A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated

Continuous-Time Graph Learning for Cascade Popularity Prediction

Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing research treats a cascade as an individual sequence. Actually, the cascades might be correlated with each other due to the shared users or similar topics. Moreover, the preferences of users and semantics of a cascade are usually continuously evolving over time. In this paper, we propose a continuous-time graph learning method for cascade popularity prediction, which first connects different cascades via a universal sequence of user-cascade and user-user interactions and then chronologically learns on the sequence by maintaining the dynamic states of users and cascades. Specifically, for each interaction, we present an evolution learning module to continuously update the dynamic states of the related users and cascade based on their currently encoded messages and previous dynamic states. We also devise a cascade representation learning component to embed the temporal information and structural information carried by the cascade. Experiments on real-world datasets demonstrate the superiority and rationality of our approach.Comment: 9 pages, 5 figures, IJCAI 202

Methylcap-Seq Reveals Novel DNA Methylation Markers for the Diagnosis and Recurrence Prediction of Bladder Cancer in a Chinese Population

PURPOSE: There is a need to supplement or supplant the conventional diagnostic tools, namely, cystoscopy and B-type ultrasound, for bladder cancer (BC). We aimed to identify novel DNA methylation markers for BC through genome-wide profiling of BC cell lines and subsequent methylation-specific PCR (MSP) screening of clinical urine samples. EXPERIMENTAL DESIGN: The methyl-DNA binding domain (MBD) capture technique, methylCap/seq, was performed to screen for specific hypermethylated CpG islands in two BC cell lines (5637 and T24). The top one hundred hypermethylated targets were sequentially screened by MSP in urine samples to gradually narrow the target number and optimize the composition of the diagnostic panel. The diagnostic performance of the obtained panel was evaluated in different clinical scenarios. RESULTS: A total of 1,627 hypermethylated promoter targets in the BC cell lines was identified by Illumina sequencing. The top 104 hypermethylated targets were reduced to eight genes (VAX1, KCNV1, ECEL1, TMEM26, TAL1, PROX1, SLC6A20, and LMX1A) after the urine DNA screening in a small sample size of 8 normal control and 18 BC subjects. Validation in an independent sample of 212 BC patients enabled the optimization of five methylation targets, including VAX1, KCNV1, TAL1, PPOX1, and CFTR, which was obtained in our previous study, for BC diagnosis with a sensitivity and specificity of 88.68% and 87.25%, respectively. In addition, the methylation of VAX1 and LMX1A was found to be associated with BC recurrence. CONCLUSIONS: We identified a promising diagnostic marker panel for early non-invasive detection and subsequent BC surveillance

Primary gastric non-Hodgkin's lymphoma in Chinese patients: clinical characteristics and prognostic factors

<p>Abstract</p> <p>Background</p> <p>Optimal management and outcome of primary gastric lymphoma (PGL) have not been well defined in the rituximab era. This study aimed to analyze the clinical characteristics, prognostic factors, and roles of different treatment modalities in Chinese patients with PGL.</p> <p>Methods</p> <p>The clinicopathological features of 83 Chinese patients with PGL were retrospectively reviewed. Staging was performed according to the Lugano staging system for gastrointestinal non-Hodgkin's lymphoma.</p> <p>Results</p> <p>The predominant pathologic subtype among Chinese patients with PGL in our study was diffuse large B cell lymphoma (DLBCL), followed by mucosa-associated lymphoid tissue (MALT) lymphoma. Among the 57 patients with gastric DLBCL, 20 patients (35.1%) were classified as the germinal center B cell-like (GCB) subtype and 37 patients (64.9%) as the non-GCB subtype. The 83 patients had a five-year overall survival (OS) and event-free survival (EFS) of 52% and 59%, respectively. Cox regression analysis showed that stage-modified international prognostic index (IPI) and performance status (PS) were independent predictors of survival. In the 67 B-cell lymphoma patients who received chemotherapy, 36 patients treated with rituximab (at least 3 cycles) had a mean OS of 72 months (95% CI 62-81) versus 62 months (95% CI 47-76) for patients without rituximab treatment (P = 0.021).</p> <p>Conclusion</p> <p>The proportion of Chinese gastric DLBCL cases with non-GCB subtype was higher than the GCB subtype. Stage-modified IPI and PS were effective prognostic factors in Chinese patients with PGL. Our data suggested that primary gastric B-cell lymphoma might have an improved outcome with rituximab in addition to chemotherapy. More studies are necessary, preferentially large prospective randomized clinical trials to obtain more information on the impact of the rituximab in the primary gastric B-cell lymphoma.</p

