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31 |
Discovering the shared biology of cognitive traits determined by genetic overlap
کشف زیست شناسی مشترک صفات شناختی که با همپوشانی ژنتیکی تعیین می شوند-2020 Investigating the contribution of biology to human cognition has assumed a bottom-up causal cascade where
genes influence brain systems that activate, communicate, and ultimately drive behavior. Yet few studies have
directly tested whether cognitive traits with overlapping genetic underpinnings also rely on overlapping brain
systems. Here, we report a step-wise exploratory analysis of genetic and functional imaging overlaps among
cognitive traits. We used twin-based genetic analyses in the human connectome project (HCP) dataset (N ¼ 486),
in which we quantified the heritability of measures of cognitive functions, and tested whether they were driven by
common genetic factors using pairwise genetic correlations. Subsequently, we derived activation maps associated
with cognitive tasks via functional imaging meta-analysis in BrainMap (N ¼ 4484), and tested whether cognitive
traits that shared genetic variation also exhibited overlapping brain activation. Our genetic analysis determined
that six cognitive measures (cognitive flexibility, no-go continuous performance, fluid intelligence, processing
speed, reading decoding and vocabulary comprehension) were heritable (0.3 < h2 < 0.5), and genetically
correlated with at least one other heritable cognitive measure (0.2 < ρg < 0.35). The meta-analysis showed that
two genetically-correlated traits, cognitive flexibility and fluid intelligence (ρg ¼ 0.24), also had a significant brain
activation overlap (ρperm ¼ 0.29). These findings indicate that fluid intelligence and cognitive flexibility rely on
overlapping biological features, both at the neural systems level and at the molecular level. The cross-disciplinary
approach we introduce provides a concrete framework for data-driven quantification of biological convergence
between genetics, brain function, and behavior in health and disease. Keywords: Shared genetics | Functional imaging meta-analysis | Brain activation overlap | Cognition | Biological convergence |
مقاله انگلیسی |
32 |
Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan
هماهنگ سازی مجموعه داده های بزرگ MRI برای تجزیه و تحلیل الگوهای تصویربرداری از مغز در طول عمر-2020 As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust
pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have
become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477
structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18
different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing
methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and
deep structures and derive age trends of brain structure through the lifespan (3–96 years old). Critically, we
present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends.
We provide a web-based visualization interface to generate and present the resulting age trends, enabling future
studies of brain structure to compare their data with this reference of brain development and aging, and to
examine deviations from ranges, potentially related to disease. Keywords: MRI | Segmentation | FreeSurfer | MUSE | Brain | ROI |
مقاله انگلیسی |
33 |
Artificial intelligence (AI) and big data in cancer and precision oncology
هوش مصنوعی و داده های بزرگ در سرطان و انکولوژی دقیق -2020 Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare
sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and
time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer
is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic
interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative
to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance
treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these
demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that
are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging,
accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery.
NGS generates large datasets that demand specialised bioinformatics resources to analyse the
data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and
prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images.
Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical
application of NGS remains to be validated. By continuing to enhance the progression of innovation
and technology, the future of AI and precision oncology show great promise. Keywords: Artificial intelligence | Machine learning | Deep learning | Big datasets | Precision oncology | NGS and bioinformatics | Medical imaging | Digital pathology | Diagnosis | Treatment | Prognosis and drug discovery |
مقاله انگلیسی |
34 |
Imaging of microdefects in ZnGeP2 single crystals by X-ray topography
تصویربرداری از ریزگردها در بلورهای تک ZnGeP2 توسط توپوگرافی با اشعه X-2020 The contrast from microdefects in ZnGeP2 crystals is studied. Simulation of images
in X-ray topography based on the Borrmann effect is carried out for a model of a coherent
inclusion of spherical form in an infinite isotropic matrix. For this simulation, a semiphenomenological
theory of contrast from defects with a slowly changing deformation field is
applied. It is shown that the contrast from the inclusion is a complex function, depending on
the nature of defect (sign of the deformation of the matrix), the magnitude of the deformation
caused by the defect, its depth in the crystal, the modulus of the diffraction vector g and the
topography used (reflection or transmission). The most common images are intensity rosettes
of double or triple contrast, whose lobes are elongated along the diffraction vector. These are
created by inclusions, located near the X-ray exit surface of the sample. Analysis of
experimental data shows that the majority of microdefects in ZnGeP2 revealed by Borrmann
method (~96%) show good agreement with proposed model. All the features of the
experimental images are explained by the theory. Additionally, the contrast from dislocation
loops and from groups of big inclusions which have non-Coulombic deformation fields is
observed Keywords: B2. Nonlinear optic materials | A2. Bridgman technique | A2. Seed crystals | A1. Xray topography | A1. Defects| A1. Computer simulation |
مقاله انگلیسی |
35 |
comSensitivity analysis of nondestructive magnetictechniques for the restoration of stamped markson low carbon steel
تجزیه و تحلیل حساسیت تکنیکهای مغناطیسی غیر مخرب برای بازسازی مهر فولادی کم کربن مارکسون-2020 Restoration of obliterated stamped marks is a common investigative technique used incriminal investigation. Considering the evolution of the obliteration techniques used bycriminals, a study of the feasibility of sensitive, effective, and nondestructive techniques isneeded. This study aims to evaluate the performance and sensibility of two nondestructivetechniques: magnetic particle restoration and magneto-optical imaging. To this end, steelsamples from automobile chassis were analyzed. The samples were characterized in orderto obtain information regarding material microstructure, magnetic characteristic, and hard-ness, followed by the restoration of stamp marks. Both magnetic particle restoration andmagneto-optical imaging were capable of partially restoring the identification code obliter-ated by overstamping. In addition, nondestructive techniques showed a restoration depthlimit of 260 m below the cavity. Keywords:Restoration | Sensibility | Obliterated marks | Magneto-optic imaging | Magnetic particle |
مقاله انگلیسی |
36 |
تحلیل لبه ای مبتنی بر موجک چند جهته برای تشخیص سطح توسط پروفیلومتری نوری
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 18 دانشمندان، مهندسان و تولید کنندگان نیاز ضروری به تکنیک های بهتر تشخیص و کنترل کیفیت دارند. مترولوژی نوری با استفاده از علوم نور و علوم کامپیوتر به دنبال شبیه سازی، طراحی، محاسبات و بازرسی برای بسیاری از برنامه های کاربردی علمی و صنعتی مانند اپتیک، مکانیک، هواپیما، الکترونیک و … است. آنالیز الگوی fringe روشی برای انجام برخی عملیات در تصاویر نوری و به منظور دریافت نقشه فاز اینترفرومتری و سپس استخراج برخی اطلاعات مفید از آن است. در این مقاله، بهبود محرک الگوریتم دمدولاسیون fringe محلی ارائه شده است، که بر اساس موجک جدید چند جهته است. کارهای عددی و تجربی در مقایسه با سایر الگوریتم های استاندارد، سود جالبی را نشان می دهد. رویکرد ما به سرعت به عنوان فاز روش های بازیابی پرطرفدار اجرا می شود، اما با دقت قابل توجهی دمدولاسیون fringe های نویز را بهبود می دهد. همه این مسائل بدون هیچ پیش پردازش توسط فیلتر کردن مدل ها رخ می دهد.
کليدواژه ها: تصویربرداری نوری | علوم کامپیوتر | پردازش تصویر | موجک چند جهته | فاز بازیابی | طرح ریزی fringe . |
مقاله ترجمه شده |
37 |
TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set
TOP-GAN: طبقه بندی سلول های سرطانی بدون لکه با استفاده از یادگیری عمیق با یک مجموعه آموزشی کوچک-2019 We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is hybridization between transfer learning and generative ad- versarial networks (GANs). Healthy cells and cancer cells of different metastatic potential have been im- aged by low-coherence off-axis holography. After the acquisition, the optical path delay maps of the cells are extracted and directly used as inputs to the networks. In order to cope with the small number of clas- sified images, we use GANs to train a large number of unclassified images from another cell type (sperm cells). After this preliminary training, we change the last layers of the network and design automatic classifiers for the correct cell type (healthy/primary cancer/metastatic cancer) with 90–99% accuracies, although small training sets of down to several images are used. These results are better in comparison to other classic methods that aim at coping with the same problem of a small training set. We believe that our approach makes the combination of holographic microscopy and deep learning networks more accessible to the medical field by enabling a rapid, automatic and accurate classification in stain-free imaging flow cytometry. Furthermore, our approach is expected to be applicable to many other medical image classification tasks, suffering from a small training set. Keywords: Holography | Quantitative phase imaging | Deep learning | Machine learning algorithms | Image classification | Biological cells |
مقاله انگلیسی |
38 |
Neurobiological divergence of the positive and negative schizophrenia subtypes identified upon a new factor-structure of psychopathology using non-negative factorization: An international machine-learning study
واگرایی عصبی از زیرگروه های اسکیزوفرنی مثبت و منفی مشخص شده بر یک ساختار جدید از روانشناسی با استفاده از فاکتورسازی غیر منفی: یک مطالعه بین المللی یادگیری ماشین-2019 Objective: Disentangling psychopathological heterogeneity in schizophrenia is challenging and previous
results remain inconclusive. We employed advanced machine-learning to identify a stable and
generalizable factorization of the “Positive and Negative Syndrome Scale (PANSS)”, and used it to identify
psychopathological subtypes as well as their neurobiological differentiations.
Methods: PANSS data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients,
586 followed up after 1.35±0.70 years) were used for learning the factor-structure by an orthonormal
projective non-negative factorization. An international sample, pooled from nine medical centers across
Europe, USA, and Asia (490 patients), was used for validation. Patients were clustered into
psychopathological subtypes based on the identified factor-structure, and the neurobiological divergence
between the subtypes was assessed by classification analysis on functional MRI connectivity patterns.
Results: A four-factor structure representing negative, positive, affective, and cognitive symptoms was
identified as the most stable and generalizable representation of psychopathology. It showed higher
internal consistency than the original PANSS subscales and previously proposed factor-models. Based on
this representation, the positive-negative dichotomy was confirmed as the (only) robust
psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly
assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional
connectivity profiles of the ventro-medial frontal cortex, temporoparietal junction, and precuneus.
Conclusions: Machine-learning applied to multi-site data with cross-validation yielded a factorization
generalizable across populations and medical systems. Together with subtyping and the demonstrated
ability to predict subtype membership from neuroimaging data, this work further disentangles the
heterogeneity in schizophrenia Keywords: non-negative factorization | brain imaging | subtyping | machine learning | multivariate classification | schizophrenia |
مقاله انگلیسی |
39 |
Combined machine learning and diffusion tensor imaging reveals altered anatomic fiber connectivity of the brain in primary open-angle glaucoma
یادگیری ماشین ترکیبی و تصویربرداری با تانسور انتشار ، ارتباط فیبر آناتومیک مغز را در گلوکوم زاویه باز اولیه تغییر داده است-2019 Parameters derived from diffusion tensor imaging (DTI) have been found to be significantly altered in the optic
tracts, optic nerves, and optic radiations in patients with primary open-angle glaucoma (POAG). In this study,
DTI-derived parameters were further constructed into fiber connectivity, and we investigated anatomical fiber
connectivity changes within and beyond the visual pathway in POAG patients. DTI and T1-weighted magnetic
resonance images were acquired in 18 POAG patients and 26 healthy controls (HC). White matter tracts based on
the Brodmann atlases (BA) were constructed using the deterministic fiber tracking method. The mean fractional
anisotropy (FA), fiber number (FN), and mean fiber length (FL) were measured and then evaluated using twosample
t-tests between POAG and HC. The fiber connectivity between regions was taken as the features for
classifying HC and POAG using a machine learning method known as naïve Bayesian classification. The mean FA
decreased in connections between visual cortex BA17/BA18 and cortex BA23/BA25/BA35/BA36, while it increased
in the connections between cortex BA3/BA7/BA9 and BA5/BA6/BA45/BA25 in POAG. Classification
using fibers where a significant difference in FN had been identified produced better accuracy (ACC=0.89) than
using FA or FL (ACC=0.77 and 0.75, respectively). The FN of individual fiber connections with higher accuracy
and significant changes in POAG involved brain regions associated with vision (BA19), depression (BA10/BA46/
BA25), and memory (BA29). These findings strengthen the hypothesis that POAG involves changes in anatomical
connectivity within and beyond the visual pathway. Classification using the machine learning method reveals
that mean FN has the potential to be used as a biomarker for detecting white matter microstructure changes in
POAG. Keywords: Glaucoma | Anatomic white matter connectivity | Diffusion tensor imaging | Fiber tracking | Machine learning |
مقاله انگلیسی |
40 |
A deep learning model for early prediction of Alzheimers disease dementia based on hippocampal magnetic resonance imaging data
یک مدل یادگیری عمیق برای پیش بینی اولیه زوال عقل بیماری آلزایمر بر اساس داده های تصویربرداری رزونانس مغناطیسی هیپوکامپ-2019 It is challenging at baseline to predict when and which individuals who meet criteria
for mild cognitive impairment (MCI) will ultimately progress to Alzheimer’s disease (AD) dementia.
Methods: A deep learning method is developed and validated based on magnetic resonance imaging
scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects’ progression
to AD dementia in a time-to-event analysis setting.
Results: The deep-learning time-to-event model predicted individual subjects’ progression to AD
dementia with a concordance index of 0.762 on 439 Alzheimer’s Disease Neuroimaging Initiative
testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a
concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging
testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted
progression risk also clustered individual subjects into subgroups with significant differences
in their progression time to AD dementia (P ,.0002). Improved performance for predicting progression
to AD dementia (concordance index 5 0.864) was obtained when the deep learning–based progression
risk was combined with baseline clinical measures.
Discussion: Our method provides a cost effective and accurate means for prognosis and potentially
to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal
period. Keywords: Deep learning | Hippocampus | Time-to-event analysis | Alzheimer’s disease |
مقاله انگلیسی |