Complete nucleotide sequence of a novel partitivirus from Brassica campestris L. ssp. chinensis.

In the present work, we report the discovery and complete genome sequence of a novel partitivirus identified from Brassica campestris L. ssp. chinensis, which we have named "Brassica campestris chinensis cryptic virus 1" (BCCV1). Next-generation sequencing (NGS) combined with adapter-ligation-mediated amplification allowed assembly of the full-length genome sequence of BCCV1. The genome of BCCV1 contains two dsRNA segments, dsRNA1 (1595 bp) and dsRNA2 (1591 bp), which encode a conserved RNA-dependent RNA polymerase (RdRp) and a putative capsid protein (CP), respectively. Homology searches and phylogenetic analysis of the 479-aa RdRp and 438-aa CP showed that BCCV1 is a new member of the genus Deltapartitivirus, family Partitiviridae. This is the first report of the identification of a member of the family Partitiviridae in Brassica campestris L. ssp. chinensis.

A novel negative-stranded RNA virus of the order Bunyavirales identified in Brassica campestris L. ssp. chinensis.

Here, we report the full-length genome sequence of a novel cogu-like virus identified in Brassica campestris L. ssp. Chinensis (B. campestris), an economically important vegetable in China. This virus, tentatively named "Brassica campestris chinensis coguvirus 1" (BCCoV1), has a bipartite genome that consists of two RNA molecules (RNA1 and RNA2). The negative-stranded (ns) RNA1 is 6757 nt in length, encoding the putative RNA-dependent RNA polymerase (RdRp), and the ambisense RNA2 is 3061 nt long, encoding the putative movement protein (MP) and nucleocapsid protein (NP). A homology search of the RdRp, MP, and NP showed that they are closely related to five other recently discovered negative-stranded RNA (nsRNA) viruses infecting plants, belonging to the new genus Coguvirus. Phylogenetic analysis of the 252-kDa RdRp confirmed the classification of this virus, showing that BCCoV1 possibly belongs to the genus Coguvirus, family Phenuiviridae, order Bunyavirales. The present study improves our understanding of the viral diversity in B. campestris and the evolution of nsRNA viruses.

Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors.

The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameters or some specific behaviors. However, few research works focus on detecting the implicit irregularity involving the combination of diverse behaviors, which can assess the cognitive and physical wellbeing of elders but cannot be directly identified based on sensor data. This paper proposes an Implicit IRregularity Detection (IIRD) mechanism that aims to detect the implicit irregularity by developing the unsupervised learning algorithm based on daily behaviors. The proposed IIRD mechanism identifies the distance and similarity between daily behaviors, which are important features to distinguish the regular and irregular daily behaviors and detect the implicit irregularity of elderly health condition. Performance results show that the proposed IIRD outperforms the existing unsupervised machine-learning mechanisms in terms of the detection accuracy and irregularity recall.