Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning - Maryam Rahmani; Donna Dierker; Lauren Yaeger; Andrew Saykin; Patrick H. Luckett; Andrei G. Vlassenko; Christopher Owens; Hussain Jafri; Kyle Womack; Jurgen Fripp; Ying Xia; Duygu Tosun; et al. (Brain Imaging and Behavior, 2024)
This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.

Predicting preferences for chemical treatment of aquatic invasive species and implications for outreach - Bret Randall Shaw; Richard James Heinrich; Dominique Brossard; Theresa Vander Woude; Tim Campbell (Biological Invasions, 2024)

Feasibility of Microlearning for Improving the Self-Efficacy of Cancer Patients Managing Side Effects of Chemotherapy - Anna Janssen; Kavisha Shah; Melanie Rabbets; Adnan Nagrial; Christopher Pene; Clare Zachulski; Jane L. Phillips; Paul Harnett; Tim Shaw (Journal of Cancer Education, 2023)
Lung cancer patients have a high symptom burden that negatively affects their quality of life. Increasing patient self-efficacy to deal with treatment side effects can ameliorate their symptom burden. Education programs can help enhance patient self-efficacy by giving patients more control over their condition through increased disease literacy. This study aimed to evaluate the feasibility of microlearning for delivering lung cancer patients’ information on side effects of chemotherapy. Secondary objectives of the program are to understand the acceptability of microlearning for delivery this type of education to lung cancer patients and the potential impact of microlearning on patient self-efficacy, knowledge and confidence managing side effects of chemotherapy. A mixed-methods prepost test (or quasi-experimental) study design was used to better enable patients to identify and manage the side effects of their condition and chemotherapy. Participants were patients diagnosed with stage II to stage IV lung cancer, who had a life expectancy of greater than 3 months and were aged 18 years or older. Multiple validated scales were used to assess patient self-efficacy pre- and post-intervention. The online program was evaluated using quantitative data of completion rates extracted from the online platform. Semi-structured interviews were used to explore the impact of the online program on perceived self-efficacy and quality of life. Twenty-three participants agreed to participate in the study and five agreed to complete a semi-structured interview. Participants found the content comprehensive, relevant and engaging. The program improved perceived disease literacy and helped participants develop coping strategies to manage side effects. Participants also found the platform easy to use and navigate. Additional courses and features were requested. Patients with a diagnosis of cancer receive a large amount of information about the side effects of chemotherapy and how to manage them. This information is often provided soon after diagnosis or upon commencement of therapy, which can be overwhelming for some patients. Microlearning, a method of online learning that spaces distributing of content over several weeks, may be a useful tool for supporting delivering of health information to this group of patients.

Learning Together for and with the Martuwarra Fitzroy River - Rosemary Hill; Pia Harkness; Nat Raisbeck-Brown; Ilisapeci Lyons; Jorge G. Álvarez-Romero; Milena Kiatkoski Kim; Dennis Chungalla; Heather Wungundin; Mary Aiken; Jean Malay; Bernadette Williams; Rachel Buissereth; et al. (Sustainability Science, 2022)
Co-production across scientific and Indigenous knowledge systems has become a cornerstone of research to enhance knowledge, practice, ethics, and foster sustainability transformations. However, the profound differences in world views and the complex and contested histories of nation-state colonisation on Indigenous territories, highlight both opportunities and risks for Indigenous people when engaging with knowledge co-production. This paper investigates the conditions under which knowledge co-production can lead to improved Indigenous adaptive environmental planning and management among remote land-attached Indigenous peoples through a case study with ten Traditional Owner groups in the Martuwarra (Fitzroy River) Catchment in Western Australia’s Kimberley region. The research team built a 3D map of the river and used it, together with an interactive table-top projector, to bring together both scientific and Indigenous spatial knowledge. Participatory influence mapping, aligned with Traditional Owner priorities to achieve cultural governance and management planning goals set out in the Fitzroy River Declaration, investigated power relations. An analytical framework, examining underlying mechanisms of social learning, knowledge promotion and enhancing influence, based on different theories of change, was applied to unpack the immediate outcomes from these activities. The analysis identified that knowledge co-production activities improved the accessibility of the knowledge, the experiences of the knowledge users, strengthened collective identity and partnerships, and strengthened Indigenous-led institutions. The focus on cultural governance and management planning goals in the Fitzroy River Declaration enabled the activities to directly affect key drivers of Indigenous adaptive environmental planning and management—the Indigenous-led institutions. The nation-state arrangements also gave some support to local learning and decision-making through a key Indigenous institution, Martuwarra Fitzroy River Council. Knowledge co-production with remote land-attached Indigenous peoples can improve adaptive environmental planning and management where it fosters learning together, is grounded in the Indigenous-led institutions and addresses their priorities.

CropPol : A dynamic, open and global database on crop pollination - Alfonso Allen‐Perkins; Ainhoa Magrach; Matteo Dainese; Lucas A. Garibaldi; David Kleijn; Romina Rader; James R. Reilly; Rachael Winfree; Ola Lundin; Carley M. McGrady; Claire Brittain; David J. Biddinger; et al. (Ecology, 2022)
Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open, and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e., berry mass, number of fruits, and fruit density [kg/ha], among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), North America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001–2005 (21 studies), 2006–2010 (40), 2011–2015 (88), and 2016–2020 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non‐commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY‐NC‐SA).

