Identification of the causal agent of Botryosphaeria stem canker in Ethiopian Eucalyptus plantations

Plantations of exotic Eucalyptus make up more than 30% of Ethiopia's plantations, providing fuel and construction timber to the country. Species such as E. camaldulensis, E. saligna, E. grandis, E. citriodora and E. globulus are most commonly planted. During a survey of Eucalyptus diseases in 2000 and 2001, Botryosphaeria stem canker was observed in most plantations. The disease symptoms included tip die- back, coppice failure and stem cankers characterised by kino exudation. The aim of this study was to identify the species responsible for Botryosphaeria stem canker in Ethiopia. Culture and conidial morphology, as well as DNA-based identification involving Restriction Fragment Length Polymorphisms (RFLPs) and sequencing of the Internal Transcribed Spacer regions (ITS) of the ribosomal RNA gene and the elongation factor 1-alpha (EF1-α) gene, were used to identify isolates. Pathogenicity studies were conducted in the greenhouse and under field conditions. Results showed that Botryosphaeria parva is responsible for Botryosphaeria stem canker of Eucalyptus in Ethiopia. This is the first report of the fungus from this country. Greenhouse and field inoculation studies showed that the Ethiopian isolates are highly virulent. Careful site species selection and breeding trials are thus needed to reduce the impact of this disease in Ethiopia

Structural, item, and test generalizability of the psychopathology checklist - revised to offenders with intellectual disabilities

The Psychopathy Checklist–Revised (PCL-R) is the most widely used measure of psychopathy in forensic clinical practice, but the generalizability of the measure to offenders with intellectual disabilities (ID) has not been clearly established. This study examined the structural equivalence and scalar equivalence of the PCL-R in a sample of 185 male offenders with ID in forensic mental health settings, as compared with a sample of 1,212 male prisoners without ID. Three models of the PCL-R’s factor structure were evaluated with confirmatory factor analysis. The 3-factor hierarchical model of psychopathy was found to be a good fit to the ID PCL-R data, whereas neither the 4-factor model nor the traditional 2-factor model fitted. There were no cross-group differences in the factor structure, providing evidence of structural equivalence. However, item response theory analyses indicated metric differences in the ratings of psychopathy symptoms between the ID group and the comparison prisoner group. This finding has potential implications for the interpretation of PCL-R scores obtained with people with ID in forensic psychiatric settings

Analysis of the dynamics of the FT4 powder rheometer

Traditional powder flow measurement devices, such as shear cells, operate in the quasi-static regime of shear strain rate. The FT4 powder rheometer of Freeman Technology, developed over the last two decades, has provided a clearer differentiation of powder flowability in some instances. This has been attributed to the instrument operating in the dynamic regime of shear strain rates, a feature that has yet to be established. We report an analysis of the dynamic behaviour of a bed of glass beads made cohesive by silanisation and subjected to standard FT4 testing procedure, where a rotating blade is driven into a cylindrical bed, using a combination of experimental measurements and numerical simulations by the Distinct Element Method. The DEM analysis underestimates the flow energy measured experimentally, although the agreement is improved when sliding friction is increased. The shear stress of the powder in front of the blade is shown to be roughly constant along the radial direction and increasing as the impeller penetrates the bed, suggesting a characteristic shear stress can be determined for a powder under a given test conditions in the FT4. For ease of simulations large beads were used (1.7 – 2.1 mm). Future work will investigate the influence of particle properties and operational conditions on the prevailing stresses and strain rates

Mental health and substance use screening in HIV primary care before and during the early COVID-19 pandemic

