text:
Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine
Practice review
http://orcid.org/0000-0003-1792-0842Shammi Ramlakhan1,
Reza Saatchi2,
http://orcid.org/0000-0001-6488-3314Lisa Sabir1,
Yardesh Singh3,
Ruby Hughes4,
Olamilekan Shobayo2,
Dale Ventour3
1 Emergency Department, Sheffield Children's Hospital, Sheffield, UK
2 Electronics and Computer Engineering Research Institute, Sheffield Hallam University, Sheffield, UK
3 Department of Clinical Surgical Sciences, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
4 Simulation and Modelling Unit, Advanced Forming Research Centre, University of Strathclyde, Sheffield, UK
Correspondence to Dr Shammi Ramlakhan, Emergency Department, Sheffield Children's Hospital, Sheffield, UK; sramlakhan{at}nhs.net
http://dx.doi.org/10.1136/emermed-2021-212068
The field of artificial intelligence (AI) has been developing more prominently for over half a century. Innovations in computer processing power and analytical capabilities coupled with the availability of huge amounts of routinely collected data has meant that AI research and technology development has grown exponentially in recent years. The results of this growth can be seen in emergency medicine (EM)—with the Food and Drug Administration approving the first AI software as a medical device for wrist fracture detection in 2018. As of 2021, several more have been approved—for triage, X-ray identification of pneumothorax and notification and triage software for CT images.1
Between 2015 and 2021, there were over 500 publications indexed in MEDLINE involving AI in acute and emergency care, with more than half of these published within the last 2 years alone. There is recognition that AI technology can potentially play an important role in ED decision making, workflow and operations.2–4 However, concerns with unstructured and often opaque reporting, inappropriate algorithm selection, proxy bias, data privacy and safety have led to calls for better standards for undertaking and reporting of research involving AI.5–9 For practising ED clinicians, this will facilitate interpretation and understanding of AI research prior to model deployment or generalisation.
The aim of this paper is to serve as a primer for clinicians and researchers in understanding common AI methods as they relate to EM, and to provide a framework for interpreting AI research. A companion paper provides a more detailed exploration of the AI model building pipeline in an EM context.
What is the promise of AI for EM?
AI technology is seen in multiple aspects of day-to-day living—from email spam filters, voice activated devices, suggestions from entertainment streaming services and social media to self-driving cars—all are powered by AI of varying complexities. The natural extension into healthcare, and EM in particular, is expected given the generalist, public-facing nature of the specialty.
AI has the potential to influence and improve ED triage and outcome prediction,10 11 forecasting and operations,3 diagnosis2 and assessment of prognosis.12
In addition, AI is facilitating the harnessing of new technology suitable for ED applications and research, such as natural language processing,13 radiomics14 and machine vision.15 Large data repositories are being curated and leveraged to explore correlations between patient variables and urgent care outcomes.10 16 17 Examples of recent studies with a reasonable rationale for using AI methods are summarised in box 1.
Emergency medicine-relevant studies using artificial intelligence (AI)/machine learning (ML)
Fracture recognition2
Problem: misinterpretation of limb X-rays by less experienced ED clinicians is not uncommon. Because contemporaneous radiologist reporting is not widely available, a system which automatically highlights abnormalities on an image may improve fracture detection.
Why AI? Deep neural networks (DNN) can be trained to identify areas (pixels) on an image where a fracture is likely. A DNN trained on a large number of images could improve the diagnostic accuracy of a clinician by providing a visual 'opinion' of the presence of a fracture. This principle can be extended to most imaging modalities.
Issues: although the reported DNN-aided accuracy was better than the clinician-only interpretation, this was assessed using images on which the DNN had been trained. The DNN would therefore have had an advantage, as it would have 'seen' the images before, and biasing direct comparison with human interpretation. In addition, the clinical significance of a missed or delayed fracture diagnoses must be considered—with the actual patient benefit of any diagnostic improvement from AI clearly shown.
Waiting time (WT) estimation17
Problem: knowledge of expected WT are helpful for ED patients, families and providers. Traditional triage or estimation based on historical time/day WTs are considered crude, and often do not reflect actual time spent waiting.
Why AI? Traditional statistical models derived from small datasets/single sites may not capture important predictors and are prone to overfitting. AI algorithms can look for important variables in large multicentre routine datasets and derive predictive models which should theoretically give accurate estimates of WTs across a network or at single sites. This approach would be more resource efficient than each site developing their own model.
Issues: even within a geographically defined area using a large dataset, estimates of WT from AI models were only accurate within 30 min of the actual wait time between 40% and 63% of the time. This may not be sufficient for operational benefit but may be for patient information purposes. External validation of models developed for one setting also appear to be less accurate at others, emphasising the importance of robust external evaluation and the need for site-specific customisation.
Predicting severe sepsis31
Problem: early recognition and treatment of sepsis improves outcomes. Several sepsis scoring systems are used to predict severe sepsis and outcomes. However, these generally cannot leverage trends or correlate individual measures and have moderate predictive value. Automated electronic patient record (EPR) sepsis alerting systems using these scores suffer from low specificity and false positives.
Why AI? Large datasets with physiological and outcome variables are available using EPR repositories. Conventional regression methods may not capture the interdependencies of individual parameters at specific times as well as relevant time-trends. The complexity of rationalising multiple variable combinations and the volume of data is challenging for human-designed analysis; however, gradient boosted ensemble ML algorithms can predict time-series events, and select important parameters/parameter combinations while handling class imbalance. They can therefore predict an outcome at multiple given time points, making it particularly useful in the ED where patients may present at different stages in disease progression. It also allows prediction, in this case, at up to 48 hours before the onset of severe sepsis.
Issues: the model demonstrated impressively high area under the receiver operating characteristics (AUROC), which was stable on external validation up to 6 hours before onset of severe sepsis. However, performance at 48 hours deteriorated markedly on external validation, suggesting overfitting (overtraining so it fits too closely to training data and is unable to generalise well for new data). For imbalanced datasets, AUROC is not an ideal metric for head-to-head comparison (given that the false positive rate (FPR) changes minimally due to the high number of true negatives—see Fig 4). Whether predicting the onset of severe sepsis translates into improved outcomes still requires evaluation—with further external validation and/or a randomised trial.
Adverse outcomes from COVID-1932
Problem: the rapid spread and variable disease progression of SARS-CoV-2 infections have meant that clinicians are working with an unprecedented lack of data, knowledge base or decision tools for predicting outcomes.
Why AI? The combination of individual patient variables which determine outcome are largely unknown. There is a paucity of quality data which considers disease outcomes at variable time points and at different stages of illness. In order to leverage available, good quality datasets to explore multiple predictor variables and their relative importance, an interpretable ensemble algorithm (Random Forest) can be used. This allows various models to be constructed with iterations of setting, clinical features, laboratory data and temporal measures, thus allowing flexibility in deployment and interpretability. In this case, the availability of a relatively small amount of good quality data to train and externally validate a model is preferable to larger poor-quality datasets from a single institution or region.
Issues: as with any AI model, deterministic capability is lacking. This is compounded by similar lack of pathophysiological knowledge of poor outcomes in COVID-19, for example, is obesity associated with poor outcomes due to immunomodulation or simply reduced respiratory capacity and is there therefore covariance and imputation issues which impacts on the model? A relatively small dataset makes the model potentially unstable, however external validation is reassuring other than for predicting intensive care unit admission (likely due to the small numbers). As additional data become available, the model performance and parameters may change, and validation in more diverse settings would be warranted.
However, the promise of AI must be tempered with acknowledgement of its evolving nature. This must be coupled with a realistic assessment of the acceptability and quality of current EM applications of the technology, especially compared with those which have been conventionally derived using traditional statistical methods. Certainly, in other specialties, AI models have not been shown to be superior to those derived by traditional logistic regression.18
The vast majority of AI EM diagnostic and prognostic studies use retrospective data, that is, where the data were not collected specifically for the AI application (which also raises questions about the use of personal data without explicit consent). Importantly, <20% are externally validated in a traditional manner,19 with less than half of those externally validated according to AI standards.8 20 21 Randomised trials are even rarer.
To compound matters, AI models can involve deep neural networks or similarly complex algorithms. The way that these models arrive at a decision cannot be interrogated in the clinical setting, making transparency and acceptability challenging to clinicians, patients and regulators.22 23
Although most published research compares AI with a clinician,19 24 this is arguably largely irrelevant. In EM practice, AI solutions have potential for secondary benefits by facilitating effective use of clinician time and reducing cognitive load. This can be through automating non-clinical tasks, allocating resources or prioritising workflows for efficiency. Rather than replacing clinicians or clinical tasks, AI's true promise lies in supplementing or improving on clinicians' care by harnessing the most appropriate human and machine abilities.
Artificial intelligence, machine learning and deep neural networks
Much of the terminology relating to AI can be confusing as they are often used interchangeably—at least partly due to popular usage stemming from science fiction references. Even within the fields of computing and data science (not to mention philosophy!), there is no clear definition of AI, and this is at least partly attributable to the evolving nature of the technology. Box 2 contains a glossary to aid in understanding AI terminology and descriptions uses in this paper. From a clinician's perspective, AI can be considered any human-like intelligence displayed by a machine (computer). This definition is valid insofar as humans demonstrate intelligence by learning from experience or observations, then use this knowledge to recognise, interpret and take autonomous actions when faced with similar situations.
Glossary of artificial intelligence/machine learning terms
Algorithm: programming code/pseudocode or formulae which has been developed for a certain task or process.
Model: a representation of a trained algorithm, that is, an algorithm which has learnt from data.
Ensemble: combinations of diverse simpler algorithms to improve overall performance.
Data leakage: where data from outside the training set manage to leak into the model building (training) process, hence inappropriately influencing the model and its validation.
Feature: a measurable characteristic of the data or population of interest (commonly a variable).