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Testing Emphasis Message Frames and Metaphors on Social Media to Engage Boaters to Learn about Preventing the Spread of Zebra Mussels - Bret Shaw; Tim Campbell; Barry T. Radler (Environmental Management, 2021)

Using structured eradication feasibility assessment to prioritize the management of new and emerging invasive alien species in Europe - Olaf Booy; Pete A. Robertson; Niall Moore; Jess Ward; Helen E. Roy; Tim Adriaens; Richard Shaw; Johan Van Valkenburg; Gabrielle Wyn; Sandro Bertolino; Olivier Blight; Etienne Branquart; et al. (Global Change Biology, 2020)
Prioritizing the management of invasive alien species (IAS) is of global importance and within Europe integral to the EU IAS regulation. To prioritize management effectively, the risks posed by IAS need to be assessed, but so too does the feasibility of their management. While the risk of IAS to the EU has been assessed, the feasibility of management has not. We assessed the feasibility of eradicating 60 new (not yet established) and 35 emerging (established with limited distribution) species that pose a threat to the EU, as identified by horizon scanning. The assessment was carried out by 34 experts in invasion management from across Europe, applying the Non‐Native Risk Management scheme to defined invasion scenarios and eradication strategies for each species, assessing the feasibility of eradication using seven key risk management criteria. Management priorities were identified by combining scores for risk (derived from horizon scanning) and feasibility of eradication. The results show eradication feasibility score and risk score were not correlated, indicating that risk management criteria evaluate different information than risk assessment. In all, 17 new species were identified as particularly high priorities for eradication should they establish in the future, whereas 14 emerging species were identified as priorities for eradication now. A number of species considered highest priority for eradication were terrestrial vertebrates, a group that has been the focus of a number of eradication attempts in Europe. However, eradication priorities also included a diverse range of other taxa (plants, invertebrates and fish) suggesting there is scope to broaden the taxonomic range of attempted eradication in Europe. We demonstrate that broad scale structured assessments of management feasibility can help prioritize IAS for management. Such frameworks are needed to support evidence‐based decision‐making.

Hierarchical analysis of wild Atlantic salmon ( Salmo salar ) fecundity in relation to body size and developmental traits - Nora Hanson; James Ounsley; Tim Burton; Sonya Auer; James H. Hunt; Brian Shaw; Jim Henderson; Stuart J. Middlemas (Journal of Fish Biology, 2020)
Using data from wild Atlantic salmon Salmo salar returning to spawn in seven Scottish rivers, we developed a model of fecundity based on individual body size and key developmental traits. We used a novel approach to model selection which maximises predictive accuracy for application to target river stocks to select the best from a suite of Bayesian hierarchical models. This approach aims to ensure the optimal model within the candidate set includes covariates that best predict out‐of‐sample data to estimate fecundity in areas where no direct observations are available. In addition to body size, the final model included the developmental characteristics of age at smolting and years spent at sea. Using two independent long‐term monitoring datasets, the consequences of ignoring these characteristics was revealed by comparing predictions from the best model with models that omitted them.

Horizon scanning for invasive alien species with the potential to threaten biodiversity in Great Britain - Helen E. Roy; Jodey Peyton; David C. Aldridge; Tristan Bantock; Tim M. Blackburn; Robert Britton; Paul Clark; Elizabeth Cook; Katharina Dehnen‐Schmutz; Trevor Dines; Michael Dobson; François Edwards; et al. (Global Change Biology, 2014)
Invasive alien species ( IAS ) are considered one of the greatest threats to biodiversity, particularly through their interactions with other drivers of change. Horizon scanning, the systematic examination of future potential threats and opportunities, leading to prioritization of IAS threats is seen as an essential component of IAS management. Our aim was to consider IAS that were likely to impact on native biodiversity but were not yet established in the wild in Great Britain. To achieve this, we developed an approach which coupled consensus methods (which have previously been used for collaboratively identifying priorities in other contexts) with rapid risk assessment. The process involved two distinct phases: Preliminary consultation with experts within five groups (plants, terrestrial invertebrates, freshwater invertebrates, vertebrates and marine species) to derive ranked lists of potential IAS. Consensus‐building across expert groups to compile and rank the entire list of potential IAS. Five hundred and ninety‐one species not native to Great Britain were considered. Ninety‐three of these species were agreed to constitute at least a medium risk (based on score and consensus) with respect to them arriving, establishing and posing a threat to native biodiversity. The quagga mussel, Dreissena rostriformis bugensis , received maximum scores for risk of arrival, establishment and impact; following discussions the unanimous consensus was to rank it in the top position. A further 29 species were considered to constitute a high risk and were grouped according to their ranked risk. The remaining 63 species were considered as medium risk, and included in an unranked long list. The information collated through this novel extension of the consensus method for horizon scanning provides evidence for underpinning and prioritizing management both for the species and, perhaps more importantly, their pathways of arrival. Although our study focused on Great Britain, we suggest that the methods adopted are applicable globally.

Variation in microbial communities colonizing horticultural slow sand filter beds: implications for filter function - Paul J. Hunter; Leo A. Calvo-Bado; Nick R. Parsons; Tim R. Pettitt; Geoff M. Petch; Eve Shaw; J. Alun W. Morgan; John M. Whipps (Irrigation Science, 2013)

Analysis on variety and characteristics of maghemite - XiuMing Liu; John Shaw; JianZhong Jiang; Jan Bloemendal; Paul Hesse; Tim Rolph; XueGang Mao (Science China Earth Sciences, 2010)

Abstracts of presentations on plant protection issues at the fifth international Mango Symposium Abstracts of presentations on plant protection issues at the Xth international congress of Virology - J. E. Peña; M. Wysoki; Gajendra Singh; Nancy Boscán de M.; Freddy J. Godoy; A. Obligado; C. J. Rossetto; I. J. A. Ribeiro; P. B. Gallo; N. B. Soares; J. C. Sabino; A. L. M. Martins; et al. (Phytoparasitica, 1997)