Learning and Forgetting with Local Information of New Objects

The performance of supervised learners depends on the presence of a relatively large labeled sample. This paper proposes an automatic ongoing learning system, which is able to incorporate new knowledge from the experience obtained when classifying new objects and correspondingly, to improve the efficiency of the system. We employ a stochastic rule for classifying and editing, along with a condensing algorithm based on local density to forget superfluous data (and control the sample size). The effectiveness of the algorithm is experimentally evaluated using a number of data sets taken from the UCI Machine Learning Database Repository

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Genomic organization of the bovine class II MHC studied with field inversion gel electrophoresis

In a study Bovine DNA was prepared from a Theileria parva-transformed lymphobastoid cell line derived from the PBM of an heterozygons Boran (Bos indicus) steer. It was observed that linkage between the bovine DR alpha and DR beta subregions on the basis that a 1-2 Mbp Sfi1 and a 0.6 Mbp not 1 fragment hybridized with both probes. In view of the fact that these probes revealed, in addition, urigne extra bands with both enzymes, it is concluded that they may be considered locus-specific in cattle. The DP beta cDNA hybridized with a 0.32 Mbp Sfi1 and 0.6 and 0.1 Mbp Not1 fragments. Only the highest molecular weight fragment seems to be common with the DR region. Sight differences in mobility between the Sfi1 DR beta and DP beta bands (0.3 versus 0.32 Mbp) have been observed. The result suggest that the bovine MHC spans more than 3 MbThe class II region is composed of tightly linked DR alpha and DR beta subregions

Emotional state inference using face related features

Obtaining reliable and complete systems able to extract human emotional status from streaming videos is of paramount importance to Human Machine Interaction (HMI) applications. Side views, unnatural postures and context are challenges. This paper presents a semi-supervised fuzzy emotional classification system based on Russell\u2019s circumplex model. This emotional inference system relies only on face related features codified with the Facial Action Coding System (FACS). These features are provided by a morphable 3D tracking system robust to posture, occlusion and illumination changes