Training set: data used by an algorithm to create or fit a model.
Validation set: data used to evaluate the model fit while tuning hyperparameters, or to select features.
Test set: data used to assess a model's performance on unseen data.
Ground truth: analogous to gold standard.
Underfitting: where the model does not learn adequately, and performs poorly in training and testing. It has a high bias and low variance.
Overfitting: where the model has learnt the training set too well, and thus performs well in training but poorly in testing. It has a low bias and high variance.
Bias: the difference between predicted and actual values.
Variance: how varied the predictions are between different sets of input.
Parameter: a variable whose value is calculated from the data and forms part of the model itself. It is independent of the analyst and cannot be manually adjusted.
Hyperparameter: a setting or weight whose value cannot be calculated from the data and is external to the model. It can be tuned by the analyst.
Loss function: a measure of how the predicted output of each training instance differs from the actual outcome.
Regularisation: modifications to a learning algorithm intended to reduce its generalisation error but not its training error.33 This is usually by penalising or limiting weights or early stopping of training. This can be achieved by algorithms using least absolute shrinkage and selection operator or Ridge regression.
Decision tree: a type of algorithm which splits data based on probabilities or attributes in a branching pattern, until an output is determined.
Support vector machine: a classification algorithm which separates classes by finding a (hyper)plane of separability between them.
Naïve Bayes: uses Bayes theorem, that is, how pre-event variables influences postevent outcome probability. It is naïve as it assumes all variables are independent.
K-nearest neighbours: an algorithm which predicts an outcome by finding the k-nearest instances of the input variable and averaging their outcomes.
Random Forest: a decision-tree-based bagging ensemble model. It Randomly selects variables/data and builds a Forest of multiple decision trees.
Machine learning (ML) is a subset of AI (figure 1), and involves the use of algorithms which can identify patterns in data, learn from these patterns, improve with experience and come to conclusions when faced with new data—all without being explicitly programmed.25 In EM, these data are generally from images, electronic patient records and ED or population databases, but can reasonably include any type of data which can be interpreted by an algorithm. These algorithms usually consist of programming code (written in programming languages such as Python), mathematical formulae or combinations of these represented as pseudocode. There are several ML algorithm repositories/libraries where researchers can access 'off-the-shelf' algorithms, which have functions relevant to specific tasks. An AI algorithm which has 'learnt' from data is referred to as a model.
Relationship between artificial intelligence, machine learning, artificial neural networks (ANN) and deep learning (deep neural networks (DNN)).
The term artificial neural network is used to describe an algorithm which consists of an input, output and often intermediate (hidden) layer(s). Data (input variables, also called features) is fed into the algorithm, which then weighs various combinations of these variables in the intermediate layer, and feeds forward an output which is dependent on being activated at set thresholds. As the number of hidden (intermediate) layers increase, this layer becomes deeper—hence deep learning (DL) or deep neural networks—and so does its ability to undertake more complex learning tasks. Output is passed from one layer to the next and is dependent on multiple inputs into multiple layers. DL models can also correct their own errors by backpropagation of data in order to refine and improve on performance.
The fundamental principle of the subtypes of AI is the requirement for learning from data. In ML, this is generally through either supervised or unsupervised learning. There are other types such as semi-supervised or reinforcement learning, which are less common in EM applications. Supervised learning is used in most ML applications and involves the use of labelled data (usually a type of variable as well as outcome of interest) fed to the algorithm so that it can learn the relationships and differences between the variables and outcome(s). Conversely, in unsupervised learning, unlabelled data are provided to the algorithm, and it is allowed to determine patterns and features of the data on its own, without human input. Unsupervised learning is used primarily in exploratory (clustering) algorithms, and generally requires much more data to train than supervised models.
AI or conventional methods?
ML/AI uses some biostatistical methods and measures that will be familiar to emergency physicians (EP). Some algorithms use methods based on linear regression, decision trees and Bayesian reasoning, among others and these can usually be represented mathematically. Significantly, AI/ML can go further, by using algorithms to leverage previously unrecognised variables (or features), which are different to the human-determined variables used in traditional statistics. In addition, models built on these algorithms can harness non-linear relationships between variables and outcomes, which make them well suited to identifying complex or unapparent data inter-relationships. They are therefore better at exploratory predictive (regression or classification) modelling with large or computationally heavy datasets, whereas classic statistical approaches are arguably better for confirmatory, hypotheses-light analyses with relatively smaller datasets. Of note, the process of evaluation and comparison of different models is almost always based on conventional statistical methods. These methods can often be adapted for ML models, commonly using programming code written specifically to simplify some types of analysis.
AI models learn from the data they are given—the algorithms can incorporate multiple data inputs and make predictions based on their analysis of the relationships between inputs and outputs. Just as an experienced EP who has looked at thousands of ECGs can interpret an ECG at a glance by what would be considered tacit knowledge, an AI/ML model should theoretically be able to similarly interpret specific ECGs—provided that it has learnt to do so by being trained on a sufficient number of similar ECGs for it to discern the relationships between the labelled ECG 'variables' and diagnosis. It can do this without explicitly learning—for example, it will not 'learn' the voltage criteria for left bundle branch block (LBBB), the electro-pathological reasons for the pattern or how it affects ST-elevation myocardial infarction (STEMI) diagnosis. It may however, still be able to diagnose LBBB or a STEMI. This analogy highlights an often-ignored aspect of AI—it has no deterministic capability and cannot discern physical, physiological or pathological determinants or constraints. It can therefore find spurious relationships which may lead to inappropriate predictions. With large datasets with multiple combinations of input variables, it is not difficult to see how 'statistically' significant relationships can be derived purely by chance—so-called 'p-hacking'.
Another driver for using AI/ML stems from the type of data that is being made available. Besides large-scale routinely collected data, other types of data which were previously relatively untapped due to their complexity are well suited for AI algorithmic analysis. These include diagnostic imaging, laboratory or physiological data and free text from clinical notes.
Understanding the differences between AI and traditional statistical methods also comes from appreciating the focus of the main proponents of AI development—usually in data-rich technology and marketing industries—where the performance of a model takes priority over describing how it works. ML/AI is focused on real-life predictions or decisions based on new or dynamic data, whereas traditional statistics is more focused on understanding and articulating data relationships using established statistical theories and assumptions. This difference is critical—the point of creating an AI model is not to show how it performs in vitro, but to deploy it in the real world (clinically or operationally). The ultimate goal therefore, is developing a model which can be confidently generalised.
Stages in developing an AI (machine learning) model
EM research in AI is almost entirely focused on ML models, and therefore the focus henceforth will be on ML, although the framework and principles will also apply to almost any AI model. There is significant variability in the reporting and conduct of ML research, which can make interpretation challenging—particularly when some of the models used are by their nature 'black boxes'.22 An ML model should be developed systematically, with the same structure and clinical focus as any traditional EM clinical research. The steps in model development from conception to deployment are outlined in figure 2.
Steps in artificial intelligence (AI) model development. Square brackets denote steps which are sometimes described during data management, model development or evaluation. Feedback and iterations of preceding steps are expected. ML, machine learning. PICO/PECO - Population/ Intervention (Exposure)/Comparison/Outcome
An understanding of model development is useful when interpreting an AI research paper. More detail on this process is discussed in a linked companion paper26.
Interpreting an AI/ML paper
The ability to interpret and understand ML methodology is vitally important to EM clinicians. Key considerations when appraising an EM AI/ML paper are summarised in table 1.
Key considerations when appraising an EM AI/ML paper
There are concerns that AI in clinical medicine is overhyped and suffers from poor methodology, reporting and transparency.9 This is reflected in the findings of systematic reviews which have highlighted incomplete and non-standardised methods and reporting in ML studies.6 18 19 24 27 This has led to calls for reporting standards to address these shortcomings, with extensions to current reporting guidelines8 28 29 developed along with suggested general20 30 and specialist21 frameworks and checklists being proposed.
There are several ML repositories and libraries which allow researchers to use ML techniques out-of-box using accessible platforms such as R(r-project.org). As with any novel tool, there is a risk of inappropriate or clinically disconnected use. In addition, internal validation processes are heterogeneous with heuristics and analyst preference regarding algorithms, procedures, metrics and validation being common. Because of the interdependencies of various steps in model development, bias or poor choice of processes at any part of data selection, preparation or model development will have a knock-on effect, and impact negatively on the performance of the final model(s). This and other model building considerations are explored and expanded on in a linked paper, along with discussion of the pitfalls to be aware of when interpreting AI model performance.
This paper should provide the reader with conceptual insight on how AI models are developed and a framework to interpret AI applications as it relates to EM practice and research. There is scope for synergism between AI and EM clinicians to maximise efficiency within EDs by using these methods in workflow, triage processes, automating certain tasks and diagnostic applications. There is likely to be an ongoing increase in the number of studies describing these methods. Therefore, the ability to critically appraise these papers and careful awareness that despite exciting promise, AI models still have to be methodologically sound and applicable to real-world practice. The true benefit of AI is likely in assisting clinicians rather than replacing them.
This study does not involve human participants.
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Handling editor Katie Walker
Twitter @shammi_ram
Contributors Conceptualisation, writing original draft: SLR. Writing—review and editing: all other authors.
Competing interests SLR is a Decision Editor for the EMJ.
Building artificial intelligence and machine learning models : a primer for emergency physicians
Shammi L Ramlakhan Reza Saatchi Lisa Sabir Dale Ventour Olamilekan Shobayo Ruby Hughes Yardesh Singh
Emergency Medicine Journal 2022; 39 e1-e1 Published Online First: 03 Mar 2022. doi: 10.1136/emermed-2022-212379


