Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus

BACKGROUND

A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences.

PURPOSE

To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI).

STUDY TYPE

Retrospective.

POPULATION

Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation.

FIELD STRENGTH/SEQUENCE

3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI).

ASSESSMENT

We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality.

STATISTICAL TESTS

Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant.

RESULTS

Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes.

DATA CONCLUSION

Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Health-Related Quality of Life Among Chinese Pregnant Women in the Third Trimester Based on Theory of Unpleasant Symptoms: A Complex Network Analysis

Health-related quality of life (HRQOL) is the result of complex interactions between many different factors. But few studies to date have explored the structure of HRQOL. This study aimed to investigate the complex inter-relationship between HRQOL and influencing factors using network analysis. In all, 624 Chinese pregnant women in third trimester were recruited through recruiting sampling. We used regularized network analysis to create a complex network. The relationship with mother-in-law was the most central node followed by relationship with partner. We found the comorbidity of anxiety and depressive symptoms using network analysis. Physical- and mental-related quality of life were a "bridge node" connecting psychological factors with physiologic factors. The present network analysis highlights the strong link between relationship with mother-in-law or partner and HRQOL, which is also a unique phenomenon under Chinese culture. This analysis provides key variables for future intervention or improvement of pregnant women' HRQOL.

Assessing Functional Brain Network Dynamics in Dyslexia from fNIRS Data

Developmental dyslexia is characterized by a deficit of phonological awareness whose origin is related to atypical neural processing of speech streams. This can lead to differences in the neural networks that encode audio information for dyslexics. In this work, we investigate whether such differences exist using functional near-infrared spectroscopy (fNIRS) and complex network analysis. We have explored functional brain networks derived from low-level auditory processing of nonspeech stimuli related to speech units such as stress, syllables or phonemes of skilled and dyslexic seven-year-old readers. A complex network analysis was performed to examine the properties of functional brain networks and their temporal evolution. We characterized aspects of brain connectivity such as functional segregation, functional integration or small-worldness. These properties are used as features to extract differential patterns in controls and dyslexic subjects. The results corroborate the presence of discrepancies in the topological organizations of functional brain networks and their dynamics that differentiate between control and dyslexic subjects, reaching an Area Under ROC Curve (AUC) up to 0.89 in classification experiments.

External supply risk of agricultural products trade along the Belt and Road under the background of COVID-19

Agricultural product trade along the Belt and Road (B&R) is an important part of the international food security system, the vulnerabilities of which have been highlighted by the recent COVID-19 pandemic. Based on the complex network analysis, this study analyzes the characteristics of agricultural products trade network along the B&R. It also combines the effects of COVID-19 with the import trade volume of agricultural products in countries along the B&R to build a risk supply model of agricultural products. The results show that: (1) In 2021, the spatial correlation structure of agricultural products trade along the B&R became increasingly sparse, and the network connectivity and density also decreased. (2) The network showed obvious scale-free distribution characteristics and obvious heterogeneity. Five communities emerged under the influence of the core node countries, but the formation of community in 2021 had obvious geopolitical characteristics. (3) Under the influence of the COVID-19 epidemic, the number of countries with medium-risk and high-risk level along the route facing external dependence risk (R_EDI), import concentration risk (R_HHI) and COVID-19 epidemic risk (R_RICI) increased in 2021, and the number of countries with extremely low-risk level decreased. (4) The dominant risk type of external supply of agricultural products along the route changed from compound risk type in 2019 to epidemic risk in 2021. Hence, the results can be expected to prevent external risk impact from reducing excessive concentration of agricultural products trade and excessive dependence on the external market.

[Material basis and molecular mechanism of Angelicae Sinensis Radix in activating blood:based on computer-aided drug design]

