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\"In 1923, Nikola Tesla's career is in its twilight until he unveils a robot with automatic intelligence--Atomic Robo! Granted full American citizenship in return for his participation in a top secret military operation in 1938, Atomic Robo goes on to found Tesladyne, a think tank dedicated to exploring the fringes of scientific inquiry. After decades of dealing with all manner of weirdness, Atomic Robo and the so-called Action Scientists of Tesladyne became the go-to defense force against the unexplained\"--V. 1, cover p. [4].

Unfortunately for the Cincinnati Zoo and Botanical Garden, one of their favorite snacks happens to be wires used in their annual holiday light display.

Protists are ubiquitous members of soil microbial communities, but the structure of these communities, and the factors that influence their diversity, are poorly understood. We used barcoded pyrosequencing to survey comprehensively the diversity of soil protists from 40 sites across a broad geographic range that represent a variety of biome types, from tropical forests to deserts. In addition to taxa known to be dominant in soil, including Cercozoa and Ciliophora, we found high relative abundances of groups such as Apicomplexa and Dinophyceae that have not previously been recognized as being important components of soil microbial communities. Soil protistan communities were highly diverse, approaching the extreme diversity of their bacterial counterparts across the same sites. Like bacterial taxa, protistan taxa were not globally distributed, and the composition of these communities diverged considerably across large geographic distances. However, soil protistan and bacterial communities exhibit very different global-scale biogeographical patterns, with protistan communities strongly structured by climatic conditions that regulate annual soil moisture availability.

Little is known about co-occurring tuberculosis (TB) and COVID-19 in low TB incidence settings. We obtained a cross-section of 333 persons in the United States co-diagnosed with TB and COVID-19 within 180 days and compared them to 4,433 persons with TB only in 2020 and 18,898 persons with TB during 2017‒2019. Across both comparison groups, a higher proportion of persons with TB–COVID-19 were Hispanic, were long-term care facility residents, and had diabetes. When adjusted for age, underlying conditions, and TB severity, COVID-19 co-infection was not statistically associated with death compared with TB infection only in 2020 (adjusted prevalence ratio 1.0 [95% CI 0.8‒1.4]). Among TB–COVID-19 patients, death was associated with a shorter interval between TB and COVID-19 diagnoses, older age, and being immunocompromised (non-HIV). TB–COVID-19 deaths in the United States appear to be concentrated in subgroups sharing characteristics known to increase risk for death from either disease alone.

Evanescent Bloch waves are eigensolutions of spatially periodic problems for complex-valued wavenumbers at finite frequencies, corresponding to solutions that oscillate in time and space and that exponentially decay in space. Such evanescent waves are ubiquitous in optics, plasmonics, elasticity, and acoustics. In the limit of zero frequency, the wave “freezes” in time. We introduce frozen evanescent waves as the eigensolutions of the Bloch periodic problem at zero eigenfrequency. Elastic waves, i.e., phonons, in metamaterials serve as an example. We show that, in the complex plane, the Cauchy-Riemann equations for analytical functions connect the minima of the phonon band structure to frozen evanescent phonons. Their exponential decay length becomes unusually large if a minimum in the band structure tends to zero and thereby approaches a soft mode. This connection between unusual static and dynamic behaviors allows to engineer large characteristic decay lengths in static elasticity. For finite-size samples, the static solutions for given boundary conditions are linear combinations of frozen evanescent phonons, leading to interference effects. Theory and experiment are in excellent agreement. Anomalous behavior includes the violation of Saint Venant’s principle, which means that large decay-length frozen evanescent phonons can potentially be applied in terms of remote mechanical sensing. The authors introduce frozen evanescent waves as the Bloch eigenmodes of periodic problems at zero frequency. Elastic waves serve as an example. Under special conditions, the decay length of the frozen evanescent phonons can become anomalously large.

Background Androgen deprivation therapy (ADT) improves outcomes in men undergoing definitive radiotherapy for prostate cancer but carries significant toxicities. Clinical parameters alone are insufficient to accurately identify patients who will derive the most benefit, highlighting the need for improved patient selection tools to minimize unnecessary exposure to ADT’s side effects while ensuring optimal oncological outcomes. The ArteraAI Prostate Test, incorporating a multimodal artificial intelligence (MMAI)-driven digital histopathology-based biomarker, offers prognostic and predictive information to aid in this selection. However, its clinical utility in real-world settings has yet to be measured prospectively. Methods This multicentre implementation trial aims to collect real-world data on the use of the previously validated Artera MMAI-driven prognostic and predictive biomarkers in men with intermediate-risk prostate cancer undergoing curative radiotherapy. The prognostic biomarker estimates the 10-year risk of metastasis, while the predictive biomarker determines the likely benefit from short-term ADT (ST-ADT). A total of 800 participants considering ST-ADT in conjunction with curative radiotherapy will be recruited from multiple Australian centers. Eligible patients with intermediate-risk prostate cancer, as defined by the National Comprehensive Cancer Network, will be asked to participate. The primary endpoint is the percentage of patients for whom testing led to a change in the shared ST-ADT recommendation, analyzed using descriptive statistics and McNemar’s test comparing recommendations before and after biomarker testing. Secondary endpoints include the impact on quality of life and 5-year disease control, assessed through linkage with the Prostate Cancer Outcomes Registry. The sample size will be re-evaluated at an interim analysis after 200 patients. Discussion ASTuTE will determine the impact of a novel prognostic and predictive biomarker on shared decision-making in the short term, and both quality of life and disease control in the medium term. If the biomarker demonstrates a significant impact on treatment decisions, it could lead to more personalized treatment strategies for men with intermediate-risk prostate cancer, potentially reducing overtreatment and improving quality of life. A potential limitation is the variability in clinical practice across different centers inherent in real-world studies. Trial Registration Australian New Zealand Clinical Trials Registry, ACTRN12623000713695p. Registered 5 July 2023.

BackgroundNearly half of individuals who sustain orthopaedic trauma develop chronic pain and experience significant levels of depression and anxiety. Emotional awareness and expression therapy (EAET) is a newly developed psychological intervention designed to treat chronic pain by helping patients process psychological trauma and conflict to reduce pain. The purpose of this study is to examine the feasibility of delivering EAET to individuals who sustained traumatic orthopaedic injuries requiring surgery and who have chronic pain 6 months after hospital discharge.Methods and analysisThe study will consist of a single-arm design. Thirty individuals who sustained traumatic orthopaedic injuries requiring surgery and who reported chronic pain 6 months after hospital discharge will be recruited. Participants will receive eight sessions of individually administered EAET delivered via telehealth and complete self-report questionnaires at three timepoints (pretreatment, post-treatment and 3-month follow-up). Quantitative sensory testing will also be done before and after treatment. The primary outcome of the study is feasibility (eg, per cent of eligible patients recruited and per cent of study completers) and acceptability as reported by responses to a self-report questionnaire.Ethics and disseminationThis study has been approved by the Johns Hopkins Institutional Review Board. All data are expected to be collected by 2026, with results of this study to be disseminated via relevant peer-reviewed journals and scientific conferences.Trial registration numberClinicalTrials.gov NCT05989230. Registered on 14 August 2023.

Peatlands store approximately one-third of the world’s soil carbon (C), but their functioning is highly variable at fine spatial scales due to differences in vegetation cover and environmental conditions such as water table depth. This fine-scale heterogeneity plays a key role in carbon dynamics yet capturing it—particularly in relation to green leaf phenology (GLP)—is challenging with traditional remote sensing methods. To address this, we developed a smartphone-based methodology and community-science project called the PeatPic Project. We gathered over 3700 photographs from 27 sites across 10 countries in 2021 and 2022, representing different peatland types (bog, fen, and swamp), at 1–2 week intervals. We calculated GLP metrics, such as the data of the start of the season and end of the season, based on the red-blue-green bands from these photographs. We found that GLP metrics varied significantly across peatland types and dominant vegetation communities. Notably, peak greenness at bog sites occurring approximately 10 days later in the year compared to fen sites. Furthermore, variables relation to peatland/vegetation type and energy balance were key predictors of peatland GLP. The PeatPic Project’s readily available methodology offers low-cost opportunities for further research into peatland phenology, enabling the calculation of additional phenological indices and integration with other data types. By refining our understanding of peatland GLP, we can improve predictive C modelling and better assess the impacts of future changes on these important ecosystems.