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Coconut rhinoceros beetle (Oryctes rhinoceros; CRB) was discovered in 2015 in a small outbreak in Honiara, Guadalcanal, Solomon Islands. This was the first record of CRB from Solomon Islands and a response plan was prepared. An awareness programme was launched and where CRB sightings were confirmed, delimitation surveys were carried out. Following the launch of the awareness programme, later in 2015, CRB was also reported from the Shortland Islands in the Western Province. Other islands were surveyed from 2015 to 2020. If CRB presence was confirmed, beetles were collected and analysed for haplotype and presence of the classical biological control agent, Oryctes rhinoceros nudivirus (OrNV). A distribution map and timeline of invasion were developed. The initial populations belonged to two distinct haplotypes: CRB-G (clade IA) in Honiara and CRB-S (clade II) in Shortland Islands. Despite control measures, by 2020 CRB-G had spread to islands in eight provinces and CRB-S had spread to islands in seven provinces. CRB-S and CRB-G co-occur in Guadalcanal and Malaita provinces. In 2019, OrNV was detected from field collected CRB from Guadalcanal and has since spread to Malaita Island. In both cases the virus was detected where CRB-G and CRB-S co-occur. Other outbreak areas in Solomon Islands remain OrNV-free. The two haplotypes appear to have spread following patterns of air and sea movement between the point of origin (CRB-G from Honiara and CRB-S from the Shortland Islands) to other islands/provinces.

Detecting and improving the scientific understanding of global trends in complex Earth systems, such as climate, increasingly depend on assimilating datasets from multiple instruments and platforms over decadal timescales. Calibration accuracy, stability, and inter-consistency among different instruments are key to developing reliable composite data records from sensors in low Earth and geostationary orbits, however, achieving sufficiently low uncertainties for these performance metrics poses a significant challenge. Space-borne instruments commonly carry on-board references for calibration at various wavelengths, but these increase mass and mission complexity and are subject to degradation in the space environment. The Moon can be considered a natural solar diffuser which can be utilized as an on-orbit calibration target by most space-borne Earth-observing instruments. Since the lunar surface reflectance is effectively time-invariant, establishing the Moon as a high-accuracy calibration reference enables broad inter-calibration opportunities even between temporally non-overlapping instruments and provides an exo-atmospheric absolute radiometric standard. This on-orbit radiometric spectral reference helps enable high-accuracy absolute calibrations and inter-calibrations of past, current, and future Earth-observing sensors, meteorological imagers, and long-term climatemonitoring satellite systems. The goal of the ARCSTONE In-Space Validation of Earth Science Technologies (InVEST) project, funded by NASA ESTO, is to demonstrate its novel lunar measurement approach from space and improve the Moon as a radiometric SI-traceable reference for high-accuracy on-orbit calibrations spanning the visible and nearinfrared spectral regions. The ARCSTONE instrument is a compact spectrometer which will be accommodated on a 6U CubeSat intended for low Earth orbit with planned readiness for launch in about 2 years. It will measure the lunar spectral reflectance with combined standard uncertainty of 0.5% (k=1) across the 350 nm to 2300 nm spectral range. The ARCSTONE InVEST team will present the status of the project, the instrument design, calibration approach, concept of operations, and the planned path toward mission implementation.

This private OSF component documents presentations and discussions at the ICE concerning coconut rhinoceros beetle.

Results and scripts for under review paper: Dissociable Cerebellar-Prefrontal Networks Underlying Executive Function: Evidence from the Human Connectome Project. A pre-print of the paper is available on bioRxiv: https://doi.org/10.1101/431593

Recent research found increased functional connectivity (FC) between the (DN) and cerebral cortex in Alzheimer’s disease (AD; Olivito, 2020) and points to the potential importance of cerebrocerebellar connectivity as a biomarker in the progression of the disease. The cerebellum, which projects to the thalamus via the dentate nucleus (DN), is connected to both cognitive and motor regions of the cerebral cortex via separate, trans-thalamic, reciprocal loops (Kelly & Strick, 2003). Research has delineated at least two distinct dentate-cortical networks in both the non-human primate and human brain: the dorsal DN, associated with motor functions and circuits, and the ventral DN, associated with cognitive processing and the corresponding circuits (Dum & Strick, 2003; Bernard et al., 2014; Steele et al., 2017). Importantly, cerebellar regions prone to atrophy in AD (e.g., Crus I/II) are functionally connected to atrophied regions of the cerebral cortex (e.g., primary motor cortex and regions related to the default mode network; de Jong et al. 2008, Suvà et al., 1999, Guo, 2016), indicating dysfunction occurs at a network level. A crucial step in elucidating the role of the cerebellum in AD was to investigate functional connectivity alterations between cerebral structures and the DN (Olivito, 2020) and an important next step is to investigate functional connectivity differences in the individual networks within the DN. Additionally, loss of motor function and the presence of motor symptoms can predict AD outcomes and the risk of developing AD (Buchman& Bennett, 2011; Scarmeas, 2005; Albers, 2015). Therefore, it is of interest to explore whether the dorsal and ventral dentate-cortical networks have differences in functional connectivity in the presence of mild cognitive impairment (MCI) and AD and whether any differences seen in these networks are related to scores on motor and cognitive tasks. This work may potentially inform future clinical work in the diagnosis and understanding of AD, as well as help decipher AD subtypes and the progression of symptoms as they relate to brain network dysfunction.