Britta U. Westner, Daniel R. McCloy, Eric Larson, Alexandre Gramfort, Daniel S. Katz, Arfon M. Smith, invited co-signees
Most scientists need software to perform their research (Barker et al., 2020; Carver et al., 2022; Hettrick, 2014; Hettrick et al., 2014; Switters and Osimo, 2019), and neuroscientists are no exception. Whether we work with reaction times, electrophysiological signals, or magnetic resonance imaging data, we rely on software to acquire, analyze, and statistically evaluate the raw data we obtain - or to generate such data if we work with simulations. In recent years there has been a shift toward relying on free, open-source scientific software (FOSSS) for neuroscience data analysis (Poldrack et al., 2019), in line with the broader open science movement in academia (McKiernan et al., 2016) and wider industry trends (Eghbal, 2016). Importantly, FOSSS is typically developed by working scientists (not professional software developers) which sets up a precarious situation given the nature of the typical academic workplace (wherein academics, especially in their early careers, are on short and fixed term contracts). In this paper, we will argue that the existing ecosystem of neuroscientific open source software is brittle, and discuss why and how the neuroscience community needs to come together to ensure a healthy growth of our software landscape to the benefit of all.
大多数科学家都需要软件来完成他们的研究（Barker et al.，2020；Carver et al.，2022；Hettrick，2014；Hettrick et al.，2014；Switters and Osimo，2019），神经科学家也不例外。无论我们研究的是反应时、电生理信号还是磁共振成像数据，我们都依赖软件来获取、分析和统计评估我们获得的原始数据，如果我们研究的是模拟数据，则需要软件来生成这些数据。近年来，神经科学数据分析开始转向依赖免费开源科学软件（FOSSS）（Poldrack 等人，2019 年），这与学术界更广泛的开放科学运动（McKiernan 等人，2016 年）和更广泛的行业趋势（Eghbal，2016 年）是一致的。重要的是，FOSSS 通常是由在职科学家（而非专业软件开发人员）开发的，鉴于典型学术工作场所的性质（学术界人士，尤其是处于职业生涯早期的人士，都是签订短期和固定期限合同），这就造成了一种不稳定的局面。在本文中，我们将论证现有的神经科学开源软件生态系统是脆弱的，并讨论为什么神经科学社区需要团结起来，以确保我们的软件环境健康发展，造福所有人。
{"title":"Cycling on the Freeway: The Perilous State of Open Source Neuroscience Software","authors":"Britta U. Westner, Daniel R. McCloy, Eric Larson, Alexandre Gramfort, Daniel S. Katz, Arfon M. Smith, invited co-signees","doi":"arxiv-2403.19394","DOIUrl":"https://doi.org/arxiv-2403.19394","url":null,"abstract":"Most scientists need software to perform their research (Barker et al., 2020;\u0000Carver et al., 2022; Hettrick, 2014; Hettrick et al., 2014; Switters and Osimo,\u00002019), and neuroscientists are no exception. Whether we work with reaction\u0000times, electrophysiological signals, or magnetic resonance imaging data, we\u0000rely on software to acquire, analyze, and statistically evaluate the raw data\u0000we obtain - or to generate such data if we work with simulations. In recent\u0000years there has been a shift toward relying on free, open-source scientific\u0000software (FOSSS) for neuroscience data analysis (Poldrack et al., 2019), in\u0000line with the broader open science movement in academia (McKiernan et al.,\u00002016) and wider industry trends (Eghbal, 2016). Importantly, FOSSS is typically\u0000developed by working scientists (not professional software developers) which\u0000sets up a precarious situation given the nature of the typical academic\u0000workplace (wherein academics, especially in their early careers, are on short\u0000and fixed term contracts). In this paper, we will argue that the existing\u0000ecosystem of neuroscientific open source software is brittle, and discuss why\u0000and how the neuroscience community needs to come together to ensure a healthy\u0000growth of our software landscape to the benefit of all.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140323437","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}
Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu
The year 2023 marked a significant surge in the exploration of applying large language model (LLM) chatbots, notably ChatGPT, across various disciplines. We surveyed the applications of ChatGPT in various sectors of bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future development.
