A cross-sectional study was used to identify and assess prevalence and phenotypic antimicrobial resistance (AMR) profiles of Escherichia coli and other enterobacteria isolated from healthy wildlife and livestock cohabiting at a 10,000 acres game ranch near Lusaka, Zambia. Purposive sampling was used to select wildlife and livestock based on similarities in behavior, grazing habits and close interactions with humans. Isolates (n = 66) from fecal samples collected between April and August 2018 (n = 84) were examined following modified protocols for bacteria isolation, biochemical identification, molecular detection, phylogenetic analysis, and antimicrobial susceptibility testing by disc diffusion method. Data were analyzed using R software, Genetyx ver.12 and Mega 6. Using Applied Profile Index 20E kit for biochemical identification, polymerase chain reaction assay and sequencing, sixty-six isolates were identified to species level, of which Escherichia coli (72.7%, 48/66), E. fergusonii (1.5%, 1/66), Shigella sonnei (22.7%, 14/66), Sh. flexinerri (1.5%, 1/66) and Enterobacteriaceae bacterium (1.5%, 1/66), and their relationships were illustrated in a phylogenetic tree. Phenotypic antimicrobial resistance or intermediate sensitivity expression to at least one antimicrobial agent was detected in 89.6% of the E. coli, and 73.3% of the Shigella isolates. The E. coli isolates exhibited the highest resistance rates to ampicillin (27%), ceftazidime (14.3%), cefotaxime (9.5%), and kanamycin (9.5%). Multidrug resistance (MDR) was detected in 18.8% of E. coli isolates while only 13.3% Shigella isolates showed MDR. The MDR was detected among isolates from impala and ostrich (wild animals in which no antimicrobial treatment was used), and in isolates from cattle, pigs, and goats (domesticated animals). This study indicates the possible transmission of drug-resistant microorganisms between animals cohabiting at the wildlife–livestock interface. It emphasizes the need for further investigation of the role of wildlife in the development and transmission of AMR, which is an issue of global concern.

This paper proposes a customer interest estimation method using security camera to meet the demand of the retail industry. In the field of retail industry, it is considered that the understanding of customers’ interests in the real store can be used for various marketing activities such as the product development and the layout of the store. Then, it is important to pay attention to customers’ behavior in the real store. Their behavior is often recorded by the cameras installed in the store for security purposes. A method for estimating their interests from the videos of the security camera is presented in this paper. The novelty of our method is three-fold. Firstly, the experimental data of subjects in our group were taken by using the security camera already installed in the real store. Secondly, we used a pre-trained posture estimation model and treated the results as the features to be trained by a two-layer neural network model. Finally, a professional have annotated the subjects’ interests. The effectiveness of our method was confirmed by comparing with benchmark supervised machine learning models.

Aim of our study is to establish models for predicting the number of migratory wild birds based on the meteorological data. From 136 species of wild birds, which have been observed at Futatsudate in Miyazaki, Japan, from 2008 to 2016, we selected the potential high-risk species, which can introduce highly pathogenic avian influenza (HPAI) virus into Miyazaki; we defined them as “risky birds”. We then performed regression analysis to model the relationship between the number of risky birds and meteorological data. We selected 10 wild bird species as risky birds: Mallard ( Anas platyrhynchos ), Northern pintail ( Anas acuta ), Eurasian wigeon ( Anas penelope ), Eurasian teal ( Anas crecca ), Common pochard ( Aythya ferina ), Eurasian coot ( Fulica atra ), Northern shoveler ( Anas clypeata ), Common shelduck ( Tadorna tadorna ), Tufted duck ( Aythya fuligula ), and Herring gull ( Larus argentatus ). We succeeded in identifying five meteorological factors associated with their migration: station pressure, mean value of global solar radiation, minimum of daily maximum temperature, days with thundering, and days with daily hours of daylight under 0.1 h. We could establish some models for predicting the number of risky birds based only on the published meteorological data, without manual counting. Dynamics of migratory wild birds has relevance to the risk of HPAI outbreak, so our data could contribute to save the cost and time in strengthening preventive measures against the epidemics.