MGIML: Cancer Grading With Incomplete Radiology-Pathology Data via Memory Learning and Gradient Homogenization.

Taking advantage of multi-modal radiology-pathology data with complementary clinical information for cancer grading is helpful for doctors to improve diagnosis efficiency and accuracy. However, radiology and pathology data have distinct acquisition difficulties and costs, which leads to incomplete-modality data being common in applications. In this work, we propose a Memory- and Gradient-guided Incomplete Modal-modal Learning (MGIML) framework for cancer grading with incomplete radiology-pathology data. Firstly, to remedy missing-modality information, we propose a Memory-driven Hetero-modality Complement (MH-Complete) scheme, which constructs modal-specific memory banks constrained by a coarse-grained memory boosting (CMB) loss to record generic radiology and pathology feature patterns, and develops a cross-modal memory reading strategy enhanced by a fine-grained memory consistency (FMC) loss to take missing-modality information from well-stored memories. Secondly, as gradient conflicts exist between missing-modality situations, we propose a Rotation-driven Gradient Homogenization (RG-Homogenize) scheme, which estimates instance-specific rotation matrices to smoothly change the feature-level gradient directions, and computes confidence-guided homogenization weights to dynamically balance gradient magnitudes. By simultaneously mitigating gradient direction and magnitude conflicts, this scheme well avoids the negative transfer and optimization imbalance problems. Extensive experiments on CPTAC-UCEC and CPTAC-PDA datasets show that the proposed MGIML framework performs favorably against state-of-the-art multi-modal methods on missing-modality situations.

A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data.

A recent technology breakthrough in spatial molecular profiling (SMP) has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in SMP data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns. However, the Gaussian process models rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy-based modeling approach led to higher accuracy in detecting SV genes than those kernel-based methods. When applied to two real spatial transcriptomics (ST) datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing ST data.

Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network.

Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.

Analysis of the Government Health Expenditure in the First Decade of Chinese New Medical Reform (2009-2018): Xinjiang Uygur Autonomous Region as an Example.

OBJECTIVE: To analyze the status of government health expenditure in Xinjiang Uygur Autonomous Region since the first 10 years from the new medical reform, and find the existing problems in order to provide evidence for the government to formulate medical and health policies. METHODS: Based on the health expenditure monitoring data of Xinjiang Uygur Autonomous Region government from Urumqi Central Sub-branch of the People's Bank of China, combined with the relevant data in Xinjiang statistical yearbook, Excel2013 and SPSS19.0 were used to conduct a comparative analysis of government expenditure data from 2009 to 2018. RESULTS: The average annual growth rate of the government's health expenditure in Xinjiang Uygur Autonomous Region was 12.25%, which was similar to the national level. The proportion of government health expenditure in gross domestic product increased from 0.97% to 1.07%, while the proportion in the total fiscal expenditure decreased from 3.06% to 2.63%, which led to far behind the national and even western area level. The Gini Coefficient of per capita government health expenditure in every city (state) of the autonomous region fell from 0.46 to 0.32 between 2014 and 2018. In the past decade, the ratio between public health expenditure and medical institution expenditure has decreased from 1.01 to 0.42, led to insufficient proportion of public health expenditure. The health expenditure level of the four prefectures especially Kashgar and Hotan in Southern Xinjiang was still far lower than the whole autonomous region and the national average level. CONCLUSION: The government of Xinjiang Uygur Autonomous Region should continuously strengthen the financial expenditure in health, maintain the current situation of preferential policy implementation for rural and grassroots expenditure, constantly optimize the proportion of various financial expenditures, and strive for the transfer payment from the central and autonomous regional governments to the four prefectures in Southern Xinjiang.

Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

OBJECTIVE: Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on functional magnetic resonance imaging (fMRI) has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown. METHODS: To address the two issues, we propose a novel computational framework of group-wise sparse representation of whole-brain fMRI temporal segments to assess the temporal dynamic spatial patterns of SOPFNs that are consistent across different subjects. RESULTS: Experimental results based on the recently publicly released Human Connectome Project grayordinate task fMRI data demonstrate that meaningful SOPFNs exhibiting dynamic spatial patterns across different time periods are effectively and robustly identified based on the reconstructed concurrent functional networks via the proposed framework. Specifically, those SOPFNs locate significantly more on gyral regions than on sulcal regions across different time periods. CONCLUSION: These results reveal novel functional architecture of cortical gyri and sulci. SIGNIFICANCE: Moreover, these results help better understand functional dynamics mechanisms of cerebral cortex in the future.

Optimizing a desirable fare structure for a bus-subway corridor.

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has.

Burden of acute gastroenteritis caused by norovirus in China: A systematic review.

