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1. A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

   https://doi.org/10.3390/agriengineering5040146  

   Yan, Kevin; Shisher, Md Kamran; Sun, Yin (December 2023, AgriEngineering)

   During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops. 

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2. Preserving the Biologically Coherent Generic Concept of Phytophthora, ‘Plant Destroyer’

   https://doi.org/10.1094/PHYTO-11-24-0372-LE  

   Brasier, Clive M; Grünwald, Niklaus J; Bourret, Tyler B; Govers, Francine; Scanu, Bruno; Cooke, David_E L; Bose, Tanay; Hawksworth, David L; Abad, Z Gloria; Albarracin, M Victoria; et al (March 2025, Phytopathology®)

   Phytophthora is a long-established, well known and globally important genus of plant pathogens. Phylogenetic evidence has shown that the biologically distinct, obligate biotrophic downy mildews evolved from Phytophthora at least twice. Since, cladistically, this renders Phytophthora ‘paraphyletic’, it has been proposed that Phytophthora evolutionary clades be split into multiple genera (Runge et al. 2011; Crous et al. 2021; Thines et al. 2023; Thines 2024). In this letter, we review arguments for the retention of the generic name Phytophthora with a broad circumscription made by Brasier et al. (2022) and by many delegates at an open workshop organized by the American Phytopathological Society. We present our well-considered responses to this proposal in general terms and to the specific proposals for new genera; together with new information regarding the biological properties and mode of origin of the Phytophthora clades. We consider that the proposals for new genera are mostly non-rigorous and not supported by the scientific evidence. Further, given (1) the apparent lack of any distinguishing biological characteristics (synapomorphies) between the Phytophthora clades; (2) the fundamental monophyly of Phytophthora in the original Haeckelian sense; (3) the fact that paraphyly is not a justification for taxonomic splitting; and (4) the considerable likely damage to effective scientific communication and disease management from an unnecessary break-up of the genus, we report that Workshop delegates voted unanimously in favour of preserving the current generic concept and for seeking endorsement of this view by a working group of the International Commission on the Taxonomy of Fungi. 

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3. Wanted: Standards for FAIR taxonomic concept representations and relationships 

   https://doi.org/10.3897/biss.5.75587  

   Sterner, Beckett; Upham, Nathan; Gupta, Prashant; Powell, Caleb; Franz, Nico (September 2021, Biodiversity Information Science and Standards)

   Making the most of biodiversity data requires linking observations of biological species from multiple sources both efficiently and accurately (Bisby 2000, Franz et al. 2016). Aggregating occurrence records using taxonomic names and synonyms is computationally efficient but known to experience significant limitations on accuracy when the assumption of one-to-one relationships between names and biological entities breaks down (Remsen 2016, Franz and Sterner 2018). Taxonomic treatments and checklists provide authoritative information about the correct usage of names for species, including operational representations of the meanings of those names in the form of range maps, reference genetic sequences, or diagnostic traits. They increasingly provide taxonomic intelligence in the form of precise description of the semantic relationships between different published names in the literature. Making this authoritative information Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) would be a transformative advance for biodiversity data sharing and help drive adoption and novel extensions of existing standards such as the Taxonomic Concept Schema and the OpenBiodiv Ontology (Kennedy et al. 2006, Senderov et al. 2018). We call for the greater, global Biodiversity Information Standards (TDWG) and taxonomy community to commit to extending and expanding on how FAIR applies to biodiversity data and include practical targets and criteria for the publication and digitization of taxonomic concept representations and alignments in taxonomic treatments, checklists, and backbones. As a motivating case, consider the abundantly sampled North American deer mouse— Peromyscus maniculatus (Wagner 1845)—which was recently split from one continental species into five more narrowly defined forms, so that the name P. maniculatus is now only applied east of the Mississippi River (Bradley et al. 2019, Greenbaum et al. 2019). That single change instantly rendered ambiguous ~7% of North American mammal records in the Global Biodiversity Information Facility (n=242,663, downloaded 2021-06-04; GBIF.org 2021) and ⅓ of all National Ecological Observatory Network (NEON) small mammal samples (n=10,256, downloaded 2021-06-27). While this type of ambiguity is common in name-based databases when species are split, the example of P. maniculatus is particularly striking for its impact upon biological questions ranging from hantavirus surveillance in North America to studies of climate change impacts upon rodent life-history traits. Of special relevance to NEON sampling is recent evidence suggesting deer mice potentially transmit SARS-CoV-2 (Griffin et al. 2021). Automating the updating of occurrence records in such cases and others will require operational representations of taxonomic concepts—e.g., range maps, reference sequences, and diagnostic traits—that are FAIR in addition to taxonomic concept alignment information (Franz and Peet 2009). Despite steady progress, it remains difficult to find, access, and reuse authoritative information about how to apply taxonomic names even when it is already digitized. It can also be difficult to tell without manual inspection whether similar types of concept representations derived from multiple sources, such as range maps or reference sequences selected from different research articles or checklists, are in fact interoperable for a particular application. The issue is therefore different from important ongoing efforts to digitize trait information in species circumscriptions, for example, and focuses on how already digitized knowledge can best be packaged to inform human experts and artifical intelligence applications (Sterner and Franz 2017). We therefore propose developing community guidelines and criteria for FAIR taxonomic concept representations as "semantic artefacts" of general relevance to linked open data and life sciences research (Le Franc et al. 2020). 

