Feline immunodeficiency virus (FIV) is a lentivirus in the family Retroviridae that infects domestic cats resulting in an immunodeficiency disease featuring a progressive and profound decline in multiple sets of peripheral lymphocytes. Despite compelling evidence of FIV-associated immunopathology, there are conflicting data concerning the clinical effects of FIV infection on host morbidity and mortality. To explore FIV-associated immunopathogenesis and clinical disease, we experimentally inoculated a cohort of 4 specific pathogen free kittens with a biological isolate of FIV clade C and continuously monitored these animals along with 2 uninfected control animals for more than 13 years, from the time of inoculation to the humane euthanasia endpoint. Here we report the results obtained during the late asymptomatic and terminal phases of FIV infection in this group of experimentally FIV-infected cats.

Abstract: This article reports the results of an ecological study of cancer incidence rates by state in the US for the period 2016-2020. The goals of this study were to determine the extent to which solar UVB doses still reduced cancer risk compared to findings reported in 2006 for cancer mortality rates for the periods 1950–1969 and 1970-1794 as well as cancer incidence rates for the period 1998–2002 and to determine which factors were recently associated with cancer risk. The cancer data for non-Hispanic white (European American) men and women were obtained from the Centers for Disease Control and Prevention. Indices were obtained for solar UVB at the surface for July 1992, and alcohol consumption, and diabetes and obesity prevalence near the 2016–2020 period. Lung cancer incidence rates were also used in the analyses as a surrogate for smoking, diet, and air pollution. The cancers for which solar UVB is significantly associated with reduced incidence are bladder, brain (males), breast, corpus uteri, esophageal, gastric, non-Hodgkin’s lymphoma, pancreatic, and renal cancer. Lung cancer was significantly associated with colorectal, laryngeal, and renal cancer. Diabetes was also significantly associated with breast, liver, and lung cancer. Obesity prevalence was significantly associated with breast, colorectal and renal cancer. Alcohol consumption was associated with bladder and esophageal cancer. Thus diet has become a very important driver of cancer incidence rates. The role of solar UVB in reducing risk of cancer has been reduced due to people spending less time outdoors, wearing sunscreen that blocks UVB but not UVA radiation, and population increases in overweight and obesity, which lowers 25-hydroxyvitamin D concentrations and generates systemic inflammation, a risk factor for cancer. The dietary approach that would reduce the risk of diabetes, obesity, lung cancer, and, therefore cancer, would be one based mostly on whole-plants and restrictions on red and processed meats and ultraprocessed foods. Solar UVB exposure for a few minutes before applying sunscreen and taking vitamin D supplements would also help reduce risk of cancer.Keywords: alcohol consumption; cancer incidence;

Recent analyses of cosmic microwave background surveys have revealed hints that there may be a non-trivial running of the running of the spectral index. If future experiments were to confirm these hints, it would prove a powerful discriminator of inflationary models, ruling out simple single field models. We discuss how isocurvature perturbations in multi-field models can be invoked to generate large runnings in a non-standard hierarchy, and find that a minimal model capable of practically realising this would be a two-field model with a non-canonical kinetic structure. We also consider alternative scenarios such as variable speed of light models and canonical quantum gravity effects and their implications for runnings of the spectral index.

In this data deposit, I describe a dataset that is the result of content mining 167,318 published articles for statistical test results. As a result of this content mining, 688,112 results from 50,845 articles were extracted. In order to provide a comprehensive set of data, the statistical results are supplemented with metadata from the article they originate from. The dataset is provided in a comma separated file (CSV) in long-format. For each of the 688,112 results, 20 variables are included, of which seven are article metadata and 13 pertain to the individual statistical results (e.g., reported and recalculated p-value). A five-pronged approach was taken to generate the dataset: (i) collect journal lists, (ii) spider journal pages for articles, (iii) download articles, (iv) add article metadata, and (v) mine articles for statistical results.

Chronic fatigue syndrome (CFS) and multiple sclerosis (MS) share similarities regarding their epidemiology, symptomatology and craniospinal physiology. Indeed, the cardinal feature of CFS, fatigue, is also a major factor in the symptomatology of the majority of MS patients. Recently, we have found that there is a significant reduction in the craniospinal compliance in MS which affects both the stiffness of the walls of the spinal canal and the walls of the cerebral venous system. This change in compliance brings about an alteration in the effectiveness of the pulse wave dampening in the craniospinal system. The result is an impedance mismatch between the cortical veins and their draining sinuses, leading to dilatation of these upstream veins. We deduce this dilatation can only be brought about by an increase in the pressure gradient between the vein lumen and the subarachnoid space (i.e. the transmural pressure gradient). We hypothesise that given the similarities between MS and CFS, a similar mechanism underlies the physiology of CFS. We present two case studies to highlight the expected findings in CFS patients if this hypothesis were proven to be correct.

This paper presents the design of a digital twin that blends aviation, gaming, simulation, and Geographic Information Systems (GIS) to create a synthetic environment within which strategies, laws and platforms for electric aviation may be tested out. This digital twin has been called Future Urban Synthetic Environment (FUSE). FUSE includes an in-built Unmanned Traffic Management (UTM) that can be used to run simulations to test the coordination of all urban traffic. It uses real GIS tagged imagery data and implements it at runtime into the game engine and thus link the optimised imagery loading to the visual performance of the simulation engine. FUSE provides a 3D digital twin of specified areas designed to simulate the effects (in terms of noise, visual impact, privacy) of drones and electric air taxis operating under various operational scenarios (such as number of deliveries allowed per day, maximum payload weight, no-fly areas, position of depots, vertiports, etc.). With so much high-fidelity data it is difficult for any game system to effectively render the environment and do justice to the detail whilst enabling enough of the landscape to be rendered to keep in focus the detail when looking out at the horizon.

Modern drugs have changed epilepsy, which affects people of all ages. But for young people with epilepsy, the framework of drug development has stalled. In the wake of the thalidomide catastrophe, the misconception emerged that for people <18y antiseizure medications (ASMs) need separate proof of efficacy and safety (E&S), overall called “pediatric drug development.” This has been corrected to some extent. Authorities accept that ASMs are effective in <18y as well, but they still require "extrapolation of efficacy," as if minors were another species. In our view, the real problem is less that relevant parts of pediatric clinical epilepsy research over the past decades were useless, but that this has hampered research on meaningful challenges. We do not need to show that ASMs work also before the 18th birthday. But we need to learn how to best use ASMs to prevent brain damage in young patients and optimize ASMs use considering a broader range of aspects

The global energy transition is motivated largely by the need to combat climate change. Other reasons include limits to available fossil fuels, energy-related environmental impacts of many kinds, and societal goals such as equity and providing sustainable energy for the poor. This transition will significantly change the world both technically, economically and socially. It is taking shape with characteristics which prioritise certain types of policy, interventions and solutions, as well as certain actors and institutional forces. More fundamentally, it embodies underlying assumptions about what constitutes a desirable type of economy and society. Briefly stated, the energy transition is being geared to a conventional economic growth-focused type of development; including the not unchallenged concept of green growth. Not everyone, however, would agree on those priorities or the underlying assumptions. We point out various constraints and limitations which this approach places on the energy transition. Whilst much of the change being initiated is positive, we argue that different views, which lie closer to discourses such as strong sustainability (1), deep ecology (2) and ecological economics (3, 4) might prioritise differently and offer both necessary and desirable outcomes. Given the predominant focus on energy and climate, equally essential goals such as equity and eradication of poverty appear to be at particular risk of being missed. Building on examples and recent experience we argue that achieving global sustainable energy, not least the societal goals, remains unlikely within socio-political frameworks permeated by the imperatives of market-driven growth, the commodification of resources, and intense competitive dynamics. Technology and economics, the themes of this issue, have far-reaching societal implications and outcomes. We argue that it is essential that they be considered within the broad context discussed here.

A variant of the von Neumann-Wigner Interpretation is proposed. Problems arising from the quantum Zeno effect are addressed as we have described previously. We do, however, offer some new, and perhaps unexpected observations. We are accustomed to thinking of wave function collapse as occurring consequent to laboratory measurements. We speculate that, whatever physical correlate of consciousness exists within the brain, it is quantum mechanical in the sense that a brain, left to itself, would eventually decohere into a state no longer compatible with its conscious functioning. Wave function collapse returns it to a state compatible with consciousness. Indeed, this may be its important reason for occurring. A universe without it simply could not play host to conscious brains. The fact that it also prevents us from encountering "absurd" situations in the laboratory is merely a fortunate dividend. Whenever a quantum measurement is made the universe's future history splits into a number of possibilities. This number may be very large or infinite. And we believe consciousness plays a vital role in this happening. A "conscious" universe where quantum measurements are being made allows for an enormous number of equally acceptable world-histories. An "unconscious" one, always evolving in a unitary fashion, allows for only one. If we assume that the decision as to which worldhistory is the real one (i.e. this one) is made at random we see that the universe is overwhelmingly more likely to be "conscious" than not.

A variant of the von Neumann-Wigner Interpretation is proposed. It does not make use of the familiar language of wave functionsand observers. Instead it pictures the state of the physical world as a vector in a Fock space and, therefore not, literally, a functionof any spacetime coordinates. And, rather than segregating consciousness into individual points of view (each carrying with it asense of its proper time), this model proposes only unitary states of consciousness, Q(t), where t represents a fiducial time withrespect to which both the state of the physical world and the state of consciousness evolve. States in our world's Fock space areclassified as either 'admissible' (meaning they correspond to definite states of consciousness) or 'inadmissible' (meaning they donot). The evolution of the state vector of the world is such as to always keep it restricted to 'admissible' states. Consciousness istreated very much like what Chalmers calls an "M-Property." But we try to show that problems with the quantum Zeno effect do not arise from this model.

BackgroundIt is unclear what underpins the large global variations in the prevalence of fluoroquinolone resistance in gram-negative bacteria. We tested the hypothesis that different intensities in the use of quinolones for food-animals plays a role. MethodsWe used Spearman’s correlation to assess if the country-level prevalence of fluoroquinolone resistance in human infections with Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa was correlated with the use of quinolones for food producing animals. Linear regression was used to assess the relative contributions of country-level quinolone consumption for food-animals and humans on fluoroquinolone resistance in these 4 species. ResultsThe prevalence of fluoroquinolone resistance in each species was positively associated with quinolone use for food-producing animals (E. coli [ρ=0.55; P<0.001], K. pneumoniae [ρ=0.58; P<0.001]; A. baumanii [ρ=0.54; P=0.004]; P. aeruginosa [ρ=0.48; P=0.008]). Linear regression revealed that both quinolone consumption in humans and food animals were independently associated with fluoroquinolone resistance in E. coli and A. baumanii. ConclusionsReducing quinolone use in food-producing animals may help retard the spread of fluoroquinolone resistance in various gram negative bacterial species.

