.76550842/v1","url":null,"abstract":"Pharmacometrics is instrumental in drug development, guiding decisions on dose selection, study design, formulation optimization, biomarker identification and commercial viability. While traditional Pharmacokinetic-Pharmacodynamic (PK/PD) modeling is widely embraced, Kinetic-Pharmacodynamic (KPD) modeling remains relatively underutilized. This paper introduces KPD modeling as an alternative approach for understanding dose-effect relationships in scenarios where conventional PK data is limited. KPD models use dose as the primary input to predict key parameters, offering a valuable tool for clinical applications. To explore KPD modeling’s scope and potential benefits, we conducted a systematic review following PRISMA guidelines. The research question was “Where can KPD modeling be applied, and what are the main outcomes from KPD models?”. We searched databases, including PubMed, Web of Science, Cochrane and EMBASE, using specific terms. Eligible articles had to be in english and discuss KPD modeling applications or its role in model development. Our review covered 132 articles published from January 2004 to October 2023, identifying 51 meeting inclusion criteria. Data included publication year, country, institution, study type, studied compounds, software tools, KPD applications, and outcomes. This paper presents a comprehensive analysis of reviewed studies, highlighting diverse KPD modeling applications in clinical and preclinical settings. It outlines limitations and suggests avenues for rational KPD integration into research, clinical trials, and regulatory approvals. By harnessing KPD modeling’s power, pharmacometrics can enhance decision-making, addressing challenges posed by limited PK data, ultimately advancing drug development and patient care.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"51 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169995548.84464946/v1
Shivaraj S, Manju M, Rissi Kumar P, Shivesh PR
Agriculture is a cornerstone of India’s economy, supporting a vast majority of its population. However, farmers grapple with selecting the right crop due to diverse soil characteristics, environmental factors, plant diseases, and the need for consistent crop monitoring. This paper presents a smart system assisting farmers in specific crop selection, integrating plant diseases and consistent monitoring as vital features. By considering comprehensive data on environmental parameters(moisture), soil characteristics (including N, P, K levels), plant diseases, and consistent crop monitoring, the system recommends the most suitable crop for each season. Moreover, it offers fertilizer suggestions aligned with optimal nutrient requirements, particularly focusing on N, P, and K levels, aiming to enhance farming efficiency and sustainability.
{"title":"Smart Fields: Enhancing Agriculture with Machine Learning","authors":"Shivaraj S, Manju M, Rissi Kumar P, Shivesh PR","doi":"10.22541/au.169995548.84464946/v1","DOIUrl":"https://doi.org/10.22541/au.169995548.84464946/v1","url":null,"abstract":"Agriculture is a cornerstone of India’s economy, supporting a vast majority of its population. However, farmers grapple with selecting the right crop due to diverse soil characteristics, environmental factors, plant diseases, and the need for consistent crop monitoring. This paper presents a smart system assisting farmers in specific crop selection, integrating plant diseases and consistent monitoring as vital features. By considering comprehensive data on environmental parameters(moisture), soil characteristics (including N, P, K levels), plant diseases, and consistent crop monitoring, the system recommends the most suitable crop for each season. Moreover, it offers fertilizer suggestions aligned with optimal nutrient requirements, particularly focusing on N, P, and K levels, aiming to enhance farming efficiency and sustainability.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"15 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/essoar.170000364.41564926/v1
Estel Font, Sebastiaan Swart, Gerd Bruss, Peter M. F. Sheehan, Karen J. Heywood, Bastien Yves Queste
Dense overflows from marginal seas are critical pathways of oxygen supply to the Arabian Sea Oxygen Minimum Zone (OMZ), yet these remain inadequately understood. Climate models struggle to accurately reproduce the observed extent and intensity of the Arabian Sea OMZ due to their limited ability to capture processes smaller than their grid scale, such as dense overflows. Multi-month repeated sections by underwater gliders off the coast of Oman resolve the contribution of dense Persian Gulf Water (PGW) outflow to oxygen supply within the Arabian Sea OMZ. We characterize PGW properties, seasonality, transport and mixing mechanisms to explain local processes influencing water mass transformation and oxygen fluxes into the OMZ. Atmospheric forcing at the source region and eddy mesoscale activity in the Gulf of Oman control spatiotemporal variability of PGW as it flows along the shelf of the northern Omani coast. Subseasonally, it is modulated by stirring and shear-driven mixing driven by eddy-topography interactions. The oxygen transport from PGW to the OMZ is estimated to be 1.3 Tmol yr over the observational period, with dramatic inter- and intra-annual variability (±1.6 Tmol yr). We show that this oxygen is supplied to the interior of the OMZ through the combined action of double-diffusive and shear-driven mixing. Intermittent shear-driven mixing enhances double-diffusive processes, with mechanical shear conditions (Ri<0.25) prevailing 14% of the time at the oxycline. These findings enhance our understanding of fine-scale processes influencing oxygen dynamics within the OMZ that can provide insights for improved modeling and prediction efforts.
