Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits - the Hispanic/Latino Anthropometry Consortium

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely-imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (Stage 1, n=59,771) and generalized our findings in 9 additional studies (HISLA Stage 2, n=10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA Stage 1 and existing consortia of European and African ancestries. In our HISLA Stage 1+2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified three secondary signals for BMI, 28 for height, and two for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification

Arteriovenous shunt graft ulceration with sinus and graft epithelialization

Arteriovenous fistula and grafts are used as access sites for patients with chronic kidney disease and are prone for complications. Stent grafts are used to treat access site complications. We report a rare and unusual finding of epithelialization of the sinus tract and the lumen of a polytetrafluoroethylene graft, following ulceration of the overlying skin

Intracardiac extension of intravenous leiomyomatosis in a woman with previous hysterectomy and bilateral salpingo-oophorectomy: A case report and review of the literature

Intravenous leiomyomatosis (IVL) is a rare tumor, characterized by benign smooth muscle growth inside veins. The tumor arises from the uterine venous wall or uterine leiomyomas and is usually confined to the pelvic cavity. However, on rare instances, it may extend into the cardiac cavity (Pathol Annu 1988;23 Pt 2:153–158), and the pulmonary system (Arch Gynecol Obstet 2001;264:209–210). Treatment consists of surgical removal of the tumor, cessation of ovarian function and avoidance of estrogen replacement therapy (Gynecol Obstet Invest 2004;58:168–170). We present a case of intravenous leiomyomatosis with extension from IVC to RA, RV and PA, with an unusually rapid course of progression in the absence of estrogen (TAH-BSO, without concomitant hormonal therapy)

MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA

Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning

Deep neural networks can be potentially vulnerable to adversarial samples. For example, by introducing tiny perturbations in the data sample, the model behaviour may be significantly altered. While the adversarial samples can be leveraged to enhance the model robustness and performance with adversarial training, one critical attribute of the adversarial samples is the perturbation rate. A lower perturbation rate means a smaller difference between the adversarial and the original sample. It results in closer features learnt from the model for the adversarial and original samples, resulting in higher-quality adversarial samples. How to design a successful attacking algorithm with a minimum perturbation rate remains challenging. In this work, we consider pruning algorithms to dynamically minimise the perturbation rate for adversarial attacks. In particularly, we propose, for the first time, an attribution based perturbation reduction method named Min-PR for white-box adversarial attacks. The comprehensive experiment results demonstrate Min-PR can achieve minimal perturbation rates of adversarial samples while providing guarantee to train robust models. The code in this paper is available at: https://github.com/LMBTough/Min-PR

FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning

The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of network neurons involvement. In our experiments, our approach can effectively quantify the importance of network neuron. We extend the evaluation through comprehensive experiments conducted on a range of datasets, including CIFAR-10, CIFAR-100 and Caltech101. The results demonstrate that, our method have consistently achieved the state-of-the-art performance over many existing methods. We anticipate that this work will help to reduce the heavy training and inference cost of deep neural network models where a lightweight deep learning enhanced service and system is possible. The source code is open source at https://github.com/LMBTough/FVW

PGLYRP-2 and Nod2 Are Both Required for Peptidoglycan-Induced Arthritis and Local Inflammation

SummaryPeptidoglycan recognition proteins (PGRPs) are structurally conserved from insects to mammals. Insect PGRPs have diverse host-defense functions. Mammalian PGRPs PGLYRP-1, PGLYRP-3, and PGLYRP-4 have bactericidal activity, while PGLYRP-2 has amidase activity. To extend the known functions of mammalian PGRPs, we examined whether they have immunomodulating activities in peptidoglycan-induced arthritis in mice. We demonstrate that PGLYRP-2 and Nod2 are both required for arthritis in this model. The sequence of events in peptidoglycan-induced arthritis is activation of Nod2, local expression of PGLYRP-2, chemokine production, and recruitment of neutrophils into the limbs, which induces acute arthritis. Only PGLYRP-2 among the four mammalian PGRPs displays this proinflammatory function, and PGLYRP-1 is anti-inflammatory. Toll-like receptor 4 (TLR4) and MyD88 are required for maturation of neutrophils before peptidoglycan challenge. Our results demonstrate that PGRPs, Nod2, and TLR4, representing three different types of pattern-recognition molecules, play interdependent in vivo roles in local inflammation