Zika virus (ZIKV) is a mosquito-borne flavivirus. Infection results in a dengue-like illness with fever, headache, malaise, and a maculopapular rash. Nearly all cases are mild and self-limiting but in 2007, a large outbreak of ZIKV was reported from the island of Yap (in Micronesia, northwest of Indonesia). Singapore is already endemic for dengue, and its impact on public health and economic burden is significant. Other dengue-like infections (e.g., Chikungunya virus) are present. Yet only 10% of reported dengue cases have laboratory confirmation. The identification and control of other dengue-like, mosquito-transmitted infections is thus important for the health of Singapore's population, as well as its economy. Given that ZIKV shares the same Aedes mosquito vector with both dengue and Chikungunya, it is possible that this virus is present in Singapore and causing some of the mild dengue-like illness. A specific and sensitive one-step, reverse transcription polymerase chain reaction (RT-PCR) with an internal control (IC) was designed and tested on 88 archived samples of dengue-negative, Chikungunya-negative sera from patients presenting to our hospital with a dengue-like illness, to determine the presence of ZIKV in Singapore. The assay was specific for detection of ZIKV and displayed a lower limit of detection (LoD) of 140 copies viral RNA/reaction when tested on synthetic RNA standards prepared using pooled negative patient plasma. Of the 88 samples tested, none were positive for ZIKV RNA, however, the vast majority of these were from patients admitted to hospital and further study may be warranted in community-based environments.

Influenza B viruses have circulated in humans for over 80 y, causing a significant disease burden. Two antigenically distinct lineages (“B/Victoria/2/87-like” and “B/Yamagata/16/88-like,” termed Victoria and Yamagata) emerged in the 1970s and have cocirculated since 2001. Since 2015 both lineages have shown unusually high levels of epidemic activity, the reasons for which are unclear. By analyzing over 12,000 influenza B virus genomes, we describe the processes enabling the long-term success and recent resurgence of epidemics due to influenza B virus. We show that following prolonged diversification, both lineages underwent selective sweeps across the genome and have subsequently taken alternate evolutionary trajectories to exhibit epidemic dominance, with no reassortment between lineages. Hemagglutinin deletion variants emerged concomitantly in multiple Victoria virus clades and persisted through epistatic mutations and interclade reassortment—a phenomenon previously only observed in the 1970s when Victoria and Yamagata lineages emerged. For Yamagata viruses, antigenic drift of neuraminidase was a major driver of epidemic activity, indicating that neuraminidase-based vaccines and cross-reactivity assays should be employed to monitor and develop robust protection against influenza B morbidity and mortality. Overall, we show that long-term diversification and infrequent selective sweeps, coupled with the reemergence of hemagglutinin deletion variants and antigenic drift of neuraminidase, are factors that contributed to successful circulation of diverse influenza B clades. Further divergence of hemagglutinin variants with poor cross-reactivity could potentially lead to circulation of 3 or more distinct influenza B viruses, further complicating influenza vaccine formulation and highlighting the urgent need for universal influenza vaccines.

Spatial transcriptomics enable us to dissect tissue heterogeneity and map out inter-cellular communications. Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting the data. We present SEDR, an unsupervised spatially embedded deep representation of both transcript and spatial information. The SEDR pipeline uses a deep autoencoder to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. We applied SEDR on human dorsolateral prefrontal cortex data and achieved better clustering accuracy, and correctly retraced the prenatal cortex development order with trajectory analysis. We also found the SEDR representation to be eminently suited for batch integration. Applying SEDR to human breast cancer data, we discerned heterogeneous sub-regions within a visually homogenous tumor region, identifying a tumor core with pro-inflammatory microenvironment and an outer ring region enriched with tumor associated macrophages which drives an immune suppressive microenvironment.

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