{"title":"Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review","authors":"Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu","doi":"arxiv-2403.15274","DOIUrl":"https://doi.org/arxiv-2403.15274","url":null,"abstract":"The year 2023 marked a significant surge in the exploration of applying large\u0000language model (LLM) chatbots, notably ChatGPT, across various disciplines. We\u0000surveyed the applications of ChatGPT in various sectors of bioinformatics and\u0000biomedical informatics throughout the year, covering omics, genetics,\u0000biomedical text mining, drug discovery, biomedical image understanding,\u0000bioinformatics programming, and bioinformatics education. Our survey delineates\u0000the current strengths and limitations of this chatbot in bioinformatics and\u0000offers insights into potential avenues for future development.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300703","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}
Due to the concerns of the society about the increase of antibiotic resistant infections, many studies and research have been done on nanoparticles and applications of nano-biotechnology. Zirconium Oxide ($text{ZrO}_{2}$) in which called zirconia, is a white oxide of zirconium metal that its diameter is 20 nm. The colloidal size of these particles is often smaller than bacterial and eukaryotic cells. The main intention of this paper is to investigate the effect of different doses of $text{ZrO}_{2}$ NPs on the sequences changes for the $textit{Escherichia coli}$ ($textit{E. coli}$) genome. At the first step, $textit{E. coli}$ was cultured in eosin methylene blue agar and brain heart broth (BHB) mediums, respectively. Then, bacteria were treated with $text{ZrO}_{2}$ NPs at concentrations of 100, 250, and 350 $mu$g/ml. After treatment, the growth of bacteria was evaluated by utilizing spectrophotometry at 600 nm after incubation times including 2, 4, 6, 8, and 24 hours at 37 $^{circ}$C. At the second step, the extraction of DNA was performed by using control and treated samples. Then, the changes in the sequence of bacterial genome were investigated using RAPD markers. Finally, NTSYS-PC platform was employed in order to analyze of the results extracted by electrophoresis of products on agarose gel. In this paper, it was observed that $text{ZrO}_{2}$ NPs can inhibit the growth of bacteria at concentrations of 250 and 350 $mu$g/ml after 8 hours of treatment. It was also found that the $text{ZrO}_{2}$ NPs at different concentrations have not changed the genome sequence of $textit{E. coli}$. Furthermore, it was concluded that the $text{ZrO}_{2}$ NPs with the concentration of 350 $mu$g/ml had the highest inhibitory properties without significant changing in the genomic sequence of $textit{E. coli}$.
{"title":"Investigation of Genomic Effect of Zirconium Oxide Nanoparticles in Escherichia coli Bacteria","authors":"Simin Rashidia, b, Bahram Golestani Eimania","doi":"arxiv-2403.14728","DOIUrl":"https://doi.org/arxiv-2403.14728","url":null,"abstract":"Due to the concerns of the society about the increase of antibiotic resistant\u0000infections, many studies and research have been done on nanoparticles and\u0000applications of nano-biotechnology. Zirconium Oxide ($text{ZrO}_{2}$) in which\u0000called zirconia, is a white oxide of zirconium metal that its diameter is 20\u0000nm. The colloidal size of these particles is often smaller than bacterial and\u0000eukaryotic cells. The main intention of this paper is to investigate the effect\u0000of different doses of $text{ZrO}_{2}$ NPs on the sequences changes for the\u0000$textit{Escherichia coli}$ ($textit{E. coli}$) genome. At the first step,\u0000$textit{E. coli}$ was cultured in eosin methylene blue agar and brain heart\u0000broth (BHB) mediums, respectively. Then, bacteria were treated with\u0000$text{ZrO}_{2}$ NPs at concentrations of 100, 250, and 350 $mu$g/ml. After\u0000treatment, the growth of bacteria was evaluated by utilizing spectrophotometry\u0000at 600 nm after incubation times including 2, 4, 6, 8, and 24 hours at 37\u0000$^{circ}$C. At the second step, the extraction of DNA was performed by using\u0000control and treated samples. Then, the changes in the sequence of bacterial\u0000genome were investigated using RAPD markers. Finally, NTSYS-PC platform was\u0000employed in order to analyze of the results extracted by electrophoresis of\u0000products on agarose gel. In this paper, it was observed that $text{ZrO}_{2}$\u0000NPs can inhibit the growth of bacteria at concentrations of 250 and 350\u0000$mu$g/ml after 8 hours of treatment. It was also found that the\u0000$text{ZrO}_{2}$ NPs at different concentrations have not changed the genome\u0000sequence of $textit{E. coli}$. Furthermore, it was concluded that the\u0000$text{ZrO}_{2}$ NPs with the concentration of 350 $mu$g/ml had the highest\u0000inhibitory properties without significant changing in the genomic sequence of\u0000$textit{E. coli}$.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300715","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}
Of MaminiainaFOFIFA-DRZVP, IMVAVET, M. KokoIMVAVET, J. J. RajaonarisonIMVAVET, R. RazafindrakotoIMVAVET, J. RavaomananaFOFIFA-DRZVP, A. D. Shannon
From 1994, we began to use ELISA (Enzyme Linked Immunosorbent Assay) in the diagnosis of PCP. This is aELISA for capturing antigens (PACE) possibly contained in the samples. The advantage of this test comes from the fact that it is completelyindependent of cell cultures. In addition, it is fast: the result can be obtained in less than 36 hours. A study of its standardizationcarried out in Australia gave a sensitivity (Se) of 99%, a specificity (Sp) close to 100% and a negative predictive value (NPV) of 99.7%. Due to its high specificity, the test gives a negative result to all true negatives, in other words, the negatives of the test correspond to thetrue negatives. A variant of the capture ELISA, the CTB-ELISA or complex trapping blocking ELISA allows the quantity of antibodies to be measureddirected against the non-structural protein, p80 (or NS3), contained in animal sera. Evaluation of the level of anti-NS3 antibodiesconstitutes an excellent assessment of the level of neutralizing antibodies because the correlation coefficient between these two types of antibodies, the firstobtained by CTB-ELISA, and the second by serum neutralization (VNT), is very high (r = 0.98).The two tests being capable, one of detecting pestiviral antigens and the other of measuring antibodies specific to each of thegroups, constitutes an excellent tool for the qualitative control of anti-CSF vaccine.
{"title":"Valeur des tests PACE et CTB_ELISA dans le diagnostic de la peste porcine classique (PPC) et le contr{ô}le de qualit{é} du vaccin correspondant {à} Madagascar","authors":"Of MaminiainaFOFIFA-DRZVP, IMVAVET, M. KokoIMVAVET, J. J. RajaonarisonIMVAVET, R. RazafindrakotoIMVAVET, J. RavaomananaFOFIFA-DRZVP, A. D. Shannon","doi":"arxiv-2403.13853","DOIUrl":"https://doi.org/arxiv-2403.13853","url":null,"abstract":"From 1994, we began to use ELISA (Enzyme Linked Immunosorbent Assay) in the\u0000diagnosis of PCP. This is aELISA for capturing antigens (PACE) possibly\u0000contained in the samples. The advantage of this test comes from the fact that\u0000it is completelyindependent of cell cultures. In addition, it is fast: the\u0000result can be obtained in less than 36 hours. A study of its\u0000standardizationcarried out in Australia gave a sensitivity (Se) of 99%, a\u0000specificity (Sp) close to 100% and a negative predictive value (NPV) of 99.7%.\u0000Due to its high specificity, the test gives a negative result to all true\u0000negatives, in other words, the negatives of the test correspond to thetrue\u0000negatives. A variant of the capture ELISA, the CTB-ELISA or complex trapping\u0000blocking ELISA allows the quantity of antibodies to be measureddirected against\u0000the non-structural protein, p80 (or NS3), contained in animal sera. Evaluation\u0000of the level of anti-NS3 antibodiesconstitutes an excellent assessment of the\u0000level of neutralizing antibodies because the correlation coefficient between\u0000these two types of antibodies, the firstobtained by CTB-ELISA, and the second\u0000by serum neutralization (VNT), is very high (r = 0.98).The two tests being\u0000capable, one of detecting pestiviral antigens and the other of measuring\u0000antibodies specific to each of thegroups, constitutes an excellent tool for the\u0000qualitative control of anti-CSF vaccine.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197657","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: To provide an overall quality assessment of the methods used for COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on PubMed to identify COVID-19-related studies that use the PSM analysis between 2020 and 2021. Key information about study design and PSM analysis were extracted, such as covariates, matching algorithm, and reporting of estimated treatment effect type. Results: One-hundred-and-fifty (87.72%) cohort studies and thirteen (7.60%) case-control studies were found among 171 identified articles. Forty-five studies (26.32%) provided a reasonable justification for covariates selection. One-hundred-and-three (60.23%) and Sixty-nine (40.35%) studies did not provide the model that was used for calculating the propensity score or did not report the matching algorithm, respectively. Seventy-three (42.69%) studies reported the method(s) for checking covariates balance. Forty studies (23.39%) had a statistician co-author. All the case-control studies (n=13) did not have a statistician co-author (p=0.006) and all studies that clarified the treatment effect estimation (n=6) had a statistician co-author (p<0.001). Conclusions: The reporting quality of the PSM analysis is suboptimal in some COVID-19 epidemiological studies. Some pitfalls may undermine study findings that involve PSM analysis, such as a mismatch between PSM analysis and study design.