BACKGROUND: To understand the epidemiology and disease burden of norovirus (NoVs) gastroenteritis in China, a systematic review was conducted. METHODS: Studies on acute gastroenteritis (AGE) caused by NoVs from mainland China, published before 2017 were searched. All retrieved articles were screened and reviewed by a standardized algorithm. NoVs detection rates as well as strain variations by ages, seasonal variations and geographic locations were analyzed using random-effects model. RESULTS: A total of 225 articles were included in the final analysis. Similar detection rates at 21.0% and 19.8% were obtained from the North and the South, respectively. NoVs infection occurred year round, with a peak between October and January in the North and between August and November in the South. High detection rates (∼29%) of NoVs were found in adults and the elderly and in children aged 6-35 months (∼22%). The predominant strains were GII.4 (70.4%), followed by GII.3 (13.5%). CONCLUSION: NoVs cause significant disease burden in China which warrants development of vaccines against NoVs, particularly for children and the elderly who are vulnerable to gastroenteritis diseases. To achieve a broad protection, continual monitoring NoV epidemics and strain variations for selection of proper vaccine strains is critical.

Histo-blood group antigens as receptors for rotavirus, new understanding on rotavirus epidemiology and vaccine strategy.

The success of the two rotavirus (RV) vaccines (Rotarix and RotaTeq) in many countries endorses a live attenuated vaccine approach against RVs. However, the lower efficacies of both vaccines in many low- and middle-income countries indicate a need to improve the current RV vaccines. The recent discovery that RVs recognize histo-blood group antigens (HBGAs) as potential receptors has significantly advanced our understanding of RV diversity, evolution and epidemiology, providing important new insights into the performances of current RV vaccines in different populations and emphasizing a P-type-based vaccine approach. New understanding of RV diversity and evolution also raises a fundamental question about the 'Jennerian' approach, which needs to be addressed for future development of live attenuated RV vaccines. Alternative approaches to develop safer and more cost-effective subunit vaccines against RVs are also discussed.

Alberta Provincial Pediatric EnTeric Infection TEam (APPETITE): epidemiology, emerging organisms, and economics.

BACKGROUND: Each year in Canada there are 5 million episodes of acute gastroenteritis (AGE) with up to 70% attributed to an unidentified pathogen. Moreover, 90% of individuals with AGE do not seek care when ill, thus, burden of disease estimates are limited by under-diagnosing and under-reporting. Further, little is known about the pathogens causing AGE as the majority of episodes are attributed to an "unidentified" etiology. Our team has two main objectives: 1) to improve health through enhanced enteric pathogen identification; 2) to develop economic models incorporating pathogen burden and societal preferences to inform enteric vaccine decision making. METHODS/DESIGN: This project involves multiple stages: 1) Molecular microbiology experts will participate in a modified Delphi process designed to define criteria to aid in interpreting positive molecular enteric pathogen test results. 2) Clinical data and specimens will be collected from children aged 0-18 years, with vomiting and/or diarrhea who seek medical care in emergency departments, primary care clinics and from those who contact a provincial medical advice line but who do not seek care. Samples to be collected will include stool, rectal swabs (N = 2), and an oral swab. Specimens will be tested employing 1) stool culture; 2) in-house multiplex (N = 5) viral polymerase chain reaction (PCR) panel; and 3) multi-target (N = 15) PCR commercially available array. All participants will have follow-up data collected 14 days later to enable calculation of a Modified Vesikari Scale score and a Burden of Disease Index. Specimens will also be collected from asymptomatic children during their well child vaccination visits to a provincial public health clinic. Following the completion of the initial phases, discrete choice experiments will be conducted to enable a better understanding of societal preferences for diagnostic testing and vaccine policy. All of the results obtained will be integrated into economic models. DISCUSSION: This study is collecting novel samples (e.g., oral swabs) from previously untested groups of children (e.g., those not seeking medical care) which are then undergoing extensive molecular testing to shed a new perspective on the epidemiology of AGE. The knowledge gained will provide the broadest understanding of the epidemiology of vomiting and diarrhea of children to date.

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment.

Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.

Predictive models of resting state networks for assessment of altered functional connectivity in MCI.

Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.

[Assessment of lymph node metastasis in gastric cancer: status quo, recent advances and new perspectives].

The precondition of accurate gastric cancer surgery is precise assessment of lymph node metastasis. To date, no imaging modality achieves both high sensitivity and high specificity in detecting lymph node metastasis in gastric cancer. Intraoperative sentinel node tracing and biopsy are the most popular method to identify the localization of tumor cell, but is limited to early gastric cancer. Nano-composite materials, designed for tumor imaging and tracing, show us a newly emerging domain for tumor detection in gastric cancer. The function of these nano-composite materials to detect lymph node metastasis in gastric cancer relies on the effective backflow of lymph system. However, the lymph vessels can be obstructed by tumor cells in advanced gastric cancer, which may restrain the application of these nanoparticles. Therefore, more methods to detect lymph node metastasis in gastric cancer should be explored. This review summarizes the characteristic of the targeted nanosphere. Based on the reported studies, a novel idea is conceived that targeted multifunctional nanosphere may be a potential method to achieve precise assessment of lymph node metastasis in gastric cancer.