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Iran's Import of Consumer Goods Rises But Capital Goods Fall
EghtesadOnline: Iran's imports of consumer and intermediate goods jumped in the last fiscal year (March 2019-20) while the other major import category, i.e. capital goods, declined dramatically compared with the previous year.
The country imported 2.8 million tons of consumer goods worth $7.6 billion in the fiscal 2019-20 to register a 2.33% rise in weight and a 10.54% increase in value year-on-year.
Consumer goods are purchased for consumption. Alternately called final goods, consumer goods are the end result of production and manufacturing, and are what a consumer will see on the store shelf. Clothing, food and jewelry are all examples of consumer goods.
Imports of intermediate goods surged by 19.63% in weight and 9.78% in value last year to stand at 31.65 million tons worth $29.44 billion, Fars News Agency reported.
An intermediate good is utilized to produce a final or finished product. These goods are sold between industries for resale, or for the production of other goods. Examples include steel, wood, glass, gold and silver.
Imports of capital goods into Iran, however, declined by 10.2% in tonnage and 12.92% in value to hover around 484,000 tons worth $5.93 billion.
Capital goods are tangible assets such as buildings, machinery, equipment, vehicles and tools that an organization uses to produce goods or services to produce consumer goods and goods for other businesses.
Overall Imports Rise 1.3% to $43.73b
The country's overall imports last year stood at 35.36 million tons worth $43.73 billion, indicating a 9% growth in weight and a 1.3% increase in value compared with the preceding year.
China was the main exporter to Iran during the period under review, according to Mehdi Mirashrafi, the head of Islamic Republic of Iran Customs Administration.
"The Asian country sold $11.2 billion worth of non-oil goods to Iran last year. The UAE with $8.9 billion, Turkey $4.9 billion, India $3.6 billion and Germany with $2.1 billion worth of exports to Iran were Iran's other key trading partners in imports," he said.
The imports chiefly included material used in livestock feed, essential goods, medical equipment and pharmaceuticals.
Last year's imports of livestock feed and essential goods were 3 million tons more compared with the year before, he added.
Meanwhile, exports of non-oil goods stood at $41.3 billion in the last Iranian year.
Oil-based products and by-products as well as petrochemical products are included in IRICA's "non-oil" export data. In fact, petrochemicals and gas condensates constitute the greater share of total exports.
Exports weighed 133.9 million tons—three times as much as the imports in weight and about 13.5% more than the previous year. In terms of value, however, exports show a 7% decline year-on-year, according to the IRICA chief.
"Imports reached 35.3 million tons in the fiscal 2019-20, indicating a 9.3% growth year-on-year. A $2.4 billion trade deficit was registered for the country during the period under review. Raw materials, machinery and intermediate goods accounted for 85% of the imports," he was quoted as saying by Mehr News Agency.
Essential Goods Take Center Stage
Amid the intensifying unilateral US sanctions, in addition to the detrimental effects of the spread of the new coronavirus on Iran's economy, the trade regime of the country has been geared to importing essential goods to ensure meeting the basic needs of the country.
Also known as necessity goods, essential goods are products consumers will buy, regardless of changes in income levels.
A total of 25.09 million tons of essential goods worth close to $15.5 billion were imported into Iran in the last fiscal year to register a 20.77% and 17.13% increase in weight and value respectively, compared with the year before, according to the spokesman of the Islamic Republic of Iran Customs administration, Rouhollah Latifi.
"This amount of essential goods imports accounted for close to 71% and 35% of the volume and value of last year's total imports respectively," the official was quoted as saying by ISNA.
"The imported essential commodities included wheat, sugar, corn, rubber, barley, processed tea, rice, different kinds of seeds, red meat, soybeans, pulses, paper, chemical fertilizers and industrial machinery."
Q1 Imports Fall 26% to $7.6b
Latest data released by IRICA show a total of 8.92 million tons of goods worth $7.62 billion were imported into Iran during the first quarter of the current fiscal year (March 20-June 20), posting a decrease of 26.76% in value and 0.78% in tonnage year-on-year.
Q1 imports of field corn stood at $588 million with a share of 7.72% of overall imports; wheat at $537 million with 7.05%; cellphones at $358 million with 4.7%; semi-milled rice or wholly-milled rice worth $205 million with 2.69% and soybeans at $170 million with a share of 2.23% from total imports.
Top exporters to Iran in Q1 were China with $1.93 billion and a share of 25.4% of Iran's total imports, the UAE with $1.7 (22.3%), Turkey with $815 million (10.7%), India with $529 (7%) and Russia with $421 million (5.5%).
Imports from China, Turkey and India respectively decreased by 1,834%, 36.1% and 57.8% when compared with last year's same period while imports from the UAE and Russia improved by 8.5% and 43.22% YOY.
Q1 imports from other countries with a share of 29.1% of Iran's total exports plunged by 39% YOY to stand at $2.23 billion.
Iran exported over 21.92 million tons of non-oil goods worth $6.36 million during the period to register a decline of 43.98% in value and 45.12% in weight compared with the corresponding period of last year.
The country posted a trade deficit of $1.26 billion over the first quarter of the current year.
Noting that the Covid-19 pandemic disrupted normal economic activity around the world, Mirashrafi said, "The decline in our exports in recent months is to blame on the outbreak of coronavirus and closure of borders. However, exports have begun to get back to normal since mid-June."
Goods Iran import Consumer
Consumer Inflation at 27.8%
Q1 Exports From Mazandaran Ports Reach 185K Tons
Furniture, Home Appliances Inflation at 37%
SCI Surveys Latest Consumer Price Changes in Provinces
Tobacco Inflation at 14.5%

redpajama_set_name: RedPajamaCommonCrawl


This was an issue wherever I tested it. Adding a "wait" seems to fix things. I'm not 100% sure if it is the right fix. I think it is.
Before this patch I found that proc.returncode was none until it called proc.communicate at which point it always returned 0. The previous behavior was to return zero regardless what was clicked.
hd.txt (567 bytes) - added by Nathan Hallquist 5 months ago.
wsl.patch (1.8 KB) - added by Antoine Martin 5 months ago.
returncode is meant to be None until the process has terminated.
I'm not sure reading from the pipes after the process has already terminated is going to work everywhere. That could cause SIGPIPEs.
Can we detect WSL and only wait prematurely when really needed?
You are right. I'm not sure what is going on in WSL. Everything is fine in MinGW. I will test Linux later.
Please try the patch above, according to Provide a way to positively detect WSL from an app compiled on Linux, this is the correct way - untested.
The returncode always starts as None, it is set after one either calls wait or communicate, and that's true on all platforms AFAIK. So what is different with WSL?
This patch works. I apologize for not having had time to follow up more rapidly.
After your comment about SIGPIPE I doubted my initial observations.
I can confirm that it is broken in OpenSuSE 42.3 WSL and Ubuntu 18.04 WSL. I can also confirm that this patch fixes everything.
I can also confirm that this patch fixes everything.
At some point in the future they are likely to fix the WSL bug, and then we'll have to remove the workaround.


text:
Death toll in Haiti earthquake adjusted to 2200 - 60000 homes destroyed
Almost 2,200 people have been reported dead, many more are injured and tens of thousands of families are homeless in Haiti after the magnitude 7,2 earthquake that shook the country on Saturday.
On Wednesday, the death toll was adjusted upwards to nearly 2,200 victims. Many thousands more are injured.
Hours later, an aftershock shook southwestern Haiti during the night until Thursday, local time.
Nearly 60,000 houses are estimated to have been destroyed in the country, which is considered to be the poorest in North America.
The earthquake has also caused damage to hospitals, churches and hotels.
According to Bruno Maes, UNICEF's representative in the country, around 500, 000 Haitian children are without, or with limited access to shelter, clean water, medical care and food.
The UN has set aside just over € 7 million for humanitarian efforts in Haiti. Several neighboring countries have also sent supplies and rescue workers. Organizations such as the Red Cross and Doctors Without Borders are already in place and are involved in relief work.
However many of the emergency shipments have been delayed.
Saturday´s earthquake was reportedly stronger than the 2010 earthquake, but had less damaging impact.
The magnitude 7.0 earthquake that struck Haiti on the afternoon of January 12, 2010 affected 3 million people and was the most devastating natural disaster ever experienced in Haiti. Roughly 250,000 lives were lost and 300,000 people were injured. About 1.5 million individuals were forced to live in makeshift camps.
Cover Photo ( Above:) Flickr Licence. UK Fire Service Search and Rescue team at work in Haiti during the great earthquake in 2010.
UK firefighters at work in Port-au-Prince, Haiti.To find out more about how the UK is helping in Haiti, please visit www.dfid.gov.uk/haiti-earthquake Image credit: UK Fire Service, 15th of January 2010.


text:
Collide With the Sky es el tercer álbum de estudio de la banda de post-hardcore Pierce the Veil, lanzado por la compañía discográfica Fearless Records el 17 de julio del 2012. El primer sencillo del álbum, «King for a Day», fue lanzado el 5 de junio de 2012. Cuenta con la participación de Kellin Quinn, vocalista de la banda Sleeping With Sirens. «Bulls In the Bronx» fue elegido como el segundo sencillo y fue lanzado en junio de 2012. Los respectivos videos de ambos sencillos fueron lanzados el 6 de agosto y 7 de mayo. El álbum también cuenta con la participación de Jason Butler en la canción «Tangled In the Great Scape» y de Lindsay Stamey en el tema «Hold on Till May».

El álbum apareció en el Billboard 200 de Estados Unidos en el puesto número doce, vendiendo más de 27 000 copias en su primera semana. Esto marca un gran éxito para la banda, sobre todo teniendo en cuenta que el álbum se filtró en Internet cuatro semanas antes del lanzamiento oficial.

Producción
El álbum fue grabado en el estudio House of Loud en Nueva Jersey por los productores Dan Korneff y Kato Khandwala quienes anteriormente habían trabajado con bandas como Mayday Parade. La batería del álbum sin embargo, fue grabada en una casa abandonada, localizada muy cerca del estudio.

Diseño de la portada
Según Vic Fuentes, «el tema de la portada del álbum es saltar fuera de la tierra que se rompe debajo de ti. La idea es inspirar esperanza en medio del caos que puede estar sucediendo a tu alrededor. Si el suelo se rompe bajo sus pies, tu primera reacción puede ser de correr y saltar a la seguridad, y es ese momento en el que están me he inspirado. Un cuadro en el que no se está seguro de si la persona se cae o vuela. Se trata de la liberación de las cosas que se rompen o caen a pedazos, e inspiran un sentimiento de esperanza en la desesperación».

Recepción
El álbum ha sido bastante bien recibido por la crítica. Alternative Press calificó al álbum con cuatro estrellas de cinco, donde el crítico correspondiente declaró que «Collide With the Sky es el mejor trabajo hasta la fecha de Pierce the Veil». También elogió a la canción «Props & Mayhem» por sus ganchos power pop. AbsolutePunk comentó que el álbum es "seguro para continuar el ascenso de Pierce The Veil a la cima de la liga", concluyendo que "La prueba es que Pierce the Veil está aparentemente un paso por delante de todos los demás a la hora de inventar cortes energéticos de post-hardcore y que no dejan caer el balón a corto plazo".

Lista de canciones

Personal
Personal digital.

Pierce the Veil
Vic Fuentes – voz, guitarra rítmica, teclados
Mike Fuentes – batería, percusión, beats
Jaime Preciado – bajo, coros, programación
Tony Perry – guitarra líder, coros

Músicos Adicionales
Kellin Quinn (Sleeping with Sirens) – voces en "King for a Day"
Jason Butler (letlive.) – voces en "Tangled in the Great Escape"
 Lindsey Stamey (Oh No Fiasco) – voces en "Hold on Till May"
 Dave Yaden – piano, teclados

Producción
 Dan Korneff, Kato Khandwala – producción
 Dan Korneff – mezclado
 Brian Robbins – ingeniero adicional, edición digital
 John Bender – ingeniero
 Jim Romano – edición digital, ingeniero asistente
 Alex Prieto – edición digital, guitarra tecnológica
 Ted Jensen – masterización
 Aaron Marsh para Forefathers Group – illustración de la portada, plan de diseño
 Daniel Danger - concepto artístico

Certificaciones

Referencias

Álbumes de 2012
Álbumes de Pierce the Veil
Álbumes en inglés

redpajama_set_name: RedPajamaWikipedia


WESTPORT — Standing outside an empty Old County Road building, parts of which date to the 18th century, Deborah Weaver stuck a ceremonial shovel in a pile of ceremonial dirt and smiled.
Finally, a new day had come for Westport River Watershed Alliance.
"I've always thought organizations at a certain point in their evolution need some roots in the community," said Weaver, the alliance's executive director. "It's been a good organization. This gives it some depth that is quite different."
The 42-year-old alliance is gearing up to leave its rented Main Road headquarters to put down roots at the Head of Westport in an old wood-and-granite building overlooking the river. The building fittingly will be called the River Center, a space designed chiefly to educate the public about the fragile watershed and make the organization more visible and accessible.
"We get to transform a blighted building into something that people can benefit from," Weaver said.
Officials broke ground on the project June 8 with renovations, overseen by Page Building Construction of Stoughton, set to begin later this year. The alliance has raised more than $1.6 million toward a $2 million goal, according to its website. In addition to private donations, the project is receiving funding from the Massachusetts Cultural Facilities Fund, Westport Community Preservation Committee and BayCoast Bank.
"We're down in the middle of nowhere, so this brings us right into the heart of our watershed community," said Gay Gillespie, the alliance's development director.
The groundbreaking comes after more than three years of planning, designing and acquiring the necessary permits for the undertaking. Weaver said permitting was the most challenging aspect, noting that while the alliance owns the 493 Old County Road building, the town owns the land.
"It was such an arduous process, but it's exciting to think we can start doing the project," she said.
Weaver also said, along the way, the work helped the alliance foster stronger relationships with town boards and many people in the community.
In a brief ceremony across the street, by the Head of Westport Country Store, Sen. Michael Rodrigues (D - Westport) speculated that a ribbon-cutting ceremony could happen in a year.
The building has survived centuries of use, having once served as a blacksmith shop and later, in the early 1900s, as a Ford dealership with a service garage on the first floor and a showroom on the second, according to Steve Connors, community engagement manager for the alliance.
"This whole area was once the center of commerce for Westport," Connors said, opening the side door to the building for a walk around. On the first floor, Connors pointed out where educational displays will likely go, before heading upstairs to future staff office and meeting space.
"We'll be able to engage with the public on an everyday basis," he said as the window behind him offered a view of kayakers setting out from the landing for an afternoon adventure.
The alliance envisions a floor with a painted or mosaic watershed map, a ceiling-mounted projector to provide virtual tours of the river and tanks for habitat exhibits. Connors said the building could also house information on the history of the area.
According to the alliance website, Page Building Construction has some big projects in its portfolio, including New Bedford Whaling Museum Wattles-Jacobs Education Center, Fairfield Inn and Suites in New Bedford and Tanglewood Performing Arts Center in Lenox.
The center won't be the only change coming to this spot in Westport. In 2017, the town received a $249,000 grant from the state Seaport Economic Council to fund additional improvements to the area, including to sidewalks, lighting and parking.


text:
Anthroposophical Society
General Secretary Report Dec 2018
Sue Simpson, General Secretary, Anthroposophical Society in NZ
There are times when you step onto a path not knowing where it's leading, who'll you meet or what will evolve, there's a mood of anticipation and slight nervousness that describes how people felt on arrival in Dornach. When the AGM at the Goetheanum closed, people dispersed quickly and there was no time to reflect with one another on the days and what had eventuated. The articles and commentaries that appeared in AWW, expressed the polarisation and distress felt by some members, and though much can change with the passage of time, it was not clear what any unresolved issues, carried by a general secretary or country representative would have on our work. I can now look back with deep gratitude to those who prepared and facilitated the meetings. We first met without the executive or section leaders, the space was created for open, honest and sensitive interaction. We were able to work through some challenging moments, reconnect and strengthen the circle in a way that bodes well for the future. This inner work was reflected in nature, a real festival of fire and the warm colours of autumn.
As the circle expanded to include the Goetheanum Leadership, we became acutely aware of the diminished numbers on the executive, especially with the absence of Matthias Girke who was called away, leaving three members: Constanza Kalics, Joan Sleigh and Justus Wittich. From them we heard of developments at the Goetheanum, and the commitment of all section leaders to share in the responsibility for carrying the tasks at the Goetheanum. I had already experienced the cooperative work of Claus-Peter Röh (Pedagogical Section) and Johannes Kühl (Natural Sciences) with Matthias Girke in facilitating and carrying the Class Holders meetings. Christiane Haid (Literature Arts Section) is working with Constanza Kalics on a new membership booklet; Georg Soldner (Medical Section) with Justus Wittich on the World Association in development and Gerald Haffner (Social Science Section) and Justus Wittich are researching the wider inclusion of members vote and decision making at the AGM. Joan Sleigh is working with English speaking class holders on a new publication of the class texts where the German text will run parallel to the English, providing the possibility to view both languages on opposite pages.
Several of the above topics are or will be reported in AWW. One thought I will share is that there is no simple resolution to the question of including members in the decision-making process at the AGM. During the discussions it became clear that it's important to review the process concerning motions and the process of review and confirmation of the executive. In 2019 Justus Wittich will complete his seven-year term and requires the confirmation of members in order to continue as an executive member. It's important that there be good communication with members prior to the AGM and that questions be well answered. A group are working towards shaping the next AGM and presenting any suggested future changes.
Two sessions were given to explore two themes, the future Michael Festival and the Foundation Stone Meditation. Is there a relationship between the second verse of the FSM and a Michael Festival? Concentrating on the words, 'the rhythms of time' and 'entering into your own soul being', we can create the space and find the strength that can bring us into a working relationship with the Zeitgeist Michael. In my working group, Georg Soldner reflected on the festivals of Easter, Whitsun and Michael, with the perspective of Easter, in the West being an experience of denial. Peter and the disciples denying Christ was followed by the crucifixion. He noted that experience of denial is also a part of the Anthroposophical Society's history and Steiner's experience. Whitsun is a very different experience, though people speak different languages they can understand one another. He posed the question, is the experience of Whitsun a necessary step towards creating a future Michael festival? We find our identity in our language, yet it is a prison that we struggle to free ourselves from. Learning the language of another is to live with no set form, no repetition and not base our ideas on tradition. The gift is in not knowing what to do.
Sitting in my room, observing the deep colours of the sunset in the west, I'm reminded of a conversation that asked the question, what does 'Let from the East be fired, what in the West is formed' truly mean? Several country representatives come from eastern Europe. In countries such as Hungary, between 1950 – 1989 people longed to be freed from the darkness of communist pressure. In 1989 the wall came down, but an aggressive invasion of western materialism followed. A physical wall came down but thirty years on a kind of iron curtain remains in the soul. Has the West taken up the gifts of the East? Bridges of understanding and cooperation can be built that give strength through what come to life in the in-between. There is a danger of branding a culture under labels such as intellectual or mind, or any other soul, in that having prejudged we fail to perceive the diversity of life. Clearly the place where we are born and live will have an influence on us in life, but it doesn't have to limit us. Wherever we stand on the earth we experience the sun rise and set, as human beings we hold the center and have the possibility to bridge East and West wherever we are.
At the instigation of Constanza Kalics a group of young people were invited to join us for a session. This proved to be a highlight in the days of meeting. Any expectation that they would come with questions around technology and such challenges of today were quickly forgotten as they opened with their concerns for the Society and their experience at the AGM. How can we develop a way of working that ensures we nurture and respect the dignity of every human being? Their way of processing the events at the AGM was to work through the Christmas Conference 1923/24 lectures and procedures. Their questions were our questions, age was irrelevant as we listened and spoke with people who shared common themes. They came well prepared and were inspiring. As they said, they don't always agree and that is fine. When asked what they saw as important for the future, several spoke to the need for research in all fields. They see the work demands on the executive and others and want to help but finding a way into a busy world isn't always easy or possible. Some of us joined them for an evening meal and the room reverberated with lively conversations. They also took the opportunity to share with us some of the 2019 projects.
At the conclusion of the meeting, there was a definite sense of consolidation and positive support for the other, whether it be the representatives around the table or the Goetheanum Leadership. We need one another to progress and it's essential that we develop the capacities to work with one another if the Anthroposophical Society is to truly find its meaningful place the world, with members having a lively interest and purposeful engagement in the world.
Visiting the Nordic Lands
En route to Dornach, Jan Baker-Finch, General Secretary for Australia, and I visited the Nordic countries. Our impulse was to meet the general secretaries and where possible their councils and other initiatives in their own environment. Again and again we heard words of appreciation, "Thank you for making the effort to visit us." Flying to Finland, we sailed to Sweden before flying to Norway and finally Basel, Switzerland. Though we only caught a glimpse of each country, the differences were apparent in the landscape and people. While there is something familiar in the Swedish and Norwegian languages, Finish flowed over us like beautiful but incomprehensible music. We felt lucky that everyone we met spoke English!
Noticeable in Finland are how low the clouds hang over the land. The Baltic Sea gently laps against the land, the rocks of ancient basalt rounded and smoothed by the ice call forth the image of snow-covered lands. Soon the snow will return but we caught the last flame of autumn. Finland is a bilingual country with Swedish and Finnish, the latter an ancient language that has survived the invasion of both Russia and Sweden. The cultures co-exist, with their own Steiner Schools, two Societies the Finnish (circa 500 members) and the Finnish-Swedish (circa 50 members). The council is made up of members from different professions and we arrived to join an extended meeting, held twice a year, that invites a greater diversity of people and professions. Reference was made to Denis de Rougement's work on the principles of federalism and his suggestion that Europe form a federation not of nations but of folk souls to enable the recognition and maintenance of cultural rather than border-defined differences.
We visited Snellman College and Research Center, met with some of the lecturers and its director, Markku Niinivirta. He spoke of the colleges founding and shared the Fins great love of learning. It is a small College that offers a number of courses, amongst them philosophy, arts, a fulltime eurythmy and teacher' trainings. Many do their masters through the university in Norway and some go on to complete their doctorates in Finland. Being a private college, they face the annual struggle to find the necessary funds and yet they manage to continue. During our stay, we also learned that despite the relative freedom that schools in Finland have, they too are experiencing the encroaching influence of technology and the state.
An overnight ferry saw us sailing up the beautiful archipelago at dawn, to Stockholm. There we were met by the General Secretary, Mats Ola Olsen who gave us a brief tour of the city before heading to Yitterjärna. There we met with a group of young people from YIP (Youth Initiative Programme) and the co-founder Reinoud Meijer. The discussion was very different to the one we would later have with young people in Dornach. The Yippies are taking that exciting and tentative step into life and the unknown, they are fully occupied with the course and their assignments. They share a rich life in Järna and clearly the bonding and social interaction is strong and important for the year and their future.
Standing in Järna we were all too aware of the changes we see so often around the world. Once this was a vibrant center with all kinds of trainings, workshops, performances and conferences. It has grown quiet with the echoes of a past culture needing to give way to future impulses. Visiting the Vidar Medical Clinic, we heard the sad news that it is struggling to remain open. It is a beautiful building and it doesn't take much to imagine the healing influences, all the more tragic the possible loss.
In the evening we shared with members and friends, reflections of Australia and New Zealand. This led into a conversation that continued the following day with some of the council members. The theme, colonization of lands, peoples and places, raised the question of the anthroposophical movement, and whether wisdom has been missed or lost because there was an intention to bring knowledge and ultimately not see what was already there. I see and hear what I know; what am I not seeing and hearing? Themes were raised that were later discussed in Dornach, others we shared such as finding initiative and connecting with the world. Our visit closed with the special gift of viewing the glass windows in the Culture House and having Kristoffer Laurén, who was one of the three artists, who created the windows with no prior experience in colouring or cutting glass. He spoke to the images and imaginations behind each window and remained inspired by the trust and openness Arne Klingborg had given them to learn and develop their skills and techniques. It had been a unique experience in his life and a great gift for us. Our last hour in Sweden was sitting in on a Mystery drama rehearsal in one of the several curative pedagogy centres in Ytter Järna
Last stop Oslo where we visited the training college and two anthroposophical houses. The college was busy and every space occupied. Norway offers university degrees and the possibility to do a masters. We had the opportunity to meet with some of the lecturers and tutors before meeting with the full Norwegian council. The council shares the tasks between them, employing one person for the treasurer-secretarial-publishing work. They prefer the name ambassador to general secretary, the role Ingrid Reistad has as representative to Dornach for Norway. On their council, a lawyer who has a high profile in society, is highly respected and well known in Norway. This gives weight and affirmation to Anthroposophy in Norway, but we also understood that anthroposophy is positively recognised in Norway. The council held their meeting in English so that we could participate. There was a youthfulness in Norway that comes across in how they are and what they do. Elizabeth Wirsching, who visited New Zealand a few times, when she was leading the Youth Section at the Goetheaum, is also on the council. On our last day, she guided us on a brief but interesting tour of central Oslo.
We left the Nordic countries with a sense of the connectedness. Though our languages, cultures, history and environments differ, there are human and world themes that draw us together and create a world connectivity.
Pedagogical Section Conference in Dornach, 9 – 13 November 2018
Jan Baker-Finch (general secretary Australia) and I were invited by Claus-Peter Röh (Pedagogical Section) to take the daily eurythmy sessions during the conference. I was also asked to give what is called a free-rendering of a lesson. With at least twelve English speakers attending, the groups were divided into English and German speakers for conversations while the workshops were mixed. This was the first section conference based in the School of Spiritual Science that I had attended in Dornach. I left with a greater appreciation for the group through the way we worked together. This I experienced in the capacity to listen and live with silence, enabling the conversations to expand and deepen. The participants fed back their great appreciation for the eurythmy, as an art but more for the deepening experience it gave to the lessons. This conference enabled colleagues to connect and develop their work. They came from USA, Great Britain and Europe, extending to Slovenia in the east, and Australia/New Zealand in the South. Our thanks to the Pedagogical Section for organising this conference.


text:
Stintino, Nort-West Sardinia
Nights: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Modify/Delete reservation
Park Hotel Asinara ***
The hotel is approximately 25 km from Porto Torres and 45 km from Alghero-Fertilia.
Visit Stintino
For information on how to reach the hotel, call us at telephone number 079/523127 or 079/523267
For information on how to reach the hotel, call us at telephone number 079/520058, or fill out the form below.
The origins of Stintino dates back to 1885 with the settlement of 45 families, mostly related to the activities of fishing, coming from Asinara.
Previously the territory, part of Nurra of Sassari, was populated by scattered settlements of herdsmen-farmers, who lived in residences-admissions of cattle called "li cuili."
The village of Stintino, already part of the municipality of Sassari became an autonomous municipality the 10 August 1988. The first elections for the appointment of the municipal council of Stintino were held in March 1989.
Recommended link: www.overingdiving.it
Park Hotel Asinara Stintino - Loc. Le Vele - Tel. & Fax: 079/520058 - 07040 Stintino - P.I. e C.F: 01722160908
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One of the things I love about my area of Capitol Hill is that I am walking distance from a variety of restaurants. Almost everytime LL and I walk to Jamjuree or 22 Doors or QFC we pass Jalisco's, mention that we should check it out, but then continue on. Tonight we decided that after almost 3 years in this neck of the woods it was time to sample the neighborhood Mexican fare.
This entry was posted in Food on April 27, 2006 by kenny.
It's really too bad that you didn't enjoy the food at Jalisco's. My family has been dining there for over 20 years and have rarely been disappointed by this family run restaurant. We have found the food to be consistently good. We also like the Mexican staff even if the accent can be difficult from time to time. And the number of Mexican customers who we have seen dining thereseem to enjoy it as well.
This is a very moderately priced restaurant. For those who want gourmet food, expect to pay considerably more.
I'm glad to hear that you had a better experience than me. I wasn't looking for gourmet food, but I was looking for quality and taste at least on par with El Gallito or Tacos Guaymas (which is even more moderately priced as well).


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Hepatitis A is one of the most common human infections. The infective agent of disease is hepatitis A virus (HAV), belonging to the family Picornaviridae. Viral genome is represented by single-strand RNA. Feature of hepatitis A virus is its stability in the environment: at room temperature it can remain viable for several weeks or months, and at 4 °C – for a few months or years. Only thermal (...) continue »
Hepatitis A is one of the most common human infections. The infective agent of disease is hepatitis A virus (HAV), belonging to the family Picornaviridae. Viral genome is represented by single-strand RNA. Feature of hepatitis A virus is its stability in the environment: at room temperature it can remain viable for several weeks or months, and at 4 °C – for a few months or years. Only thermal inactivation at 100 °C for 5 minutes can destroy the virus.
Annually about ten million people in the world are infected with hepatitis A virus. Children and adolescents may have asymptomatic hepatitis A; the disease is severer for babies and seniors. For adults, the infection often occurs with severe intoxication and jaundice. The duration of the disease depends on the immune system, the person's age and other factors, but on average it takes about 40 days. 15% of patients have a chronic form of hepatitis A in the course of 6 to 9 months. The risk of death from the disease ranges from 0,1 to 2,4% for people older than 40 years. Full recovery is observed for 90% of patients, in other cases there are residual effects.
The source of infection is a person sick of hepatitis A who excretes with faeces billions of viruses into environment. With contaminated water and food the virus enters the intestine, penetrate the epithelial wall and get into the bloodstream and penetrate the liver and its cells - hepatocytes and Kupffer cells. After virus replication cells are destroyed by its cytolytic action, resulting in the development of inflammation and abnormal liver function. Viral particles are secreted into bile and excreted by defecation (with stools). On average, viral particles are excreted in significant amounts for about 11 days before the emergence of symptoms or IgM class antibodies against hepatitis A virus emergence in the blood. The incubation period for hepatitis A lasts 15 to 50 days. IgM class antibodies appear 3-5 days before the first symptoms and store in the patient's blood for 3-6 months, decreasing to low levels for the year. The nascence of IgG antibodies indicates the end of the acute phase and the formation of immunity to the virus. IgG antibodies to HAV also appear after the vaccination against hepatitis A and persist throughout life, providing immunity. The presence of IgG antibodies in blood indicates transferred past infection or vaccination against the virus.
Infectious diseases/
Hepatitis A/
Vitrotest HAV-IgM
ELISA test-kit for the detection of IgM class antibodies to Hepatitis A Virus in human serum or plasma
Microplate: 12 strips of 8 wells coated with monoclonal antibodies anti-IgM
Conjugate: a mixture of purified HAV antigens and monoclonal antibodies conjugated with horseradish peroxidase
Timing Analysis: 1.5 - 2 hours


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Lee talks greenscreens, Hannibal and more
Nov 3 • by Ursa • No Comments • Interviews
In November 2013 issue of F*** magazine, there's a new interview with Lee Pace and Richard Armitage (Thorin). Lee discusses the Elvenking's personality, and tells us more about the filming process. He also mentions he's seen Halt & Catch Fire pilot recently and "it's really good".
I retyped some questions for you. Full interview (I strongly recommend it) can be found in our gallery.
Tell us a little about the role that Thranduil and Mirkwood play in The Desolation Of Smaug.
Mirkwood used to be the Greenwood, but it's a corrupted forest, it's become a very dangerous, wild place, and it is also Thranduil's realm. The forest is very much a reflection of its king, just as the king is a reflection of his land. Like the Fisher King [a sovereign in Arthurian legend whose lands waste away when he suffers physical injuries]. One of the symbols I was very interested in was the Fisher King – and the story of the Fisher King was that he was away in this paradise, this utopia that would vanish, and it's surrounded by a wasteland. And that's Thranduil – he's wounded king in a paradise that's surrounded by a wasteland. It's a dangerous place; he's dangerous, dangerous king.
It's a very anticipated role, how's the fan response been so far?
I guess I've gotten some nice fan letters about it. I hope people are excited about it, I'm certainly excited to play it. It makes me feel good that people are excited to see the character. I think we've solved a lot of the riddles in the book about the character. Do you know the cartoon? Remember the cartoon? You know how the Elf King was depicted in a really nasty way? This Elf King is different.
Did you get a chance to film on location in New Zealand?
They shot at a lot of places but I never went on location. Everything I shot was on the greenscreen. I did shoot up in the Ruins of Dale. But I didn't get to go to the cool location stuff, I was bummed about that.
How was shooting on greenscreen? How does that affect the acting process?
Well, you have to imagine it. Because it never looks like that [he gestures to the poster of Bilbo in front of the Doors of Erebor] when you're on set. You just get a little rock and some twigs and then Pete's like – "Well, you know, it's gonna be very big. It's the elven king's halls, you know, it goes on and on and on." And I imagined yeah, it goes on and on and on. But then I watch what Pete does with it and it's like on and on and on and on and on and on, because he just makes everything so big. He makes it all so kind of jaw-dropping.
So, greenscreen, I mean, I like it. No complaints about it. It's easy to complain about it because it's just, you know, a weird thing. The colour's weird. Although one of my favorite places on set is the special effects table, because they've got like six screens up and they're all working on different shots from the movies.One of them is animating a dragon, one of them is creating the way the coins fall. At this level, it's awesome what they do, what they're capable of doing.
You worked on Pushing Daisies and Wonderfalls with Bryan Fuller, and there's been talk about Bryan Fuller wanting to cast you in season two of Hannibal. Can you talk a little about that?
Yeah, yeah, yeah, we've talked about it. He asked me to do something last year when I was doing a play here in New York City, so I didn't end up doing it. I've worked with Bryan twice before, so I'd love to be a part of this show too. I mean, whenever the right time comes, it'll be the right time to do it. I'm doing a show for AMC this year called Halt & Catch Fire. We shot the pilot already and it is – I saw it last week – it's really good.
Gallery Link:
Magazine Scans > F*** Magazine (November 2013)


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