A model of the early RNA world is proposed. Nearly self-complementary sequences that could adopt double-stranded, smallhairpin-like (shRNA), structures would be selected for due to their greater hydrolytic stability. These would be phosphorylated attheir 5' ends. We suppose that dehydrating conditions arise (perhaps intermittently) in the early environment allowing amino acidsto condense with these RNA molecules. The resulting phosphate-amino acid anhydrides would play the role of early, charged,tRNAs. A crude genetic code could emerge owing to the greater resistance of some amino acid-shRNA pairings to hydrolysisrelative to others. Early on there is no division of labor between mRNAs and tRNAs; the same molecules perform both functions.But the first systems would have encoded little in the way of protein sequence information. Rather they would have served as catalysts for the random polymerization of amino acids. It is speculated that the selective advantage inhering in such systems lay intheir ability to supply raw materials for the formation of coacervates within which the various molecules essential to proto-lifecould be concentrated. This would greatly facilitate the necessary chemistries. The evolution of homochiral protein and RNA populations is discussed. An appealing feature of this model is its ability to explain the transition from phosphorylated amino acids to the 3' ester-linked aminoacyl-tRNAs employed by modern life.

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Tropical forests are one of the largest terrestrial biosphere carbon pools, whereas savannas exchange carbon between the biosphere and atmosphere via frequent and extensive landscape fires. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change. Reliable mapping of tropical forest and savanna is essential to provide understanding of how anthropogenic impacts are affecting the extent of these biomes. Using Google Maps satellite imagery, we manually classified 24,239 random points as forest, savanna, or anthropogenic landscapes within the tropics. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modelling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We found there were environments where tropical forest and savanna were equally probable are geographically restricted. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modelling provides global mapping that can be used track further changes to distribution of rainforests.

A literature review was conducted to summarize the frequency of the D-allele of the angiotensin-converting enzyme-1 in all countries with available data. Using an ecological study design limited to high income countries, we found that the country-level frequency of the D-allele was associated with increased COVID-19 incidence and mortality.

It is unclear how important it is to reduce fluoroquinolone consumption in the general population to prevent the spread of fluoroquinolone resistance in Neisseria gonorrhoeae (bystander selection). Methods We assessed bystander selection by using Spearman’s correlation to assess if the country-level prevalence of fluoroquinolone resistance in N. gonorrhoeae was correlated with the prevalence of fluoroquinolone resistance in four other gram-negative species - Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. Results Fluoroquinolone resistance in N. gonorrhoeae was positively associated with homologous resistance in all 4 species - A. baumanii. (ρ=0.61, P=0.0003, E. coli (ρ=0.67, P<0.0001), K. pneumoniae (ρ=0.52, P=0.0004) and P. aeruginosa (ρ=0.40, P=0.0206). Positive associations were also found between the national prevalence of fluoroquinolone resistance and fluoroquinolone consumption in the general population in the preceding year for 4 of the 5 species. Conclusions Gonococcal fluoroquinolone resistance can be productively viewed as being part of a syndemic of fluoroquinolone resistance. Strengthening antimicrobial stewardship programs may help retard the spread of fluoroquinolone resistance in N. gonorrhoeae.

Relativistic atomic and molecular structure calculations start from very different premises to quantum electrodynamics (QED). QED predicts radiative corrections that can nly be approximated crudely in today's atomic structure programs. This paper describes rigorous simple algorithms for including accurate vacuum polarization and self-energy corrections in the GRASP programs.

Fire risk can be defined as the probability that a fire will spread. Reliable monitoring of fire risk is essential for effective landscape management. Compilation of fire risk records enable identification of seasonal and inter-annual patterns and provide a baseline to evaluate the trajectories in response to climate change. Typically, fire risk is estimated from meteorological data. In regions with sparse meteorological station coverage environmental proxies provide important additional data stream for estimating past and current fire risk. Here we use a 60-year record of daily flows from two rivers (Franklin and Davey) in the remote Tasmanian Wilderness World Heritage Area (TWWHA) to characterize seasonal patterns in fire risk in temperate Eucalyptus and rainforests. We show that river flows are strongly related to landscape soil moisture estimates derived from down-scaled re-analysis of meteorological data available since 1990. To identify river flow thresholds where forests are likely to burn, we relate river flows to known forest fires that have occurred in the previously defined ecohydrological domains that surround the Franklin and Davey catchments. Our analysis shows that the fire season in the TWWHA is centered on February (70% of all years below the median threshold), with shoulders on December-January and March. Since 1954 forest fire can occur in at least one month for all but four summers in the ecohydrological domain that includes the Franklin catchment, and since 1964 fire fires could occur in at least one month in every summer in the ecohydrological domain that includes the Davey catchment. Our analysis shows that mangers can use river flows as a simple index that provide a landscape-scale forest fire risk in the TWWHA.

In the British setting, the deployment of the phrase ‘doing god’ has become increasingly common to refer to an emerging trend whereby religion has acquired an increasingly prominent role in political spaces and discourses. This was particularly prominent while David Cameron was Prime Minister and leader of the Conservative Party. While historically, religion has not had a prominent place in either the former Prime Minister David Cameron. Here, the findings from critical analyzing a series of Cameron’s public pronouncements about religion—and Christianity in particular—is set out to try and better understand his own adherence to Christianity (the personal) how this intersected with his politics and role as Prime Minister (the political), and more importantly how this shaped his views about Britain being a Christian country (the national). Contextualised within the embryonic scholarly literature relating to the phenomenon of ‘doing god’ in the contemporary British setting, this article concludes by considering alternative and analogous frames through which greater elucidation of the true motivations of his pronouncements might be understood.

The creation of the National Muslims Women’s Advisory Group (NMWAG) in 2008 by Britain’s New Labour Government was part of a strategy which sought to engage different levels of Muslim communities beneath an overarching focus on reducing ‘Islamic extremism’. To do so however, Government acknowledged that it would need to support Muslim women to overcome some of the constraints it believed were placed on Muslim women in contemporary Britain. Deeming theology and religious interpretation to be one of those constraints, Government saw the need to empower Muslim women to ‘influence and challenge’ religious and theological discourses as a priority. This article therefore offers a case study on a project that was commissioned by Government that sought to empower Muslim women to ‘influence and challenge’ theological interpretations in collaboration with the NMWAG. Having gained unprecedented access to the NMWAG, its activities and engagement with Government, this article presents previously unpublished findings from that project to focus on two key themes: Muslim women, their identity and position; and theology, leadership and the participation of women. Having explored these in detail, this article concludes by critically reflecting on the way in which Government engaged and interacted with Muslim women, the role and relative success of the NMWAG and, most importantly, the extent to which the NMWAG was able to ‘influence and challenge’ interpretations of Islamic theology.

In many countries, governments have implemented non-pharmaceutical techniques to limit COVID-19 transmission. Restricting human mobility is one of the most common interventions, including lockdown, travel restrictions, working from home, etc. However, due to the strong transmission ability of the virus variants, further rounds of interventions, including a strict lockdown, are not considered as effective as expected. The paper aims to understand how the lockdown policy and pandemics changed human mobility in the real scenario. Here we focus on understanding the mobility changes caused by compliance with restrictions and risk perceptions, using the mobility index from the Google report during three strict lockdown periods in Leeds, the largest city in the county of West Yorkshire, England from March 2020 to March 2021. The research proposed the time-varying z-scores and Principal Component Analysis (PCA) to simulate how local people dynamically process and perceive health risk based on multi-dimensional daily COVID-19 reports first. Further modelling highlights exponentially increasing policy non-compliance through the duration of lockdown, probably attributable to factors such as mental anxiety and economic pressures. Finally, the proposed nonlinear regression model examines the mobility changes caused by the population's dynamic risk perceptions and lockdown duration. The case study at Leeds fits data well and shows that the third lockdown policy took effect much slower than the first. At the same time, the negative impact of the epidemic on population mobility decayed 40% in the third lockdown period in contrast with the first lockdown. The risk perception estimation methods could reflect that the local population became increasingly accustomed to the COVID-19 situation, and local people rationally evaluated the risks of COVID in the third lockdown period. The results prove that simulated risk perceptions and policy decay could explain urban mobility behaviour during the mobility well during lockdown periods, which could be a reference for future decision-making processes.

Mass crises are disruptive to people's mental health. The study aimed to explore mental distress during COVID-19 quarantine in a sample of university workers in Brazil. The survey included sets of questions about demographics, health, and support, an open question about major concerns, and the Clinical Outcome Routine Evaluation (CORE-OM), a measure of mental distress. 407 professionals participated in the study: mean age of 40 years (SD = 11.2), mostly female (67.8%), married (64.8%) and fulfilling social distancing to avoid COVID-19 infection (99%). Using the Consensual Qualitative Research for simple qualitative data (CQR-M) the main areas of concern were grouped into six domains, as follows: Work, Health, Isolation, Personal life and routine, Social environment, and Future. Many responses were multiple. They form categories indicating specific concerns within these domains. Quantitative data were analyzed by identifying the simple effects of potential predictors of mental distress. The results indicated medium effects of help with household chores, psychiatric treatment, age and physical exercise. Having someone available to listen was the only variable with a large effect in reducing mental suffering. The hybrid approach showed that the psychological experience during the pandemic is quite multifaceted and complex pointing new clues for public mental health.

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage doesn’t depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks

A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.

Here we report the preparation and characterization of a novel biomimetic toothpaste containing morphogenetically active amorphous polyphosphate (polyP) microparticles enriched with retinyl acetate (“a-polyP/RA-MP”). The spherical microparticles (average size, 550±120 nm), prepared by co-precipitating sodium-polyP with calcium chloride and supplemented with retinyl acetate, were incorporated into a basis toothpaste at a final concentration of 1% or 10%. The paste containing “a-polyP/RA-MP” significantly increased the growth of human mesenchymal stem cells (MSC), compared to a commercial toothpaste which acts rather inhibitory and the paste without polyP and retinyl acetate. qRT-PCR experiments revealed that the retinoid causes an induction of the expression of the MSC marker genes for osteoblast differentiation encoding collagen type I and alkaline phosphatase. On the other hand, the polyP ingredient, supplied as Zn-polyP microparticles (“Zn-a-polyP-MP”) strongly inhibited the growth of the cariogenic bacterium Streptococcus mutans. We demonstrate that the amorphous polyP-containing toothpaste, enriched with retinyl acetate, efficiently repairs both cracks/fissures and carious lesions in the tooth enamel, and reseals dentinal tubules, already after a 5 d treatment (brushing) of teeth twice daily for 5 min as examined by SEM and quantitative EDX analysis. The stability of the occlusion of dentin cracks even turned out to resist against short high power sonication treatment. Our results demonstrate that the novel toothpaste prepared here, containing amorphous polyP and retinyl acetate, is particularly suitable for prevention/repair of (cariogenic) damages of tooth enamel/dentin and for treatment of dental hypersensitivity.