Angelicae Sinensis Radix excels in activating blood, but the scientific mechanism has not been systematically analyzed, thus limiting the development of the medicinal. This study employed the computer-aided drug design methods, such as structural similarity-based target reverse prediction, complex network analysis, molecular docking, binding free energy calculation, cluster analysis, and ADMET(absorption, distribution, metabolism, excretion, toxicity) calculation, and enzyme activity assay in vitro, to explore the components and mechanism of Angelicae Sinensis Radix in activating blood. Target reverse prediction and complex network analysis yielded 40 potential anticoagulant targets of the medicinal. Gene Ontology(GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis indicated that the targets mainly acted on the complement and coagulation cascade signaling pathway to exert the anticoagulant function. Among them, the key enzymes thrombin(THR) and coagulation factor Xa(FXa) in coagulation cascade and thrombosis were the drug targets for thromboembolic diseases. At the same time, molecular docking and cluster analysis showed that the medicinal had high selectivity for FXa. According to binding free energy score, 8 potential active components were selected for enzyme activity assay in vitro. The results demonstrated that 8 components inhibited THR and FXa, and the inhibition was stronger on FXa than on THR. The pharmacophore model of 8 active compounds was constructed, which suggested that the components had the common pharmacophore AAHH. The ADMET calculation result indicated that they had good pharmacokinetic properties and were safe. Based on target reverse prediction, complex network analysis, molecular docking and binding free energy calculation, anticoagulant activity in vitro, spatial binding conformation of molecules and targets, pharmacophore model construction, and ADMET calculation, this study preliminarily clarified the material basis and molecular mechanism of Angelicae Sinensis Radix in activating blood from the perspective of big data, and calculated the pharmacology and toxicology parameters of the active components. Our study, for the first time, revealed that the medicinal had obvious selectivity and pertinence for different coagulation proteins, reflecting the unique effect of different Chinese medicinals and the biological basis. Therefore, this study can provide clues for precision application of Angelicae Sinensis Radix and the development of the blood-activating components with modern technology.

Cold Chain Food and COVID-19 Transmission Risk: From the Perspective of Consumption and Trade

Since the outbreak of the coronavirus disease 2019 (COVID-19), political and academic circles have focused significant attention on stopping the chain of COVID-19 transmission. In particular outbreaks related to cold chain food (CCF) have been reported, and there remains a possibility that CCF can be a carrier. Based on CCF consumption and trade matrix data, here, the "source" of COVID-19 transmission through CCF was analyzed using a complex network analysis method, informing the construction of a risk assessment model reflecting internal and external transmission dynamics. The model included the COVID-19 risk index, CCF consumption level, urbanization level, CCF trade quantity, and others. The risk level of COVID-19 transmission by CCF and the dominant risk types were analyzed at national and global scales as well as at the community level. The results were as follows. (1) The global CCF trade network is typically dominated by six core countries in six main communities, such as Indonesia, Argentina, Ukraine, Netherlands, and the USA. These locations are one of the highest sources of risk for COVID-19 transmission. (2) The risk of COVID-19 transmission by CCF in specific trade communities is higher than the global average, with the Netherlands-Germany community being at the highest level. There are eight European countries (i.e., Netherlands, Germany, Belgium, France, Spain, Britain, Italy, and Poland) and three American countries (namely the USA, Mexico, and Brazil) facing a very high level of COVID-19 transmission risk by CCF. (3) Of the countries, 62% are dominated by internal diffusion and 23% by external input risk. The countries with high comprehensive transmission risk mainly experience risks from external inputs. This study provides methods for tracing the source of virus transmission and provides a policy reference for preventing the chain of COVID-19 transmission by CCF and maintaining the security of the global food supply chain.

Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug-Gene Interactions

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug-gene interaction data. We obtained drug-gene interaction data from an earlier version of DrugBank, built a drug-gene interaction network, and projected it as a drug-drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug-gene interaction data to generate a comprehensive drug repurposing hint list.

Evolution of Global Food Trade Patterns and Its Implications for Food Security Based on Complex Network Analysis

Global food trade is an integral part of the food system, and plays an important role in food security. Based on complex network analyses, this paper analyzed the global food trade network (FTN) and its evolution from 1992 to 2018. The results show that: (1) food trade relations have increased and global FTN is increasingly complex, efficient, and tighter. (2) Global food trade communities have become more stable and the trade network has evolved from "unipolar" to "multipolar". (3) Over the nearly 30-year period, the core exporting countries have been stable and concentrated, while the core importing countries are relatively dispersed. The increasingly complex food trade network improves food availability and nutritional diversity; however, the food trade system, led by several large countries, has increased the vulnerability of some countries' food systems and brings about unsafe factors, such as global natural disasters and political instability. It is supposed to establish a food security community to protect the global food trade market, address multiple risks, and promote global food security.

