Epidemic cycling in a multi-strain SIRS epidemic network model

Background

One common observation in infectious diseases caused by multi-strain pathogens is that both the incidence of all infections and the relative fraction of infection with each strain oscillate with time (i.e., so-called Epidemic cycling). Many different mechanisms have been proposed for the pervasive nature of epidemic cycling. Nevertheless, the two facts that people contact each other through a network rather than following a simple mass-action law and most infectious diseases involve multiple strains have not been considered together for their influence on the epidemic cycling.

Methods

To demonstrate how the structural contacts among people influences the dynamical patterns of multi-strain pathogens, we investigate a two strain epidemic model in a network where every individual randomly contacts with a fixed number of other individuals. The standard pair approximation is applied to describe the changing numbers of individuals in different infection states and contact pairs.

Results

We show that spatial correlation due to contact network and interactions between strains through both ecological interference and immune response interact to generate epidemic cycling. Compared to one strain epidemic model, the two strain model presented here can generate epidemic cycling within a much wider parameter range that covers many infectious diseases.

Conclusion

Our results suggest that co-circulation of multiple strains within a contact network provides an explanation for epidemic cycling.

     

Estimating the subcritical transmissibility of the Zika outbreak in the State of Florida,USA, 2016

Background

Florida State has reported autochthonous transmission of Zika virus since late July 2016. Here we assessed the transmissibility associated with the outbreak and generated a short-term forecast.

Methods

Time-dependent dynamics of imported cases reported in the state of Florida was approximated by a logistic growth equation. We estimated the reproduction number using the renewal equation in order to predict the incidence of local cases arising from both local and imported primary cases. Using a bootstrap method together with the logistic and renewal equations, a short-term forecast of local and imported cases was carried out.

Results

The reproduction number was estimated at 0.16 (95 % Confidence Interval: 0.13, 0.19). Employing the logistic equation to capture a drastic decline in the number of imported cases expected through the course of 2016, together with the low estimate of the local reproduction number in Florida, the expected number of local reported cases was demonstrated to show an evident declining trend for the remainder of 2016.

Conclusions

The risk of local transmission in the state of Florida is predicted to dramatically decline by the end of 2016.

     

Logistic regression model training based on the approximate homomorphic encryption

Background

Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives.

Methods

This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier.

Results

Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable.

Conclusions

We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Genetic mutations in influenza H3N2 viruses from a 2012 epidemic in Southern China

Background

An influenza H3N2 epidemic occurred throughout Southern China in 2012.

Methods

We analyzed the hemagglutinin (HA) and neuraminidase (NA) genes of influenza H3N2 strains isolated between 2011–2012 from Guangdong. Mutation sites, evolutionary selection, antigenic sites, and N-glycosylation within these strains were analyzed.

Results

The 2011–2012 Guangdong strains contained the HA-A214S, HA-V239I, HA-N328S, NA-L81P, and NA-D93G mutations, similar to those seen in the A/ Perth/16/2009 influenza strain. The HA-NSS_061–063 and NNS_160–162 glycosylation sites were prevalent among the 2011–2012 Guangdong strains but the NA-NRS_402–404 site was deleted. Antigenically, there was a four-fold difference between A/Perth/16/2009 -like strains and the 2011–2012 Guangdong strains.

Conclusion

Antigenic drift of the H3N2 subtype contributed to the occurrence of the Southern China influenza epidemic of 2012.

     

Logistic regression over encrypted data from fully homomorphic encryption

Background

One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud.

Methods

Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process.

Results

Our algorithm for training over encrypted data takes 0.4–3.2 hours per iteration of gradient descent.

Conclusions

We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.

     

Association of mitochondrial dysfunction and lipid metabolism with type 2 diabetes mellitus: A review of literature

Background

Diabetes mellitus (DM) is one of the most prevalent chronic diseases, and its prevalence continues to increase globally. The impact of mitochondrial dysfunction and lipid metabolism on diabetes mellitus and insulin resistance (IR) has been implicated in several previous reports; however, the results of studies are confusing despite four decades of study.

Methods/Results

This review has evaluated updated understanding of the role of mitochondrial dysfunction and lipid metabolism on type 2 diabetes, and found that mitochondrial dysfunction and lipid metabolism disorder induce the dysregulation of liver and pancreatic beta cells, insulin resistance, and type 2 diabetes.

Conclusion

Mitochondrial dysfunction and lipid metabolism induce metabolic dysregulation and finally increasing the possibility of diabetes.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Modeling the elevated risk of yellow fever among travelers visiting Brazil, 2018

Background

Unlike the epidemic of yellow fever from 2016 to 17 in Brazil mostly restricted to the States of Minas Gerais and Espirito Santo, the epidemic from 2017 to 18 mainly involved São Paulo and Rio de Janeiro and resulted in multiple international disseminations. To understand mechanisms behind this observation, the present study analyzed the distribution of imported cases from Brazil, 2018.

Methods

A statistical model was employed to capture the risk of importing yellow fever by returning international travelers from Brazil. We estimated the relative risk of importation among travelers by the extent of wealth measured by GDP per capita and the relative risk obtained by random assignment of travelers’ destination within Brazil by the relative population size.

Results

Upper-half wealthier countries had 2.1 to 3.4 times greater risk of importation than remainders. Even among countries with lower half of GDP per capita, the risk of importation was 2.5 to 2.8 times greater than assuming that the risk of travelers’ infection within Brazil is determined by the regional population size.

Conclusions

Travelers from wealthier countries were at elevated risk of yellow fever, allowing us to speculate that travelers’ local destination and behavior at high risk of infection are likely to act as a key determinant of the heterogeneous risk of importation. It is advised to inform travelers over the ongoing geographic foci of transmission, and if it appears unavoidable to visit tourist destination that has the history of producing imported cases, travelers must be strongly advised to receive vaccination in advance.

     

Classification and sensitivity analysis of the transmission dynamic of hepatitis B

Background

Hepatitis B infection caused by the hepatitis B virus is one of the most serious viral infections and a global health problem. In the transmission of hepatitis B infection, three different phases, i.e. acute infected, chronically infected, and carrier individuals, play important roles. Carrier individuals are especially significant, because they do not exhibit any symptoms and are able to transmit the infection. Here we assessed the transmissibility associated with different infection stages of hepatitis B and generated an epidemic model.

Methods

To demonstrate the transmission dynamic of hepatitis B, we investigate an epidemic model by dividing the infectious class into three subclasses, namely acute infected, chronically infected, and carrier individuals with both horizontal and vertical transmission.

Results

Numerical results and sensitivity analysis of some important parameters are presented to show that the proportion of births without successful vaccination, perinatally infected individuals, and direct contact rate are highest risk factors for the spread of hepatitis B in the community.

Conclusion

Our work provides a coherent platform for studying the full dynamics of hepatitis B and an effective direction for theoretical work.

     

Best serum biomarker combination for ovarian cancer classification

Background

Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.

Methods

In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.

Results

The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.

Conclusions

We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Crosstalk between catecholamines and erythropoiesis

Background

Erythropoiesis is regulated by a range of intrinsic and extrinsic factors, including different cytokines. Recently, the role of catecholamines has been highlighted in the development of erythroid cell lineages.

Objective

This study focuses on the biological links interconnecting erythroid development and the sympathetic nervous system. The emerging evidence that underscores the role of catecholamines in the regulation of erythropoietin and other erythropoiesis cytokines are thoroughly reviewed, in addition to elements such as iron and the leptin hormone that are involved in erythropoiesis.

Methods

Relevant English-language studies were identified and retrieved from the PubMed search engine (1981–2017) using the following keywords: “Erythropoiesis”, “Catecholamines”, “Nervous system”, and “Cytokines.”

Results

Chronic social stress alters and suppresses erythroid development. However, the physiological release of catecholamines is an additional stimulator of erythropoiesis in the setting of anemia. Therefore, the severity and timing of catecholamine secretion might distinctly regulate erythroid homeostasis.

Conclusion

Understanding the relationship of catecholamines with different elements of the erythroid islands will be essential to find the tightly regulated production of red blood cells (RBCs) in both chronic and physiological catecholamine activation.

     

Simulating pedigrees ascertained for multiple disease-affected relatives

Background

Studies that ascertain families containing multiple relatives affected by disease can be useful for identification of causal, rare variants from next-generation sequencing data.

Results

We present the R package SimRVPedigree, which allows researchers to simulate pedigrees ascertained on the basis of multiple, affected relatives. By incorporating the ascertainment process in the simulation, SimRVPedigree allows researchers to better understand the within-family patterns of relationship amongst affected individuals and ages of disease onset.

Conclusions

Through simulation, we show that affected members of a family segregating a rare disease variant tend to be more numerous and cluster in relationships more closely than those for sporadic disease. We also show that the family ascertainment process can lead to apparent anticipation in the age of onset. Finally, we use simulation to gain insight into the limit on the proportion of ascertained families segregating a causal variant. SimRVPedigree should be useful to investigators seeking insight into the family-based study design through simulation.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Weekly oral alendronate in mevalonate kinase deficiency

Background

Mevalonate kinase deficiency (MKD) is caused by mutations in the MVK gene, encoding the second enzyme of mevalonate pathway, which results in subsequent shortage of downstream compounds, and starts in childhood with febrile attacks, skin, joint, and gastrointestinal symptoms, sometimes induced by vaccinations.

Methods

For a history of early-onset corticosteroid-induced reduction of bone mineral density in a 14-year-old boy with MKD, who also had presented three bone fractures, we administered weekly oral alendronate, a drug widely used in the management of osteoporosis and other high bone turnover diseases, which blocks mevalonate and halts the prenylation process.

Results

All of the patient’s MKD clinical and laboratory abnormalities were resolved after starting alendronate treatment.

Conclusions

This observation appears enigmatic, since alendronate should reinforce the metabolic block characterizing MKD, but is crucial because of the ultimate improvement shown by this patient. The anti-inflammatory properties of bisphosphonates are a new question for debate among physicians across various specialties, and requires further biochemical and clinical investigation.

     

The effect of coagulation factors polymorphisms on abortion

Objective

Recent studies showed coagulation factors play important role in controlling pregnancy duration in addition to controlling homeostasis. Recent studies showed several polymorphisms of coagulation factors genes increase the clot formation and lead to abortion. In this study, we evaluated the polymorphisms of coagulation factors and their effects on the development of the fetus.

Material and Methods

Relevant literature was identified by a PubMed search (1988-2017) of English language papers using the terms Abortion, pregnancy woman, coagulation factor and polymorphism.

Result

Several polymorphisms of coagulation factors disturb the exchange of food and other materials between the fetus and the mother, and impairs the formation of the placenta during embryonic stages.

Discussion

Evaluation of functional polymorphisms in coagulation factors gene during fetal development can be used as a prognostic factor in the prevention of the abortion.

     

Differentially private distributed logistic regression using private and public data

Background

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced.

Methodology

In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data.

Experiments and results

We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios.

Conclusion

Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.