A machine learning approach to differentiating bacterial from viral meningitis

Clinical reports indicate that differentiating bacterial from viral (aseptic) meningitis is still a difficult issue, compounded by factors such as age and time of presentation. Clinicians routinely rely on the results from blood and cerebrospinal fluid (CSF) to discriminate bacterial from viral meningitis. Tests such as the CSF Gram stain performed prior to broad-spectrum antibiotic treatment yield sensitivities between 60 and 92%. Sensitivity can be increased by performing additional laboratory testing, but the results are never completely accurate and are not cost effective in many cases. In this study, we wished to determine if a machine learning approach, based on rough sets and a probabilistic neural network could be used to differentiate between viral and bacterial meningitis. We analysed a clinical dataset containing records for 581 cases of acute bacterial or viral meningitis. The rough sets approach was used to perform dimensionality reduction in addition to classification. The results were validated using a probabilistic neural network. With an overall accuracy of 98%, these results indicate rough sets is a useful approach to differentiating bacterial from viral meningitis

A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks

In this paper, we present a medical decision support system based on a hybrid approach utilising rough sets and a probabilistic neural network. We utilised the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilised a probabilistic neural network to perform supervised classification. Our results indicate that rough sets was able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%

Vagococcus fluvialis isolation and sequencing from urine of healthy cattle

While the gram-positive bacterium Vagococcus fluvialis has been isolated from the environment as well as fish, birds, and mammals, very little is known about the species. V. fluvialis is believed to be a probiotic in fishes. However, within mammals, it is more frequently isolated from infectious tissue, including on rare occasions human and livestock lesions. Prior to the study described here, V. fluvialis had never been found in healthy bovine animals. Here, we present the complete genomes of V. fluvialis UFMG-H6, UFMG-H6B, and UFMG-H7, novel strains isolated from urine samples from healthy bovine females. These are the first genomes of mammalian isolates and the first description of V. fluvialis from urine. The genomes did not encode for any known virulence genes, suggesting that they may be commensal members of the urine microbiota

Ground-state energy of H-: a critical test of triple basis sets

We report an improved variational upper bound for the ground state energy of H- using Hylleraaslike wave functions in the form of a triple basis set having three distinct distance scales. The extended precision DQFUN of Bailey, allowing for 70 decimal digit arithmetic, is implemented to retain sufficient precision. Our result exceeds the previous record [A. M. Frolov, Euro. J. Phys. D 69, 132 (2015)], indicating that the Hylleraas triple basis set exhibits comparable convergence to the widely used pseudorandom all-exponential basis sets, but the numerical stability against roundoff error is much better. It is argued that the three distance scales have a clear physical interpretation. The new variational bound is -0.527 751 016 544 377 196 590 814 469 a.u

Escherichia coli and Pseudomonas aeruginosa Isolated From Urine of Healthy Bovine Have Potential as Emerging Human and Bovine Pathogens

The study of livestock microbiota has immediate benefits for animal health as well as mitigating food contamination and emerging pathogens. While prior research has indicated the gastrointestinal tract of cattle as the source for many zoonoses, including Shiga-toxin producing Escherichia coli and antibiotic resistant bacteria, the bovine urinary tract microbiota has yet to be thoroughly investigated. Here, we describe 5 E. coli and 4 Pseudomonas aeruginosa strains isolated from urine of dairy Gyr cattle. While both species are typically associated with urinary tract infections and mastitis, all of the animals sampled were healthy. The bovine urinary strains were compared to E. coli and P. aeruginosa isolates from other bovine samples as well as human urinary samples. While the bovine urinary E. coli isolates had genomic similarity to isolates from the gastrointestinal tract of cattle and other agricultural animals, the bovine urinary P. aeruginosa strains were most similar to human isolates suggesting niche adaptation rather than host adaptation. Examination of prophages harbored by these bovine isolates revealed similarity with prophages within distantly related E. coli and P. aeruginosa isolates from the human urinary tract. This suggests that related urinary phages may persist and/or be shared between mammals. Future studies of the bovine urinary microbiota are needed to ascertain if E. coli and P. aeruginosa are resident members of this niche and/or possible sources for emerging pathogens in humans

Application of Artificial Intelligence (AI)in Sustainable Building Lifecycle; ASystematic Literature Review

With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI’s potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI’s capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data‐driven analysis and prediction, while digital twins provide real‐time insights for informed decision‐making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices

The Rewiring of Ubiquitination Targets in a Pathogenic Yeast Promotes Metabolic Flexibility, Host Colonization and Virulence

Funding: This work was funded by the European Research Council [http://erc.europa.eu/], AJPB (STRIFE Advanced Grant; C-2009-AdG-249793). The work was also supported by: the Wellcome Trust [www.wellcome.ac.uk], AJPB (080088, 097377); the UK Biotechnology and Biological Research Council [www.bbsrc.ac.uk], AJPB (BB/F00513X/1, BB/K017365/1); the CNPq-Brazil [http://cnpq.br], GMA (Science without Borders fellowship 202976/2014-9); and the National Centre for the Replacement, Refinement and Reduction of Animals in Research [www.nc3rs.org.uk], DMM (NC/K000306/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments We thank Dr. Elizabeth Johnson (Mycology Reference Laboratory, Bristol) for providing strains, and the Aberdeen Proteomics facility for the biotyping of S. cerevisiae clinical isolates, and to Euroscarf for providing S. cerevisiae strains and plasmids. We are grateful to our Microscopy Facility in the Institute of Medical Sciences for their expert help with the electron microscopy, and to our friends in the Aberdeen Fungal Group for insightful discussions.Peer reviewedPublisher PD

A criterion for separating process calculi

We introduce a new criterion, replacement freeness, to discern the relative expressiveness of process calculi. Intuitively, a calculus is strongly replacement free if replacing, within an enclosing context, a process that cannot perform any visible action by an arbitrary process never inhibits the capability of the resulting process to perform a visible action. We prove that there exists no compositional and interaction sensitive encoding of a not strongly replacement free calculus into any strongly replacement free one. We then define a weaker version of replacement freeness, by only considering replacement of closed processes, and prove that, if we additionally require the encoding to preserve name independence, it is not even possible to encode a non replacement free calculus into a weakly replacement free one. As a consequence of our encodability results, we get that many calculi equipped with priority are not replacement free and hence are not encodable into mainstream calculi like CCS and pi-calculus, that instead are strongly replacement free. We also prove that variants of pi-calculus with match among names, pattern matching or polyadic synchronization are only weakly replacement free, hence they are separated both from process calculi with priority and from mainstream calculi.Comment: In Proceedings EXPRESS'10, arXiv:1011.601

Selfing revealed potential for higher yield performance than backcrossing among tomato segregating populations of Solanum lycopersicum × S. pimpinellifolium crosses under tropical humid climate

The objectives of this study were to assess and identify new source of phenotypic variability among F3 and BC1F2 tomato populations, and apply genotype by yield*trait (GYT) biplots for population and line selection based on multiple traits. Four diverse cultivated parents (‘CLN2498D’ [D] and ‘CLN2417H’ [H] from Ethiopia; ‘UC Dan INDIA’ [U] and ‘Tima’ [T] from Nigeria), and wild parent ‘LA2093’ [W] were used to generate 276 potential breeding lines. The lines were categorized into eight populations (‘pop_1_W/H1’, ‘pop_2_W/H2’, ‘pop_3_W/D1’, ‘pop_4_W/D2’, ‘pop_5_W/T1’, ‘pop_6_W/T2’, ‘pop_7_W/U1’, and ‘pop_8_W/U2’), and evaluated twice in the field using 19 × 15 alpha-lattice design with two replicates. Significant differences were observed among lines and populations for all yield enhancing traits. ‘Pop_1_W/H1’, ‘pop_4_W/D2’ and ‘pop_6_W/T2’ expressed the highest genetic divergence for plant height, number of leaves, total flower and fruit number, and fruit weight. GYT biplots revealed that all yield*trait interactions had a positive correlation with each other. F3 populations, ‘pop_5_W/T1’ and ‘pop_1_W/H1’ exhibited the best performance for majority of the yield*trait combinations. Hierarchical clustering on principal components (HCPC) revealed overlapping lines (70.58% of Cluster D lines) and (54.05% of Cluster U lines) from the two F3 populations. In BC1F2 population, 32.35% of the 34 original lines of Cluster D and 48.48% of Cluster T lines overlapped between Clusters D and T, while 18.18% of Cluster T lines and 8.82% of Cluster H lines were transgressive between Clusters T and H. Transgressive segregants ‘0210U1’, ‘0211U1’, and ‘0171T1’ of selfed population using multivariate analysis were believed to represent potential sources of novel genetic variation for future tomato breeding