IoT Enabled Smart Fertilization and Irrigation Aid for Agricultural Purposes

Soil is of great importance to agriculture, especially the moisture and nutrients in the soil are the essential ingredients for growing plants and crops. Therefore, benefits and importance of a soil moisture and nutrient monitoring system in modern agriculture and gardening is undeniable. It can also be an interesting feature of an intelligent home or smart agriculture system using the internet of things (IoT) technology. This paper presents an IoT application in Arduino platform aiming to monitor the change in soil moisture and Nitrogen (N), Phosphorus (P), Potassium (K) (NPK) value for an indoor plant using moisture sensors and optical transducers. Other functionalities and important features of this prototype include online data display infographic as user feedback, level-based nutrient classification for enabling proper type of fertilizer selection, hardware and e-mail notification of moisture and nutrients' easily accessible and user-friendly smartphone app

Multiple Recurrent De Novo CNVs, Including Duplications of the 7q11.23 Williams Syndrome Region, Are Strongly Associated with Autism

SummaryWe have undertaken a genome-wide analysis of rare copy-number variation (CNV) in 1124 autism spectrum disorder (ASD) families, each comprised of a single proband, unaffected parents, and, in most kindreds, an unaffected sibling. We find significant association of ASD with de novo duplications of 7q11.23, where the reciprocal deletion causes Williams-Beuren syndrome, characterized by a highly social personality. We identify rare recurrent de novo CNVs at five additional regions, including 16p13.2 (encompassing genes USP7 and C16orf72) and Cadherin 13, and implement a rigorous approach to evaluating the statistical significance of these observations. Overall, large de novo CNVs, particularly those encompassing multiple genes, confer substantial risks (OR = 5.6; CI = 2.6–12.0, p = 2.4 × 10-7). We estimate there are 130–234 ASD-related CNV regions in the human genome and present compelling evidence, based on cumulative data, for association of rare de novo events at 7q11.23, 15q11.2-13.1, 16p11.2, and Neurexin 1

Machine learning in clinical neuroimaging

This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track).The rise of neuroimaging data, bolstered by the rapid advancements in computational resources and algorithms, is poised to drive significant breakthroughs in clinical neuroscience. Notably, deep learning is gaining relevance in this domain. Yet, there’s an imbalance: while computational methods grow in complexity, the breadth and diversity of standard evaluation datasets lag behind. This mismatch could result in findings that don’t generalize to a wider population or are skewed towards dominant groups. To address this, it’s imperative to foster inter-domain collaborations that move state-of-the art methods quickly into clinical research. Bridging the divide between various specialties can pave the way for methodological innovations to smoothly transition into clinical research and ultimately, real-world applications.Ourworkshop aimed to facilitate this by creating a forum for dialogue among engineers, clinicians, and neuroimaging specialists. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) was held on October 8th, 2023, as a satellite event of the 26th International Conference on Medical Imaging Computing & Computer-Assisted Intervention (MICCAI 2023) in Vancouver to continue the yearly recurring dialog between experts in machine learning and clinical neuroimaging. The call for papers was made on May 2nd, 2023, and submissions were closed on July 4th, 2023. Each of the 27 submitted manuscripts was reviewed by three or more program committee members in a double-blinded review process. The sixteen accepted papers showcase the integration of machine learning techniques with clinical neuroimaging data. Studied clinical conditions include Alzheimer’s disease, autism spectrum disorder, stroke, and aging. There is a strong emphasis on deep learning approaches to analysis of structural and functional MRI, positron emission tomography, and computed tomography. Research also delves into multi-modal data synthesis and analysis. The conference encapsulated the blend of methodological innovation and clinical applicability in neuroimaging. The proceedings mirror the hallmarks in the sections “Machine learning” and “Clinical applications”, although all papers carry clinical relevance and provide methodological novelty. For the sixth time, this workshop was put together by a dedicated community of authors, program committee, steering committee, and workshop participants. We thank all creators and attendees for their valuable contributions that made the MLCN 2023 Workshop a success

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials