Tuberculosis (TB) in humans caused by Mycobacterium tuberculosis is still a threatening disease worldwide. In domestic animals’ cases, the pathogen is usually Mycobacterium bovis, however, other M. tuberculosis complex (MTC) lineages are sometimes observed. Especially, in Asian elephant cases, the major TB pathogen is the human type, M. tuberculosis. In Nepal, we analyzed several elephant MTC isolates and found that they were M. tuberculosis possessing Nepalese isolate specific features suggesting the animals were infected by their handlers.

Bovine TB control is a serious problem in many countries including some developed countries. The failure of its control is mainly caused by the wide host range of M. bovis. Once the bacteria adopt to wild mammalian species, the eradication becomes very difficult. However, in most of the developing countries, detailed surveys on bovine TB have not been carried out, although they have noticed the existence of the disease. In Bangladesh and Nepal, we analyzed MTC isolates obtained from cattle and wild animals, namely, two monkeys, a deer, an antelope and a rhino, and found all of them were belonging to a newly identified MTC subspecies, M. orygis. Their genetic diversity suggested the introduction of the pathogen was not a recent event in those areas. TB lesions were observed in lungs in all the cases suggesting air-borne transmission of the pathogen. Several human TB cases by M. orygis have been reported and most of them were from South Asia. Thus, TB cases in animals in South Asia might be caused by M. orygis, rather by M. bovis.

Treatment regimens are different among MTC subspecies, thus, the identification of the species is important. Since the pathogen of animal TB in South Asia might be different from other areas and the pathogen, M. orygis, seems to have a wide host range, continuous surveys seem to be necessary to know the situation. To monitor the possible emerging disease in humans, the development of easy, rapid and low-cost screening methods is important.

Limited data are currently available on the occurrences and toxicological implications of equine estrogens in aquatic ecosystems. In the present study, we determined concentrations of equine estrogens, including equilin, equilenin, and their 17α- and 17β-dihydro analogues, in the river water collected from Hokkaido, Japan. Among six equine estrogens measured, equilin (2.72±2.22ng/L) concentrations were the highest in the river sample, followed by 17α-dihydroequilin (2.17±2.20ng/L). This study was the first to detect the equine estrogens in the river water collected from Hokkaido, Japan. We further investigated transcriptional profiles of estrogen-responsive genes, such as vitellogenins (Vtg1 and Vtg2), choriogenins (ChgL and ChgH) and estrogen receptor subtypes (ERα, ERβ1, and ERβ2), in the liver of male medaka (Oryzias latipes) exposed to six equine estrogens (1—300ng/L) for 3 days. Our quantitative RT-PCR analyses revealed that the expression levels of hepatic Vtg, Chg and ERα genes in male medaka responded to various types and concentrations of equine estrogens. The estrogenic potencies of the chemicals were in the order of equilin>17β-estradiol>equilenin>17β-dihydroequilin>17β-dihydroequilenin>17α-dihydroequilin>17α-dihydroequilenin, suggesting the higher lowest-observed effective concentration (LOEC) of 17β-estradiol (LOEC : 30ng/L) than that of equilin (10ng/L). Furthermore, we investigated the reproductive and transgenerational effects of equilin in paired medaka exposed to 10, 100, and 1000ng/L for 21 days. The short-term reproduction assay demonstrated that equilin (100 and/or 1000ng/L) adversely affected the reproduction (fecundity and/or fertility) of adult medaka. In F₁ generation fertilized eggs, the hatching of embryos in the 100 and 1000ng/L treatment groups also showed adverse effects, suggesting the endocrine-disrupting potential of equilin.

Dengue, global health threat, is a leading cause of morbidity and mortality in most of the countries in tropical and sub-tropical regions. With increasing globalization, dengue is becoming an increasing threat to non-endemic countries. The imported dengue cases are increasing year by year. In late August 2014, the autochthonous dengue epidemic was occurred in Japan. More than 200 dengue imported cases have been notified through national surveillance in recent years. Zika viral diseases are also mosquito-borne viral infection. The main vector mosquitoes of dengue virus and Zika virus are Aedes aegypti and Aedes albopictus. Aedes albopictus is active in summer in Japan. The controls for Aedes albopictus are important to prevent the entry of those viruses.

Missing data hinders epidemiological data analysis as it reduces the statistical power and produces biased estimates. The traditional methods for dealing with missing data, such as list-wise deletion (complete case analysis) and overall mean imputation, are known to produce biased estimations in some situations. To address these limitations, multiple imputation is becoming popular for handling missing data. In this study, a simulated data were analyzed to examine the influence of missing data on the estimates of analysis through comparing list-wise deletion and multiple imputation. For this purpose, an empirical epidemiological survey data concerning farm management practices in 563 dairy farms to investigate risk factors associated with bovine leukemia virus infection were used to create the simulated dataset with missing values.

Missing data mechanisms are classified into 3 categories based on how the probability of missing values relates to the data : (1) missing completely at random (MCAR), the probability of being missing is a completely random event ; (2) missing at random (MAR), the probability of being missing depends only on the observed data ; and (3) not missing at random (NMAR), the probability of being missing depends on unobserved data or a variable which is missing itself. Five missing data scenarios with different missing data mechanisms and varied missing value proportions were examined in this study. For each scenario, 100 simulated datasets were generated from the empirical data. For each simulated dataset, list-wise deletion and multiple imputation were performed, and estimated coefficients regarding bovine leukemia virus infection via logistic regression were compared.

Under any assumption of missing data mechanisms, estimates of coefficients obtained by list-wise deletion showed less precision than those obtained by multiple imputation. Under the MCAR assumption, list-wise deletion produced less precision in estimates as the proportion of missing data was larger, and under the MAR and NMAR assumptions it led to biased estimates. Meanwhile, multiple imputation produced less bias and greater precision under the MCAR and MAR assumptions. However, biased estimates were observed in the results of multiple imputation under the NMAR assumption. This study demonstrated that missing data induced less precision and biased estimates in analyzing epidemiological data and also showed the practical utility of multiple imputation methods to improve the precision of estimation in dealing with missing data.

One of the important measures to enhance the preparation and response against zoonosis is to strengthen the surveillance system in the fields of both medicine and veterinary medicine. The importance of zoonosis occurring in pet animal has recently been emphasized. More efficient and active response to zoonotic diseases fulfills the philosophy of “One Health” which is the concept for all the relevant groups, human, animal and environment health, to work together.