This article investigates the unexpected re-emergence of race in both the rhetoric and activities of the British radical-right. In doing so it seeks to better understand the reasons why race has re-emerged, how race has been contemporarily deployed by the radical-right, and the extent to which the re-emergence of race will force the radical right’s recent ideological focus on religion – more specifically, the religion of Islam and Muslims as adherents – begin to retreat. It does this first by setting out the methods and approaches used throughout. From here, necessary and appropriate context is provided about the British radical-right generally and the decades old ideological turn more specifically. Next, the circumstances and reasons for the re-emergence of race are critically engaged: focusing on the immediate aftermath of 2020’s Black Lives Matter protests as also the wider socio-political landscape of contemporary Britain. Taking into account both established and newly formed groups and movements from within the British radical right, the role and function played by race is considered next. As part of this, an in-depth analysis of the role and function of race in the rhetoric and activities of Patriotic Alternative is included. In conclusion, this article reflects on a number of arguments about why the re-emergence of race among the British radical-right is - albeit to varying degrees - a cause for concern.

There is great current interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a "phase" parameter that characterises the way "exceptional points" (singularities of various sorts) determine the system. These systems are briefly reviewed with an emphasis on their geometrical thermodynamics properties.

There is great current interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a "phase" parameter that characterises the way "exceptional points" (singularities of various sorts) determine the system. These systems are briefly reviewed with an emphasis on their geometrical thermodynamics properties.

Recent lines of evidence suggest the intriguing hypothesis that consuming common culinary herbs of the mint family might help prevent or treat Covid. Individual citizens could easily explore the hypothesis using ordinary kitchen materials. I offer a philosophical framework to account for the puzzling lack of public health messaging about this interesting idea.

Extremely low concentrations of ciprofloxacin may select for antimicrobial resistance. A recent global survey found that ciprofloxacin concentrations exceded safe levels at 64 sites. We assessed if national median ciprofloxacin concentrations in rivers were associated with fluoroquinolone resistance in Escherichia coli. Methods Spearman’s regression was used to assess the country-level association between the national prevalence of fluoroquinolone resistance in E. coli and the median ciprofloxacin concentration in the countries rivers. Results The prevalence of fluoroquinolone resistance in E. coli was positively correlated with the concentration of ciprofloxacin in rivers (ρ=0.36; P=0.011; N=48). Conclusions Steps to reducing the concentrations of fluoroquinolones in rivers may help prevent the emergence of resistance in E. coli and other bacterial species.

Background: It is unclear why certain countries have been more severely affected by COVID-19 than other countries. Methods: In this ecological study we compared COVID-19 mortality and incidence/100,000 as well as 4 putative explanatory factors by WHO world region. Linear regression was then used to assess the country-level predictors of COVID-19 mortality/100,000 and incidence/100,000 in 204 countries with available data. Results: COVID-19 incidence and mortality/capita were greater in Europe than other regions. This was despite a higher testing rate in Europe than other regions. Europe had an older population than all other regions and a higher prevalence of obesity than Africa, South East Asia and the Eastern Mediterranean. Country level multiple linear regression revealed positive associations between mortality/capita and testing rate, percent of the population 65 years or older, and Europe compared to Western Pacific and South East Asia (all P<0.005). Results for the analyses with cases/100,000 as outcome variable were similar. Conclusion: Our results suggest that older populations as well as other undefined regional and national factors, possibly related to efficacy of control efforts, are responsible for differences in national severity COVID-19 epidemics.

The probability of zoonoses, such as the novel coronavirus (COVID-19), emerging is strongly related to remediable factors such as habitat encroachment and trade in wild animals. Tackling these underlying determinants is important to prevent future pandemics from the approximately 700,000 viruses with the potential to cause zoonoses. Reversing habitat destruction is also vital to halt the accelerating rate of extinction of a wide array of life forms - with all the adverse consequences these extinctions will have for human health. These insights depend on viewing health and disease from within an ecological theoretical framework. We therefore argue that preventing future zoonotic outbreaks as well as dealing with a range of contemporary health issues would be facilitated by grounding our health sciences in more a more explicitly ecological conceptual framework.

It is unclear why European countries have been more severely affected by COVID-19 than East Asian countries. In this ecological study we compared the COVID-19 epidemics (cumulative incidence and mortality rates), host genetic susceptibility and national responses (testing intensity) in all European versus all Western Pacific/East Asian countries reporting cases in the first month of the epidemic. The host-genetic-susceptibility assessment was limited to the frequency of the D-allele of the angiotensin converting enzyme-1 (ACE-1) which has been found to be positively associated with COVID-19 incidence within European countries. Despite earlier epidemics, countries from the Western Pacific reported lower cumulative numbers of COVID-19 cases/100 000 inhabitants than European countries (P=0.0002). The Western Pacific countries also reported fewer cumulative COVID-19 deaths/100 000 (P=0.0024). Whilst there was little difference in the cumulative number of tests conducted/100 000, the percent of COVID-19 tests reported positive was higher in Europe than the Western Pacific (P=0.0076). The frequency of the ACE-1 D-allele was lower in the Western Pacific than European countries (P=0.0007). Our results suggest that a combination of different testing strategies and host genetic susceptibility contribute to difference in severity of the COVID-19 epidemics in the Western Pacific/East Asia and Europe.

We hypothesize that the reason severe COVID-19 is closely linked to a limited number of conditions such as obesity, hypertension, older age and diabetes is that these conditions are characterized by a pro-inflammatory state in general and an angiotensin converting enzyme-1-bias in particular. We review recent evidence that this pro-inflammatory state can be reversed via regular exercise and a healthy diet. Studies are suggested that could test this hypothesis.

In the absence of a vaccine, current antibiotic-dependent efforts to reduce the prevalence of Neisseria gonorrhoeae in high prevalence populations have been shown to result in extremely high levels of antibiotic consumption. No randomized controlled trials have been conducted to validate this strategy and an important concern of this approach is that it may induce antimicrobial resistance. To contribute to this debate, we assessed if mass treatment in the related species, Neisseria meningitidis, was associated with the emergence of antimicrobial resistance. To this end, we conducted a historical review of the effect of mass meningococcal treatment programmes on the prevalence of N. meningitidis and the emergence of antimicrobial resistance. We found evidence that mass treatment programmes were associated with the emergence of antimicrobial resistance.

On January 8, 2005, a surface melt event began on the interior portion of Ross Ice Shelf. While many surface melt events on Ross Ice Shelf are caused by the advection of warm air onto the shelf from the Ross Sea, surface winds during this event were directed offshore and the spatial pattern of surface melt was inconsistent with the Southern Ocean serving as a heat source. Rather, due to the interior location of the surface melt coupled with prevailing wind direction and surface temperature data it is thought that adiabatic warming of Föhn winds is the driving cause of this melt event. Passive Microwave (SSM/I) imagery was used to determine surface melt occurrence and the event’s extent. Spatial patterns of surface melt were then compared to NCEP/NCAR reanalysis output for several synoptic weather variables including surface temperatures, sea level pressure and surface vector winds. Synoptic-scale weather conditions were consistent with those that would produce downsloping wind (föhn) conditions in the interior of the Ross Ice Shelf where the anomalous surface melt was located.

Food security (FS) during infancy is associated with lifelong outcomes. New Zealand is a developed economy that reports poor childhood nutrition-related health statistics, particularly among minority children, yet has no measure of FS applicable to infancy. The objective was to develop a FS index for New Zealand infants and examine its association with demographic covariates and health outcomes. Within a large (n=6853) nationally representative cohort, variables describing infant food consumption, breastfeeding, and maternal food-related coping methods were collected from mothers during late infancy. A FS index was derived using confirmatory factor analysis. Associations were assessed by logistic regressions and described using odds ratios (OR) and ≥95% confidence intervals (CI). 15% of the cohort was highly FS, 43% tenuously food insecure (FIS), and 16% highly food insecure (FIS). Infants from minority ethnic groups had lower odds of being food secure, as did those born to the youngest mothers, mothers who smoked, or lived in low-income households. FIS infants had higher odds of morbidity. Interventions to improve infant FS should focus on improving dietary quality, and give particular consideration to minority infants. We identified that FIS shows wide ethnic and socioeconomic inequity, and is associated with poorer health. The most important driving factors of FIS included poor quality weaning diets, and poverty and its proxies. Any interventions to improve infant FS should focus on increasing fruit and vegetable consumption to recommended intake levels, and give particular consideration to minority infants.

Studies into the cognitive effects of alcohol have been mixed. They also present methodological challenges, often relying on self-report of alcohol consumption leading to hangover. The current study used BAC (obtained via breathalyser) and self-reported drinking behavior during a night out and related these to hangover severity and cognitive function measured over the internet in the same subjects the following morning. Volunteers were breathalysed and interviewed as they left a central entertainment district of an Australian state capital. They were provided with a unique identifier and, the following morning, logged on to a website. This included an online version of the Alcohol Hangover Severity Scale (AHSS), and number and type of drinks consumed the previous night and the eTMT-B - a validated, online analogue of the Trail Making Test B of executive function and working memory. Hangover severity was significantly correlated with one measure only, namely the previous night’s BAC (r = .228, p = .019). Completion time on the eTMT-B was significantly correlated with hangover severity ( r = .245, p = .012), previous night’s BAC (r = .197, p = .041) and time spent dinking (r = .376, p < .001). These findings confirm that alcohol hangover negatively affects cognitive functioning and that poorer working memory and executive performance correlates with hangover severity. The results also support the utility of using online measures in hangover research.

In the current study we test the hypothesis that the consumption of macrolides in food-animals is positively associated with the prevalence of macrolide resistance in M. pneumoniae at a country-level. The prevalence of M. pneumoniae macrolide resistance was positively associated with the consumption of macrolides for both food animals (Rho = 0.66; P=0.007) and humans (Rho = 0.54; P=0.040). Linear regression analysis revealed that macrolide consumption in food animals but not humans was borderline significantly associated with macrolide resistance (coef. 5547 [95% CI -596-11691] and coef. 0.006 [95% CI -0.008-0.020], respectively).

Given that population COVID-19 vaccination does not appreciably reduce SARS-CoV-2 transmission, instead, the potential to reduce hospitalization has been used to justify coercive vaccine passports. We aim to use a recently published research study as an example in order to demonstrate how data can be misinterpreted and result in deriving misleading ethical and policy implications. Bagshaw et al wrote that unvaccinated patients with COVID-19 in Alberta, Canada “had substantially greater rates of ICU admissions, ICU bed days, and ICU related costs than vaccinated patients did. This increased resource use would have been potentially avoidable had these unvaccinated patients been vaccinated.” The authors in Bagshaw et al then concluded that their findings “have important implications for discourse on the relative balance of increasingly stringent public health protection (restrictions), including mandatory vaccination policies, and the sustainability and function of health system infrastructure and capacity during the ongoing COVID-19 pandemic.” Here we show the following. First, the effect of vaccination on intensive care admissions were grossly over-estimated due to several limitations of this and almost all other vaccine studies. Second, an effect of vaccination on access to acute care and on all-cause excess deaths was grossly over-stated due to several more likely causes being omitted from discussion and from the common narrative. Third, policy implications were overstated and at best unclear due to missing consideration of more relevant aspects required to inform policy. Overall, the data cannot support what Bagshaw et al called “increasingly stringent public health protection (restrictions), including mandatory vaccination policies”.

