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Speciation analysis of fungi by liquid atmospheric pressure MALDI mass spectrometry
Get PDFFungal pathogens pose a growing threat to global health, necessitating rapid and accurate identification methods. Here, liquid atmospheric pressure matrix-assisted laser desorption/ionisation (LAP-MALDI) mass spectrometry (MS) is applied to fast lipid and protein profiling of Candida albicans and Saccharomyces cerevisiae from cultured colonies. Species-specific lipid profiles were observed in the m/z 600–1100 range, dominated by phospholipids as confirmed by tandem mass spectrometry(MS/MS). Following simple solid phase extraction clean-up, LAP-MALDI mass spectra revealed multiply charged protein ions suitable for MS/MS analysis. For C. albicans, the fully mature, species-specific WHS11 protein (~7 kDa; P43074)was detected intact and confidently identified by top-down MS/MS proteoform sequencing, including the cleavage of the N-terminal methionine initiator and the associated N-terminal acetylation. For S. cerevisiae, a set of proteoforms were sequenced by MS/MS analysis, which led to the identification of two species-specific proteins within the ‘UniProtKB reference proteomes + Swiss-Prot’ target database. One of these was also detected intact, and sequenced and identified as the fully mature HSP12 protein (~11.5 kDa; P22943). This work demonstrates the potential of LAP-MALDI MS and MS/MS biotyping as a powerful, label-free platform for rapid fungal classification and proteoform characterisation, offering substantial improvements over conventional MALDI biotyping
Integrated rumen-animal-manure analysis of dairy emission mitigation by feeding apple pomace and hempseed cake
Get PDFAgri-food by-products are underused feed resources with the potential to reduce dairy emissions, yet most studies examined rumen and manure stages separately, which mask whole-system effects and shift environmental burdens between stages. This study is the first to assess the effects of incorporating apple pomace (AP) and hempseed cake (HC) into dairy cow diets on nitrogen and methane (CH4) emissions, across the entire milk production chain, from rumen fermentation to downstream manure storage. A 3 (treatments) x 3 (Periods) Latin square design was used with 15 cows, treatment diets included (1) CON (control diet): basal diets with forage and concentrates; (2) AP: 10 % of AP replacing forage; (3) HC: 10 % of HC replacing concentrates. Faeces and urine outputs were collected separately from animals, for manure storage experiment. Feeding AP and HC decreased (P < 0.01), respectively, enteric CH4 production by 6.3 % and 6.7 %, CH4/feed intake by 10.6 % and 10.1 %, and CH4/milk yield by 9.8 % and 10.9 %. Inclusion of AP decreased urine N /total N intake, compared to CON and HC (P < 0.05). In manure storage, the AP decreased the cumulative ammonia (NH3) and CH4 emissions by 24.8 % and 27.4 % than CON, respectively (P < 0.05). The above mitigation actions through feeding AP and HC, when working together in implementation for feeding dairy cows, could decrease annual CO2 equivalent emissions by 13 % and 10 % respectively. This is the first integrated study combining rumen fermentation with manure impacts, showing that AP and HC inclusion can be a practical approach to mitigate emissions in dairy farming
Semantic segmentation of clouds and cloud shadows using state space models
Get PDFIn remote sensing image processing, cloud and cloud shadow detection is of great significance, which can solve the problems of cloud occlusion and image distortion, and provide support for multiple fields. However, the traditional convolutional or Transformer models and the existing studies combining the two have some shortcomings, such as insufficient feature fusion, high computational complexity, and difficulty in taking into account local and long-range dependent information extraction. In order to solve these problems, this paper proposes the MCloud model based on Mamba architecture is proposed, which takes advantage of its linear computational complexity to effectively model long-range dependencies and local features through the coordinated work of state space and convolutional support and the Mamba-convolutional fusion module. Experiments show that MCloud have the leading segmentation performance and generalization ability on multiple datasets, and provides more accurate and efficient solutions for cloud and cloud shadow detection
Decision-making under flood predictions: a risk perception study of coastal real estate
Get PDFFlood models, while representing our best knowledge of a natural phenomenon, are continually evolving. Their predictions, albeit undeniably important for flood risk management, contain considerable uncertainties related to model structure, parameterisation, and input data. With multiple sources of flood predictions becoming increasingly available through online flood maps, the uncertainties in these predictions present considerable risks related to property devaluation. Such risks stem from real estate decisions, measured by location preferences and willingness-to-pay to buy and rent properties, based on access to various sources of flood predictions. Here, we evaluate the influence of coastal flood predictions on real estate decision-making in the UK by adopting an interdisciplinary approach, involving flood modelling, novel experimental willingness-to-pay real estate surveys of UK residents in response to flood predictions, statistical modelling, and geospatial analysis. Our main findings show that access to multiple sources of flood predictions dominates real estate decisions relative to preferences for location aesthetics, reflecting a shift in demand towards risk-averse locations. We also find that people do not consider flood prediction uncertainty in their real estate decisions, possibly due to an inability to perceive such uncertainty. These results are robust under a repeated experimental survey using an open access long-term flood risk map. We, therefore, recommend getting flood models ‘right’ but recognise that this is a contentious issue because it implies having an error-free model, which is practically impossible. Hence, to reduce real estate risks, we advocate for a greater emphasis on effectively communicating flood model predictions and their uncertainties to non-experts
Teaching international financial management of the multinational enterprise in international business education: exploring the role of the parent firm’s corporate treasury and the use of internal debt financing by foreign subsidiaries