Complex Networks of Prion-Like Proteins Reveal Cross Talk Between Stress and Memory Pathways in Plants

Prions are often considered as molecular memory devices, generating reproducible memory of a conformational change. Prion-like proteins (PrLPs) have been widely demonstrated to be present in plants, but their role in plant stress and memory remains unexplored. In this work, we report the widespread presence of PrLPs in plants through a comprehensive meta-analysis of 39 genomes representing major taxonomic groups. We find diverse functional roles associated with these proteins in various species and term the full complement of PrLPs in a genome as its "prionome." In particular, we found the rice prionome being significantly enriched in transposons/retrotransposons (Ts/RTRs) and identified over 60 rice PrLPs that were differentially regulated in stress and developmental responses. This prompted us to explore whether and to what extent PrLPs may build stress memory. By integrating the available rice interactome, transcriptome, and regulome data sets, we could find links between stress and memory pathways that would not have otherwise been discernible. Regulatory inferences derived from the superimposition of these data sets revealed a complex network and cross talk between PrLPs, transcription factors (TFs), and the genes involved in stress priming. This integrative meta-analysis connects transient and transgenerational memory mechanisms in plants with PrLPs, suggesting that plant memory may rely upon protein-based signals in addition to chromatin-based epigenetic signals. Taken together, our work provides important insights into the anticipated role of prion-like candidates in stress and memory, paving the way for more focused studies for validating the role of the identified PrLPs in memory acclimation.

[Key factors of the severity of chronic subjective tinnitus: a complex network analysis]

Objective:To investigate the key factors related to the severity of chronic subjective tinnitus through a complex network analysis. Methods:A retrospective study about patients with chronic subjective tinnitus presented to Peking Union Medical College Hospital from December 2019 to October 2020 was conducted. The demographic information, audiometric and tinnitus-related tests, and scores of Tinnitus Handicap Inventory（THI）, Hospital Anxiety and Depression Scale（HADS）, Pittsburgh Sleep Quality Index（PSQI） and Visual Analogue Scale（VAS） of tinnitus loudness and annoyance were set as nodes in a complex network which was conducted and analyzed through the R package. Results:A total of 183 patients were enrolled, including 99（54.1%） males and 84（45.9%） females, with an average age of（41.65±1.05） years. The results of the complex network analysis showed that anxiety and sleep disorder were closely related to THI score, and anxiety was more important than sleep disorder. There was a strong correlation between self-reported anxiety and insomnia. Demographic characteristics, audiologic and tinnitus-associated tests were not significantly related to THI score. Conclusion:Anxiety and sleep disturbance were key factors related to tinnitus severity, and anxiety played a more important role. There was an obvious correlation between sleep disorder and anxiety, and they might have a superimposed effect on tinnitus severity. Future therapies of tinnitus should pay more attention on releasing anxiety and insomnia.

Dopamine Transporter Is a Master Regulator of Dopaminergic Neural Network Connectivity

Dopaminergic neurons of the substantia nigra pars compacta (SNC) and ventral tegmental area (VTA) exhibit spontaneous firing activity. The dopaminergic neurons in these regions have been shown to exhibit differential sensitivity to neuronal loss and psychostimulants targeting dopamine transporter. However, it remains unclear whether these regional differences scale beyond individual neuronal activity to regional neuronal networks. Here, we used live-cell calcium imaging to show that network connectivity greatly differs between SNC and VTA regions with higher incidence of hub-like neurons in the VTA. Specifically, the frequency of hub-like neurons was significantly lower in SNC than in the adjacent VTA, consistent with the interpretation of a lower network resilience to SNC neuronal loss. We tested this hypothesis, in DAT-cre/loxP-GCaMP6f mice of either sex, when activity of an individual dopaminergic neuron is suppressed, through whole-cell patch clamp electrophysiology, in either SNC or VTA networks. Neuronal loss in the SNC increased network clustering, whereas the larger number of hub-neurons in the VTA overcompensated by decreasing network clustering in the VTA. We further show that network properties are regulatable via a dopamine transporter but not a D2 receptor dependent mechanism. Our results demonstrate novel regulatory mechanisms of functional network topology in dopaminergic brain regions.SIGNIFICANCE STATEMENT In this work, we begin to untangle the differences in complex network properties between the substantia nigra pars compacta (SNC) and VTA, that may underlie differential sensitivity between regions. The methods and analysis employed provide a springboard for investigations of network topology in multiple deep brain structures and disorders.

