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In the United States, surveillance has been key to tracking spatiotemporal emergence of blacklegged ticks [Ixodes scapularisSay (Ixodida:Ixodidae)] and their pathogens such as Borrelia burgdorferiJohnson, Schmid, Hyde, Steigerwalt & Brenner (Spirochaetales: Spirochaetaceae), the agent of Lyme disease. On the Holt Research Forest in midcoastal Maine, collection of feeding ticks from live-trapped small mammal hosts allowed us to track the emergence and establishment of I. scapularis, 1989–2019. From 1989–1995, we collected only I. angustusNeumann (Ixodida: Ixodidae)(vole tick), Dermacentor variabilisSay (Ixodida: Ixodidae) (American dog tick), and I. marxiBanks (Ixodida: Ixodidae) (squirrel tick) from seven species of small mammals. The most abundant tick host was the white-footed mouse [Peromyscus leucopusRafinesque (Rodentia:Cricetidae)] followed by the red-backed vole (Myodes gapperiVigors (Rodentia: Cricetidae)). Emergence of I. scapulariswas signaled via the appearance of subadult I. scapularisin 1996. Emergence of B. burgdorferiwas signaled through its appearance in I. scapularisfeeding on mice in 2005. There was a substantial increase in I. scapularisprevalence (proportion of hosts parasitized) and burdens (ticks/host) on white-footed mice and red-backed voles in 2007. The ~11-yr time-to-establishment for I. scapulariswas consistent with that seen in other studies. White-footed mice comprised 65.9% of all captures and hosted 94.1% of the total I. scapularisburden. The white-footed mouse population fluctuated interannually, but did not trend up as did I. scapularisprevalence and burdens. There were concurrent declines in I. angustusand D. variabilis. We discuss these results in the broader context of regional I. scapularisrange expansion.

Vertebrate surveillance for eastern equine encephalitis virus (EEEV) activity usually focuses on three types of vertebrates: horses, passerine birds, and sentinel chicken flocks. However, there is a variety of wild vertebrates that are exposed to EEEV infections and can be used to track EEEV activity. In 2009, we initiated a pilot study in northern New England, United States, to evaluate the effectiveness of using wild cervids (free-ranging white-tailed deer and moose) as spatial sentinels for EEEV activity. In Maine, New Hampshire, and Vermont during 2009–2017, we collected blood samples from hunter-harvested cervids at tagging stations and obtained harvest location information from hunters. U.S. Centers for Disease Control and Prevention processed the samples for EEEV antibodies using plaque reduction neutralization tests (PRNTs). We detected EEEV antibodies in 6 to 17% of cervid samples in the different states and mapped cervid EEEV seropositivity in northern New England. EEEV antibody-positive cervids were the first detections of EEEV activity in the state of Vermont, in northern Maine, and northern New Hampshire. Our key result was the detection of the antibodies in areas far outside the extent of documented wild bird, mosquito, human case, or veterinary case reports of EEEV activity in Maine, New Hampshire, and Vermont. These findings showed that cervid (deer and moose) serosurveys can be used to characterize the geographic extent of EEEV activity, especially in areas with low EEEV activity or with little or no EEEV surveillance. Cervid EEEV serosurveys can be a useful tool for mapping EEEV activity in areas of North America in addition to northern New England.

Geographical range expansions of blacklegged tick [Ixodes scapularisSay (Acari: Ixodidae)] populations over time in the United States have been attributed to a mosaic of factors including 20th century reforestation followed by suburbanization, burgeoning populations of the white-tailed deer [Odocoileus virginianusZimmerman (Artiodactyla: Cervidae)], and, at the northern edge of I. scapularis’ range, climate change. Maine, a high Lyme disease incidence state, has been experiencing warmer and shorter winter seasons, and relatively more so in its northern tier. Maine served as a case study to investigate the interacting impacts of deer and seasonal climatology on the spatial and temporal distribution of I. scapularis.A passive tick surveillance dataset indexed abundance of I. scapularisnymphs for the state, 1990–2013. With Maine’s wildlife management districts as the spatial unit, we used a generalized additive model to assess linear and nonlinear relationships between I. scapularisnymph abundance and predictors. Nymph submission rate increased with increasing deer densities up to ~5 deer/km2(13 deer/mi2), but beyond this threshold did not vary with deer density. This corroborated the idea of a saturating relationship between I. scapularisand deer density. Nymphs also were associated with warmer minimum winter temperatures, earlier degree-day accumulation, and higher relative humidity. However, nymph abundance only increased with warmer winters and degree-day accumulation where deer density exceeded ~2 deer/km2(~6/mi2). Anticipated increases in I. scapularisin the northern tier could be partially mitigated through deer herd management.