{"title":"Propensity-score matching analysis in COVID-19-related studies: a method and quality systematic review","authors":"Chunhui Gu, Ruosha Li, Guoqiang Zhang","doi":"arxiv-2403.07023","DOIUrl":"https://doi.org/arxiv-2403.07023","url":null,"abstract":"Objectives: To provide an overall quality assessment of the methods used for\u0000COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on\u0000PubMed to identify COVID-19-related studies that use the PSM analysis between\u00002020 and 2021. Key information about study design and PSM analysis were\u0000extracted, such as covariates, matching algorithm, and reporting of estimated\u0000treatment effect type. Results: One-hundred-and-fifty (87.72%) cohort studies and thirteen (7.60%)\u0000case-control studies were found among 171 identified articles. Forty-five\u0000studies (26.32%) provided a reasonable justification for covariates selection.\u0000One-hundred-and-three (60.23%) and Sixty-nine (40.35%) studies did not provide\u0000the model that was used for calculating the propensity score or did not report\u0000the matching algorithm, respectively. Seventy-three (42.69%) studies reported\u0000the method(s) for checking covariates balance. Forty studies (23.39%) had a\u0000statistician co-author. All the case-control studies (n=13) did not have a\u0000statistician co-author (p=0.006) and all studies that clarified the treatment\u0000effect estimation (n=6) had a statistician co-author (p<0.001). Conclusions: The reporting quality of the PSM analysis is suboptimal in some\u0000COVID-19 epidemiological studies. Some pitfalls may undermine study findings\u0000that involve PSM analysis, such as a mismatch between PSM analysis and study\u0000design.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115335","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}
Dong ShengSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China, Siyuan JingSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai, China, Xueqing HeSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaDepartment of Health and Environmental Sciences, Xian Jiaotong-Liverpool University, Suzhou, China, Alexandra-Maria KleinNature Conservation and Landscape Ecology, University of Freiburg, Freiburg, Germany, Heinz-R. KöhlerAnimal Physiological Ecology, Institute of Evolution and Ecology, University of Tuebingen, Tuebingen, Germany, Thomas C. WangerSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaAgroecology, University of Goettingen, Goettingen, Germany
Biodiversity-associated ecosystem services such as pollination and biocontrol may be severely affected by emerging nano/micro-plastics (NMP) pollution. We synthesized the little-explored effects of NMP on pollinators and biocontrol agents on the organismal, farm and landscape scale. For instance ingested NMP trigger organismal changes from gene expression, organ damage to behavior modifications. At the farm and landscape level, NMP will likely amplify synergistic effects with other threats such as pathogens and antibiotics, and may alter landscape properties such as floral resource distributions in high NMP concentration areas, what we call NMP islands. It is essential to understand the functional exposure pathways of NMP on pollinators and biocontrol agents to comprehensively evaluate the risks for agricultural ecosystems and global food security.
{"title":"Nano/micro-plastics effects in agricultural landscapes: an overlooked threat to pollination, biological pest control, and food security","authors":"Dong ShengSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China, Siyuan JingSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai, China, Xueqing HeSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaDepartment of Health and Environmental Sciences, Xian Jiaotong-Liverpool University, Suzhou, China, Alexandra-Maria KleinNature Conservation and Landscape Ecology, University of Freiburg, Freiburg, Germany, Heinz-R. KöhlerAnimal Physiological Ecology, Institute of Evolution and Ecology, University of Tuebingen, Tuebingen, Germany, Thomas C. WangerSustainable Agricultural Systems & Engineering Lab, School of Engineering, Westlake University, Hangzhou, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaAgroecology, University of Goettingen, Goettingen, Germany","doi":"arxiv-2403.04920","DOIUrl":"https://doi.org/arxiv-2403.04920","url":null,"abstract":"Biodiversity-associated ecosystem services such as pollination and biocontrol\u0000may be severely affected by emerging nano/micro-plastics (NMP) pollution. We\u0000synthesized the little-explored effects of NMP on pollinators and biocontrol\u0000agents on the organismal, farm and landscape scale. For instance ingested NMP\u0000trigger organismal changes from gene expression, organ damage to behavior\u0000modifications. At the farm and landscape level, NMP will likely amplify\u0000synergistic effects with other threats such as pathogens and antibiotics, and\u0000may alter landscape properties such as floral resource distributions in high\u0000NMP concentration areas, what we call NMP islands. It is essential to\u0000understand the functional exposure pathways of NMP on pollinators and\u0000biocontrol agents to comprehensively evaluate the risks for agricultural\u0000ecosystems and global food security.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140098648","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}
Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.
{"title":"Optical Screening of Citrus Leaf Diseases Using Label-Free Spectroscopic Tools: A Review","authors":"Saurav Bharadwaj, Akshita Midha, Shikha Sharma, Gurupkar Singh Sidhu, Rajesh Kumar","doi":"arxiv-2403.04820","DOIUrl":"https://doi.org/arxiv-2403.04820","url":null,"abstract":"Citrus diseases pose threats to citrus farming and result in economic losses\u0000worldwide. Nucleic acid and serology-based methods of detection and,\u0000immunochromatographic assays are commonly used but these laboratory tests are\u0000laborious, expensive and might be subjected to cross-reaction and\u0000contamination. Modern optical spectroscopic techniques offer a promising\u0000alternative as they are label-free, sensitive, rapid, non-destructive, and\u0000demonstrate the potential for incorporation into an autonomous system for\u0000disease detection in citrus orchards. Nevertheless, the majority of optical\u0000spectroscopic methods for citrus disease detection are still in the trial\u0000phases and, require additional efforts to be established as efficient and\u0000commercially viable methods. The review presents an overview of fundamental\u0000working principles, the state of the art, and explains the applications and\u0000limitations of the optical spectroscopy technique including the spectroscopic\u0000imaging approach (hyperspectral imaging) in the identification of diseases in\u0000citrus plants. The review highlights (1) the technical specifications of\u0000optical spectroscopic tools that can potentially be utilized in field\u0000measurements, (2) their applications in screening citrus diseases through leaf\u0000spectroscopy, and (3) discusses their benefits and limitations, including\u0000future insights into label-free identification of citrus diseases. Moreover,\u0000the role of artificial intelligence is reviewed as potential effective tools\u0000for spectral analysis, enabling more accurate detection of infected citrus\u0000leaves even before the appearance of visual symptoms by leveraging\u0000compositional, morphological, and chemometric characteristics of the plant\u0000leaves. The review aims to encourage stakeholders to enhance the development\u0000and commercialization of field-based, label-free optical tools for the rapid\u0000and early-stage screening of citrus diseases in plants.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140098784","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}
As microscopy diversifies and becomes ever-more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they've generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by understanding tools and tradeoffs, optimizing data quality, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.
