Authorea (Authorea)最新文献_第6页

Practical Synthesis of Valbenazine via 1,3-Dipolar Cycloaddition 1,3-偶极环加成法实用合成缬苯那嗪

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/au.169994410.04755017/v1

Yalan Peng, Zuming Lin, Lili Zhu, Shiqing Han, Sha-Hua Huang, Ran Hong

引用次数: 0

Impact of Ambient Air Pollutants on Influenza-like illness, Influenza A and Influenza B: A Nationwide Time-Series Study in China 环境空气污染物对流感样疾病、甲型流感和乙型流感的影响:中国全国时间序列研究

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/au.169993986.65715321/v1

shenglan xiao, Dina Wang, Yadi Wu, Xi Huang, Qing Zhang, Dayan Wang, Yuelong Shu

{"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}

引用次数: 0

Knowledge graph modeling of college students' independent learning style and application of knowledge-based reasoning 大学生自主学习风格的知识图谱建模与知识推理的应用

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/au.169997327.74961038/v1

Chenwen Zhang, Yugang Shan

引用次数: 0

Translations Of Neural Networks based on Fuzzy Weights for Binary Keys within Delayed and Actual Time 基于模糊权重的神经网络在延迟和实际时间内的二进制密钥转换

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/essoar.170000021.14010690/v1

Amitesh Kumar Singam

引用次数: 0

Genotype Informed Bayesian Dosing of Tacrolimus in Paediatric Solid Organ Transplant Individuals 他克莫司在儿童实体器官移植个体中的基因型贝叶斯剂量

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/au.169996506.63522482/v1

Dhrita Khatri, Ben Felmingham, Claire Moore, Smaro Lazarakis, Tayla Stenta, Lane Collier, David Elliott, David Metz, Rachel Conyers

{"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}

引用次数: 0

kinetic-pharmacodynamic models: applications, limitations and perspectives: A systematic review 动力学-药效学模型:应用、局限性和前景:系统综述

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/au.169996507.76550842/v1

Leonardo Xavier, Sandro Filho, Izabel Alves

{"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}

引用次数: 0

Smart Fields: Enhancing Agriculture with Machine Learning 智能领域:用机器学习增强农业

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/au.169995548.84464946/v1

Shivaraj S, Manju M, Rissi Kumar P, Shivesh PR

引用次数: 0

Ventilation of the Arabian Sea Oxygen Minimum Zone by Persian Gulf Water 波斯湾水域对阿拉伯海最低氧区的通气

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/essoar.170000364.41564926/v1

Estel Font, Sebastiaan Swart, Gerd Bruss, Peter M. F. Sheehan, Karen J. Heywood, Bastien Yves Queste

{"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}

引用次数: 0

More Frequent Spaceborne Sampling of XCO2 Improves Detectability of Carbon Cycle Seasonal Transitions in Arctic-Boreal Ecosystems 更频繁的XCO2星载采样提高了北极-北方生态系统碳循环季节转换的可探测性

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 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

{"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}

引用次数: 0

Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML 机器学习的不确定性量化不是魔法:用ML模拟辐射传递的经验教训

Authorea (Authorea) Pub Date : 2023-11-14 DOI: 10.22541/essoar.170000340.08902129/v1

Ryan Lagerquist, Imme Ebert-Uphoff, David D Turner, Jebb Q. Stewart

{"title":"Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML","authors":"Ryan Lagerquist, Imme Ebert-Uphoff, David D Turner, Jebb Q. Stewart","doi":"10.22541/essoar.170000340.08902129/v1","DOIUrl":"https://doi.org/10.22541/essoar.170000340.08902129/v1","url":null,"abstract":"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.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"14 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":"134954739","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}

引用次数: 0