Electrical microgrids are deemed to be the future of modern power systems. Microgrids are sophisticated, decentralized, and self-sufficient small-scale power systems consisting of various resources ranging from wind turbines, solar photovoltaics, electric vehicles, smart energy storage, and complex communication infrastructure. However, renewable energy sources such as solar photovoltaics and wind-based generators are highly intermittent, and if not appropriately planned, they can compromise the stability of the grid. Formal methods can define and analyze the functionality and behavior of any system and show if the system design is correct before the actual system is implemented. Although formal methods have been around for many years, it is surprising that little to none are utilized in the design of safety-critical electrical power systems. Currently, in modeling microgrids, few to no attempts of formalization are being used to improve the design reliability and reduce system operating costs and time. This work demonstrates how complex systems such as microgrids can be modeled elegantly using a formal specification method. In this work, the Z state-based formal specification language (Z-Method) is used to model and verify microgrid designs. In this work, 3-interconnected microgrid systems with a high penetration level of solar and wind-based renewable energy sources with plugin hybrid electric vehicles (PHEV) as battery energy storage systems (BESS) are modeled using the Z-method. To the best of authors, the knowledge presented formal method is one of the first reported attempts in modeling microgrid communities using Software Engineering formalism.

Soon after the Conservative-led Coalition government came to power in 2010, Baroness Sayeeda Warsi announced that Islamophobia had passed the ‘dinner-table test’ in contemporary Britain. Resultantly, the need to address Islamophobia was identified as a priority for the Coalition. This article critically analyses how the Coalition sought to achieve this and the extent to which it was successful. Focusing on the period 2010-5, this article initially frames what is meant by Islamophobia before briefly setting out how it had been responded to by previous British governments. As regards the Coalition, a threefold approach is adopted that considers the All-Party Parliamentary Group on Islamophobia, the Cross-Government Working Group on Anti-Muslim Hate and the political discourses used by the Coalition about Muslims and Islam more generally. Concluding that the Coalition failed to meet the high expectations set by Warsi’s speech, this article considers why this might have been so.

The COVID-19 crisis has brought unprecedented strain on healthcare systems around the world. It has perhaps taught us some key lessons that are worth considering and addressing to help build more sustainable health systems as well as improve our ability to combat future epidemics.

This study aimed to determine whether camelina oil is safe for use in canine diets, using canola oil and flax oil as controls as they are similar and generally regarded as safe (GRAS) for canine diets. Thirty privately-owned adult dogs of various breeds (17 females; 13 males), with an average age of 7.2 ± 3.1 years (mean ± SD) and body weight (BW) of 27.4 ± 14.0 were used. After a 4-week wash-in period using sunflower oil and kibble, dogs were blocked by breed, age, and size, and randomly allocated to one of three treatment oils (camelina (CAM), flax (FLX), or canola (OLA)) at a level of 8.2 g oil/100g total dietary intake. Body condition score (BCS), BW, food intake (FI), and hematological and select biochemical parameters were measured at various timepoints over a 16-week feeding period. All data were analyzed with ANOVA using PROC GLIMMIX of SAS. No biologically significant differences were seen between treatment groups for BW, BCS, FI, hematological and biochemical results. Statistically significant differences noted among some serum biochemical results were considered small and due to normal biological variation. These results support a conclusion that camelina oil is safe for use in canine nutrition.

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communications network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.

We have considered a SEIR-SEI Vector-host mathematical model which captures malaria transmission dynamics, described and built on 7-dimensional nonlinear ordinary differential equations. We compute the basic reproduction number of the model; examine the positivity and boundedness of the model compartments in a region using well established methods viz: Cauchy’s differential theorem, Birkhoff & Rota’s theorem which verifies and reveals the well-posedness, and carrying capacity of the model respectively, the existence of the Disease-Free (DFE) and Endemic (EDE) equilibrium points were determined and examined. Using the Gaussian elimination method and the Routh-hurwitz criterion, we convey stability analyses at DFE and EDE points which indicates that the DFE (malaria-free) and the EDE (epidemic outbreak) point occurs when the basic reproduction number is less than unity (one) and greater than unity (one) respectively. We obtain a solution to the model using the Variational iteration method (VIM) (an unprecedented method) to each population compartments and verify the efficacy, reliability and validity of the proposed method by comparing the respective solutions via tables and combined plots with the computer in-built Runge-kutta-Felhberg of fourth-fifths order (RKF-45). We illustrate the combined plot profiles of each compartment in the model, showing the dynamic behavior of these compartments; then we speculate that VIM is efficient and capable to conduct analysis on Malaria models and other epidemiological models.

Microbiome research has gained considerable interest due to the emerging evidence of its impact on human and animal health. Similar to higher organisms, the gut-associated microbiota of mosquitoes affect host fitness and other phenotypes. It is now well established that microbes can alter pathogen transmission in mosquitoes, either positively or negatively, and avenues are being explored to exploit microbes for vector control. However, less attention has been paid to how microbiota affect phenotypes that impact vectorial capacity. Several mosquito and pathogen components, such as vector density, biting rate, survival, vector competence and pathogen extrinsic incubation period all influence pathogen transmission. Interestingly, the mosquito gut-associated microbes can impact each of these components, and therefore ultimately modulate vectorial capacity. Promisingly, this expands the options available to exploit microbes for vector control by also targeting parameters that affect vectorial capacity. However, there are still many knowledge gaps in the biology of the mosquito – microbe symbiosis that need to be addressed in order to understand these interactions more thoroughly and exploit them efficiently. Here, we review current evidence of the impacts of the microbiome on aspects of vectorial capacity highlighting opportunities for novel vector control strategies and areas where further studies are required.

This article responds to an identified and significant gap in the existing scholarly canon to consider the extent to which cinematic representations construct heroin users—the ‘junkie’—as a criminalised ‘Other’ which confer legitimacy on the notion that such are criminogenic and deviant. Positioned within the disciplinary bounds of cultural criminology, this article focuses on five films - Sid and Nancy (1986); The Basketball Diaries (1995); Trainspotting (1996); Requiem for a Dream (2000); and, T2 Trainspotting (2017). Drawing together Hall’s (1997) theories of representation and Hjelm’s (2014) theories of social constructionism, the findings from a narrative analysis of each of the films—individually and comparatively—explores the following themes, junkies: as criminogenic; as dangerous underclass; as embodying decay and depravity; and in relation to female junkies, as junkie whores. In doing so, this article elucidates new thinking and ideas about cinematic representations of junkies and how this shapes and influences social norms and mores.

The effects of buckwheat intake on cardiovascular diseases (CVD) have not been systematically investigated. The aim of the present study was to comprehensively summarise studies in humans and animals evaluating the impact of buckwheat consumption on CVD risk markers and to conduct a meta-analysis of relevant data. Thirteen randomised, controlled human studies, two cross-sectional human studies and twenty-one animal studies were identified. Using random effects models, the weighted mean difference of post-intervention concentrations of blood glucose, total cholesterol and triglycerides were significantly decreased following buckwheat intervention compared with controls [differences in blood glucose: -0.85 mmol/L (95% CI: -1.31, -0.39), total cholesterol: 0.50 mmol/L (95% CI: -0.80, -0.20) and triglycerides: 0.25 mmol/L (95% CI: -0.49, -0.02)]. Responses of a similar magnitude were seen in two cross-sectional studies. For animal studies, nineteen of twenty-one studies showed a significant reduction in total cholesterol of between 12 and 54%, and fourteen of twenty studies showed a significant reduction in triglycerides of between 2 and 74%. All exhibited high unexplained heterogeneity. There was inconsistency in HDL cholesterol outcomes in both human and animal studies. It remains unclear whether increased buckwheat intake significantly benefits other markers of CVD risk, such as weight, blood pressure, insulin, and LDL-cholesterol, and underlying mechanisms responsible for any effects are unclear.

The present paper presents findings from the follow-up of 185 of these nurses who had been involved in the front-line care of COVID-19 patients in urban centres in Northern England, during and after the “second wave” of COVID patients in November, 2020. Measures of adjustment, stress (including PTSD), and Maslowian self-actualization were added to earlier measures of personality, adjustment, work-life stress, and career intention. Cluster analysis identified three types in the 2020-2023 cohort: I ‘Actualizing Professionals’; (N=62); II ‘Strong Professionals’ (N=56); III ‘Highly Stressed Nurses’ (N=45). These latter very stressed nurses included all nurses (N=16) identified as having PTSD symptoms.

Milk fat production is important in the New Zealand (NZ) dairy industry. Elsewhere, an amino acid substitution (K232A) in the enzyme diacylglycerol acyl-CoA acyltransferase (DGAT1) has been reported to explain variation in some milk traits, including variation in milk fatty acid (FA) profile. In this study, associations between K232A, and milk traits including milk FA composition, were studied in wholly pasture-fed NZ Holstein-Friesian × Jersey (HF×J) cross-bred dairy cows. With a high frequency of K variant (61.9%), the KK cows produced more milk fat than the AA cows (5.41 ± 0.04 % vs 4.42 ± 0.05 %). The milk volume, fat concentration and protein concentration of AK cows were between the genotypes, AA and KK. More C16:0, CLA and C18:3 cis-9, 12, 15 FA were found in the milk of Kiwi-cross cows feed in outdoor pasture grazing system., and the influence of DGAT1 K232A, on these FAs from mid- and late lactation stages were significant. The AA cows produced (P < 0.001) more CLA and C18:3 cis-9, 12, 15, but less C16:0 (1.137 ± 0.047, 0.855 ± 0.015 and 35.170 ± 0.355) than the KK cows (0.934 ± 0.025, 0.778 ± 0.009 and 38.010 ± 0.250).

There is considerable interest in the use of doxycycline post exposure prophylaxis (PEP) to reduce the incidence of bacterial sexually transmitted infections (STIs). An important concern is that this could select for tetracycline resistance in these STIs and other species. We searched PubMed and Google Scholar, (1948–2023) for randomized controlled trials comparing tetracycline PEP with non-tetracycline controls. The primary outcome was antimicrobial resistance (AMR) to tetracyclines in all bacterial species with available data. Our search yielded 140 studies, of which three met the inclusion criteria. Tetracycline PEP had no effect on the prevalence of tetracycline resistance in Neisseria gonorrhoeae in any of the three trials or the pooled estimates. PEP did, however, have a marked effect on the tetracycline MIC distribution in the one study where this was assessed. Prophylactic efficacy was 100% at low MICs and 0% at high MICs. In the one study where this was assessed, PEP resulted in a significant increase in tetracycline resistance in commensal Neisseria species compared to the control group but no significant effect on the prevalence of tetracycline resistance in Staphylococcus aureus. The available evidence suggests that PEP with tetracyclines could be associated with selecting tetracycline resistance in N. gonorrhoeae and commensal Neisseria species.

Mountain fire can become more complex than fires at lower elevation due to the complex interaction of fire, topography, and weather. The Gell River Fire in Tasmania, Australia occurred in rugged terrain where there are abundant fire sensitive vegetation communities, as well as the presence of infrastructure including high-voltage transmission lines. The fire began at the end of December 2018 and lasted a few months, with a final burnt area of approximately 350km² despite significant fire suppression effort. The fire was investigated by employing wind vector maps, numerical weather model vertical sounding charts (NWMVS) and Prototype 2, which is an integrated fire simulator and can detect lateral fire channeling (LFC). Our analysis of the fire found its spread was likely to be introduced into a valley by forced channeling (FC), which is modified synoptic wind, and showed rapid spread in the valley. The simulated fire also showed wider spread than the observed data in the valley, with the simulated fire impacting highly sensitive vegetation communities on the fringes of the valley. This alludes to some potential conclusions: (1) The loss of fire sensitive vegetation would have increased if fire suppression activity had not been conducted. (2) Spotting fires could be produced by LFC because these spotting fire would allow spreading fire in a shorter period. (3) Heterogeneity of vegetation, such as combination of buttongrass and forest, could help carry fire rapidly in the valley with LFC. Fire can propagate faster in buttongrass than in forests while the forests allow the spotting fire.

In 1990 the Malaysian Government launched Vision 2020 with the goal of Malaysia becoming a ‘fully developed country’ by the year 2020. In this drive for development the country has been examining many of its key natural assets, exploring how to both preserve them whilst ensuring that they make the maximum possible contribution to national development goals. Endau Rompin is one of these assets, being the second largest National Park in Peninsula Malaysia. The rich tropical rainforest flora and fauna of the Park and its hinterland, however, are threatened by various anthropogenic forces, most notably deforestation and poaching; both of which have taken the Park’s flagship species - the Tiger Panthera tigris jacksoni – towards the brink of local extinction. Malaysia’s statutory East Coast Economic Region Development Council commissioned a study to consider ways of increasing tourism revenues from the Park while at the same time reversing these threats. The key conclusion drawn is that a more holistic, landscape-scale approach should be adopted, with Endau Rompin forming part of a wider strategic economic zone based around sustainable tourism and land management. The proposed expanded zone extends eastwards beyond the confines of the Park, across the coastal plain to the South China Sea, and onwards to the neighbouring Mersing Islands. This expanded zone is referred to as Malaysia’s new ‘Rainforest to Reef Region’.

In this paper, we introduce a non-invasive approach for monitoring bridge infrastructure with ground-based interferometric radar. This approach is called the mirror mode, since it utilises the flat surface of the bridge underside as a mirror to reflect the signal to a corner reflector on the ground placed opposite of the radar sensor. For proving the feasibility of this approach, a measurement campaign has been carried out at an exemplary bridge in Karlsruhe (Germany) including a radar sensor in mirror mode, a second radar sensor in the default mode and a laser profile scanner. We investigate the potential of this approach to monitor the bridge displacement in vertical direction and compare the results with the two other sensors. The derived results reveal the potential for monitoring bridge infrastructure. Finally, we propose further research aspects of this approach to analyse its capabilities and limitation in the context of non-invasive infrastructure monitoring.

Navigating growth cones are exposed to multiple signals simultaneously and have to integrate competing cues into a coherent navigational response. Integration of guidance cues is traditionally thought to occur at the level of cytoskeletal dynamics. Drosophila studies indicate that cells exhibit a low level of continuous caspase protease activation, and that axon guidance cues can activate or suppress caspase activity. We base a model for axon guidance on these observations. By analogy with other systems in which caspase signaling has non-apoptotic functions, we propose that caspase signaling can either reinforce repulsion or negate attraction in response to external guidance cues by cleaving cytoskeletal proteins. Over the course of an entire trajectory, incorrectly navigating axons may pass the threshold for apoptosis and be eliminated, whereas axons making correct decisions will survive. These observations would also explain why neurotrophic factors can act as axon guidance cues and why axon guidance systems such as Slit/Robo signaling may act as tumor suppressors in cancer.

We consider the restrictions on photovoltaic (PV) capacity that are caused by limitations on where panels can be sited, and find quantitative evidence for the need for high efficiencies. We define 15% of the UK’s energy consumption as a “significant” contribution and, with London as an exemplar, we perform an idealised calculation that makes the most optimistic possible assumptions about the capabilities of future PV technologies, and use published surveys of energy usage, dwelling type and insolation. We find that covering every UK domestic roof with the highest power-conversion efficiency (PCE) solar panels currently commercially available, could produce up to 9% of the UK's energy. A 15% contribution would require PV technologies with &gt;37% PCE, more than the theoretical Shockley-Queisser limit. Replacing the idealising assumptions with more realistic estimates increases this by 2-3 times. Alternatively, a solar farm using the currently available PCEs would require a politically challenging ~1200km2 of new land, roughly the area of Greater London, for this 15% contribution. We conclude that PCEs must be driven higher even than the Shockley-Queisser limit for PV to play a significant part in carbon mitigation.

We exploit the properties of complex time to obtain an analytical relationship based on considerations of causality between the two conserved quantities of a system’s Hamiltonian and its Entropy Production. In natural units, when complexified, the one is simply the Wick-rotated complex-conjugate of the other. A Hilbert transform relation is constructed in the formalism of Quantitative Geometrical Thermodynamics which enables system irreversibility to be handled analytically within a framework that unifies both the microscopic and macroscopic scales, and which also unifies the treatment of both reversibility and irreversibility as complementary parts of a single physical description. In particular the thermodynamics of two unitary entities are considered: the alpha particle which is absolutely stable (that is, trivially reversible with zero entropy production), and a black hole whose absolute disequilibrium (unconditional irreversibility) is characterized by a non-zero entropy production for which we show an alternate derivation confirming our previous one (Universe 7, 2021, 325). The thermodynamics of a canonical decaying harmonic oscillator are also considered. In this treatment the complexification of time also enables a meaningful physical interpretation of both “imaginary time” and “imaginary energy”.

As Christopher Alexander discovered, all space or matter – either organic or inorganic – has some degree of order in it according to its structure and arrangement. The order refers to a kind of structural character, called living structure, which is defined as a mathematical structure that consists of numerous substructures with an inherent hierarchy. Across the hierarchy, there are far more small substructures than large ones, while on each level of the hierarchy the substructures are more or less similar in size. In this paper we develop a new approach to representing geographic space as a hierarchy of recursively defined subspaces for computing the degree of order. A geographic space is first represented as a hierarchy of recursively defined subspaces, and all the subspaces are then topologically represented as a network for computing the degree of order of the geographic space, as well as that of its subspaces. Unlike conventional geographic representations, which are mechanical in nature, this new geographic representation is organic, conceived, and developed under the third view of space; that is, space is neither lifeless nor neutral, but a living structure capable of being more living or less living. Thus, the order can also be referred to as life, beauty, coherence, or harmony. We applied the new representation to three urban environments, 253 patterns, and 35 black-white strips to verify it and to demonstrate advantages of the new approach and the new kind of order. We further discuss the implications of the approach and the order on geographic information science and sustainable urban planning.

Double-spiral galaxies are common in the Universe. It is known that the logarithmic double spiral is a Maximum Entropy geometry and represents spiral galaxies well. It is also known that the virial mass of such a galaxy can be approximately determined from the entropy of its central supermassive black hole. But over time the black hole must accrete mass, and therefore the overall galactic entropy must increase. From the associated entropic Euler-Lagrange equation (forming the basis of the Principle of Least Exertion, and also enabling the application of Nöther’s theorem) we show that the galactic entropy production is a conserved quantity, and we derive an appropriate expression for the relativistic entropic Hamiltonian of an idealised spiral galaxy. We generalise Onsager’s celebrated expression for entropy production and demonstrate that galactic entropy production has two parts, one many orders of magnitude larger than the other, and where the smaller is comparable to the Hawking radiation of the central supermassive black hole. We conclude that galaxies cannot be isolated, since even idealised spiral galaxies have non-zero entropy production.

This paper examines the spatial and temporal distribution of all COVID-19 cases from January to June 2020 against the underlying distribution of population in the United States. It is found that, as time passes, COVID-19 cases become a power law with cut-off, resembling the underlying spatial distribution of populations. The power law implies that many states and counties have a low number of cases, while only a few highly populated states and counties have a high number of cases. To further differentiate patterns between the underlying populations and COVID-19 cases, we derived their inherent hierarchy characterized by the ht-index. We found that the ht-index of COVID-19 cases persistently approaches that of the populations; that is, 5 and 7 at the state and county levels, respectively. Mapping the ht-index of COVID-19 cases against that of populations provides new insights into the development of the pandemic in the United States.

Learning models of the cerebellum propose that the cerebellum implements an algorithm which makes iterative adjustments to synaptic transmission strength that collectively determine the response to input in learned patterns following training. We propose instead that pattern recognition and control of firing by output cells are separately handled functions which process independently variable data. This can account for the evidence without a machine learning algorithm. The model is a hybrid of physiological arguments and computational methods used to test and quantify the ideas. We argue inter alia that learning operates at the level of functional groups of Purkinje cells defined by their shared climbing fibre input; Golgi cells have several functions and regulation of parallel fibre activity by Golgi cells is not in the expected way; recoding of input to the cerebellum received in the granular layer converts the number of input variables (variables expressed in mossy fibre input to the system) into a much reduced number of functional variables expressed by internal signals traffic; circuits simultaneously execute separate but integrated functions of pattern memory and output coding; they are able to operate separately because the expression of data used in each varies independently of the other; output rates are not learned but controlled by recent relevant input signals in a window opened by pattern memory; the moment-to-moment probability that a Purkinje cell spikes is synchronised across a microzone; a principal function of functional organisation of Purkinje cells into microzones is to increase resolution of rate coded information received by the output cells of the circuit, and to do so in a very short integration window, so that circuit architecture can be explained partly as a device with this function.

