MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts

Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive and annotated real-world human datasets has been a laborious task, especially for highly interactive scenarios. On the other hand, algorithmic data generation methods are usually limited by their model capacities, making them unable to offer realistic and diverse data needed by various application users. In this work, we study trajectory-level data generation for multi-human or human-robot interaction scenarios and propose a learning-based automatic trajectory generation model, which we call Multi-Agent TRajectory generation with dIverse conteXts (MATRIX). MATRIX is capable of generating interactive human behaviors in realistic diverse contexts. We achieve this goal by modeling the explicit and interpretable objectives so that MATRIX can generate human motions based on diverse destinations and heterogeneous behaviors. We carried out extensive comparison and ablation studies to illustrate the effectiveness of our approach across various metrics. We also presented experiments that demonstrate the capability of MATRIX to serve as data augmentation for imitation-based motion planning.Comment: IEEE International Conference on Robotics and Automation (ICRA 2024

Serving Deep Learning Model in Relational Databases

Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of representative architectures to address the requirement. We highlight three pivotal paradigms: The state-of-the-artDL-Centricarchitecture offloadsDL computations to dedicated DL frameworks. The potential UDF-Centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the database system. The potentialRelation-Centricarchitecture aims to represent a large-scale tensor computation through relational operators. While each of these architectures demonstrates promise in specific use scenarios, we identify urgent requirements for seamless integration of these architectures and the middle ground between these architectures. We delve into the gaps that impede the integration and explore innovative strategies to close them. We present a pathway to establish a novel database system for enabling a broad class of data-intensive DL inference applications.Comment: Authors are ordered alphabetically; Jia Zou is the corresponding autho

Potential effects of specific gut microbiota on periodontal disease: a two-sample bidirectional Mendelian randomization study

IntroductionPeriodontal disease (PD) presents a substantial global health challenge, encompassing conditions from reversible gingivitis to irreversible periodontitis, often culminating in tooth loss. The gut-oral axis has recently emerged as a focal point, with potential gut microbiota dysbiosis exacerbating PD.MethodsIn this study, we employed a double-sample bidirectional Mendelian randomized (MR) approach to investigate the causal relationship between specific gut microbiota and periodontal disease (PD) and bleeding gum (BG) development, while exploring the interplay between periodontal health and the gut microenvironment. We performed genome-wide association studies (GWAS) with two cohorts, totalling 346,731 (PD and control) and 461,113 (BG and control) participants, along with data from 14,306 participants’ intestinal flora GWAS, encompassing 148 traits (31 families and 117 genera). Three MR methods were used to assess causality, with the in-verse-variance-weighted (IVW) measure as the primary outcome. Cochrane’s Q test, MR-Egger, and MR-PRESSO global tests were used to detect heterogeneity and pleiotropy. The leave-one-out method was used to test the stability of the MR results. An F-statistic greater than 10 was accepted for instrument exposure association.Results and conclusionSpecifically, Eubacterium xylanophilum and Lachnoclostridium were associated with reduced gum bleeding risk, whereas Anaerotruncus, Eisenbergiella, and Phascolarctobacterium were linked to reduced PD risk. Conversely, Fusicatenibacter was associated with an elevated risk of PD. No significant heterogeneity or pleiotropy was detected. In conclusion, our MR analysis pinpointed specific gut flora with causal connections to PD, offering potential avenues for oral health interventions

A multi-tissue transcriptomic landscape of female mice in estrus and diestrus provides clues for precision medicine

Female reproductive cycle, also known as menstrual cycle or estrous cycle in primate or non-primate mammals, respectively, dominates the reproductive processes in non-pregnant state. However, in addition to reproductive tissues, reproductive cycle could also perform global regulation because the receptors of two major female hormones fluctuating throughout the cycle, estrogen and progesterone, are widely distributed. Therefore, a multi-tissue gene expression landscape is in continuous demand for better understanding the systemic changes during the reproductive cycle but remains largely undefined. Here we delineated a transcriptomic landscape covering 15 tissues of C57BL/6J female mice in two phases of estrous cycle, estrus and diestrus, by RNA-sequencing. Then, a number of genes, pathways, and transcription factors involved in the estrous cycle were revealed. We found the estrous cycle could widely regulate the neuro-functions, immuno-functions, blood coagulation and so on. And behind the transcriptomic alteration between estrus and diestrus, 13 transcription factors may play important roles. Next, bioinformatics modeling with 1,263 manually curated gene signatures of various physiological and pathophysiological states systematically characterized the beneficial/deleterious effects brought by estrus/diestrus on individual tissues. We revealed that the estrous cycle has a significant effect on cardiovascular system (aorta, heart, vein), in which the anti-hypertensive pattern in aorta induced by estrus is one of the most striking findings. Inspired by this point, we validated that two hypotensive drugs, felodipine and acebutolol, could exhibit significantly enhanced efficacy in estrus than diestrus by mouse and rat experiments. Together, this study provides a valuable data resource for investigating reproductive cycle from a transcriptomic perspective, and presents models and clues for investigating precision medicine associated with reproductive cycle

Predictive model for inflammation grades of chronic hepatitis B: Large‐scale analysis of clinical parameters and gene expressions

BackgroundLiver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus‐infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)‐infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV‐DNA) in large‐scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions.MethodsWe analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine‐learning methods including Random Forest, K‐nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model.ResultsSignificant genes related to clinical parameters were found enriching in the immune system, interferon‐stimulated, regulation of cytokine production, anti‐apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77‐0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65‐0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible.ConclusionsThis is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139116/1/liv13427.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139116/2/liv13427_am.pd

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Patients' experience of QR code-based health education program in university general practice waiting room

Background: The general practice waiting room is a novel setting for health promotion and education. Research suggests that waiting rooms are a potential location for engaging patients with health education resources, with some studies showing positive patient response to digital-based content such as videos. Quick response (QR) codes have recently surged in popularity and are an emerging platform for engaging the public with various types of information. Despite this, little is known about the public’s views towards QR codes as means of engaging with health education material. Aim/Objectives: This student project aims to assess the reach and explore participant experience and views of a QR code-based health education program in a university-based general practice waiting room. Methods: A mixed-methods approach will be used. Posters with information about the research project and the QR code linked to health education videos, healthy recipes, and community digital resources that promote healthy dietary behaviours will be displayed at a university health service waiting room. Patients who scanned the QR code will have access to the education program and invited at the end of the program to be considered for semi-structured interviews to explore their experience of the health education program and their views of QR codebased health information. The interviews will be recorded and transcribed. Quantitatively, data scan rates of QR codes will be collected and analysed to produce descriptive statistics. Qualitatively, interview data will be thematically analysed. Findings: Ethics approval for this project is pending. Data collection is planned to start mid-June for a two-month period. Preliminary results will be presented at this conference. Implications: Understanding of patients’ experience and views of QR code-based health education programs will inform future development of engaging and effective preventative health resources to promote community health literacy

Predictors of Outcome in Clinically Diagnosed Viral Encephalitis Patients: A 5-Year Prospective Study

Background. Viral encephalitis is the most common infectious disease of the central nervous system and is associated with high morbidity, mortality, and disability. The objective of this study was to analyze the clinical characteristics, auxiliary examinations, therapeutic management, and outcomes of patients clinically diagnosed with viral encephalitis and identify the outcome predictors. Methods. We conducted a prospective observational study by collecting information from patients clinically diagnosed with viral encephalitis at the First Affiliated Hospital of Chongqing Medical University and Yongchuan Hospital of Chongqing Medical University from January 2013 to December 2018. Univariate and multivariate analyses were performed to identify factors that influenced good patient outcomes (mRS<3) and poor patient outcomes (mRS≥3) at discharge. Results. In total, 216 patients were enrolled in the study. The multivariate analysis suggested that the following factors were associated with a poor outcome: Glasgow Coma Scale (GCS) score (OR 0.154, 95% CI (0.078-0.302), and P<0.001), focal neurological deficits (OR 9.403, 95% CI (1.581-55.928), and P=0.014), and total length of hospital stay (OR 1.119, 95% CI (1.002-1.250), and P=0.045). However, neurological intensive care unit (NICU) treatment, status epilepticus, and abnormal electroencephalogram (EEG) findings did not influence the prognosis of patients. Conclusion. Our study suggests that low GCS scores at admission, focal neurological deficits at admission, and a prolonged total hospital stay are predictors of a poor outcome at discharge in clinically diagnosed viral encephalitis patients. Whether early and effective neurological rehabilitation can improve the prognosis of viral encephalitis patients with focal neurological deficits remains to be confirmed in further studies