Unsupervised deep structured semantic models for commonsense reasoning

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.Comment: To appear in NAACL 2019, 10 page

Conforming an Extracorporeal Lithotripter System for Video Urodynamic Studies

Objectives: This study aimed to evaluate the efficiency using existing fluoroscopic unit and lithotripter table of an extracorporeal lithotripter system for video urodynamic studies (VUDS) to determine anatomical abnormalities in patients with neurogenic lower urinary tract dysfunction (NLUTD). Methods: The extracorporeal lithotripsy system was adapted to obtain optimum fluoroscopic view according to body shape and observed organs of patients. We reviewed the VUDS data of 25 patients with NLUTD. Results: “Christmas tree bladder” (CTB) was found in 5 (20%) patients. Vesicoureteral reflux (VUR) and external detrusor sphincter dyssynergia (DESD) were detected in 3 (12%) and 4 (16%) patients, respectively. Four (16%) patients with normal coordination between detrusor contraction and external sphincter relaxation were proven by VUDS. CTB, VUR, or DESD was not observed in 10 (40%) patients with flaccid bladder. Hematuria, urinary tract infection, or autonomic dysreflexia did not occur in any of the patients. Conclusions: VUDS can discern anatomical abnormalities of the urinary tract, and patients in undeveloped areas of the world who have NLUTD can have easier access to VUDS because of the decreasing capital cost of VUDS

Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available

Functional connectivity decreases in autism in emotion, self, and face circuits identified by knowledge-based enrichment analysis

A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity

BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys

Rapid progress has been made in instruction-learning for image editing with natural-language instruction, as exemplified by InstructPix2Pix. In biomedicine, such methods can be applied to counterfactual image generation, which helps differentiate causal structure from spurious correlation and facilitate robust image interpretation for disease progression modeling. However, generic image-editing models are ill-suited for the biomedical domain, and counterfactual biomedical image generation is largely underexplored. In this paper, we present BiomedJourney, a novel method for counterfactual biomedical image generation by instruction-learning from multimodal patient journeys. Given a patient with two biomedical images taken at different time points, we use GPT-4 to process the corresponding imaging reports and generate a natural language description of disease progression. The resulting triples (prior image, progression description, new image) are then used to train a latent diffusion model for counterfactual biomedical image generation. Given the relative scarcity of image time series data, we introduce a two-stage curriculum that first pretrains the denoising network using the much more abundant single image-report pairs (with dummy prior image), and then continues training using the counterfactual triples. Experiments using the standard MIMIC-CXR dataset demonstrate the promise of our method. In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen. To facilitate future study in counterfactual medical generation, we plan to release our instruction-learning code and pretrained models.Comment: Project page & demo: https://aka.ms/biomedjourne

Knowledge-Rich Self-Supervision for Biomedical Entity Linking

Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ($\tt KRISS$) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach can easily incorporate entity descriptions and gold mention labels if available. We conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces $\tt KRISSBERT$, a universal entity linker for four million UMLS entities that attains new state of the art, outperforming prior self-supervised methods by as much as 20 absolute points in accuracy

The Role of Gaseous Molecules in Traumatic Brain Injury: An Updated Review

Traumatic brain injury (TBI) affects millions of people in China each year. TBI has a high mortality and often times a serious prognosis. The causative mechanisms of TBI during development and recovery from an injury remain vague, leaving challenges for the medical community to provide treatment options that improve prognosis and provide an optimal recovery. Biological gaseous molecules including nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H2S), and molecular hydrogen (H2) have been found to play critical roles in physiological and pathological conditions in mammals. Accumulating evidence has found that these gaseous molecules can execute neuroprotection in many central nervous system (CNS) conditions due to their highly permeable properties allowing them to enter the brain. Considering the complicated mechanisms and the serious prognosis of TBI, effective and adequate therapeutic approaches are urgently needed. These four gaseous molecules can be potential attractive therapeutic intervention on TBI. In this review, we will present a comprehensive overview on the role of these four biological gasses in the development of TBI and their potential therapeutic applications