Extensive polymorphism screening of elacestrant dihydrochloride, an Active Pharmaceutical Ingredient (API), was conducted using various methods and solvents. The solubility assessment and polymorph screening of elacestrant dihydrochloride (referred to as elacestrant throughout the paper) utilized both crystalline and amorphous forms of the API as input material with 40 non-conventional solvents, employing slow cooling, maturation, and/or evaporation. Crystalline material underwent further screening via anti-solvent addition, polymer templating, and solvent-drop grinding. A total of 7 new polymorphs were identified. No solvated forms were identified, but some samples contained notable water content, tentatively classified as hydrated forms. Comprehensive solid-state characterization data for representative crystalline samples of each polymorph is presented. Polymorph A exhibited the highest melt (225-226 °C, onset). Thermal analysis of polymorphs B, C, D, E, F and G showed an endothermic-exothermic event (with an onset temperature between 136 and 184 °C), indicating a form conversion, followed by a melt at 205-222 °C (onset), consistent with the previously identified and most stable form of elacestrant. This suggests that these materials convert to the most stable form prior to melting. Pattern G does not show an endothermic-exothermic event, but after desolvation, the material melts at 222 °C (onset temperature).

The cause of sarcoidosis is unknown, and vitamin D is contraindicated to treat the condition. We therefore ask what causes sarcoidosis and why vitamin D can be dangerous for those so afflicted. We propose a contrary hypothesis: sarcoidosis is a physiological process of defense against cancer that requires the substrate vitamin D. We tested this hypothesis, finding many case reports involving sarcoidosis and cancer—further cases of cancer mimicry by sarcoidosis. Several reports describe spontaneous healing of cancer in the presence of sarcoidosis. In the context of the granulomas of sarcoidosis, monocytes are the site of vitamin D release. Furthermore, active vitamin D can control cancer cells by using the vitamin D receptors of the nucleus. That granulomas consistently do not caseate is explained as a confrontation with human molecules and the cancer cells’ complete absorption. The striking fact that granulomas of sarcoidosis mostly lack cancerous cells is interpreted as a result of a well-functioning sarcoidosis process. We view the typical sarcoidosis monocytic synthesis of active vitamin D as a successful defense against cancer: a theory we call the “endogenous program organizing cancer apoptosis” (EPOCA).

The Principle of Indifference (“PI”: the simplest non-informative prior in Bayesian probability) has been shown to lead to paradoxes since Bertrand (1889). Von Mises (1928) introduced the “Wine/Water Paradox” as a resonant example of a “Bertrand paradox”, and which has been presented as demonstrating that the PI must be rejected. We now resolve these paradoxes by a Maximum Entropy (MaxEnt) treatment of the PI that also includes information provided by Benford’s “Law of Anomalous Numbers” (1938). We show that the PI should be understood to represent a family of informationally-identical MaxEnt solutions; each solution being identified with its own explicitly justified boundary condition. In particular, our solution of the Wine/Water Paradox exploits Benford’s Law to construct a non-uniform distribution representing the universal constraint of scale invariance, which is a physical consequence of the Second Law of Thermodynamics.

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulates heat balances and gaseous exchanges in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of learning techniques is one such tool that is used to identify the characteristics of seasonal ice variability. This paper utilizes the Sentinel-1 RADAR imagery with a support vector machine learning scheme through Google Earth Engine to classify lead types in the Beaufort Sea.

A significant percentage of children suffer from neurodevelopmental aberrations, which have long-term effects on both individuals and society. Since the developmental timelines of different brain regions vary, the type, severity and timing of harmful exposures are crucial for understanding the specific pathological processes and consequences involved. During development, the cerebellum is one of the first brain structures to begin to differentiate, but one of the last to achieve maturity. This relatively long period underscores its vulnerability to detrimental environmental exposures throughout gestation. Moreover, as the postnatal functionality of the cerebellum is multifaceted, enveloping sensorimotor, cognitive, and emotional domains, prenatal disruptions in cerebellar development can result in a variety of neurological and mental health disorders. Here, we review major intrauterine insults that affect cerebellar development in both humans and rodents, ranging from abuse of toxic chemical agents such as alcohol and nicotine, to stress and sleep, malnutrition as well as infections. Understanding these pathological mechanisms in the context of the different stages of cerebellar development in humans and rodents may help us to identify critical and vulnerable periods and thereby to prevent the risk of associated prenatal and early postnatal damage that can lead to lifelong neurological and cognitive disabilities.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

Highly-correlated electrons – electrons that engage in strong electron-electron interactions – have been observed in transition metal oxides and quantum dots and can create unusual material behavior that is difficult to model, such as switching between a low resistance metal state and a high resistance Mott insulator state. Tests of devices using a layer-by-layer deposition process for forming multilayer arrays of ferritin (a transition metal (iron) oxide storage protein) have been previously reported that indicate that highly-correlated electron transport is occurring, consistent with models of electron transport in quantum dots. This paper reports the results of the effect of various degrees of structural homogeneity on the electrical characteristics of these ferritin arrays, as well as demonstrating that these structures can provide a switching function associated with the circuit that they are contained within, consistent with the observed behavior of highly-correlated electrons.

The Azores Archipelago is a collection of volcanic islands near northwestern Portugal in the Atlantic Ocean. Because of the importance of accurate environmental monitoring for the advancement of research, transportation and tourism, a LoRaWan Sensor Network was developed to monitor levels of air pollution across these islands. This study presents an approach to the implementation of a blockchain network that is both lightweight and scalable, with the goal of preventing man-in-the-middle attacks on the design of sensor networks.

We explore the importance of curvature in carbonaceaous species transformation and stability, using laser-induced vaporisation to evaporate and ionise a source of curved polyaromatic hydrocarbons: carbon nanobelts. Collision impacts between species cause mass loss and the resultant ions are catalogued via mass-spectrometry. To interpret the mass spectra, we perform a series of “in-silico” simulated systematic hydrogen-loss studies using density functional theory (DFT) modelling, sequentially removing hydrogen atoms using thermodynamic stability as a selection for subsequent dehydrogenation. In an initial sequence of H2 removal, the rings are maintained through stable carbyne chain and pentagon-chain formation, giving rise to circular strained dehydrobenzoannulene species. The chains subsequently break, releasing CH and C2. While theoretical closed-cage routes are identified, they are not observed experimentally. The results can serve as a useful guide to high-energy impact conditions observed in some astrochemical environments.

A recent publication defined lockdown “revisionism” as “the spread of misinformation on lockdowns and other public health measures.” We used this publication to analyze the claim that questioning lockdowns or other public health interventions and mandates amounts to lockdown “revisionism” and “misinformation”. We suggest that the term ‘revisionism’, like the term ‘misinformation’ contained in its definition, were merely labels used to denigrate evidence-based contrary conclusions so as to avoid having to critically appraise the best evidence itself. We aim to describe how, by glossing over topics without fully engaging with the best evidence available, the assertions made do not withstand critical scrutiny. We suggest that, to ensure lessons are learned for the future, we must be willing to engage in rigorous and open debate – calling reasonable critical scrutiny of evidence ‘misinformation’, ‘disinformation’, or ‘revisionism’ is not supportive of this goal. Finally, we suggest that a main lesson from the COVID-19 pandemic is not to have an increased focus on so-called ‘misinformation’ and ‘revisionism’, but rather to re-discover the emergency management process of making decisions that ensure multidisciplinary representation, transparency, cost-benefit analyses of courses open using the best evidence available, and that protects against censorship and groupthink.

Although mountain areas account for approximately one fifth of the terrestrial surface, there has been less research focused on fire in these areas compared to lowlands. Mountain fires have distinct behavior due to dynamic winds interacting with the terrain, which can influence the fireline intensity and propagation. For the sake of fire safety of fire crews, it is essential to know how difficult to control the fire is in the mountain regions, with fireline intensity providing a useful indicator of risk and suppressibility. We studied one of the major disasters, wildfire, in Australia in such a highland by using the Great Pine Tier Fire, which occurred 15^th January in 2019, ending up burning approximately 511.86km². Weather and fire intensity at pseudo weather stations located at key points of fire progression were analyzed by wind vector maps and numerical weather model vertical sounding (NWMVS). Fire propagation was then simulated in Prototype 2, a fire simulator capable of detecting the potential for lateral fire channeling (LFC), and simulating fireline intensity using Australian vegetation sub-models. We found that the synoptic wind appeared to be modified by the interaction with the terrain in windward and the fire intensified the most in its leeward. In practice, the fire moved out of the valley axis and up its sidehill by following the wind which had been modified by local vertex of the curved valley axis before reaching this location.

Background: We studied Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 and burnt more than 637.19 km² in southern Tasmania, Australia. Aims: We focused on fire weather, such as identification of dynamic wind and vegetation type, in a valley of the study area. Methods: We employed two methods: numerical weather model vertical sounding (NWMVS) and the use of a fire simulator, to quantify and examine the contribution of dynamic winds to fire behaviour. The NWMVSs allow rapid diagnosis of changes in wind, temperature, dew point temperature and cloud coverage. Prototype 2 is a fire simulator based on the specification of Australian Fire Danger Rating System (AFDRS). Key results: We found fires to be guided by terrain-forced channelling primarily and by downslope wind conditionally in the valleys. In addition, the fire intensity periodically changed with the magnitude of surface wind, in buttongrass moorland, in which the fire often smoulders, during the fire period according to the satellite image. Conclusions and Implications: Therefore, there should be caution for not only terrain and dynamic wind but also vegetation type during fire spread in rugged terrain.

Consumption of prebiotic fibers to modulate the human gut microbiome is a promising strategy to positively impact health. Nevertheless, given the compositional complexity of the microbiome and its inter-individual variances, generalized recommendations on the source or amount of fiber supplements remain vague. This problem is further compounded by availability of tractable in vitro and in vivo models to validate certain fibers. We employed a gnotobiotic mouse model containing an a priori characterized 14-member synthetic human gut microbiome (SM) for their ability to metabolize a suit of fibers in vitro; the SM contains 14 different strains belonging to five distinct phyla. Since soluble purified fibers have been a common subject of studies, we specifically investigated the effects of concentrated raw fibers (CRFs)—containing fibers from pea, oat, psyllium, wheat and apple—on the compositional and functional alterations in the SM. We demonstrate that, compared to a fiber-free diet, CRF supplementation increased the abundance of fiber-degraders namely Eubacterium rectale, Roseburia intestinalis and Bacteroides ovatus and decreased the abundance of the mucin-degrader Akkermansia muciniphila. These results were corroborated by a general increase of bacterial fiber-degrading α-glucosidase enzyme activity. Overall, our results highlight the ability of CRFs to enhance the microbial fiber-degrading capacity.

A case study is presented which demonstrates the value and validity of a novel approach to the use of consolidated amateur (‘ham’) radio reception reports as indicators of the presence of intense ionospheric sporadic E (Es). It is shown that the use of amateur data can provide an important supplement to other techniques, allowing the detection and tracking of Es where no suitable ionosonde or other measurements are available. The effectiveness of the approach is demonstrated by reference to ionosonde data, and the advantages and limitations of the technique are discussed.

