GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.

We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer's disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model's median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model's median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance-This study provides a deep-learning model that can predict Alzheimer's disease severity levels while identifying consistent and clinically relevant biomarkers.

Function of gamma delta (γδ) T cell in cancer with special emphasis on cervical cancer.

Cervical cancer is the second-most prevalent gynecologic cancer in India. It is typically detected in women between the ages of 35 and 44. Cervical cancer is mainly associated with the human papillomavirus (HPV). The report shows that 70 % of cervical cancer is caused by HPV 16 and 18. There are few therapeutic options and vaccines available for cervical cancer treatment and γδ T cell therapy is one of them. This therapy can kill various types of cancers, including cervical cancer. The major γδ T cell subset is the Vγ9Vδ2 T cell, mainly distributed in peripheral blood which recognize non-MHC peptide antigens and can eliminate MHC-downregulated cancer. Moreover, γδ T cells can express different types of receptors that bind to the molecules of stressed cells, often produced on cancerous cells but absent from healthy tissue. γδ T cells possess both direct and indirect cytotoxic capabilities against malignancies and show potential antitumoral responses. However, γδ T cells also encourage the progression of cancer. Cancer immunotherapy using γδ T cells will be a potential cancer treatment, as well as cervical cancer. This review focused on the γδ T cell and its function in cancer, with special emphasis on cervical cancer. It also focused on the ligand recognition site of γδ T cells, galectin-mediated therapy and pamidronate-treated therapy for cervical cancer. Instead of the great potential of γδ T cell for the eradication of cervical cancer, no comprehensive in-depth review is available to date, so there is a need to jot down the various roles and modes of action and different applications of γδ T cells for cancer research, which we believe will be a handy tool for the researchers and the readers.

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS summary statistics (https://github.com/sayangsep/Beatrice-Finemapping). Identifying causal variants is challenging due to their sparsity and to highly correlated variants in the nearby regions. To account for these challenges, our approach relies on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. Using this simulated data, we perform a comparative analysis against two state-of-the-art baseline methods for fine-mapping. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. Thus, BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space.

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

Distinct mechanical properties in homologous spectrin-like repeats of utrophin.

Patients with Duchenne muscular dystrophy (DMD) lack the protein dystrophin, which is a critical molecular component of the dystrophin-glycoprotein complex (DGC). Dystrophin is hypothesized to function as a molecular shock absorber that mechanically stabilizes the sarcolemma of striated muscle through interaction with the cortical actin cytoskeleton via its N-terminal half and with the transmembrane protein ß-dystroglycan via its C-terminal region. Utrophin is a fetal homologue of dystrophin that can subserve many dystrophin functions and is therefore under active investigation as a dystrophin replacement therapy for DMD. Here, we report the first mechanical characterization of utrophin using atomic force microscopy (AFM). Our data indicate that the mechanical properties of spectrin-like repeats in utrophin are more in line with the PEVK and Ig-like repeats of titin rather than those reported for repeats in spectrin or dystrophin. Moreover, we measured markedly different unfolding characteristics for spectrin repeats within the N-terminal actin-binding half of utrophin compared to those in the C-terminal dystroglycan-binding half, even though they exhibit identical thermal denaturation profiles. Our results demonstrate dramatic differences in the mechanical properties of structurally homologous utrophin constructs and suggest that utrophin may function as a stiff elastic element in series with titin at the myotendinous junction.

Note: Design of FPGA based system identification module with application to atomic force microscopy.

The science of system identification is widely utilized in modeling input-output relationships of diverse systems. In this article, we report field programmable gate array (FPGA) based implementation of a real-time system identification algorithm which employs forgetting factors and bias compensation techniques. The FPGA module is employed to estimate the mechanical properties of surfaces of materials at the nano-scale with an atomic force microscope (AFM). The FPGA module is user friendly which can be interfaced with commercially available AFMs. Extensive simulation and experimental results validate the design.

A novel non-rigid registration algorithm for zebrafish larval images.

Precise three-dimensional mapping of a large number of gene expression patterns, neuronal types and connections to an anatomical reference helps us to understand the vertebrate brain and its development. Zebrafish has evolved as a model organism for such study. In this paper, we propose a novel non-rigid registration algorithm for volumetric zebrafish larval image datasets. A coarse affine registration using the L-BFGS algorithm is applied first on the moving dataset. We then divide this coarsely registered moving image and the reference image into a union of overlapping patches. Minimum weight bipartite graph matching algorithm is employed to find the correspondence between the two sets of patches. The corresponding patches are then registered using the diffeomorphic demons method with proper intra-patch regularization. For each voxel lying in the overlapping regions, we impose inter-patch regularization through a composite transformation obtained from the adjacent transformation fields. Experimental results on four multi-view confocal 3D datasets show the advantage of the proposed solution over the existing ViBE-Z software.

Toward quantitative estimation of material properties with dynamic mode atomic force microscopy: a comparative study.

In this article, we explore methods that enable estimation of material properties with the dynamic mode atomic force microscopy suitable for soft matter investigation. The article presents the viewpoint of casting the system, comprising of a flexure probe interacting with the sample, as an equivalent cantilever system and compares a steady-state analysis based method with a recursive estimation technique for determining the parameters of the equivalent cantilever system in real time. The steady-state analysis of the equivalent cantilever model, which has been implicitly assumed in studies on material property determination, is validated analytically and experimentally. We show that the steady-state based technique yields results that quantitatively agree with the recursive method in the domain of its validity. The steady-state technique is considerably simpler to implement, however, slower compared to the recursive technique. The parameters of the equivalent system are utilized to interpret storage and dissipative properties of the sample. Finally, the article identifies key pitfalls that need to be avoided toward the quantitative estimation of material properties.