B-type lamins are critical nuclear envelope proteins that interact with the 3D genomic architecture. However, identifying the direct roles of B-lamins on dynamic genome organization has been challenging as their joint depletion severely impacts cell viability. To overcome this, we engineered mammalian cells to rapidly and completely degrade endogenous B-type lamins using Auxin-inducible degron (AID) technology. Paired with a suite of novel technologies, live-cell Dual Partial Wave Spectroscopic (Dual-PWS) microscopy, in situ Hi-C, and CRISPR-Sirius, we demonstrate that lamin B1 and lamin B2 depletion transforms chromatin mobility, heterochromatin positioning, gene expression, and loci-positioning with minimal disruption to mesoscale chromatin folding. Using the AID system, we show that the disruption of B-lamins alters gene expression both within and outside lamin associated domains, with distinct mechanistic patterns depending on their localization. Critically, we demonstrate that chromatin dynamics, positioning of constitutive and facultative heterochromatic markers, and chromosome positioning near the nuclear periphery are significantly altered, indicating that the mechanism of action of B-type lamins is derived from their role in maintaining chromatin dynamics and spatial positioning. Our findings suggest that the mechanistic role of B-type lamins is stabilization of heterochromatin and chromosomal positioning along the nuclear periphery. We conclude that degrading lamin B1 and lamin B2 has several functional consequences related to both structural disease and cancer.
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within thebrain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detectdisease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis usinga single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3Dwhole-brain structure using standard post-processing methods. A deep learning model was then developed,optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia.Our proposed model outperformed the benchmark model, which was also trained with structural MR imagesusing a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987)distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regionalanalysis localized subcortical regions and ventricles as the most predictive brain regions. Subcorticalstructures serve a pivotal role in cognitive, affective, and social functions in humans, and structuralabnormalities of these regions have been associated with schizophrenia. Our finding corroborates thatschizophrenia is associated with widespread alterations in subcortical brain structure and the subcorticalstructural information provides prominent features in diagnostic classification. Together, these results furtherdemonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structuralneuroimaging signatures from a single, standard T1-weighted brain MRI.
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.
This paper presents a series of methods for automatically determining the gender of proper names, based on their co-occurrence with words and grammatical features in a large corpus. Although the results obtained were for Spanish given names, the method presented here can be easily replicated and used for names in other languages. Most methods reported in the literature use pre-existing lists of first names that require costly manual processing and tend to become quickly outdated. Instead, we propose using corpora. Doing so offers the possibility of obtaining real and up-to-date name-gender links. To test the effectiveness of our method, we explored various machine-learning methods as well as another method based on simple frequency of co-occurrence. The latter produced the best results: 93% precision and 88% recall on a database of ca. 10,000 mixed names. Our method can be applied to a variety of natural language processing tasks such as information extraction, machine translation, anaphora resolution or large-scale delivery or email correspondence, among others.
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