We consider the problem of coordinating the charging of an entire fleet of electric vehicles (EV), using a model-free approach, i.e. purely data-driven reinforcement learning (RL). The objective of the RL-based control is to optimize charging actions, while fulfilling all EV charging constraints (e.g. timely completion of the charging). In particular, we focus on batch-mode learning and adopt fitted Q-iteration (FQI). A core component in FQI is approximating the Q-function using a regression technique, from which the policy is derived. Recently, a dueling neural networks architecture was proposed and shown to lead to better policy evaluation in the presence of many similar-valued actions, as applied in a computer game context. The main research contributions of the current paper are that (i)we develop a dueling neural networks approach for the setting of joint coordination of an entire EV fleet, and (ii)we evaluate its performance and compare it to an all-knowing benchmark and an FQI approach using EXTRA trees regression technique, a popular approach currently discussed in EV related works. We present a case study where RL agents are trained with an epsilon-greedy approach for different objectives, (a)cost minimization, and (b)maximization of self-consumption of local renewable energy sources. Our results indicate that RL agents achieve significant cost reductions (70--80%) compared to a business-as-usual scenario without smart charging. Comparing the dueling neural networks regression to EXTRA trees indicates that for our case study's EV fleet parameters and training scenario, the EXTRA trees-based agents achieve higher performance in terms of both lower costs (or higher self-consumption) and stronger robustness, i.e. less variation among trained agents. This suggests that adopting dueling neural networks in this EV setting is not particularly beneficial as opposed to the Atari game context from where this idea originated.

Understanding the ionic channel network of proton exchange membranes, which dictate fuel cell performance, is crucial when developing proton exchange membrane fuel cells. However, itis difficult to characterize due to complicated nano structure and differing changes to their structure with different amounts of water uptake. Electrostatic force microscopy (EFM) can map surface charge distribution as nano special resolution by measuring the electrostatic force between a vibrating conductive tip and a charged surface under an applied voltage, . In this study, the ionic channel network of a proton exchange membrane is analyzed using EFM. A mathematical approximation model of the ionic channel network is first derived, to explain changes in force gradient on the surface using EFM. The phase lag of dry and wet Nafion under stepwise changes to bias voltage is then measured. Based on the model, variations in the ionic channel network of Nafion with different amounts of water uptake are analyzed numerically. The mean surface charge density of both membranes, which is connected with the ionic channel network, is calculated using the model. The results show that the difference between the mean surface charge of the dry and wet membranes is consistent with the variation in their proton conductivity.

Generating a range-wide population status of the diamondback terrapin (Malaclemys terrapin spp.) is challenging due to a combination of species ecology and behavior, and limitations associated with traditional sampling methods. Visual counting of emergent heads offers an efficient, non-invasive and promising method for generating abundance estimates of terrapin populations across broader spatial scales and can be used to explain spatial variation in population size. We conducted repeated visual head count surveys at 38 predetermined sites along the shoreline of Wellfleet Bay in Wellfleet, Massachusetts. We analyzed the count data using a hierarchical modeling framework designed specifically to analyze repeated count data: the so-called N-mixture model. This approach allows for simultaneous modeling of imperfect detection to generate estimates of true terrapin abundance. We found detection probability was lowest when skies were overcast and when wind speed was highest. Site specific abundance varied but we found that abundance estimates were, on average, higher in unexposed sites compared to exposed sites. We demonstrate the utility of pairing visual head counts and N-mixture models as an efficient method for estimating terrapin abundance and show how the approach can be used to identifying environmental factors that influence detectability and distribution.

IntroductionDiversity is known to be important but diversity of staff working in dental schools in the UK and Ireland is low in comparison with the dental profession and the overall population. AimsTo provide a detailed snapshot of the diversity of dental academic staff in 2021 including staff working in academic and clinical roles.MethodsAn online survey was circulated to dental schools by Dental Schools Council. Questions included dates of graduation and key career milestones, demographics and indicators of esteem. Free text questions were also included.ResultsThere was a high degree of variability in dental academic careers which made direct comparisons difficult. Neither gender nor race appeared to be associated with a faster career progression. Females and those of racialised minority were less represented at the most senior grades. For those who had completed specialty training, racialised minority staff were far less likely to be working at consultant grade than their White equivalents.ConclusionThere are differences in career progression between staff in dental schools. The high amount of individual variability makes these difficult to quantify except at the most senior positions. Qualitative information is needed to contextualise the results and provide additional information.

The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics, and language sciences more generally. Information-theory gives us tools at hand to measure precisely the average amount of choice associated with words—the word entropy. Here we use three parallel corpora—encompassing ca. 450 million words in 1916 texts and 1259 languages—to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present three main results: 1) a text size of 50K tokens is sufficient for word entropies to stabilize throughout the text, 2) across languages of the world, word entropies display a unimodal distribution that is skewed to the right. This suggests that there is a trade-off between the learnability and expressivity of words across languages of the world. 3) There is a strong linear relationship between unigram entropies and entropy rates, suggesting that they are inherently linked. We discuss the implications of these results for studying the diversity and evolution of languages from an information-theoretic point of view.

The ability to predict physical characteristics from DNA presents significant opportunities for forensic science. Giving scientists an ability to make predictions about the donor of genetic material at a crime scene can then give investigators new intelligence leads for cold cases where DNA evidence has not identified any person of interest. However, the interpretation of this new form of intelligence requires careful analysis. The responses to an online survey, conducted in 2018-19, were used to examine how actors in the criminal justice system assess and interpret different types of DNA evidence and intelligence. The groups of focus for the survey were investigators, legal practitioners and the general public (as potential jurors). Several statistically significant effects were identified based on occupation and whether an individual had prior exposure to new DNA technology. Monitoring how those involved in interpreting reports from different types of DNA evidence and intelligence interpret them helps to ensure that decisions are made based on a sound understanding of their capabilities and limitations and may inform broader training and awareness strategies.

The Semantic-Enhanced Tree-LSTM (SeT-LSTM) network, a novel advancement in the realm of linguistic modeling, marks a significant step forward from traditional Tree-based Long Short Term Memory (LSTM) networks. By intricately weaving in the nuances of typed grammatical dependencies, SeT-LSTMs offer a more nuanced understanding of language semantics. Traditional models often overlook the semantic shift caused by variations in word or phrase roles, a gap this paper aims to fill by focusing on the types of grammatical connections, or typed dependencies, within sentences. Our proposed architecture, dubbed the Semantic Relationship-Guided LSTM (SRG-LSTM), leverages a control mechanism to model the interplay between sequence elements. Additionally, we present a novel Tree-LSTM variant, the Semantic Dependency Tree-LSTM (SDT-LSTM), which integrates dependency parse structures with dependency types for more robust sentence embedding. The SDT-LSTM demonstrates superior performance in Semantic Relatedness Scoring and Sentiment Analysis compared to its predecessors. Qualitatively, it shows resilience to changes in sentence voice and heightened sensitivity to nominal alterations, aligning well with human intuition. This research underlines the pivotal role of grammatical relationships in sentence understanding and paves the way for further exploration in this domain.

There is a paucity of data on the progression of track cycling sprinters, and the evolution of training and performance over a training cycle. Following prior research showing relationships between sprint cycling power and endurance cycling power, we compared these relations over a 3-month period. Our hypothesis is large improvements in power would come from training either for sprint power or sprint capacity, and this would be reflected in the data. A total of publicly available 25 datasets. These data were plotted against the line of best fit for 30-s and 2-min power to assess if training towards the line led to better 30-s power. We observe the best performances came from riders who start below the 30-s vs 2-min power line of best fit, with greater capacity and ability to ride fast over a sprint series, and progressing towards focusing on specific power in the final block leading into a competition. These results support the hypothesis of combined capacity and power training based on rider-specific relationship to the line of best fit between these measures versus a strictly maximal power training focus.

Physics has been thought to truly represent reality since at least Galileo, and the foundations of physics are always established using philosophical ideas. In particular, the elegant naming of physical entities is usually very influential in the acceptance of physical theories. We here demonstrate (using current developments in thermodynamics as an example) that both the epistemology and the ontology of physics ultimately rest on poetic language. What we understand depends essentially on the language we use. We wish to establish our knowledge securely, but strictly speaking this is impossible using only analytic language. Knowledge of the meanings of things must use a natural language designed to express meaning, that is, poetic language. Although the world is really there, and although we can indeed know it truly, this knowledge is never either complete or certain but ultimately must rest on intuition. Reading a recently discovered artefact with a palaeo-Hebrew inscription as from the first century, we demonstrate from it that this ontological understanding long predates the Hellenic period. Poetic language is primary, both logically and temporally.

We survey the main results emerging in the rapidly developing field that considers the geometry of thermodynamics and statistical mechanics. Our ideas of stability depend on variational principles – Maximum Entropy together with Least Action (and the isomorphic Least Exertion) – and this stability (invariance in time) indicates the centrality of the geometries of the systems. We explore a variety of examples of the application of thermodynamics in both macro- and micro-systems, and propose that irreversibility is an intrinsic and fundamental property of the physical realm (instead of being merely emergent). Using the concept of complex time, we examine the underlying mathematical unity of quantum mechanics, statistical mechanics, and geometrical thermodynamics. We show how the Planck and Boltzmann constants provide the fundamental quantum units for the phenomenological characteristics of the two Noether-conserved quantities controlling overall thermodynamic behaviour: respectively, energy and entropy production. We discuss the far-reaching isomorphisms (between all of complex time, temperature and the system symmetry) that always exist for Maximum Entropy systems, even as they exhibit either irreversible (dissipative, yet still dynamically stable) or reversible (including stable and static geometries) behaviours.

This paper proposes the localisation of room occupants in home environments using Unobtrusive Sensing Solutions (USSs). The ability to localise room occupants in home environments can help in the objective monitoring of sedentary behaviour. While wearable sensors can provide tangible information on health and wellness, they have battery life issues and the inability to perform prolonged monitoring. This work uses heterogeneous USSs in the form of an Infrared Thermopile Array (ITA-64) thermal sensor and a Multi-Chirp Frequency Modulated Continuous Wave Mono-pulse (MC-FMCW-M) Radar sensor to monitor room occupants. Digital filters and background subtraction algorithms were used to process the thermal images gleaned from the ITA-64 thermal sensors. The MC-FMCW-M Radar sensor used multi-chirp and Doppler shift principles to estimate the exact location of the targeted room occupants. The estimated distances from the Radar Sensor were compared with ground truth values. Experimental results demonstrated the ability to identify thermal blobs of occupants present in the room at any particular time. Data analyses indicated no significant difference (p = 0.975) and a very strong positive correlation (r = 0.998) between the ground truth distance values and those obtained from the Radar Sensor.

The world is in the grips of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increase concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D [25(OH)D] concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome, and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D₃ for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40–60 ng/ml (100–150 nmol/l). For treatment of people who become infected with COVID-19, higher vitamin D₃ doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

Background: Nutrient supplements are widely used for type 2 diabetes (T2D) yet evidence-based guidance for clinicians is lacking. Methods: We searched the four electronic databases from November 2015–December 2021. The most recent, most comprehensive, high-ranked systematic reviews, meta-analyses and/or umbrella reviews of randomised controlled trials in adults with T2D were included. Data were extracted on study characteristics, aggregate outcome measures per group (glycemic control, measures of insulin sensitivity and secretion), adverse events, and GRADE assessments. Quality was assessed using AMSTAR-2. Results: Twelve meta-analyses and one umbrella review were included. There was very low certainty evidence that chromium, Vitamin C and omega-3 polyunsaturated fatty acids (-3 PUFAs) were superior to placebo for the primary outcome of HbA1c (MD -0.54%, -0.54% and ES -0.27 respectively). Probiotics were superior to placebo for HbA1c (WMD -0.43%). There was very low certainty evidence that Vitamin D was superior to placebo for lowering HbA1c in trials of <6 months (MD -0.17%). Magnesium, zinc, Vitamin C, probiotics and polyphenols were superior to placebo for FBG. Vitamin D was superior to placebo for insulin resistance. Data on safety was limited. Conclusions: Future research should identify who may benefit from nutrient supplementation, safety, and optimal regimens and formulations.

Age is the primary risk factor for neurodegenerative diseases such as Alzheimer’s and Huntington’s disease. Alzheimer’s disease is the most common form of dementia and a leading cause of death in the elderly population of the United States. No effective treatments for these diseases currently exist. Identifying effective treatments for Alzheimer’s, Huntington’s, and other neurodegenerative diseases is a major current focus of national scientific resources, and there is a critical need for novel therapeutic strategies. Here we investigate the potential for targeting the kynurenine pathway metabolite 3-hydroxyanthranilic acid (3HAA) using Caenorhabditis elegans expressing amyloid-beta or a polyglutamine peptide in body wall muscle, modeling proteotoxicity in Alzheimer’s and Huntington’s disease-related, respectively. We show that knocking down the enzyme that degrades 3HAA, 3HAA dioxygenase (HAAO) delays age-associated paralysis in both models. This effect on paralysis was independent of protein aggregation in the polyglutamine model. We also show that the mechanism of protection against proteotoxicity from HAAO knockdown is mimicked by 3HAA supplementation, supporting elevated 3HAA as the mediating event liking HAAO knockdown to delayed paralysis. This work demonstrates potential for 3HAA as a targeted therapeutic in neurodegenerative disease, though the mechanism is yet to be explored.

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable modes of transport in urban mobility to help reduce the environmental impact. However, its traffic-related, economic and environmental effects are merely represented in academic research. We used passenger car traffic data in Berlin generated by the multi-agent transport simulation framework MATSim to develop a python-based simulation, resembling an e-moped sharing system. Based on the results, a total cost of ownership and a life cycle assessment for fleet sizes of 2,500, 10,000 and 50,000 vehicles were conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2,500 vehicles exhibits the lowest environmental emissions per kilometer driven over the expected lifespan of a shared e-moped. Based on the renewable energy potential for 2050 forecasted by the German Federal Environment Agency, a significant overall decline in environmental impacts can be achieved.

The 2030 Agenda for Sustainable Development, the SDGs, are high on the agenda for most countries of the world. In its publication of the SDGs, the UN has provided the goals and target descriptions that, if implemented at a country level, would lead towards a sustainable future. The IAEG (InterAgency Expert Group of the SDGs) was tasked with disseminating indicators and methods to countries that can be used to gather data describing the global progress towards sustainability. However 2030 Agenda leaves it to countries to adopt the targets with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances. At present, guidance on how to go about this is scant, but it is clear that the responsibility is with countries to implement and that it is actions at a country level that will determine the success of the SDGs. SDG reporting by countries takes on two forms 1) global reporting using prescribed indicator methods and data; 2) National Voluntary Reviews where a country reports on its own progress in more detail but is also able to present data that are more appropriate for the country. For the latter, countries need to be able to adapt the global indicators to fit national priorities and context, thus the global description of an indicator could be reduced to describe only what is relevant to the country. Countries may also, for the National Voluntary Review, use indicators that are unique to the country but nevertheless contribute to measurement of progress towards the global SDG target. Importantly, for those indicators that relate to the security of natural resources security (e.g. water) indicators, there are no prescribed numerical targets/standards or benchmarks. Rather countries will need to set their own benchmarks or standards against which performance can be evaluated. This paper presents a procedure that would enable a country to describe national targets with associated benchmarks that are appropriate for the country. The procedure focusses on those SDG targets that are natural resource-security focussed e.g. extent of water-related ecosystems (6.6), desertification (15.3) etc., because the selection of indicator methods and benchmarks is based on the location of natural resources, their use and present state and how they fit into national strategies.

This study assesses the durability of severe acute respiratory coronavirus-2 (SARS-CoV-2) anti-nucleocapsid (anti-N) immunoglobulin G (IgG) after infection, and examines its association with established risk factors among South African healthcare workers (HCWs). Blood samples were obtained from 390 HCWs with diagnosis of coronavirus disease 2019 (COVID-19) for assay of the SARS-CoV-2 anti-N IgG at two time points (Phase 1 &amp; 2) between November 2020 and February 2021. Out of 390 HCWs with COVID-19 diagnosis, 267 (68.5%) had detectable SARS-CoV-2 anti-N IgG antibodies at the end of phase I. These antibodies persisted for 4-5 and 6-7 months in 76.4% and 16.1%, respectively. In the multivariate logistic regression model analysis, black participants were more likely to sustain SARS-CoV-2 anti-N IgG for 4-5 months. However, participants who were HIV positive were less likely to sustain SARS-CoV-2 anti-N IgG antibodies for 4-5 months. In addition, individuals who were &lt;45 years of age were more likely to sustain SARS-CoV-2 anti-N IgG for 6-7 months. Of the 202 HCWs selected for phase 2, 116 participants (57.4%) had persistent SARS-CoV-2 anti-N IgG for an extended mean period of 223 days (7.5 months). Findings support the longevity of vaccine responses against SARS CoV-2 in black Africans.

Hydrogenated small fullerenes (Cn, n<60) are of interest as potential astrochemical species, and as intermediates in hydrogen catalysed fullerene growth. However computational identification of key stable species is difficult due to the vast combinatorial space of structures. In this study we explore routes to predict stable hydrogenated small fullerenes. We show that neither local fullerene geometry nor local electronic structure analysis are able to correctly predict subsequent low energy hydrogenation sites, and indeed sequential stable addition searches also sometimes fail to identify most stable hydrogenated fullerene isomers. Of the empirical and semi-empirical methods tested, GFN2-xTB consistently gives highly accurate energy correlation (r>0.99) to full DFT-LDA calculations at a fraction of the computational cost. This allows identification of the most stable hydrogenated fullerenes up to 4H for four fullerenes, namely two isomers of C28 and C40, via “brute force” systematic testing of all symmetry inequivalent combinations. The approach shows promise for wider systematic studies of smaller hydrogenated fullerenes.

The application of electrospray ionization mass spectrometry (ESI-MS) as a direct method for detecting reactive intermediates is a technique of developing importance in the routine monitoring of solution-phase reaction pathways. Here, we utilize a novel on-line photolysis ESI-MS approach to detect the photoproducts of riboflavin in aqueous solution under mildly alkaline conditions. Riboflavin is a constituent of many food products, so its breakdown processes are of wide interest. Our on-line photolysis setup allows for solution-phase photolysis to occur within a syringe using UVA LEDs, immediately prior to being introduced into the mass spectrometer via ESI. Gas-phase photofragmentation studies via laser-interfaced mass spectrometry of deprotonated riboflavin, [RFH], the dominant solution-phase species under the conditions of our study, are presented alongside the solution-phase photolysis. The results obtained illustrate the extent to which gas-phase photolysis methods can inform our understanding of the corresponding solution-phase photochemistry. We determine that the solution-phase photofragmentation observed for [RFH] closely mirrors the gas-phase photochemistry, with the m/z 241 ion being the only major condensed-phase photoproduct. Further gas-phase photoproducts are observed at m/z 255, 212, and 145. The value of exploring both the gas- and solution-phase photochemistry to characterize photochemical reactions is discussed.

Verteporfin, a free base benzoporphyrin derivative monoacid ring A, is a photosensitizing drug for photodynamic therapy (PDT) used in the treatment of the wet form of macular degeneration and activated by red light of 689 nm. Here, we present the first direct study of its photofragmentation channels in the gas-phase, conducted using a laser interfaced mass spectrometer across a broad photoexcitation range from 250-790 nm. The photofragmentation channels are compared with the collision-induced dissociation (CID) products revealing similar dissociation pathways characterized by the loss of the carboxyl and ester groups. Complementary solution-phase photolysis experiments indicate that photobleaching occurs in verteporfin in acetonitrile; a notable conclusion, as photoinduced activity in Verteporfin was not thought to occur in homogenous solvent conditions. These results provide unique new information on the thermal break-down products and photoproducts of this light-triggered drug.

This brief review highlights some current issues in Galactic stellar nucleosynthesis, and some recent laboratory studies by the Wisconsin atomic physics group that have direct application to stellar spectroscopy, in order to advance our understanding of the chemical evolution of our Galaxy. The relevant publication history of the lab studies are summarized, and investigations into the abundances of neutron-capture and iron-peak elements in low metallicity stars are described. Finally, new initatives in near-infrared spectroscopy are briefly explored.

The accurate virus detection, strain discrimination, and source attribution of contaminated food items remains a persistent challenge because of the high mutation rates anticipated to occur in foodborne RNA viruses, such as Hepatitis A virus (HAV). This has led to predictions of the existence of more than one sequence variant between the hosts (inter-host) or within an individual host (intra-host). However, there have been no reports of intra-host variants from an infected single individual, and little is known about the accuracy of the single nucleotide variations (SNVs) calling with various methods. In this study, the presence and identity of viral SNVs, either between HAV clinical specimens or among a series of samples derived from HAV clone1-infected FRhK4 cells, were determined following analyses of nucleotide sequences generated using next-generation sequencing (NGS) and pyrosequencing methods. The results demonstrate the co-existence of inter- and intra-host variants both in the clinical specimens and the cultured samples. The discovery and confirmation of multi-viral RNAs in an infected individual is dependent on the strain discrimination at the SNV level, and critical for successful outbreak traceback and source attribution investigations. The detection of SNVs in a time series of HAV infected FRhK4 cells improved our understanding on the mutation dynamics determined probably by different selective pressures. Additionally, it demonstrated that NGS could potentially provide a valuable investigative approach toward SNV detection and identification for other RNA viruses.

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.