Prevalence of antibodies against Chlamydophila spp. in herds with bovine abortion of Paraná state, Brazil

Chlamydophila abortus é o agente etiológico do aborto epizoótico bovino, cujas manifestações clínicas mais freqüentes são aborto, nascimento de bezerros prematuros e de animais fracos, natimorto e repetição de cio em intervalos irregulares. O objetivo deste trabalho foi estimar a prevalência de anticorpos anti-Chlamydophila spp. em fêmeas bovinas de propriedades rurais com histórico de aborto, selecionadas dentro do delineamento amostral do Plano Nacional de Controle e Erradicação da Brucelose e Tuberculose no estado do Paraná. Foram testadas pela prova de fixação de complemento 3.102 amostras de soro de fêmeas bovinas (idade > 24 meses), provenientes de 373 propriedades. Ao total, 44 (1,42%) animais foram positivos com títulos > 32. A prevalência de focos foi de 8,82% (6,15%-12,17%). Animais confinados ou semi-confinados (OR=3.339, P=0.004), propriedade com menos de 35 matrizes (OR=3.339, P=0.017), presença de produtos do aborto na pastagem (OR=2.372, P=0.037) e aluguel de pasto (OR=3.398, P=0.006) foram considerados fatores de risco para Chlamydophila spp. A infecção por Chlamydophila spp. acometeu um número pequeno de animais, oriundos de propriedades com histórico de aborto. A importância deste agente como causa de aborto em bovinos no estado do Paraná, se existir, é muito pequena.Chlamydophila abortus is a recognized cause of bovine epizootic abortion. Abortion, premature birth and weak lamb/calf, stillbirth and repeat breeding in irregular intervals are the most frequent disease manifestations. The complement fixation test is the recommended by the World Organization of Animal Health (OIE) for Chlamydophila spp. serologic diagnosis. The aim of this study was estimate the prevalence of antibodies against Chlamydophila spp. in cattle herds with abortion, selected inside the sampling design of National Program of Control and Erradication of Brucellosis in Paraná state. Serum samples of 3,102 cows (age > 24 months) from 373 herds were analyzed by complement fixation test. Totally, 44 (1.42%) animal were positive with titers > 32. The seroprevalence of Chlamydophila spp. in the herds was 8.82% (6.15%-12.17%). Four variables were associated with seroprevalence for Chlamydophila spp. in the final model of logistic regression: confined or semi-confined breeding (OR=3.339, P=0.004), farms with less than 35 cows (OR=3.339, P=0.017), abortion in the pasture (OR=2.372, P=0.037) and pasture rent (OR=3.398, P=0.006) were risk factors for Chlamydophila spp. This bacterium infected a small number of cattle from herds with abortion in Paraná state. Chlamydophila spp impact as abortion cause is reduced in this state

Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections.</p

Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers

An introduction to the Philosophy of Information

This book serves as the main reference for an undergraduate course on Philosophy of Information. The book is written to be accessible to the typical undergraduate student of Philosophy and does not require propaedeutic courses in Logic, Epistemology or Ethics. Each chapter includes a rich collection of references for the student interested in furthering her understanding of the topics reviewed in the book. The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers

The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine