Evaluation of recombinant Babesia gibsoni thrombospondin-related adhesive protein (BgTRAP) for the sero-diagnosis of canine babesiosis.

Canine babesiosis, caused by Babesia gibsoni is one of the most significant tick-borne illnesses across the world. Light microscopy as well as polymerase chain reaction may fail in the diagnosis of disease when the level of parasitaemia is very low during subclinical and chronic cases. The serological techniques using a recombinant protein will be useful for the accurate and sensitive surveillance of the disease, especially in chronic cases.

Molecular detection of pathogens in the ova and unfed larvae of Rhipicephalus annulatus and Haemaphysalis bispinosa ticks infesting domestic cattle of south India.

This study aimed to investigate the presence of pathogens in the engorged ticks infesting domestic cattle, their ova, and unfed larvae. The engorged female ticks infesting domestic cattle of Wayanad district of Kerala, south India were collected and kept for oviposition. The dead females after the complete oviposition, their egg masses, and unfed larvae were screened for the presence of various pathogens by specific PCRs.

Point Cloud Video Super-Resolution via Partial Point Coupling and Graph Smoothness.

Point cloud (PC) is a collection of discrete geometric samples of a physical object in 3D space. A PC video consists of temporal frames evenly spaced in time, each containing a static PC at one time instant. PCs in adjacent frames typically do not have point-to-point (P2P) correspondence, and thus exploiting temporal redundancy for PC restoration across frames is difficult.

Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment.

Point cloud (PC)-a collection of discrete geometric samples of a 3D object's surface-is typically large, which entails expensive subsequent operations. Thus, PC sub-sampling is of practical importance. Previous model-based sub-sampling schemes are ad-hoc in design and do not preserve the overall shape sufficiently well, while previous data-driven schemes are trained for specific pre-determined input PC sizes and sub-sampling rates and thus do not generalize well.