No abstract

Rickettsia spp. associated with ticks infesting wild animals have been mostly neglected in several countries, including Pakistan. To address this knowledge gap, ticks were collected during 2017 to 2021 from wild animals including cats (Felis chaus), Indian hedgehogs (Paraechinus micropus), and wild boars (Sus scrofa). The collected ticks were morpho-molecularly identified and screened for the detection of Rickettsia spp. Morphologically identified ticks were categorized into four species of the genus Rhipicephalus: Rhipicephalus haemaphysaloides, Rh. turanicus, Rh. sanguineus sensu lato (s.l), and Rh. microplus. Among 53 wild animals examined, 31 were infested by 531 ticks, an overall prevalence of 58.4%. Adult female ticks were predominant (242 out of 513 ticks collected, corresponding to 46%) in comparison with males (172, 32%), nymphs (80, 15%) and larvae (37, 7%). The most prevalent tick species was Rh. turanicus (266, 50%), followed by Rh. microplus (123, 23%), Rh. sanguineus (106, 20%), and Rh. haemaphysaloides (36, 7%). Among the screened wild animals, wild boars were the most highly infested, with 268 ticks being collected from these animals (50.4%), followed by cats (145, 27.3%) and hedgehogs (118, 22.3%). Tick species Rh. haemaphysaloides, Rh. turanicus, and Rh. sanguineus were found on wild boars, Rh. haemaphysaloides, and Rh. microplus on cats, and Rh. turanicus on hedgehogs. In a phylogenetic analysis, mitochondrial cytochrome C oxidase 1 (cox1) sequences obtained from a subsample (120) of the collected ticks clustered with sequences from Bangladesh, China, India, Iran, Myanmar, and Pakistan, while 16S ribosomal DNA (16S rDNA) sequences clustered with sequences reported from Afghanistan, Egypt, India, Pakistan, Romania, Serbia, and Taiwan. Among Rickettsia infected ticks (10/120, 8.3%), Rh. turanicus (7/10, 70%), and Rh. haemaphysaloides (3/10, 30%) were found infesting wild boars in the districts Mardan and Charsadda. The obtained rickettsial gltA gene sequences showed 99% and ompA gene sequences showed 100% identity with Rickettsia massiliae, and the phylogenetic tree shows ompA clustered with the same species reported from France, Greece, Spain, and USA. This study emphasizes the need for effective surveillance and control programs in the region to prevent health risks due to tick-borne pathogens, and that healthy infested wild animals may play a role in the spread of these parasites.

Personally identifiable information (PII) affects individual privacy because PII combinations may yield unique identifications in published data. User PII such as age, race, gender, and zip code contain private information that may assist an adversary in determining the user to whom such information relates. Each item of user PII reveals identity differently, and some types of PII are highly identity vulnerable. More vulnerable types of PII enable unique identification more easily, and their presence in published data increases privacy risks. Existing privacy models treat all types of PII equally from an identity revelation point of view, and they mainly focus on hiding user PII in a crowd of other users. Ignoring the identity vulnerability of each type of PII during anonymization is not an effective method of protecting user privacy in a fine-grained manner. This paper proposes a new anonymization scheme that considers the identity vulnerability of PII to effectively protect user privacy. Data generalization is performed adaptively based on the identity vulnerability of PII as well as diversity to anonymize data. This adaptive generalization effectively enables anonymous data, which protects user identity and private information disclosures while maximizing the utility of data for performing analyses and building classification models. Additionally, the proposed scheme has low computational overheads. The simulation results show the effectiveness of the scheme and verify the aforementioned claims.

This paper presents the flight path planning algorithm in a 3-dimensional environment with fixed obstacles for small unmanned aerial vehicles (SUAVs). The emergence of SUAVs for commercial uses with low-altitude flight necessitates efficient flight path planning concerning economical energy consumption. We propose the visibility roadmap based on the visibility graph approach to deal with this uprising problem. The objective is to approximate the collision-free and energy-efficient flight path of SUAVs for flight missions in a considerable time complexity. Stepwise, we describe the construction of the proposed pathfinding algorithm in a convex static obstacle environment. The theoretical analysis and simulation results prove the effectiveness of our method.