We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and ...robust question engine that leverages Visual Genome scene graph structures to create 22M diverse reasoning questions, which all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. A careful analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains a mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding of vision and language.
Peritoneal dialysis (PD) - associated peritonitis is a serious complication of peritoneal dialysis (PD). The 2022 International Society of Peritoneal Dialysis (ISPD) guidelines do not recommend ...intraperitoneal (IP) ampicillin for treatment of Enterococcal PD - associated peritonitis. To date, there is no in vivo data to support use of IP ampicillin for the treatment of
.
A 69-year-old man with a past medical history of end stage kidney disease (ESKD) requiring continuous cycling peritoneal dialysis (CCPD) was admitted to the hospital and treated for peritonitis with
. The patient's CCPD prescription was 2.5% Dianeal with 5 total exchanges. IP ampicillin was added to the first 4 exchanges and additional ampicillin was added to the last fill. The patient successfully completed the treatment course with clinical cure.
The use of IP ampicillin for
.
peritonitis is controversial and previously lacked compelling clinical evidence for or against its use. This case demonstrates treatment of peritonitis using a modified dosing strategy with ampicillin added to each CCPD exchange and last fill. The loss of ampicillin antimicrobial activity reported in vitro with
was not supported by this case.
Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor ...mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 × 10
and p = 6 × 10
, respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers.
Response to Li and Hopper Thomas, Minta; Sakoda, Lori C; Hoffmeister, Michael ...
American journal of human genetics,
03/2021, Letnik:
108, Številka:
3
Journal Article
Background and Purpose
Hyperglycemia following acute ischemic stroke (AIS) is associated with adverse outcomes including, hemorrhagic conversion and increased length of stay; however, the impact of ...glycemic variability is largely unknown. This study aims to evaluate the effect of glycemic variability on discharge outcomes in patients treated with alteplase for AIS.
Methods
A retrospective review of ischemic stroke patients who presented within 4.5 hours from symptom onset and received alteplase was completed. Patients hospitalized for at least 48 hours were included. Glycemic variability was measured using J-index. Groups were defined by normal or abnormal J-indices. Logistic regression models were developed to determine odds ratios for select clinical characteristics, NIHSS score, mRS, and disposition at discharge.
Results
Of the 229 patients, 97 (42%) had an abnormal J-index. In the univariate analysis, abnormal J-index was associated with worse outcomes in terms of NIHSS score, mRS, and discharge disposition compared to a normal J-index. In the unadjusted multivariate analysis, abnormal J-index was associated with higher odds of unfavorable mRS (3-6) at discharge (OR 2.1; 95% CI 1.2 – 3.5, P = .009). In the adjusted multivariate analysis, patients with an abnormal J-index had higher odds of hemorrhagic transformation (OR 5.7; 95% CI 2.1 – 15.6, P < .0001). There was no difference in mortality.
Conclusion
Glycemic variability with abnormal J-index following AIS is associated with adverse functional outcomes at discharge and increased odds of hemorrhagic conversion in patients treated with alteplase. Additional studies validating glycemic variability indices post-ischemic stroke are needed to determine the full clinical impact.
Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and ...interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect ...the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages: a fast and lightweight planning phase, where we draft a high-level scene layout, followed by an attention-based execution phase, where the layout is being refined, evolving into a rich and detailed picture. Our model moves away from conventional black-box GAN architectures that feature a flat and monolithic latent space towards a transparent design that encourages efficiency, controllability and interpretability. We demonstrate GANformer2's strengths and qualities through a careful evaluation over a range of datasets, from multi-object CLEVR scenes to the challenging COCO images, showing it successfully achieves state-of-the-art performance in terms of visual quality, diversity and consistency. Further experiments demonstrate the model's disentanglement and provide a deeper insight into its generative process, as it proceeds step-by-step from a rough initial sketch, to a detailed layout that accounts for objects' depths and dependencies, and up to the final high-resolution depiction of vibrant and intricate real-world scenes. See https://github.com/dorarad/gansformer for model implementation.
We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning. Given an ...image, we first predict a probabilistic graph that represents its underlying semantics and serves as a structured world model. Then, we perform sequential reasoning over the graph, iteratively traversing its nodes to answer a given question or draw a new inference. In contrast to most neural architectures that are designed to closely interact with the raw sensory data, our model operates instead in an abstract latent space, by transforming both the visual and linguistic modalities into semantic concept-based representations, thereby achieving enhanced transparency and modularity. We evaluate our model on VQA-CP and GQA, two recent VQA datasets that involve compositionality, multi-step inference and diverse reasoning skills, achieving state-of-the-art results in both cases. We provide further experiments that illustrate the model's strong generalization capacity across multiple dimensions, including novel compositions of concepts, changes in the answer distribution, and unseen linguistic structures, demonstrating the qualities and efficacy of our approach.
We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range ...interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model's strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency. Further qualitative and quantitative experiments offer us an insight into the model's inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at https://github.com/dorarad/gansformer.
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