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4. Computational Classrooms: A Research-Based Approach to Designing a Computer Science Course for Elementary and Middle School Teachers

   Rivera, J; Radoff, J (March 2025, National Association for Research in Science Teaching)

   To address the complex threats to Earth's life-sustaining systems, students need to learn core concepts and practices from various disciplines, including mathematics, civics, science, and, increasingly, computer science (NRC, 2012; United Nations, 2021). Schools must therefore equip students to navigate and integrate these disciplines to tackle real-world problems. Over the past two decades, STEM educators have advocated for an interdisciplinary approach, challenging traditional barriers between subjects and emphasizing contextualized real-world issues (Hoachlander & Yanofsky, 2011; Vasquez et al., 2013; Ortiz-Revilla et al., 2020; Honey et al., 2014; Takeuchi et al., 2020). Despite extensive evidence supporting integrated approaches to STEM education, subject boundaries remain, with disciplines often taught separately and computer science and computational thinking (CS & CT) not consistently included in elementary and middle school curricula. In today's digital age, CS and CT are crucial for a well-rounded education and for addressing sustainability challenges (ESSA, 2015; NGSS Lead States, 2013; NRC, 2012). While there's consensus on the importance of introducing computational concepts and practices to elementary and middle school students, integrating them into existing curricula poses significant challenges, including how to effectively support teachers to deliver inquiry instruction confidently and competently (Ryoo, 2019). Existing frameworks and tools for teaching CS and CT often focus on maintaining fidelity to canonical concepts and formalized taxonomies rather than on practical applications (Grover & Pea, 2013; Kafai et al., 2020; Wilkerson et al., 2020). This focus can lead teachers to learn terminology without fully understanding its relevance or application in different contexts. In response, some researchers suggest using a learning sciences perspective to consider “how the complexity of everyday spaces of learning shapes what counts, and what should be counted, as ‘computational thinking’” (Wilkerson et al., 2020, p. 265). These scholars emphasize the importance of drawing on learners’ everyday experiences and problems to make computational practices more meaningful and contextually relevant for both teachers and their students. Thus, this paper aims to address the following question: How can we design learning experiences for in-service teachers that support (1) their authentic engagement with computational concepts, practices, and tools and (2) more effective integration within classroom contexts? In the limited space of this proposal, we primarily address part 1. 

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5. No to Neocosmospora : Phylogenomic and Practical Reasons for Continued Inclusion of the Fusarium solani Species Complex in the Genus Fusarium

   https://doi.org/10.1128/mSphere.00810-20  

   O’Donnell, Kerry; Al-Hatmi, Abdullah M.; Aoki, Takayuki; Brankovics, Balázs; Cano-Lira, José F.; Coleman, Jeffrey J.; de Hoog, G. Sybren; Di Pietro, Antonio; Frandsen, Rasmus J.; Geiser, David M.; et al (October 2020, mSphere)

   Mitchell, Aaron P. (Ed.)

   This article is to alert medical mycologists and infectious disease specialists of recent name changes of medically important species of the filamentous moldFusarium.Fusariumspecies can cause localized and life-threating infections in humans. Of the 70Fusariumspecies that have been reported to cause infections, close to one-third are members of theFusarium solanispecies complex (FSSC), and they collectively account for approximately two-thirds of all reportedFusariuminfections. 

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