Lyme disease is caused by the bacterial spirochete Borrelia burgdorferiJohnson, Schmid, Hyde, Steigerwalt, and Brenner (Spirocheatales: Spirochaetaceae) which is transmitted through the bite of an infected blacklegged tick Ixodes scapularisSay (Ixodida: Ixodidae). Maine, USA, is a high Lyme disease incidence state, with rising incidence of Lyme disease and other tick-borne illnesses associated with increasing I. scapularisabundance and northward range expansion. Members of the public submitted ticks to a tick identification program (1990–2013). From these passive surveillance data, we characterized temporal trends in I. scapularissubmission rate (an index of abundance), comparing Maine’s northern tier (seven counties) versus southern tier (nine counties). In the northern tier, the I. scapularissubmission rate increased throughout the duration of the time series, suggesting I. scapulariswas emergent but not established. By contrast, in the southern tier, submission rate increased initially but leveled off after 10–14 yr, suggesting I. scapulariswas established by the mid-2000s. Active (field) surveillance data from a site in the southern tier—bird tick burdens and questing adult tick collections—corroborated this leveling pattern. Lyme disease incidence and I. scapularissubmission rate were temporally correlated in the northern but not southern tier. This suggested a decoupling of reported disease incidence and entomological risk.

Assistive health care system is a viable solution for elderly care to offer independent living. Such health care systems are feasible through smart homes, which are intended to enhance the living quality of the occupant. Activities of daily living (ADL) are considered in the design of a smart home and are extended to abnormality detection in the case of health care. Abnormality in occupant behavior is the deviation of ongoing activity with that of the built activity model. Generally, supervised machine learning strategies or knowledge engineering strategies are employed in the process of activity modeling. Supervised machine learning approaches incur overheads in annotating the dataset, while the knowledge modeling approaches incur overhead by being dependent on the domain expert for occupant specific knowledge. The proposed approach on the other hand, employs an unsupervised machine learning strategy to readily extract knowledge from unlabelled data using contextual pattern clustering and subsequently represents it as ontology activity model. Ontology offers enhanced activity recognition through its semantically clear representation and reasoning, it has restriction in handling temporal data. Hence, this article in addition to unsupervised modeling focuses at enabling temporal reasoning within ontology using fuzzy logic. The proposed fuzzy ontology activity recognition (FOAR) framework represents an activity model as a fuzzy temporal ontology. Fuzzy SWRL rules modeled within ontology aid activity recognition and abnormality detection for health care. The experimental results show that the proposed FOAR has better performance in abnormality detection than that of the existing systems.

While gait recognition is the mapping of a gait sequence to an identity known to the system, gait authentication refers to the problem of identifying whether a given gait sequence belongs to the claimed identity. A typical gait authentication system starts with a feature representation such as a gait template, then proceeds to extract its features, and a transformation is ultimately applied to obtain a discriminant feature set. Almost every authentication approach in literature favours the use of Euclidean distance as a threshold to mark the boundary between a legitimate subject and an impostor. This article proposes a method that uses the posterior probability of a Bayes &#x02BC classifier in place of the Euclidean distance. The proposed framework is applied to template-based gait feature representations and is evaluated using the standard CASIA-B gait database. Our study experimentally demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclidean distance approach and the cosine distance which is established in research to be the current state of the art.