Background: Mental health and substance use disorders disproportionately affect people with HIV (PWH), and may have been exacerbated during COVID-19. The Promoting Access to Care Engagement (PACE) trial was designed to assess the effectiveness of electronic screening for mental health and substance use in HIV primary care and enrolled PWH from October 2018 to July 2020. Our objective here was to compare screening rates and results for PWH before (October 2018 – February 2020) and early in the COVID-19 pandemic (March-July 2020). Methods: Adult (≥ 18 years) PWH from 3 large HIV primary care clinics in a US-based integrated healthcare system were offered electronic screening online or via in-clinic tablet computer every 6 months. Screening completion and results (for depression, suicidal ideation, anxiety, and substance use) were analyzed using logistic regression with generalized estimating equations to estimate prevalence ratios (PR) before and after the start of the regional COVID-19 shelter-in-place orders on March 17, 2020. Models adjusted for demographics (age, sex, race/ethnicity), HIV risk factors (men who have sex with men, injection drug use, heterosexual, other), medical center, and modality of screening completion (online or tablet). We conducted qualitative interviews with providers participating in the intervention to evaluate how the pandemic impacted patient care. Results: Of 8,954 eligible visits, 3,904 completed screenings (420 during COVID, 3,484 pre-COVID), with lower overall completion rates during COVID (38% vs. 44%). Patients completing screening during COVID were more likely to be White (63% vs. 55%), male (94% vs. 90%), and MSM (80% vs., 75%). Adjusted PRs comparing COVID and pre-COVID (reference) were 0.70 (95% CI), 0.92 (95% CI), and 0.54 (95% CI) for tobacco use, any substance use, and suicidal ideation, respectively. No significant differences were found by era for depression, anxiety, alcohol, or cannabis use. These results were in contrast to provider-reported impressions of increases in substance use and mental health symptoms. Conclusion: Findings suggest PWH had modest declines in screening rates early in the COVID-19 pandemic which may have been affected by the shift to telemedicine. There was no evidence that mental health problems and substance use increased for PWH in primary care. Trial registration: NCT03217058 (First registration date: 7/13/2017); https://clinicaltrials.gov/ct2/show/NCT03217058

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

An Emerging Natural History in the Development, Mechanisms and Worldwide Prevalence of Major Mental Disorders

Conciliating recent findings from molecular genetics, evolutionary biology, and clinical observations together point to new understandings regarding the mechanism, development and the persistent worldwide prevalence of major mental disorders (MMDs), which should be considered the result of an evolutionary downside trade off. Temperamental/trait variability, by facilitating choices for individual and group responses, confers robustness flexibility and resilience crucial to success of our species. Extreme temperamental variants, originating evolutionarily from the asocial aspect of human nature, also constitute the premorbid personality of the disorders. The latter create vulnerable individuals out of whom some will develop MMDs but at much higher rate to that of the general population. Significantly, similar temperamental “lopsidedness� enables many of these vulnerable individuals, if intelligent, tenacious, and curious, to be creative and contribute to our survival while some may also develop MMDs. All have a common neural-developmental origin and share characteristics in their clinical expression and pharmacological responses also expressed as mixed syndromes or alternating ones over time. Over-pruning of synaptic neurons may be considered the trigger of such occurrences or conversely, the failure to prevent them in spite of it. The symptoms of the major mental disorders are made up of antithetical substitutes as an expression of a disturbed over-all synchronizing property of brain function for all higher faculties previously unconsidered in their modeling. The concomitant presence of psychosis is a generic common occurrence

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization

Global Search for New Physics with 2.0/fb at CDF

Data collected in Run II of the Fermilab Tevatron are searched for indications of new electroweak-scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with the standard model prediction. A model-independent approach (Vista) considers gross features of the data, and is sensitive to new large cross-section physics. Further sensitivity to new physics is provided by two additional algorithms: a Bump Hunter searches invariant mass distributions for "bumps" that could indicate resonant production of new particles; and the Sleuth procedure scans for data excesses at large summed transverse momentum. This combined global search for new physics in 2.0/fb of ppbar collisions at sqrt(s)=1.96 TeV reveals no indication of physics beyond the standard model.Comment: 8 pages, 7 figures. Final version which appeared in Physical Review D Rapid Communication

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem