This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells and supported by computer simulations. The model decomposes self-replication into core components, each of which is executed by a single machine constructed from a set of basic block types. Key functionalities such as sorting, copying, and building, are demonstrated. The model provides valuable insights into the constraints of self-replicating systems. The discussion also addresses the spatial and timing behavior of the system, as well as its efficiency and complexity. This work provides a foundational framework for future studies on self-replicating mechanisms and their information-processing applications.
{"title":"Mechanical Self-replication","authors":"Ralph P. Lano","doi":"arxiv-2407.14556","DOIUrl":"https://doi.org/arxiv-2407.14556","url":null,"abstract":"This study presents a theoretical model for a self-replicating mechanical\u0000system inspired by biological processes within living cells and supported by\u0000computer simulations. The model decomposes self-replication into core\u0000components, each of which is executed by a single machine constructed from a\u0000set of basic block types. Key functionalities such as sorting, copying, and\u0000building, are demonstrated. The model provides valuable insights into the\u0000constraints of self-replicating systems. The discussion also addresses the\u0000spatial and timing behavior of the system, as well as its efficiency and\u0000complexity. This work provides a foundational framework for future studies on\u0000self-replicating mechanisms and their information-processing applications.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774446","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}
Belinda Neo, Dale Tilbrook, Noel Nannup, Alison Daly, Eleanor Dunlop, John Jacky, Carol Michie, Cindy Prior, Brad Farrant, Carrington C. J. Shepherd, Lucinda J. Black
Background/Objective: Vitamin D deficiency (serum 25-hydroxyvitamin D [25(OH)D] concentration <50 nmol/L) is prevalent among Aboriginal and Torres Strait Islander peoples in Australia. Alternative to sun exposure (the primary source of vitamin D), vitamin D can also be obtained from food (e.g., fish, eggs, and meat) and supplements. However, vitamin D intake among Aboriginal and Torres Strait Islander peoples is currently unknown. We aimed to provide the first quantification of vitamin D intake using nationally representative data from Aboriginal and Torres Strait Islander peoples. Methods: We used food consumption data collected in the 2012-2013 National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (n = 4,109) and vitamin D food composition data to quantify mean absolute vitamin D intake by sex, age group, and remoteness of location. Differences in mean vitamin D intake between sexes and between remoteness of location were assessed using the 95% confidence interval (95% CI). Results: The mean (standard deviation (SD)) vitamin D intake among Aboriginal and Torres Strait Islander peoples was 2.9 (3.0) {mu}g/day. Males had a statistically significantly higher mean (SD) [95% CI] vitamin D intake (3.2 (3.1) [3.0-3.4] {mu}g/day) than females (2.6 (2.7) [2.4-2.7] {mu}g/day). There were no statistically significant differences between mean (SD) [95% CI] vitamin D intake in non-remote (2.9 (2.2) [2.7-3.1] {mu}g/day) and remote areas (2.8 (4.8) [2.6-3.0] {mu}g/day). Conclusions: Vitamin D intake among Aboriginal and Torres Strait Islander peoples is low. Food-based public health strategies could be developed to promote higher vitamin D intake among this population.
背景/目的：维生素 D 缺乏症（血清 25- 羟维生素 D[25(OH)D] 浓度低于 50 nmol/L）在澳大利亚土著居民和托雷斯海峡岛民中十分普遍。除了阳光照射（维生素 D 的主要来源），维生素 D 还可以从食物（如鱼、蛋和肉）和补充剂中获得。然而，原住民和托雷斯海峡岛民的维生素 D 摄入量目前尚不清楚。我们的目的是首次利用具有全国代表性的原住民和托雷斯海峡岛民数据对维生素 D 的摄入量进行量化。方法：我们利用2012-2013年全国原住民和托雷斯海峡岛民营养与体育活动调查（n = 4,109）中收集的食物消费数据和维生素D食物成分数据，按性别、年龄组和地点偏远程度对维生素D绝对摄入量的平均值进行了量化。性别间和偏远地区间维生素 D 平均摄入量的差异采用 95% 置信区间 (95% CI) 进行评估。结果男性的维生素D平均摄入量（标准差（SD）[95% CI]）（3.2（3.1）[3.0-3.4]{mu}g/天）明显高于女性（2.6（2.7）[2.4-2.7]{mu}g/天）。非偏远地区（2.9 (2.2) [2.7-3.1] {mu}克/天）和偏远地区（2.8 (4.8) [2.6-3.0] {mu}克/天）的维生素 D 平均摄入量（标度）[95% CI]之间没有明显的统计学差异。结论：土著居民和托雷斯海峡岛民的维生素D摄入量较低。可以制定以食物为基础的公共卫生策略，以促进该人群摄入更多的维生素D。
{"title":"Quantifying vitamin D intake among Aboriginal and Torres Strait Islander peoples in Australia","authors":"Belinda Neo, Dale Tilbrook, Noel Nannup, Alison Daly, Eleanor Dunlop, John Jacky, Carol Michie, Cindy Prior, Brad Farrant, Carrington C. J. Shepherd, Lucinda J. Black","doi":"arxiv-2407.13797","DOIUrl":"https://doi.org/arxiv-2407.13797","url":null,"abstract":"Background/Objective: Vitamin D deficiency (serum 25-hydroxyvitamin D\u0000[25(OH)D] concentration <50 nmol/L) is prevalent among Aboriginal and Torres\u0000Strait Islander peoples in Australia. Alternative to sun exposure (the primary\u0000source of vitamin D), vitamin D can also be obtained from food (e.g., fish,\u0000eggs, and meat) and supplements. However, vitamin D intake among Aboriginal and\u0000Torres Strait Islander peoples is currently unknown. We aimed to provide the\u0000first quantification of vitamin D intake using nationally representative data\u0000from Aboriginal and Torres Strait Islander peoples. Methods: We used food\u0000consumption data collected in the 2012-2013 National Aboriginal and Torres\u0000Strait Islander Nutrition and Physical Activity Survey (n = 4,109) and vitamin\u0000D food composition data to quantify mean absolute vitamin D intake by sex, age\u0000group, and remoteness of location. Differences in mean vitamin D intake between\u0000sexes and between remoteness of location were assessed using the 95% confidence\u0000interval (95% CI). Results: The mean (standard deviation (SD)) vitamin D intake\u0000among Aboriginal and Torres Strait Islander peoples was 2.9 (3.0) {mu}g/day.\u0000Males had a statistically significantly higher mean (SD) [95% CI] vitamin D\u0000intake (3.2 (3.1) [3.0-3.4] {mu}g/day) than females (2.6 (2.7) [2.4-2.7]\u0000{mu}g/day). There were no statistically significant differences between mean\u0000(SD) [95% CI] vitamin D intake in non-remote (2.9 (2.2) [2.7-3.1] {mu}g/day)\u0000and remote areas (2.8 (4.8) [2.6-3.0] {mu}g/day). Conclusions: Vitamin D\u0000intake among Aboriginal and Torres Strait Islander peoples is low. Food-based\u0000public health strategies could be developed to promote higher vitamin D intake\u0000among this population.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738505","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}
Trevor Reckell, Beckett Sterner, Petar Jevtić, Reggie Davidrajuh
Petri nets are a promising modeling framework for epidemiology, including the spread of disease across populations or within an individual. In particular, the Susceptible-Infectious-Recovered (SIR) compartment model is foundational for population epidemiological modeling and has been implemented in several prior Petri net studies. However, the SIR model is generally stated as a system of ordinary differential equations (ODEs) with continuous time and variables, while Petri nets are discrete event simulations. To our knowledge, no prior study has investigated the numerical equivalence of Petri net SIR models to the classical ODE formulation. We introduce crucial numerical techniques for implementing SIR models in the GPenSim package for Petri net simulations. We show that these techniques are critical for Petri net SIR models and show a relative root mean squared error of less than 1% compared to ODE simulations for biologically relevant parameter ranges. We conclude that Petri nets provide a valid framework for modeling SIR-type dynamics using biologically relevant parameter values provided that the other PN structures we outline are also implemented.
Petri 网是一种很有前途的流行病学建模框架，包括疾病在人群中或个体内部的传播。其中，易感-传染-复发（SIR）区隔模型是人群流行病学建模的基础，并已在之前的多项 Petri 网研究中得到应用。然而，SIR 模型通常是一个具有连续时间和变量的常微分方程（ODE）系统，而 Petri 网则是离散事件模拟。据我们所知，此前没有任何研究探讨过 Petri 网 SIR 模型与经典 ODE 表述的数值等价性。我们介绍了在用于 Petri 网仿真的 GPenSim 软件包中实现 SIR 模型的关键数值技术。结果表明，这些技术对于 Petri 网 SIR 模型至关重要，在生物相关参数范围内，与 ODE 模拟相比，它们的均方根误差小于 1%。我们的结论是，Petri 网为利用生物相关参数值模拟 SIR 型动力学提供了一个有效的框架，前提是我们概述的其他 PN 结构也得到了实现。
{"title":"A Numerical Comparison of Petri Net and Ordinary Differential Equation SIR Component Models","authors":"Trevor Reckell, Beckett Sterner, Petar Jevtić, Reggie Davidrajuh","doi":"arxiv-2407.10019","DOIUrl":"https://doi.org/arxiv-2407.10019","url":null,"abstract":"Petri nets are a promising modeling framework for epidemiology, including the\u0000spread of disease across populations or within an individual. In particular,\u0000the Susceptible-Infectious-Recovered (SIR) compartment model is foundational\u0000for population epidemiological modeling and has been implemented in several\u0000prior Petri net studies. However, the SIR model is generally stated as a system\u0000of ordinary differential equations (ODEs) with continuous time and variables,\u0000while Petri nets are discrete event simulations. To our knowledge, no prior\u0000study has investigated the numerical equivalence of Petri net SIR models to the\u0000classical ODE formulation. We introduce crucial numerical techniques for\u0000implementing SIR models in the GPenSim package for Petri net simulations. We\u0000show that these techniques are critical for Petri net SIR models and show a\u0000relative root mean squared error of less than 1% compared to ODE simulations\u0000for biologically relevant parameter ranges. We conclude that Petri nets provide\u0000a valid framework for modeling SIR-type dynamics using biologically relevant\u0000parameter values provided that the other PN structures we outline are also\u0000implemented.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719309","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}
Objectives: Chronotypes -- comparisons of individuals' circadian phase relative to others -- can contextualize mental health risk assessments, and support detection of social jet lag, which can hamper mental health and cognition. Existing ways of determining chronotypes, such as Dim Light Melatonin Onset (DLMO) or the Morningness-Eveningness Questionnaire (MEQ), are limited by being discrete in time and time-intensive to update, rarely capturing real-world variability over time. Chronotyping users based on living schedules, as in daily planner apps, might augment existing methods by assessing chronotype and social jet lag continuously and at scale. Developing this functionality would require a novel tool to translate between digital schedules and chronotypes. Here we use a supervised binary classifier to assess the feasibility of this approach. Methods: In this study, 1,460 registered users from the Owaves app opted in to filled out the MEQ survey. Of those, 142 met the eligibility criteria for data analysis. We used multimodal app data to assess the classification of individuals identified as morning and evening types from MEQ data, basing the classifier on app time series data. This includes daily timing for 8 main lifestyle activity categories (exercise, sleep, social interactions, meal times, relaxation, work, play, and miscellaneous) as defined in the app. Results: The novel chronotyping tool was able to predict the morningness and eveningness of its users with an ROC AUC of 0.70. Conclusion: Our findings support the feasibility of chronotype classification from multimodal, real-world app data. We highlight challenges to applying binary labels to complex, multimodal behaviors. Our findings suggest a potential for real-time monitoring to support future, prospective mental health research.
{"title":"Automated and Continuous Chronotyping from a Calendar using Machine Learning","authors":"Pratiik Kaushik, Koorosh Askari, Saksham Gupta, Rahul Mohan, Kris Skrinak, Royan Kamyar, Benjamin Smarr","doi":"arxiv-2407.06478","DOIUrl":"https://doi.org/arxiv-2407.06478","url":null,"abstract":"Objectives: Chronotypes -- comparisons of individuals' circadian phase\u0000relative to others -- can contextualize mental health risk assessments, and\u0000support detection of social jet lag, which can hamper mental health and\u0000cognition. Existing ways of determining chronotypes, such as Dim Light\u0000Melatonin Onset (DLMO) or the Morningness-Eveningness Questionnaire (MEQ), are\u0000limited by being discrete in time and time-intensive to update, rarely\u0000capturing real-world variability over time. Chronotyping users based on living\u0000schedules, as in daily planner apps, might augment existing methods by\u0000assessing chronotype and social jet lag continuously and at scale. Developing\u0000this functionality would require a novel tool to translate between digital\u0000schedules and chronotypes. Here we use a supervised binary classifier to assess\u0000the feasibility of this approach. Methods: In this study, 1,460 registered\u0000users from the Owaves app opted in to filled out the MEQ survey. Of those, 142\u0000met the eligibility criteria for data analysis. We used multimodal app data to\u0000assess the classification of individuals identified as morning and evening\u0000types from MEQ data, basing the classifier on app time series data. This\u0000includes daily timing for 8 main lifestyle activity categories (exercise,\u0000sleep, social interactions, meal times, relaxation, work, play, and\u0000miscellaneous) as defined in the app. Results: The novel chronotyping tool was\u0000able to predict the morningness and eveningness of its users with an ROC AUC of\u00000.70. Conclusion: Our findings support the feasibility of chronotype\u0000classification from multimodal, real-world app data. We highlight challenges to\u0000applying binary labels to complex, multimodal behaviors. Our findings suggest a\u0000potential for real-time monitoring to support future, prospective mental health\u0000research.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566350","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}
Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piwajanusorn, Pedro J. Ballester
Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges limiting the impact and scope of AI models. Typically, these models are evaluated on benchmarks that are unlikely to anticipate their prospective performance, which inadvertently misguides their development. Indeed, while all the developed models excel in a selected benchmark, only a small proportion of them are ultimately reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). Here we discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models and which issue-specific mitigations have been effective. Next, we point out the challenges faced by uncertainty quantification techniques aimed at enhancing these AI models. We also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, we explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated with prospective studies.
{"title":"Challenges with the application and adoption of artificial intelligence for drug discovery","authors":"Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piwajanusorn, Pedro J. Ballester","doi":"arxiv-2407.05150","DOIUrl":"https://doi.org/arxiv-2407.05150","url":null,"abstract":"Artificial intelligence (AI) is exhibiting tremendous potential to reduce the\u0000massive costs and long timescales of drug discovery. There are however\u0000important challenges limiting the impact and scope of AI models. Typically,\u0000these models are evaluated on benchmarks that are unlikely to anticipate their\u0000prospective performance, which inadvertently misguides their development.\u0000Indeed, while all the developed models excel in a selected benchmark, only a\u0000small proportion of them are ultimately reported to have prospective value\u0000(e.g. by discovering potent and innovative drug leads for a therapeutic\u0000target). Here we discuss a range of data issues (bias, inconsistency, skewness,\u0000irrelevance, small size, high dimensionality), how they challenge AI models and\u0000which issue-specific mitigations have been effective. Next, we point out the\u0000challenges faced by uncertainty quantification techniques aimed at enhancing\u0000these AI models. We also discuss how conceptual errors, unrealistic benchmarks\u0000and performance misestimation can confound the evaluation of models and thus\u0000their development. Lastly, we explain how human bias, whether from AI experts\u0000or drug discovery experts, constitutes another challenge that can be alleviated\u0000with prospective studies.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566349","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}
Styliani-Christina Fragkouli, Dhwani Solanki, Leyla J Castro, Fotis E Psomopoulos, Núria Queralt-Rosinach, Davide Cirillo, Lisa C Crossman
Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive or manipulate, and displacement of human jobs across various market sectors. Here, we consider both current and future positive advances and possibilities with synthetic datasets. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models. Generative AI is an artificial intelligence genre capable of creating text, images, video or other data using generative models. The recent explosion of interest in GenAI was heralded by the invention and speedy move to use of large language models (LLM). These computational models are able to achieve general-purpose language generation and other natural language processing tasks and are based on transformer architectures, which made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this is surely the time to consider how synthetic data can be used to advance infectious disease research. In this commentary we aim to create an overview of the current and future position of synthetic data in infectious disease research.
{"title":"Synthetic data: How could it be used for infectious disease research?","authors":"Styliani-Christina Fragkouli, Dhwani Solanki, Leyla J Castro, Fotis E Psomopoulos, Núria Queralt-Rosinach, Davide Cirillo, Lisa C Crossman","doi":"arxiv-2407.06211","DOIUrl":"https://doi.org/arxiv-2407.06211","url":null,"abstract":"Over the last three to five years, it has become possible to generate machine\u0000learning synthetic data for healthcare-related uses. However, concerns have\u0000been raised about potential negative factors associated with the possibilities\u0000of artificial dataset generation. These include the potential misuse of\u0000generative artificial intelligence (AI) in fields such as cybercrime, the use\u0000of deepfakes and fake news to deceive or manipulate, and displacement of human\u0000jobs across various market sectors. Here, we consider both current and future positive advances and possibilities\u0000with synthetic datasets. Synthetic data offers significant benefits,\u0000particularly in data privacy, research, in balancing datasets and reducing bias\u0000in machine learning models. Generative AI is an artificial intelligence genre\u0000capable of creating text, images, video or other data using generative models.\u0000The recent explosion of interest in GenAI was heralded by the invention and\u0000speedy move to use of large language models (LLM). These computational models\u0000are able to achieve general-purpose language generation and other natural\u0000language processing tasks and are based on transformer architectures, which\u0000made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this\u0000is surely the time to consider how synthetic data can be used to advance\u0000infectious disease research. In this commentary we aim to create an overview of\u0000the current and future position of synthetic data in infectious disease\u0000research.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566351","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}
T Yahaya, KA Sani, E Oladele, E Yawa, M Musa, M Abubakar, R Sulaiman, M Bilyaminu
The potential health risks of cement dust exposure are increasingly raising concern worldwide as the cement industry expands in response to rising cement demand. This necessitates the need to determine the nature of the risks in order to develop appropriate measures. This study determined the effects of cement dust exposure on the weight and blood glucose levels of people residing or working around a cement company in Sokoto, Nigeria. Demographic information was obtained using questionnaires from 72 participants, which included age, gender, educational level, exposure hours, occupation, and lifestyle. The blood glucose levels and body mass index (BMI) were measured using a Fine Test glucometer and a mechanical scale, respectively. The results showed that most of the people living or working around the cement company were middle-aged men (31-40; 42.06%) with a primary (33.33%) or secondary (45.83%) school education. It showed that 30 (41.69%) of the participants were overweight while 5 (6.94%) were obese. Additionally, 52.78% of the participants were diabetic while 31.94% were prediabetic. Participants that were exposed for long hours (> 15 hours per day) were the most diabetic (20% of the participants), followed by smokers (15%), and artisans (7%). It can be concluded that exposure to cement dust from the company increased the risk of overweight, obesity, and hyperglycemia among the participants. These health risks were worsened by daily long hours of exposure, smoking, and artisanal pollutant exposure. Human settlements and artisans should not be located near the cement company, and the company should minimize pollutant emissions.
{"title":"Cement Dust Exposure and Risk of Hyperglycemia and Overweight among Artisans and Residents Close to a Cement Factory in Sokoto, Nigeria","authors":"T Yahaya, KA Sani, E Oladele, E Yawa, M Musa, M Abubakar, R Sulaiman, M Bilyaminu","doi":"arxiv-2407.00420","DOIUrl":"https://doi.org/arxiv-2407.00420","url":null,"abstract":"The potential health risks of cement dust exposure are increasingly raising\u0000concern worldwide as the cement industry expands in response to rising cement\u0000demand. This necessitates the need to determine the nature of the risks in\u0000order to develop appropriate measures. This study determined the effects of\u0000cement dust exposure on the weight and blood glucose levels of people residing\u0000or working around a cement company in Sokoto, Nigeria. Demographic information\u0000was obtained using questionnaires from 72 participants, which included age,\u0000gender, educational level, exposure hours, occupation, and lifestyle. The blood\u0000glucose levels and body mass index (BMI) were measured using a Fine Test\u0000glucometer and a mechanical scale, respectively. The results showed that most\u0000of the people living or working around the cement company were middle-aged men\u0000(31-40; 42.06%) with a primary (33.33%) or secondary (45.83%) school education.\u0000It showed that 30 (41.69%) of the participants were overweight while 5 (6.94%)\u0000were obese. Additionally, 52.78% of the participants were diabetic while 31.94%\u0000were prediabetic. Participants that were exposed for long hours (> 15 hours per\u0000day) were the most diabetic (20% of the participants), followed by smokers\u0000(15%), and artisans (7%). It can be concluded that exposure to cement dust from\u0000the company increased the risk of overweight, obesity, and hyperglycemia among\u0000the participants. These health risks were worsened by daily long hours of\u0000exposure, smoking, and artisanal pollutant exposure. Human settlements and\u0000artisans should not be located near the cement company, and the company should\u0000minimize pollutant emissions.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505128","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}
With emerging prevalence beyond traditionally endemic regions, the global burden of dengue disease is forecasted to be one of the fastest growing. With limited direct treatment or vaccination currently available, prevention through vector control is widely believed to be the most effective form of managing outbreaks. This study examines traditional state space models (moving average, autoregressive, ARIMA, SARIMA), supervised learning techniques (XGBoost, SVM, KNN) and deep networks (LSTM, CNN, ConvLSTM) for forecasting weekly dengue cases in Singapore. Meteorological data and search engine trends were included as features for ML techniques. Forecasts using CNNs yielded lowest RMSE in weekly cases in 2019.
随着登革热在传统流行地区之外的新流行，预计全球登革热病负担将成为增长最快的疾病之一。由于目前可提供的直接治疗或疫苗接种有限，人们普遍认为通过病媒控制进行预防是控制疫情的最有效方式。本研究检验了用于预测新加坡每周登革热病例的传统状态空间模型（移动平均、自回归、ARIMA、SARIMA）、监督学习技术（XGBoost、SVM、KNN）和深度网络（LSTM、CNN、ConvLSTM）。气象数据和搜索引擎趋势被作为 ML 技术的特征。在 2019 年的每周案例中，使用 CNN 进行预测的 RMSE 最低。
{"title":"Machine Learning Models for Dengue Forecasting in Singapore","authors":"Zi Iun Lai, Wai Kit Fung, Enquan Chew","doi":"arxiv-2407.00332","DOIUrl":"https://doi.org/arxiv-2407.00332","url":null,"abstract":"With emerging prevalence beyond traditionally endemic regions, the global\u0000burden of dengue disease is forecasted to be one of the fastest growing. With\u0000limited direct treatment or vaccination currently available, prevention through\u0000vector control is widely believed to be the most effective form of managing\u0000outbreaks. This study examines traditional state space models (moving average,\u0000autoregressive, ARIMA, SARIMA), supervised learning techniques (XGBoost, SVM,\u0000KNN) and deep networks (LSTM, CNN, ConvLSTM) for forecasting weekly dengue\u0000cases in Singapore. Meteorological data and search engine trends were included\u0000as features for ML techniques. Forecasts using CNNs yielded lowest RMSE in\u0000weekly cases in 2019.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513144","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}
The study was conducted to determine the best irrigation frequency for rooting hardwood cuttings of some pomegranate genotypes that are cultivated in Halabja province, Kurdistan Region, Iraq. The hardwood cuttings were collected from 11 genotypes, which were 'Salakhani Trsh' (G1), 'Salakhani Mekhosh' (G2), 'Amriki' (G3), 'Twekl Sury Trsh' (G4), 'Twekl Astury Naw Spy' (G5), 'Hanara Sherina' (G6), 'Kawa Hanary Sherin' (G7), 'Kawa Hanary Trsh' (G8), 'Malesay Twekl Asture' (G9), 'Malesay Twekl Tank' (G10), and 'Sura Hanary Trsh' (G11). The genotypes were subjected to irrigation applications by 1-day, 2-day, 7-day, or 10-day frequencies. Among pomegranates, G11, G6, and G7 produced 95, 90, and 83% rooting percentages, which were significantly higher than the rest of other genotypes. The lowest rooting percentages (28, 36, 38, and 40%) were found in G1, G5, G3, and G10, respectively. The effect of irrigation frequencies on the genotypes confirmed that a 7-day frequency was the best irrigation frequency to achieve the maximum rooting percentages (93, 86, 80, 73, 53, and 40%) in G6, G9, G2, G4, G3, and G1, respectively. In contrast, the minimum rooting percentage (20%) was recorded in G3 with a 1-day frequency and in G1 with 10-day frequency. In this study, it was found that the cuttings of G11, G6, and G7 had the best ability to form roots, and irrigation with a 7-day frequency was the best for the cuttings of all the 11 pomegranate genotypes investigated.
{"title":"Rooting behavior of pomegranate (Punica granatum L.) hardwood cuttings in relation to genotype and irrigation frequency","authors":"Kocher Omer Salih, Aram Akram Mohammed, Jamal Mahmood Faraj, Anwar Mohammed Raouf, Nawroz Abdul-Razzak Tahir","doi":"arxiv-2407.00408","DOIUrl":"https://doi.org/arxiv-2407.00408","url":null,"abstract":"The study was conducted to determine the best irrigation frequency for\u0000rooting hardwood cuttings of some pomegranate genotypes that are cultivated in\u0000Halabja province, Kurdistan Region, Iraq. The hardwood cuttings were collected\u0000from 11 genotypes, which were 'Salakhani Trsh' (G1), 'Salakhani Mekhosh' (G2),\u0000'Amriki' (G3), 'Twekl Sury Trsh' (G4), 'Twekl Astury Naw Spy' (G5), 'Hanara\u0000Sherina' (G6), 'Kawa Hanary Sherin' (G7), 'Kawa Hanary Trsh' (G8), 'Malesay\u0000Twekl Asture' (G9), 'Malesay Twekl Tank' (G10), and 'Sura Hanary Trsh' (G11).\u0000The genotypes were subjected to irrigation applications by 1-day, 2-day, 7-day,\u0000or 10-day frequencies. Among pomegranates, G11, G6, and G7 produced 95, 90, and\u000083% rooting percentages, which were significantly higher than the rest of other\u0000genotypes. The lowest rooting percentages (28, 36, 38, and 40%) were found in\u0000G1, G5, G3, and G10, respectively. The effect of irrigation frequencies on the\u0000genotypes confirmed that a 7-day frequency was the best irrigation frequency to\u0000achieve the maximum rooting percentages (93, 86, 80, 73, 53, and 40%) in G6,\u0000G9, G2, G4, G3, and G1, respectively. In contrast, the minimum rooting\u0000percentage (20%) was recorded in G3 with a 1-day frequency and in G1 with\u000010-day frequency. In this study, it was found that the cuttings of G11, G6, and\u0000G7 had the best ability to form roots, and irrigation with a 7-day frequency\u0000was the best for the cuttings of all the 11 pomegranate genotypes investigated.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"163 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513143","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}
The high demand for mango (Mangifera indica L.) fruits has led sellers to employ ripening agents. However, concerns are growing regarding the potential toxicities of induced ripening, emphasizing the need for scientific investigation. Samples of artificially and naturally ripened mangoes were analyzed for proximate composition using standard protocols. Cytogenotoxicity was then assessed using the Allium cepa L. toxicity test. Twenty (20)A. cepa (onion) bulbs were used, with 5 ripened naturally, 5 with wood ash, 5 with herbaceous ash, and 5 with rice chips, all grown over tap water for five days. The root tips of the bulbs were assayed and examined for chromosomal aberrations. The results revealed a significant (P<0.05) increase in moisture, protein, and ash content of mangoes as ripening agents were introduced. Mangoes ripened with wood ash exhibited the highest moisture content (81%), while those ripened with rice chips had the highest protein (0.5%) and ash content (1.5%). Naturally ripened mangoes displayed the highest fat (0.0095%) and fiber (11.46%) contents. The A. cepa toxicity test indicated significant (p<0.05) differences in the root growth of mangoes ripened with various agents. Wood ash resulted in the highest root growth (2.62cm), while herbaceous ash had the least (2.18%). Chromosomal aberrations, including sticky, vagrant, and laggard abnormalities, were observed in all agents, with herbaceous ash exhibiting the highest and rice chips the least. The obtained results suggest that induced ripening of the fruits could induce toxicities, highlighting the necessity for public awareness regarding the potential dangers posed by these agents.
{"title":"Comparative Evaluation of the Proximate and Cytogenotoxicity of Ash and Rice Chips Used as Mango Fruit Artificial Ripening Agents in Birnin Kebbi, Nigeria","authors":"CD Obadiah, TO Yahaya, AA Aliero, M Abdulkareem","doi":"arxiv-2408.01425","DOIUrl":"https://doi.org/arxiv-2408.01425","url":null,"abstract":"The high demand for mango (Mangifera indica L.) fruits has led sellers to\u0000employ ripening agents. However, concerns are growing regarding the potential\u0000toxicities of induced ripening, emphasizing the need for scientific\u0000investigation. Samples of artificially and naturally ripened mangoes were\u0000analyzed for proximate composition using standard protocols. Cytogenotoxicity\u0000was then assessed using the Allium cepa L. toxicity test. Twenty (20)A. cepa\u0000(onion) bulbs were used, with 5 ripened naturally, 5 with wood ash, 5 with\u0000herbaceous ash, and 5 with rice chips, all grown over tap water for five days.\u0000The root tips of the bulbs were assayed and examined for chromosomal\u0000aberrations. The results revealed a significant (P<0.05) increase in moisture,\u0000protein, and ash content of mangoes as ripening agents were introduced. Mangoes\u0000ripened with wood ash exhibited the highest moisture content (81%), while those\u0000ripened with rice chips had the highest protein (0.5%) and ash content (1.5%).\u0000Naturally ripened mangoes displayed the highest fat (0.0095%) and fiber\u0000(11.46%) contents. The A. cepa toxicity test indicated significant (p<0.05)\u0000differences in the root growth of mangoes ripened with various agents. Wood ash\u0000resulted in the highest root growth (2.62cm), while herbaceous ash had the\u0000least (2.18%). Chromosomal aberrations, including sticky, vagrant, and laggard\u0000abnormalities, were observed in all agents, with herbaceous ash exhibiting the\u0000highest and rice chips the least. The obtained results suggest that induced\u0000ripening of the fruits could induce toxicities, highlighting the necessity for\u0000public awareness regarding the potential dangers posed by these agents.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933083","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}
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