AMPNet: Attention as Message Passing for Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as a powerful representation learning framework for graph-structured data. A key limitation of conventional GNNs is their representation of each node with a singular feature vector, potentially overlooking intricate details about individual node features. Here, we propose an Attention-based Message-Passing layer for GNNs (AMPNet) that encodes individual features per node and models feature-level interactions through cross-node attention during message-passing steps. We demonstrate the abilities of AMPNet through extensive benchmarking on real-world biological systems such as fMRI brain activity recordings and spatial genomic data, improving over existing baselines by 20% on fMRI signal reconstruction, and further improving another 8% with positional embedding added. Finally, we validate the ability of AMPNet to uncover meaningful feature-level interactions through case studies on biological systems. We anticipate that our architecture will be highly applicable to graph-structured data where node entities encompass rich feature-level information.Comment: 16 pages (12 + 4 pages appendix). 5 figures and 7 table

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

Representing Cells As Sentences Enables Natural Language Processing For Single Cell Transcriptomics

Gene expression matrices commonly used in single-cell transcriptomics cannot be directly analyzed with tools developed for natural languages. Restructuring these matrices as abundance-ordered sequences of genes allows the generation of cell sentences: rank-normalized, positionally encoded sequence-structured expression data. The rank-normalization procedure also minimizes batch effects from differential sequencing depth, and comparison of cell sentences against other tools for batch integration shows that cell sentences achieve comparable performance in batch effect removal and biological effect preservation. After transformation, cell sentences can be analyzed using any existing tools from natural language processing that take text as input, enabling a host of new ways to process and understand single-cell transcriptomics data. As an example, a machine translation approach is applied to cells from neural retina, unifying cell and gene representations across species. Finally, this approach can also be used to transform a number of other data modalities to sequential formats, including imaging data. Testing of neural network architectures pretrained on language tasks against those specialized for vision tasks shows that in low-data scenarios, language models can outperform vision models in image classification tasks. These findings suggest homology in the underlying structure of natural language and natural images which may be of particular interest in machine learning for medical imaging, where small datasets are the norm

A survey-wide association study to identify youth-specific correlates of major depressive episodes.

BACKGROUND:Major depressive disorder is a common disease with high mortality and morbidity worldwide. Though peak onset is during late adolescence, the prevalence of major depressive disorder remains high throughout adulthood. Leveraging an association study design, this study screened a large number of variables in the 2017 National Survey on Drug Use and Health to characterize differences between adult and youth depression across a wide array of phenotypic measurements. METHODS:All dichotomous variables were manually identified from the survey for association screening. Association between each dichotomous variable and past-year major depressive episode (MDE) occurrence was calculated as an odds ratio for adults (≥18 years) and youth (12-17 years), and tested for significance with Fischer's exact test. Logarithm of the calculated odds ratios were plotted and fitted to a linear model to assess correlation between adult and youth risk factors. RESULTS:Many of the screened variables showed similar association between past-year depressive episode occurrence in youth and adults; Lin's concordance correlation coefficient between adult and youth associations was 0.91 (95% CI 0.89-0.92). Differentially associated variables were identified, tracking: female sex, alcohol use, cigarette use, marijuana use, Medicaid/CHIP coverage, cognitive changes due to a mental, physical or emotional condition, and respondents' identification of a single depressive event as the worst experienced. CONCLUSIONS:While some youth-specific correlates of major depressive episodes were identified through screening, including some novel associations, most examined variables showed similar association with youth and adult depression. Further study of results is warranted, especially concerning the finding of increased association between marijuana use and depressive episodes in youth

A Case of IgLON5 Encephalitis With Ophthalmoplegia

Anti-IgLON5 disease, first described in 2014, is a newly characterized progressive neurodegenerative/neuroinflammatory condition with heterogeneous presentation often involving neurocognitive changes, bulbar symptoms and movement disorders [1-3]. Patients may also display neuro-ophthalmic disturbances including gaze palsies

A comparative study of blood smear, QBC and antigen detection for diagnosis of malaria

Rapid diagnosis is prerequisite for effective treatment and reducing mortality and morbidity of malaria. This study was taken up to compare the efficacy of various methods available, i.e., thick and thin smear, quantitative buffy coat (QBC), plasmodium lactate dehydrogenase and aldolase in blood of patient. A total of 411 samples were collected from patients presenting with classic symptoms of malaria. For traditional microscopy; thick and thin smears were prepared and stained with Leishman′s stain, taking thick smear as gold standard, thin smear had a sensitivity and specificity of 54.8% and 100%, respectively. QBC and antigen detection was done using commercially available kits; out of 411 samples, QBC and Malariagen were positive in 66 and 62 cases, with a sensitivity of 78% and 75%, respectively. Leishman′s thick smear, although cost effective, is difficult to interpret for inexperienced microscopists; so if facilities are available, QBC should be used for routine diagnosis. In places where facilities are not available, rapid, simple and easy to interpret antigen detection test can be used despite low sensitivity

Maternal & perinatal outcome of fever in pregnancy in the context of dengue - A retrospective observational study

Background & objectives: Pregnant women with dengue infection may be at increased risk of adverse maternal-foetal outcomes. This study was conducted to assess the maternal and perinatal outcomes in women who presented with fever and diagnosed to have dengue infection during pregnancy. Methods: A retrospective observational study was conducted on pregnant women admitted with fever, in a tertiary referral centre in South India, during January 2015 to December 2018. We compared outcomes of women diagnosed with dengue with that of women without dengue. The study outcomes included pre-term birth, stillbirth, low-birth weight (LBW), maternal mortality and thrombocytopenia. Results: During the study period, there were six maternal deaths following complications from dengue infection. Higher rates of thrombocytopenia (24.7% vs. 14.6%, P=0.02) were noted among those with recent dengue infection. The risk of still birth was 2.67 [95% confidence interval (CI) 1.09, 6.57], LBW [risk ratio (RR) 1.13, 95% CI 0.87, 1.45] and pre-term birth (RR 1.33, 95% CI 0.89, 1.97) among the cases. Interpretation & conclusions: Occurrence of adverse maternal and foetal outcomes was increased in pregnant women with fever diagnosed with dengue infection. Future studies are needed to formulate the optimum monitoring and treatment strategies in pregnant women, where dengue can have additive adverse effects to other obstetric complications