Machine Learning Prediction of HEA Properties

High-entropy alloys (HEA) are a very new development in the field of metallurgical materials. They are made up of multiple principle atoms unlike traditional alloys, which contributes to their high configurational entropy. The microstructure and properties of HEAs are are not well predicted with the models developed for more common engineering alloys, and there is not enough data available on HEAs to fully represent the complex behavior of these alloys. To that end, we explore how the use of machine learning models can be used to model the complex, high dimensional behavior in the HEA composition space. Based on our review and experimentation, the use of a variational autoencoder will allow future research to lower the dimensionality (and therefore complexity) of the HEA composition space and allow for novel alloy generation based on one or more desired properties

Curve-lifted codes for local recovery using lines

In this paper, we introduce curve-lifted codes over fields of arbitrary characteristic, inspired by Hermitian-lifted codes over $\mathbb{F}_{2^r}$. These codes are designed for locality and availability, and their particular parameters depend on the choice of curve and its properties. Due to the construction, the numbers of rational points of intersection between curves and lines play a key role. To demonstrate that and generate new families of locally recoverable codes (LRCs) with high availabilty, we focus on norm-trace-lifted codes. In some cases, they are easier to define than their Hermitian counterparts and consequently have a better asymptotic bound on the code rate.Comment: 22 pages. Comments welcom

Evaluation of a fecal shedding test to detect badger social groups infected with Mycobacterium bovis

Bovine Tuberculosis (bTB) is an economically important disease affecting the cattle industry in England and Wales. bTB, caused by Mycobacterium bovis, also causes disease in the Eurasian badger (Meles meles), a secondary maintenance host. Disease transmission between these two species is bidirectional. Infected badgers shed M. bovis in their faeces. The UK Animal and Plant Health Agency (APHA) organised a comparative trial to determine the performance of tests in detecting M. bovis in badger faeces for the Department for Environment, Food, and Rural Affairs (DEFRA). Here we present the performance of the existing Warwick Fast24-qPCR test, and its modified version based on a high-throughput DNA extraction method (Fast96-qPCR). We found Fast24-qPCR to have a sensitivity of 96.7% (95%CI 94.5-99%, n=244) and a specificity of 99% (95%CI 97.8-100%, n=292). Fast96-qPCR requires further optimisation. Determining the disease status of badger social groups requires multiple tests per group. Therefore to increase specificity further, we independently repeated the Fast24-qPCR test on positive samples, increasing stringency by requiring a 2nd positive result. Fast24-qPCR with repeat testing had a sensitivity of 87.3% (95%CI 83.1-91.5%, n=244), and a specificity of 100% (95%CI 100-100, n=201) on an individual sample level. At the social group level, this repeat testing gives Fast24-qPCR high herd specificity, while testing multiple samples per group provides high herd sensitivity. With Fast24-qPCR we provide a social group level test with sufficient specificity and sensitivity to monitor shedding in badgers via latrine sampling, delivering a potentially valuable tool to measure the impacts of bTB control measures

Quantitative Measurement of Cyber Resilience: Modeling and Experimentation

Cyber resilience is the ability of a system to resist and recover from a cyber attack, thereby restoring the system's functionality. Effective design and development of a cyber resilient system requires experimental methods and tools for quantitative measuring of cyber resilience. This paper describes an experimental method and test bed for obtaining resilience-relevant data as a system (in our case -- a truck) traverses its route, in repeatable, systematic experiments. We model a truck equipped with an autonomous cyber-defense system and which also includes inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., "bonware") strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious mathematical models to aid in quantifying systems' resilience to cyber attacks. Using the models, we identify quantitative characteristics obtainable from experimental data, and show that these characteristics can serve as useful quantitative measures of cyber resilience.Comment: arXiv admin note: text overlap with arXiv:2302.04413, arXiv:2302.0794

An Experimentation Infrastructure for Quantitative Measurements of Cyber Resilience

The vulnerability of cyber-physical systems to cyber attack is well known, and the requirement to build cyber resilience into these systems has been firmly established. The key challenge this paper addresses is that maturing this discipline requires the development of techniques, tools, and processes for objectively, rigorously, and quantitatively measuring the attributes of cyber resilience. Researchers and program managers need to be able to determine if the implementation of a resilience solution actually increases the resilience of the system. In previous work, a table top exercise was conducted using a notional heavy vehicle on a fictitious military mission while under a cyber attack. While this exercise provided some useful data, more and higher fidelity data is required to refine the measurement methodology. This paper details the efforts made to construct a cost-effective experimentation infrastructure to provide such data. It also presents a case study using some of the data generated by the infrastructure.Comment: 6 pages, 2022 IEEE Military Communications Conference, pp. 855-86

Cardiac arrest: an interdisciplinary scoping review of clinical literature from 2021

The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct an annual search of peer-reviewed literature relevant to cardiac arrest. Now in its fourth year, the goals of this review are to highlight annual updates on clinically relevant and impactful clinical and population-level studies in the interdisciplinary world of cardiac arrest research from 2021.  To achieve these goals, a search of PubMed using keywords related to clinical research in cardiac arrest was conducted. Titles and abstracts were screened for relevance and sorted into seven categories: Epidemiology & Public Health; Prehospital Resuscitation; In-Hospital Resuscitation & Post-Arrest Care; Prognostication & Outcomes; Pediatrics; Interdisciplinary Guidelines; and Coronavirus disease 2019. Screened manuscripts underwent standardized scoring of methodological quality and impact by reviewer teams lead by a subject matter expert editor. Articles scoring higher than 99th percentile by category were selected for full critique.  Systematic differences between editors’ and reviewers’ scores were assessed using Wilcoxon signed-rank test. A total of 4,730 articles were identified on initial search; of these, 1,677 were scored after screening for relevance and deduplication.  Compared to the 2020 ICARE review, this represents a relative increase of 32% and 63%, respectively.  Ultimately, 44 articles underwent full critique. The leading category was In-Hospital Resuscitation, representing 41% of fully reviewed articles, followed by Prehospital Resuscitation (20%) and Interdisciplinary Guidelines (16%). In conclusion, several clinically relevant studies in 2021 have added to the evidence base for the management of cardiac arrest patients including implementation and incorporation of resuscitation systems, technology, and quality improvement programs to improve resuscitation

Association between a selective 5-HT4 receptor agonist and incidence of major depressive disorder: emulated target trial

Background The serotonin 4 receptor (5-HT4R) is a promising target for the treatment of depression. Highly selective 5-HT4R agonists, such as prucalopride, have antidepressant-like and procognitive effects in preclinical models, but their clinical effects are not yet established. Aims To determine whether prucalopride (a 5-HT4R agonist and licensed treatment for constipation) is associated with reduced incidence of depression in individuals with no past history of mental illness, compared with anti-constipation agents with no effect on the central nervous system. Method Using anonymised routinely collected data from a large-scale USA electronic health records network, we conducted an emulated target trial comparing depression incidence over 1 year in individuals without prior diagnoses of major mental illness, who initiated treatment with prucalopride versus two alternative anti-constipation agents that act by different mechanisms (linaclotide and lubiprostone). Cohorts were matched for 121 covariates capturing sociodemographic factors, and historical and/or concurrent comorbidities and medications. The primary outcome was a first diagnosis of major depressive disorder (ICD-10 code F32) within 1 year of the index date. Robustness of the results to changes in model and population specification was tested. Secondary outcomes included a first diagnosis of six other neuropsychiatric disorders. Results Treatment with prucalopride was associated with significantly lower incidence of depression in the following year compared with linaclotide (hazard ratio 0.87, 95% CI 0.76–0.99; P = 0.038; n = 8572 in each matched cohort) and lubiprostone (hazard ratio 0.79, 95% CI 0.69–0.91; P < 0.001; n = 8281). Significantly lower risks of all mood disorders and psychosis were also observed. Results were similar across robustness analyses. Conclusions These findings support preclinical data and suggest a role for 5-HT4R agonists as novel agents in the prevention of major depression. These findings should stimulate randomised controlled trials to confirm if these agents can serve as a novel class of antidepressant within a clinical setting

The variability and seasonality of the environmental reservoir of Mycobacterium bovis shed by wild European badgers

This is the final version of the article. Available from the publisher via the DOI in this record.The incidence of Mycobacterium bovis, the causative agent of bovine tuberculosis, has been increasing in UK cattle herds resulting in substantial economic losses. The European badger (Meles meles) is implicated as a wildlife reservoir of infection. One likely route of transmission to cattle is through exposure to infected badger urine and faeces. The relative importance of the environment in transmission remains unknown, in part due to the lack of information on the distribution and magnitude of environmental reservoirs. Here we identify potential infection hotspots in the badger population and quantify the heterogeneity in bacterial load; with infected badgers shedding between 1 × 10(3)- 4 × 10(5) M. bovis cells g(-1) of faeces, creating a substantial and seasonally variable environmental reservoir. Our findings highlight the potential importance of monitoring environmental reservoirs of M. bovis which may constitute a component of disease spread that is currently overlooked and yet may be responsible for a proportion of transmission amongst badgers and onwards to cattle.We acknowledge funding from Defra, H.C.K. was in receipt of a BBSRC DTG studentship and E.M.W. and O.C. acknowledge support from BBSRC for collaboration with Eamonn Gormley, UCD. We are also grateful to the APHA field team at Woodchester Park for support during fieldwork, and to Defra who fund the long-term stud

Cognitive dysfunction after analgesia and sedation: Out of the operating room and into the pediatric intensive care unit

In the midst of concerns for potential neurodevelopmental effects after surgical anesthesia, there is a growing awareness that children who require sedation during critical illness are susceptible to neurologic dysfunctions collectively termed pediatric post-intensive care syndrome, or PICS-p. In contrast to healthy children undergoing elective surgery, critically ill children are subject to inordinate neurologic stress or injury and need to be considered separately. Despite recognition of PICS-p, inconsistency in techniques and timing of post-discharge assessments continues to be a significant barrier to understanding the specific role of sedation in later cognitive dysfunction. Nonetheless, available pediatric studies that account for analgesia and sedation consistently identify sedative and opioid analgesic exposures as risk factors for both in-hospital delirium and post-discharge neurologic sequelae. Clinical observations are supported by animal models showing neuroinflammation, increased neuronal death, dysmyelination, and altered synaptic plasticity and neurotransmission. Additionally, intensive care sedation also contributes to sleep disruption, an important and overlooked variable during acute illness and post-discharge recovery. Because analgesia and sedation are potentially modifiable, understanding the underlying mechanisms could transform sedation strategies to improve outcomes. To move the needle on this, prospective clinical studies would benefit from cohesion with regard to datasets and core outcome assessments, including sleep quality. Analyses should also account for the wide range of diagnoses, heterogeneity of this population, and the dynamic nature of neurodevelopment in age cohorts. Much of the related preclinical evidence has been studied in comparatively brief anesthetic exposures in healthy animals during infancy and is not generalizable to critically ill children. Thus, complementary animal models that more accurately reverse translate critical illness paradigms and the effect of analgesia and sedation on neuropathology and functional outcomes are needed. This review explores the interactive role of sedatives and the neurologic vulnerability of critically ill children as it pertains to survivorship and functional outcomes, which is the next frontier in pediatric intensive care