Risk of Global External Cereals Supply under the Background of the COVID-19 Pandemic: Based on the Perspective of Trade Network

International food trade is an integral part of the food system, and the COVID-19 pandemic has exposed the fragility of external food supplies. Based on the perspective of cereals trade networks (CTN), the pandemic risk is combined with the trade intensity between countries, and an assessment model of cereals external supply risk is constructed that includes external dependence index (EDI), import concentration, and risk of COVID-19 from import countries index (RICI). The results show that: (1) the global main CTN have typical scale-free characteristics, and seven communities are detected under the influence of the core countries; (2) about 60%, 50%, and 70% of countries face risks of medium and above (high and very high) external dependence, concentration of imports, and COVID-19 in the country of origin, respectively. Under the influence of the pandemic, the risk of global external cereal supply index (RECSI) has increased by 65%, and the USA-CAN communities show the highest risk index; (3) the countries with a very high risk are mainly the Pacific island countries and the Latin American and African countries. In addition, Japan, Mexico, South Korea, and 80% of the net food-importing developing countries are at high or very high RECSI levels. Approximately 50% of countries belong to the compound risk type, and many export countries belong to the RICI risk type; (4) global external food supply is subjected to multiple potential threats such as trade interruption, "price crisis", and "payment dilemma". The geographical proximity of community members and the geographical proximity of the pandemic risk is superimposed, increasing the regional risk of external food supply; and (5) this study confirms that the food-exporting countries should avoid the adoption of food export restriction measures and can prevent potential external supply risks from the dimensions of maintaining global food liquidity and promoting diversification of import sources. We believe that our assessment model of cereals external supply risk comprises a useful method for investigations regarding the international CTN or global food crisis under the background of the pandemic.

[Complex network analysis of combination medication of patients with kidney malignant tumor based in real world]

Kidney malignant tumor is a type of primary renal cell carcinoma, and mainly refers to renal cancer. The incidence of kidney cancer and the number of hospital cases in China have been increasing. Based on the clinical medicine information of patients in the hospital information system(HIS) database of 37 hospitals in China, the combined medication of patients with kidney malignant tumor were analyzed by Tabu search algorithm, so as to analyze the combined medication of patients with kidney malignant tumor in real world. A total of 7 095 patients with kidney malignant tumor were included, the ratio of males to females was 2.11∶1, and the ratio of male patients increased gradually with age. About 3 933 patients(55.43%) showed a superior effect among those patients. The common therapies of patients with kidney malignant tumor were anti-tumor therapies and symptomatic therapies, including anti-infection, regulation of electrolyte balance, sedation and analgesia, analgesic, regulation of gastrointestinal function. The whole population of patients with kidney malignant tumor were mostly treated with anti-tumor drugs combined with more symptomatic therapies, while the anti-tumor therapies of the superiority population of patients were less combined with other drugs, with less combined medication. The result may be related to the stage of tumor or individual response to the therapeutic regimen. No matter for the whole population or for the superiority population of patients with kidney malignant tumor, the therapies was mainly Western medicines. Based on the pathogenesis of deficiency in origin and excess in superficiality with kidney malignant tumor, Chinese subgroups with formula for clearing heat and removing toxicity, formula for vigorate Qi and replenish the blood, formula for regulate Qi and invigorate the blood, laxative and hemostatic were more commonly used. In the future, further studies shall be conducted for combined therapies for patients of different stages, so as to play the advantages of multi-target, overall regulation, toxicity reduction and efficacy enhancement of traditional Chinese medicine, improve the life quality of patients with kidney malignant tumor, prolong their life time, and improve the survival rate of patients.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

Prediction of atrial fibrillation using the recurrence complex network of body surface potential mapping signals

OBJECTIVE:

Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Early diagnosis and intervention of AF is essential to avert the further fatality. The technique of noninvasive electrical mapping, especially the body surface potential mapping (BSPM), has a more practical application in the study of predicting AF, when compared with the invasive electrical mapping methods such as the epicardial mapping and interventional catheter mapping. However, the prediction of AF with noninvasive signals has been inadequately studied. Thus, the aim of this paper was to analyze the properties of atrial dynamic system based on the noninvasive BSPM signals (BSPMs), using the recurrence complex network, and consequently to evaluate its role in predicting the recurrence of AF in clinical aspect.

METHOD:

Twelve patients with persistent AF were included in this study. Their preoperative and postoperative BSPMs were recorded. Initially, the preoperative BSPMs were transformed into the recurrence complex network to characterize the complexity property of the atria. Subsequently, the parameters of recurrence ratio (REC), determinism (DET), entropy of the diagonal structure distribution (ENTR), and laminarity (LAM) were calculated. Furthermore, the difference in the parameters in the four regions of the body and the difference obtained from the dominant frequency (DF) method were compared. Finally, the results obtained for the atrial dynamic system complexity from a 12-lead electrocardiogram (ECG) from the BSPMs were discussed.

RESULTS:

Our study revealed that the patients whose REC is greater than an average threshold, and with a lower LAM presented a much higher possibility of AF recurrence, after the AF surgery.