{"title":"Ventilation of the Arabian Sea Oxygen Minimum Zone by Persian Gulf Water","authors":"Estel Font, Sebastiaan Swart, Gerd Bruss, Peter M. F. Sheehan, Karen J. Heywood, Bastien Yves Queste","doi":"10.22541/essoar.170000364.41564926/v1","DOIUrl":"https://doi.org/10.22541/essoar.170000364.41564926/v1","url":null,"abstract":"Dense overflows from marginal seas are critical pathways of oxygen supply to the Arabian Sea Oxygen Minimum Zone (OMZ), yet these remain inadequately understood. Climate models struggle to accurately reproduce the observed extent and intensity of the Arabian Sea OMZ due to their limited ability to capture processes smaller than their grid scale, such as dense overflows. Multi-month repeated sections by underwater gliders off the coast of Oman resolve the contribution of dense Persian Gulf Water (PGW) outflow to oxygen supply within the Arabian Sea OMZ. We characterize PGW properties, seasonality, transport and mixing mechanisms to explain local processes influencing water mass transformation and oxygen fluxes into the OMZ. Atmospheric forcing at the source region and eddy mesoscale activity in the Gulf of Oman control spatiotemporal variability of PGW as it flows along the shelf of the northern Omani coast. Subseasonally, it is modulated by stirring and shear-driven mixing driven by eddy-topography interactions. The oxygen transport from PGW to the OMZ is estimated to be 1.3 Tmol yr over the observational period, with dramatic inter- and intra-annual variability (±1.6 Tmol yr). We show that this oxygen is supplied to the interior of the OMZ through the combined action of double-diffusive and shear-driven mixing. Intermittent shear-driven mixing enhances double-diffusive processes, with mechanical shear conditions (Ri<0.25) prevailing 14% of the time at the oxycline. These findings enhance our understanding of fine-scale processes influencing oxygen dynamics within the OMZ that can provide insights for improved modeling and prediction efforts.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/essoar.170000369.94748519/v1
Nicholas C Parazoo, Gretchen Keppel-Aleks, Stanley Sander, Brendan Byrne, Vijay Natraj, Mingzhao Luo, Jean-Francois Blavier, Leonard Dorsky, Ray Nassar
Surface, aircraft, and satellite measurements indicate pervasive cold season CO2 emissions across Arctic regions, consistent with a hyperactive biosphere and increased metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink-to-source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. AURORA simulations demonstrate the benefits of increased CO2 sampling for detecting spatial gradients in cold season efflux and improved monitoring of rapid Arctic change.
{"title":"More Frequent Spaceborne Sampling of XCO2 Improves Detectability of Carbon Cycle Seasonal Transitions in Arctic-Boreal Ecosystems","authors":"Nicholas C Parazoo, Gretchen Keppel-Aleks, Stanley Sander, Brendan Byrne, Vijay Natraj, Mingzhao Luo, Jean-Francois Blavier, Leonard Dorsky, Ray Nassar","doi":"10.22541/essoar.170000369.94748519/v1","DOIUrl":"https://doi.org/10.22541/essoar.170000369.94748519/v1","url":null,"abstract":"Surface, aircraft, and satellite measurements indicate pervasive cold season CO2 emissions across Arctic regions, consistent with a hyperactive biosphere and increased metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink-to-source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. AURORA simulations demonstrate the benefits of increased CO2 sampling for detecting spatial gradients in cold season efflux and improved monitoring of rapid Arctic change.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"26 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/essoar.170000340.08902129/v1
Ryan Lagerquist, Imme Ebert-Uphoff, David D Turner, Jebb Q. Stewart
Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks. Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model. However, because ML-UQ is a new tool, its limitations are not yet fully appreciated. For example, some types of uncertainty are fundamentally unresolvable, including uncertainty that arises from data being out of sample, i.e. , outside the distribution of the training data. While it is generally recognized that ML-based point predictions (predictions without UQ) do not extrapolate well out of sample, this awareness does not exist for ML-based uncertainty. When point predictions have a large error, instead of accounting for this error by producing a wider confidence interval, ML-UQ often fails just as spectacularly. We demonstrate this problem by training ML with five different UQ methods to predict shortwave radiative transfer. The ML-UQ models are trained with real data but then tasked with generalizing to perturbed data containing, e.g. , fictitious cloud and ozone layers. We show that ML-UQ completely fails on the perturbed data, which are far outside the training distribution. We also show that when the training data are lightly perturbed – so that each basis vector of perturbation has a little variation in the training data – ML-UQ can extrapolate along the basis vectors with some success, leading to much better (but still somewhat concerning) performance on the validation and testing data. Overall, we wish to discourage overreliance on ML-UQ, especially in operational environments.
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