Biology subjects

Brook, A.

Publications and source records attributed to Brook, A..

3 recordsLinked to original sources

bioRxiv2025

Complex Traces: Using a Complex Adaptive Systems approach to examine morbidity and mortality among 19th Century migrants to South Australia

In multidisciplinary research interpreting interactions between diverse data sources requires a Complexity approach. A Complex Adaptive Systems (CAS) framework allows the relationships of multiple factors to be explored and may provide a more holistic and nuanced understanding. This study is innovative in explaining the potential benefits in a CAS approach to combining bioarchaeological and historical data when examining a rare archaeological skeletal sample of early migrants to South Australia (SA). Macroscopic, radiographic and micro-CT methods were used for the analysis of the skeletal remains of a group of 19th century migrants buried in an unmarked area of St Marys Anglican Church Cemetery. The relevant historical records explored were from British emigrant ships to SA (1836 to 1885) and the Church burial records (1847-1885). Evidence of poor oral and general health was present in the skeletal material. Dental developmental defects indicated earlier health insults. Pathological manifestations in bone were compatible with joint and infectious diseases, and metabolic deficiencies. Historical documents recorded that the voyages to SA were challenging, with some ships experiencing a high death rate. Diseases e.g., measles and scarlet fever, and diarrhoea were frequently recorded as causes of death at sea for both subadults and adults. In the Colony, burial records showed similar causes of death for subadults, but for adults, accidents and tuberculosis were often reported. The CAS approach provided insights beyond those from analysis of the individual sources. It increased understanding of emergent, non-predicted outcomes that resulted from interactions between multiple factors e.g., the impact of fluctuating economy, political instability and ideological pressures, on the health of migrants. The CAS framework is a valuable methodology for interpreting health patterns and can be further developed for a range of historical and contemporary health contexts.

pathology↗

bioRxiv2022

Phenotyping technology for assessing protein content in seaweed by field spectroscopy and a machine learning algorithm

Determining seaweed protein concentration and the associated phenotype is critical for food industries that require precise tools to moderate concentration fluctuations and attenuate risks. Algal protein extraction and profiling have been widely investigated, but content determination involves a costly, time-consuming, and high-energy, laboratory-based fractionation technique. The present study examines the potential of field spectroscopy technology as a precise, high-throughput, non-destructive tool for on-site detection of red seaweed protein concentration. By using information from a large dataset of 144 Gracilaria sp. specimens, studied in a land-based cultivation set-up, under six treatment regimes during two cultivation seasons, and an artificial neural network, machine learning algorithm and diffuse visible-near infrared reflectance spectroscopy, predicted protein concentrations in the algae were obtained. The prediction results were highly accurate (R2 = 0.95; RMSE = 0.84), exhibiting a high correlation with the analytically determined values. External validation of the model derived from a separate trial, exhibited even better results (R2 = 0.99; RMSE = 0.45). This model, trained to convert phenotypic spectral measurements and pigment intensity into accurate protein content predictions, can be adapted to include diversified algae species and usages. HighlightNon-destructive determination of protein content in the edible red seaweed Gracilaria sp. by in-situ, VIS-NIR spectroscopy and a machine learning algorithm.

biochemistry↗

bioRxiv2021

Canopy Volume as a Tool for Early Detection of Plant Drought and Fertilization Stress: Banana plant fine-phenotype

Irrigation and fertilization stress in plants are limitations for securing global food production. Sustainable agriculture is at the heart of global goals because threats of a rapidly growing population and climate changes are affecting agricultural productivity. Plant phenotyping is defined as evaluating plant traits. Traditionally, this measurement is performed manually but with advanced technology and analysis, these traits can be observed automatically and nondestructively. A high correlation between plant traits, growth, biomass, and final yield has been found. From the early stages of plant development, lack of irrigation and fertilization directly influence developing stages, thus the final crop yield is significantly reduced. In order to evaluate drought and fertilization stress, plant height, as a morphological trait, is the most common one used in precision-agriculture research. The present study shows that three-dimension volumetric approaches are more representative markers for alerting growers to the early stages of stress in young banana plants for fine-scale phenotyping. This research demonstrates two different group conditions: 1) Normal conditions; and 2) zero irrigation and zero fertilization. The statistical analysis results show a successfully distinguished early stress with the volumetric traits providing new insights on identifying the key phenotypes and growth stages influenced by drought stress.

physiology↗