CONCLUSIONS:

The recurrence complex network is a useful and convenient way to evaluate the nonlinear properties of the BSPMs in patients with AF. It has good immunity to the lead position and has a potential role in the understanding of predicting the recurrence of AF.

A Game-Theoretical Network Formation Model for C. elegans Neural Network

Studying and understanding human brain structures and functions have become one of the most challenging issues in neuroscience today. However, the mammalian nervous system is made up of hundreds of millions of neurons and billions of synapses. This complexity made it impossible to reconstruct such a huge nervous system in the laboratory. So, most researchers focus on C. elegans neural network. The C. elegans neural network is the only biological neural network that is fully mapped. This nervous system is the simplest neural network that exists. However, many fundamental behaviors like movement emerge from this basic network. These features made C. elegans a convenient case to study the nervous systems. Many studies try to propose a network formation model for C. elegans neural network. However, these studies could not meet all characteristics of C. elegans neural network, such as significant factors that play a role in the formation of C. elegans neural network. Thus, new models are needed to be proposed in order to explain all aspects of C. elegans neural network. In this paper, a new model based on game theory is proposed in order to understand the factors affecting the formation of nervous systems, which meet the C. elegans frontal neural network characteristics. In this model, neurons are considered to be agents. The strategy for each neuron includes either making or removing links to other neurons. After choosing the basic network, the utility function is built using structural and functional factors. In order to find the coefficients for each of these factors, linear programming is used. Finally, the output network is compared with C. elegans frontal neural network and previous models. The results implicate that the game-theoretical model proposed in this paper can better predict the influencing factors in the formation of C. elegans neural network compared to previous models.

[Complex network analysis on Shenxiong glucose injection in combined use with Chinese and Western medicine for cerebral infarction in real world study]

In order to analyze Shenxiong glucose injection in combined use with other medicines for cerebral infarction in real world, the basic information, Chinese and western medicine diagnosis information, doctors'advice information, and laboratory checking information for the patients with Shenxiong glucose injection in treatment of cerebral infarction were extracted from the hospital information system (HIS) of sixteen 3A hospitals. Apriori Algorithm was used to establish models, and Clementine 12.0 was used for correlation analysis. Then complex network was established to analyze the combined drug use and visualize the results. A total of 635 patients were included in the study, among which 599 patients (94.33%) showed superior effect. Shenxiong glucose injection was often used with platelet suppressant drug, neuroprotective agent, lipid regulating agents, free radical scavenger, vitamins and Chinese medicine blood activating and stasis eliminating agent in the treatment of cerebral infarction. In the patients with superior effect, neuroprotective agent and free radical scavengers were also used based on the combined use with Aspirin, hypolipidemic drugs and blood activating and stasis removing agents, highlighting the rain protection strategies. Shenxiong glucose injection in combined use with Chinese and western medicines for cerebral infarction complied with the latest clinical practice guideline on the treatment of cerebral infarction, and the application of neuroprotective agent was propitious to improve the therapeutic effect.

[Compatible principles of Xiebai decoction family based on complex network analysis]

In order to explore the compatible principles of Xiebai decoction family, formulae from ancient and modern Xiebai decoction family were collected and sorted in this study. The compatible characteristics, core herbs, as well as the relativity of herbs nature in Xiebai decoction family were analyzed based on scale free network and other data-mining methods such as association rules, clustering analysis and correspondence analysis. The scale free network results showed that in Xiebai decoction family, Mori Cortex-Lycii Cortex-Glycyrrhizae Radix et Rhizoma was used as the core compatible group and formed the complicated compatible network with other additional herbs; association rules results showed that the core herbs in such formulae included Mori Cortex, Lycii Cortex, Glycyrrhizae Radix et Rhizoma, scutellaria root, Platycodon root, Anemarrhena, and almond, which formed corresponding herbal pairs and compatibility; clustering analysis showed that Mori Cortex was the core herb in Xiebai decoction family, and Mori Cortex-Lycii Cortex-Glycyrrhizae Radix et Rhizoma was its main combination unit, which was always compatible with herbs of clearing heat, reducing phlegm, supplementing Qi and nourishing Yin to form the series prescriptions. The results indicated that the core compatibility features of Xiebai decoction family were clearing heat in lung and relieving cough and asthma, providing a basis for the clinical application of Xiebai decoction family.

MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram contains much drug- and pathology specific data for public health monitoring of DDI and ADR, and that complex network analysis provides an important toolbox to extract health-related associations and their support from large-scale social media data.