arXiv - QuanBio - Quantitative Methods最新文献_第2页

Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web 乳房脂肪组织中的冠状结构：利用人工智能和网络协作主动学习寻找 "大海捞针

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-12 DOI: arxiv-2409.08275

Praphulla MS Bhawsar, Cody Ramin, Petra Lenz, Máire A Duggan, Alexandra R Harris, Brittany Jenkins, Renata Cora, Mustapha Abubakar, Gretchen Gierach, Joel Saltz, Jonas S Almeida

{"title":"Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web","authors":"Praphulla MS Bhawsar, Cody Ramin, Petra Lenz, Máire A Duggan, Alexandra R Harris, Brittany Jenkins, Renata Cora, Mustapha Abubakar, Gretchen Gierach, Joel Saltz, Jonas S Almeida","doi":"arxiv-2409.08275","DOIUrl":"https://doi.org/arxiv-2409.08275","url":null,"abstract":"Crown-like structures (CLS) in breast adipose tissue are formed as a result\u0000of macrophages clustering around necrotic adipocytes in specific patterns. As a\u0000histologic marker of local inflammation, CLS could have potential diagnostic\u0000utility as a biomarker for breast cancer risk. However, given the scale of\u0000whole slide images and the rarity of CLS (a few cells in an entire tissue\u0000sample), microscope-based manual identification is a challenge for the\u0000pathologist. In this report, we describe an artificial intelligence pipeline to\u0000solve this needle-in-a-haystack problem. We developed a zero-cost,\u0000zero-footprint web platform to enable remote operation on digital whole slide\u0000imaging data directly in the web browser, supporting collaborative annotation\u0000of the data by multiple experts. The annotated images then allow for\u0000incremental training and fine tuning of deep neural networks via active\u0000learning. The platform is reusable and requires no backend or installations,\u0000thus ensuring the data remains secure and private under the governance of the\u0000end user. Using this platform, we iteratively trained a CLS identification\u0000model, evaluating the performance after each round and adding examples to the\u0000training data to overcome failure cases. The resulting model, with an AUC of\u00000.90, shows promise as a first-pass screening tool to detect CLS in breast\u0000adipose tissue, considerably reducing the workload of the pathologist. Platform available at: https://episphere.github.io/path","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213264","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

Investigation of Electrical Conductivity Changes during Brain Functional Activity in 3T MRI 通过 3T 磁共振成像研究大脑功能活动时的电导率变化

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-12 DOI: arxiv-2409.07806

Kyu-Jin Jung, Chuanjiang Cui, Soo-Hyung Lee, Chan-Hee Park, Ji-Won Chun, Dong-Hyun Kim

{"title":"Investigation of Electrical Conductivity Changes during Brain Functional Activity in 3T MRI","authors":"Kyu-Jin Jung, Chuanjiang Cui, Soo-Hyung Lee, Chan-Hee Park, Ji-Won Chun, Dong-Hyun Kim","doi":"arxiv-2409.07806","DOIUrl":"https://doi.org/arxiv-2409.07806","url":null,"abstract":"Blood oxygenation level-dependent (BOLD) functional magnetic resonance\u0000imaging (fMRI) is widely used to visualize brain activation regions by\u0000detecting hemodynamic responses associated with increased metabolic demand.\u0000While alternative MRI methods have been employed to monitor functional\u0000activities, the investigation of in-vivo electrical property changes during\u0000brain function remains limited. In this study, we explored the relationship\u0000between fMRI signals and electrical conductivity (measured at the Larmor\u0000frequency) changes using phase-based electrical properties tomography (EPT).\u0000Our results revealed consistent patterns: conductivity changes showed negative\u0000correlations, with conductivity decreasing in the functionally active regions\u0000whereas B1 phase mapping exhibited positive correlations around activation\u0000regions. These observations were consistent across both motor and visual cortex\u0000activations. To further substantiate these findings, we conducted\u0000electromagnetic radio-frequency simulations that modeled activation states with\u0000varying conductivity, which demonstrated trends similar to our in-vivo results\u0000for both B1 phase and conductivity. These findings suggest that in-vivo\u0000electrical conductivity changes can indeed be measured during brain activity.\u0000However, further investigation is needed to fully understand the underlying\u0000mechanisms driving these measurements.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213247","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

Open Source Infrastructure for Automatic Cell Segmentation 用于自动细胞划分的开源基础设施

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-12 DOI: arxiv-2409.08163

Aaron Rock Menezes, Bharath Ramsundar

引用次数: 0

Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm 借助深度聚类算法有效建立菊花种苗质量分类标准

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-12 DOI: arxiv-2409.08867

Yanzhi Jing, Hongguang Zhao, Shujun Yu

引用次数: 0

The microbiome science of composting and human excrement composting: a review 堆肥和人类排泄物堆肥的微生物组科学：综述

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-11 DOI: arxiv-2409.07376

Jeff Meilander, J. Gregory Caporaso

{"title":"The microbiome science of composting and human excrement composting: a review","authors":"Jeff Meilander, J. Gregory Caporaso","doi":"arxiv-2409.07376","DOIUrl":"https://doi.org/arxiv-2409.07376","url":null,"abstract":"Linear waste management systems are unsustainable and contribute to\u0000environmental degradation, economic inequity, and health disparities. Among the\u0000array of environmental challenges stemming from anthropogenic impacts, the\u0000management of human excrement (human feces and urine) stands as a significant\u0000concern. Over two billion people do not have access to adequate sanitation\u0000resulting in a global public health crisis. Composting is the microbial biotechnology aimed at cycling organic waste,\u0000including human excrement, for improved public health, agricultural\u0000productivity and safety, and environmental sustainability. Applications of\u0000modern microbiome-omics and related technologies have vast capacity to support\u0000continued advances in composting science and praxis. In this article, we review\u0000literature focused on applications of microbiome technologies to study\u0000composting systems and reactions. The studies we survey generally fall into the\u0000categories of animal manure composting, food and landscaping waste composting,\u0000biosolids composting, and human excrement composting. We review experiments\u0000utilizing microbiome technologies to investigate strategies for enhancing\u0000pathogen suppression and accelerating the biodegradation of organic matter.\u0000Additionally, we explore studies focused on the bioengineering potential of\u0000microbes as inoculants to facilitate degradation of toxins such as\u0000pharmaceuticals or per- and polyfluoroalkyl substances (PFAS). The findings\u0000from these studies underscore the importance of advancing our understanding of\u0000composting processes through the integration of emerging microbiome-omics\u0000technologies. We conclude that work to-date has demonstrated exciting basic and applied\u0000science potential from studying compost microbiomes, with promising\u0000implications for enhancing global environmental sustainability and public\u0000health.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213287","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

Effective management of white rust disease in red amaranth: a field study in Dhaka, Bangladesh 红苋菜白锈病的有效防治：孟加拉国达卡的一项实地研究

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-11 DOI: arxiv-2409.07579

Abu Noman Faruq Ahmmed, MD. Zahidul Islam, Raihan Ferdous

{"title":"Effective management of white rust disease in red amaranth: a field study in Dhaka, Bangladesh","authors":"Abu Noman Faruq Ahmmed, MD. Zahidul Islam, Raihan Ferdous","doi":"arxiv-2409.07579","DOIUrl":"https://doi.org/arxiv-2409.07579","url":null,"abstract":"This study aimed to evaluate the effective management strategies of Albugo\u0000candida, a pathogen of white rust disease in red amaranth (Amaranthus tricolor\u0000L.), accountable for the reduction of seed production. The study was performed\u0000during the Rabi season of 2018 and the Kharif season of 2019 at Sher-e-Bangla\u0000Agricultural University in Bangladesh. Eight treatments, including chemical,\u0000botanical, and biopesticide treatments such as Ridomil Gold 68 WG, Autostin 50\u0000WP, Dithane M 45, Goldton 50 WP, the Bordeaux mixture, G-Derma, Garlic bulb\u0000extract, and Allamanda leaf extract, were evaluated. Four foliar sprays were\u0000applied at seven-day intervals after disease symptom onset. The field\u0000experiments followed a randomized complete block design with three\u0000replications. A microscopic study confirmed that Albugo candida was the causal\u0000organism. In both seasons, Ridomil Gold demonstrated superior efficacy in\u0000reducing disease incidence in plants, disease incidence in leaves, and disease\u0000severity, which were 63.07%, 62.78.5, and 84.31%, respectively, in Rabi and\u000069.73%, 65.71%, and 88.41%, respectively, in the Kharif season. Allamanda leaf\u0000extract also had statistically similar results, while Autostin exhibited\u0000promising effectiveness. Furthermore, compared with the other treatments, the\u0000combination of Ridomil Gold and Allamanda leaf extract significantly enhanced\u0000the growth parameters and seed yield in both seasons. Assessing the collective\u0000effectiveness of the treatments, Ridomil Gold demonstrated the most efficient\u0000control of white rust disease. Consequently, Ridomil Gold holds promise for\u0000application in red amaranth seed production. Additionally, the use of Allamanda\u0000leaf extract is an environmentally friendly approach to white rust disease\u0000management and promotes safer crop production practices. This study offers\u0000significant guidance to practitioners seeking optimal disease management\u0000strategies.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213265","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

Universal scale-free representations in human visual cortex 人类视觉皮层中的通用无标度表征

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-10 DOI: arxiv-2409.06843

Raj Magesh Gauthaman, Brice Ménard, Michael F. Bonner

{"title":"Universal scale-free representations in human visual cortex","authors":"Raj Magesh Gauthaman, Brice Ménard, Michael F. Bonner","doi":"arxiv-2409.06843","DOIUrl":"https://doi.org/arxiv-2409.06843","url":null,"abstract":"How does the human visual cortex encode sensory information? To address this\u0000question, we explore the covariance structure of neural representations. We\u0000perform a cross-decomposition analysis of fMRI responses to natural images in\u0000multiple individuals from the Natural Scenes Dataset and find that neural\u0000representations systematically exhibit a power-law covariance spectrum over\u0000four orders of magnitude in ranks. This scale-free structure is found in\u0000multiple regions along the visual hierarchy, pointing to the existence of a\u0000generic encoding strategy in visual cortex. We also show that, up to a\u0000rotation, a large ensemble of principal axes of these population codes are\u0000shared across subjects, showing the existence of a universal high-dimensional\u0000representation. This suggests a high level of convergence in how the human\u0000brain learns to represent natural scenes despite individual differences in\u0000neuroanatomy and experience. We further demonstrate that a spectral approach is\u0000critical for characterizing population codes in their full extent, and in doing\u0000so, we reveal a vast space of uncharted dimensions that have been out of reach\u0000for conventional variance-weighted methods. A global view of neural\u0000representations thus requires embracing their high-dimensional nature and\u0000understanding them statistically rather than through visual or semantic\u0000interpretation of individual dimensions.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213286","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

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval 分子如何影响细胞？揭开对比表观分子检索的神秘面纱

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-10 DOI: arxiv-2409.08302

Philip Fradkin, Puria Azadi, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini

{"title":"How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval","authors":"Philip Fradkin, Puria Azadi, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini","doi":"arxiv-2409.08302","DOIUrl":"https://doi.org/arxiv-2409.08302","url":null,"abstract":"Predicting molecular impact on cellular function is a core challenge in\u0000therapeutic design. Phenomic experiments, designed to capture cellular\u0000morphology, utilize microscopy based techniques and demonstrate a high\u0000throughput solution for uncovering molecular impact on the cell. In this work,\u0000we learn a joint latent space between molecular structures and microscopy\u0000phenomic experiments, aligning paired samples with contrastive learning.\u0000Specifically, we study the problem ofContrastive PhenoMolecular Retrieval,\u0000which consists of zero-shot molecular structure identification conditioned on\u0000phenomic experiments. We assess challenges in multi-modal learning of phenomics\u0000and molecular modalities such as experimental batch effect, inactive molecule\u0000perturbations, and encoding perturbation concentration. We demonstrate improved\u0000multi-modal learner retrieval through (1) a uni-modal pre-trained phenomics\u0000model, (2) a novel inter sample similarity aware loss, and (3) models\u0000conditioned on a representation of molecular concentration. Following this\u0000recipe, we propose MolPhenix, a molecular phenomics model. MolPhenix leverages\u0000a pre-trained phenomics model to demonstrate significant performance gains\u0000across perturbation concentrations, molecular scaffolds, and activity\u0000thresholds. In particular, we demonstrate an 8.1x improvement in zero shot\u0000molecular retrieval of active molecules over the previous state-of-the-art,\u0000reaching 77.33% in top-1% accuracy. These results open the door for machine\u0000learning to be applied in virtual phenomics screening, which can significantly\u0000benefit drug discovery applications.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264957","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

PharmacoMatch: Efficient 3D Pharmacophore Screening through Neural Subgraph Matching PharmacoMatch：通过神经子图匹配进行高效的三维药效学筛选

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-10 DOI: arxiv-2409.06316

Daniel Rose, Oliver Wieder, Thomas Seidel, Thierry Langer

引用次数: 0

DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images DANCE：利用混沌增强万花筒图像进行深度学习辅助蛋白质序列分析

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-10 DOI: arxiv-2409.06694

Taslim Murad, Prakash Chourasia, Sarwan Ali, Murray Patterson

{"title":"DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images","authors":"Taslim Murad, Prakash Chourasia, Sarwan Ali, Murray Patterson","doi":"arxiv-2409.06694","DOIUrl":"https://doi.org/arxiv-2409.06694","url":null,"abstract":"Cancer is a complex disease characterized by uncontrolled cell growth. T cell\u0000receptors (TCRs), crucial proteins in the immune system, play a key role in\u0000recognizing antigens, including those associated with cancer. Recent\u0000advancements in sequencing technologies have facilitated comprehensive\u0000profiling of TCR repertoires, uncovering TCRs with potent anti-cancer activity\u0000and enabling TCR-based immunotherapies. However, analyzing these intricate\u0000biomolecules necessitates efficient representations that capture their\u0000structural and functional information. T-cell protein sequences pose unique\u0000challenges due to their relatively smaller lengths compared to other\u0000biomolecules. An image-based representation approach becomes a preferred choice\u0000for efficient embeddings, allowing for the preservation of essential details\u0000and enabling comprehensive analysis of T-cell protein sequences. In this paper,\u0000we propose to generate images from the protein sequences using the idea of\u0000Chaos Game Representation (CGR) using the Kaleidoscopic images approach. This\u0000Deep Learning Assisted Analysis of Protein Sequences Using Chaos Enhanced\u0000Kaleidoscopic Images (called DANCE) provides a unique way to visualize protein\u0000sequences by recursively applying chaos game rules around a central seed point.\u0000we perform the classification of the T cell receptors (TCRs) protein sequences\u0000in terms of their respective target cancer cells, as TCRs are known for their\u0000immune response against cancer disease. The TCR sequences are converted into\u0000images using the DANCE method. We employ deep-learning vision models to perform\u0000the classification to obtain insights into the relationship between the visual\u0000patterns observed in the generated kaleidoscopic images and the underlying\u0000protein properties. By combining CGR-based image generation with deep learning\u0000classification, this study opens novel possibilities in the protein analysis\u0000domain.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213269","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