No full textThis teaching article, which is intended for international business educators and students enrolled in undergraduate or pre-experience postgraduate courses, presents three propositions. First, international financial management of the multinational enterprise (MNE) is important in international business education. Second, theoretical concepts and frameworks need to be made relevant to the learning experiences of students by analysing the real-world practices of MNEs and their foreign subsidiaries. Third, both rigour and relevance must be well-integrated. We apply this teaching and learning approach by examining the role of internal capital markets within the MNE’s organizational structure, where the parent firm’s corporate treasury effectively mirrors the working of internal capital markets in business practice. We introduce a framework for the sources of funds in the MNE and the foreign subsidiary, discussing its theoretical insights and practical value. We examine the use of internal debt financing that foreign subsidiaries obtain from the corporate treasury. To illustrate the link between theory and practice, we use the full accounts of Nestlé (UK) Ltd., filed with the United Kingdom Companies House, as an example for classroom teaching and learning
Multilingual teachers and teachers of multilinguals: developing pre-service teachers’ multilingual identities during teacher education
Get PDFWith increased linguistic diversity in schools, it is paramount that initial teacher education and training (ITET) develops linguistically responsive teachers who can confidently work in the complex language ecologies characterising today’s multilingual classrooms. We argue that to achieve this aim, all future teachers should be given opportunities to reflect on and appreciate their multilingual repertoires during ITET. Accordingly, we present the findings of a quasi-experimental study exploring the extent to which a group of pre-service teachers expressed a multilingual identity before and after participating in an innovative, identity-oriented online intervention. Participants were 37 primary and secondary pre-service teachers enrolled on an ITET course in England. During their training, 17 pre-service teachers participated in the intervention, whilst a control group of 20 did not. All participants completed a questionnaire before and after the intervention. A comparison of pretest-posttest responses revealed statistically significant increases in scores on various items designed to capture participants’ multilingual identities in the experimental group with medium-to-large effect sizes, and no significant differences in the control group. Qualitative data from post-intervention interviews corroborated the overall finding that, after the intervention, pre-service teachers tended not only to consider themselves more multilingual, but also express a non-prescriptive view of multilingualism
Why trust is crucial – the moderating role of trust in the relationship between motivation and intention to buy healthy, sustainable and novel foods
Get PDFWithin an increasingly resource-challenged food system, consumers need greater confidence in their ability to make better and more informed food choices. We investigated the role of trust as a moderator of the relationship between the motivation to make healthy, sustainable and novel food choices and the intention to actually do so, based on the reasoning that novel healthy and sustainable food products are marketed by credence attributes, where consumers must rely on information that is provided by food chain actors. In an online survey conducted in 13 European countries over two years with a sample of 25,610 respondents, we explored how social trust, beliefs in trustworthiness and overall trust moderate the motivation-intention relationship. Results show that while trust cannot compensate for a lack of motivation to engage in healthy or sustainable behaviours, the relationship between motivation and intention was strengthened by higher levels of trust for sustainable and innovative food choices, but not for healthy food choices, and this finding was largely consistent across both years. For sustainable choices, the motivation-intention relationship was moderated by trust in farmers and retailers but not in manufacturers or authorities. All types of trust moderated the motivation-intention relationship for adoption of novel foods. This has implications for investment in trust initiatives on the part of policymakers, food businesses and food system actors
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
Get PDFPredicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behavior. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations
The pollinating network of pollinators and the service value of pollination in Hanzhong City, China
Get PDFPollinating insects are the most important pollinators in nature; they pollinate vegetables, fruits, oil crops, and wild plants, so that crop yields can be increased, wild plants can live and reproduce, and human food security and ecosystem stability are maintained. To identify the pollination network of plants–insects and the pollination service value in Hanzhong City, the methods of random net capture and transect counting in field work were used. The agricultural statistical data from Hanzhong City in 2023 was combined in the analysis. The results showed that Hanzhong City is rich in pollinator resources, with a total of 80 species of pollinators and 59 species of pollinating crops and wild nectar plants. The abundant pollinator resources provide sufficient pollination services for the production of local vegetables, fruits, and oil crops. The characteristics of the pollination networks are obvious, showing the structural characteristics of low connectivity, medium nesting, and low network specialization. In 2023, the pollination service value of pollinators in Hanzhong City was CNY 3524–4878 billion, accounting for 10.02–13.87% of the city’s agricultural output value of the year. Suggestions for the protection of pollinators in Hanzhong City: Reduce the use of pesticide, support beekeeping, intercrop nectar plants, and rationally plant crops
A psychometric analysis of the military stigma scale
No full textU.S. military populations experience a high level of mental health concerns, including post-traumatic stress disorder, clinical depression, and suicide, when compared with their civilian counterparts, and tend to access mental health services at a lower rate. Military health scholars have noted that stigma against mental health help-seeking has multiple sources, including professional, personal, and social components, though these components are rarely separated in examining why military service members avoid clinical help. Valid measurement of these factors is necessary to examine the heart of rising clinical needs. The current study replicates and extends prior work applying a bifactor model to the Military Stigma Scale (MSS). In a sample of n = 1,832 Army National Guard members, a bifactor model presented acceptable fit, though invariance testing by rank and education indicates disparate experiences with military service as deviating influences. Specifically, Private Stigma was significantly lower in higher paygrade service members and those with a college degree, while Public Stigma was higher. Results call into question the theoretical viability of a bifactor model of the MSS, especially in the evaluation of Expected Common Variance and specific factor reliability