{"title":"Creating and troubleshooting microscopy analysis workflows: common challenges and common solutions","authors":"Beth A Cimini","doi":"arxiv-2403.04520","DOIUrl":"https://doi.org/arxiv-2403.04520","url":null,"abstract":"As microscopy diversifies and becomes ever-more complex, the problem of\u0000quantification of microscopy images has emerged as a major roadblock for many\u0000researchers. All researchers must face certain challenges in turning microscopy\u0000images into answers, independent of their scientific question and the images\u0000they've generated. Challenges may arise at many stages throughout the analysis\u0000process, including handling of the image files, image pre-processing, object\u0000finding, or measurement, and statistical analysis. While the exact solution\u0000required for each obstacle will be problem-specific, by understanding tools and\u0000tradeoffs, optimizing data quality, breaking workflows and data sets into\u0000chunks, talking to experts, and thoroughly documenting what has been done,\u0000analysts at any experience level can learn to overcome these challenges and\u0000create better and easier image analyses.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140073028","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}
Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Theinmozhi ArulrajDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Alberto IppolitoDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Aleksander S. PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.
{"title":"From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling","authors":"Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Theinmozhi ArulrajDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Alberto IppolitoDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Aleksander S. PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA","doi":"arxiv-2403.03335","DOIUrl":"https://doi.org/arxiv-2403.03335","url":null,"abstract":"Virtual patients and digital patients/twins are two similar concepts gaining\u0000increasing attention in health care with goals to accelerate drug development\u0000and improve patients' survival, but with their own limitations. Although\u0000methods have been proposed to generate virtual patient populations using\u0000mechanistic models, there are limited number of applications in immuno-oncology\u0000research. Furthermore, due to the stricter requirements of digital twins, they\u0000are often generated in a study-specific manner with models customized to\u0000particular clinical settings (e.g., treatment, cancer, and data types). Here,\u0000we discuss the challenges for virtual patient generation in immuno-oncology\u0000with our most recent experiences, initiatives to develop digital twins, and how\u0000research on these two concepts can inform each other.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054125","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}
This study investigates the efficacy of alginate oligosaccharides, derived from a novel alginate lyase expressed in E. coli (Pet21a-alginate lyase), in preserving the postharvest quality of litchi (Litchi chinensis Sonn.) fruits. The alginate lyase, characterized by Huang et al. (2013), was employed to produce AOS through enzymatic degradation of alginate. The resulting oligosaccharides were applied to litchi fruits harvested from Guangzhou Zengcheng to evaluate their impact on various quality parameters under controlled storage conditions. The study focused on measuring the effects of alginate oligosaccharide treatment on the fruits' color retention, water loss rate, hardness, and susceptibility to mold infection, under a set relative humidity and temperature. Results demonstrated significant improvements in the treated fruits, with enhanced color retention, reduced water loss, maintained hardness, and lower rates of mold infection compared to untreated controls. These findings suggest that AOS offer a promising natural alternative for extending the shelf life and maintaining the quality of litchi fruits postharvest.
{"title":"Postharvest litchi (Litchi chinensis Sonn.) quality preservation by alginate oligosaccharides","authors":"Jianlie Shen, Shulin Wan, Haidong Tan","doi":"arxiv-2403.01383","DOIUrl":"https://doi.org/arxiv-2403.01383","url":null,"abstract":"This study investigates the efficacy of alginate oligosaccharides, derived\u0000from a novel alginate lyase expressed in E. coli (Pet21a-alginate lyase), in\u0000preserving the postharvest quality of litchi (Litchi chinensis Sonn.) fruits.\u0000The alginate lyase, characterized by Huang et al. (2013), was employed to\u0000produce AOS through enzymatic degradation of alginate. The resulting\u0000oligosaccharides were applied to litchi fruits harvested from Guangzhou\u0000Zengcheng to evaluate their impact on various quality parameters under\u0000controlled storage conditions. The study focused on measuring the effects of\u0000alginate oligosaccharide treatment on the fruits' color retention, water loss\u0000rate, hardness, and susceptibility to mold infection, under a set relative\u0000humidity and temperature. Results demonstrated significant improvements in the\u0000treated fruits, with enhanced color retention, reduced water loss, maintained\u0000hardness, and lower rates of mold infection compared to untreated controls.\u0000These findings suggest that AOS offer a promising natural alternative for\u0000extending the shelf life and maintaining the quality of litchi fruits\u0000postharvest.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140047224","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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