Dysregulation of respiratory center drive (P0.1) and muscle strength in patients with early stage idiopathic Parkinson's disease

Objective: The goal of this study is to evaluate pulmonary function and respiratory center drive in patients with early-stage idiopathic Parkinson's disease (IPD) to facilitate early diagnosis of Parkinson's Disease (PD). Methods: 43 IPD patients (Hoehn and Yahr scale of 1) and 41 matched healthy individuals (e.g., age, sex, height, weight, BMI) were enrolled in this study. Motor status was evaluated using the Movement Disorders Society-Unified PD Rating Scale (MDS-UPDRS). Pulmonary function and respiratory center drive were measured using pulmonary function tests (PFT). All IPD patients were also subjected to a series of neuropsychological tests, including Non-Motor Symptoms Questionnaire (NMSQ), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Beck Depression Inventory (BDI) and Mini Mental State Examination (MMSE). Results: IPD patients and healthy individuals have similar forced vital capacity (FVC), forced expiratory volume in 1s (FEV1), forced expiratory volume in 1s/forced vital capacity (FEV1/FVC), peak expiratory flow (PEF), and carbon monoxide diffusion capacity (DLCOcSB). Reduced respiratory muscle strength, maximal inspiratory pressure (PImax) and maximal expiratory pressure (PEmax) was seen in IPD patients (p = 0.000 and p = 0.002, respectively). Importantly, the airway occlusion pressure after 0.1 s (P0.1) and respiratory center output were notably higher in IPD patients (p = 0.000) with a remarkable separation of measured values compared to healthy controls. Conclusion: Our findings suggest that abnormal pulmonary function is present in early stage IPD patients as evidenced by significant changes in PImax, PEmax, and P0.1. Most importantly, P0.1 may have the potential to assist with the identification of IPD in the early stage

Epidemiology and clinical course of COVID-19 in Shanghai, China.

Background: Novel coronavirus pneumonia (COVID-19) is prevalent around the world. We aimed to describe epidemiological features and clinical course in Shanghai. Methods: We retrospectively analysed 325 cases admitted at Shanghai Public Health Clinical Center, between January 20 and February 29, 2020. Results: 47.4% (154/325) had visited Wuhan within 2 weeks of illness onset. 57.2% occurred in 67 clusters; 40% were situated within 53 family clusters. 83.7% developed fever during the disease course. Median times from onset to first medical care, hospitalization and negative detection of nucleic acid by nasopharyngeal swab were 1, 4 and 8 days. Patients with mild disease using glucocorticoid tended to have longer viral shedding in blood and feces. At admission, 69.8% presented with lymphopenia and 38.8% had elevated D-dimers. Pneumonia was identified in 97.5% (314/322) of cases by chest CT scan. Severe-critical patients were 8% with a median time from onset to critical disease of 10.5 days. Half required oxygen therapy and 7.1% high-flow nasal oxygen. The case fatality rate was 0.92% with median time from onset to death of 16 days. Conclusion: COVID-19 cases in Shanghai were imported. Rapid identification, and effective control measures helped to contain the outbreak and prevent community transmission

Simulation Study on the Coupling Relationship between Traffic Network Model and Traffic Mobility under the Background of Autonomous Driving

Autonomous driving technology will bring revolutionary changes to the development of future cities and transportation. In order to study the impact of autonomous driving on urban transportation networks, this paper first summarizes the development status of autonomous driving technology, and then three space–traffic network coupling models are proposed based on the differences of speed and space, which are the traditional difference type, scale variation type, and slow-guided type. On this basis, a new 4 * 4 km grid city model is constructed. Based on the MATSim multi-agent simulation method, the traffic parameters of the three models are studied. The results show that under the same traffic demand, the service scale and level of the three traffic networks are significantly different. The optimal service level of the traditional differential type is 2.15 times the efficiency of the slow-guided type. Under the same demand and road network mode, the travel speed of the autonomous driving mode is 1.7–2.8 times that of the traditional mode. Under the same lane area ratio, the travel speed of traditional driving is much smaller than that of autonomous driving, which is about 2.6–3.6 times greater than the former. The research conclusion has certain reference significance for formulating urban spatial development strategies and policies under autonomous driving environments and for promoting the sustainable development of urban transportation