Contraction cloze workhseet

Day -1 - Wednesday
Wednesday I arrived on the last shuttle from the airport to the event. When we arrived it was after dark (expected on my end) when we got on the internal site people mover (trying to define things differently in hopes to make it more clear, something I feel the festival should have done.) I was supposed to be in "Walk In Camping" when I told the driver this I was dropped off at preset camping (something you purchase where your campsite is already set up for you) I then asked the "camp hosts" where walk in camping was and they also insisted I was in preset camping until they couldn't find my name on their list and realized I hadn't paid for it and then couldn't direct me where to go waving heir hand vaguely in the direction we had come from on a dark road and said "that way." By this time it was 10:30pm.
I again hopped on the internal people mover and again explained I didn't have a vehicle and need the area where people would just be setting up tents and not camping directly next to their vehicles. I has dropped in an area called "Hillside Car Camping" when I again questioned this I was told to "figure it out" and left there.
I set up my tent and began my search for water (mind you I flew in and the shuttle did not make a supply stop, so everything I had had to come with me on the plane.) I brought 3 water bottles and a fillable collapsible 1 gallon water jug. I gathered all of these to go to the water station and fill them up to bring back to camp and expected to do this at least once a day since they would have water in the music area as well.
This is when I learned the map was useless. They didn't mark any roads or walkpaths on the map. I asked roughly 6 staff members at different points along the way where the water station was and no one could tell me. It was a 30 minute walk over hilly terrain (that I would later come to find out had no shade the entire way durring the day. I eventually reach the water station fill up and return to camp for a Goodnight rest.
Day 0 - Thursday
Thursday morning I wake up and begin to try and explore a bit to get better bearings and again see if I can use the map provided to get to where I wanted to go (This time the glamping area to meet a friend). I walked for over an hour through backstage and Artist and Vendor camping all the way to the commissary without security stopping me a single time and still wasn't able to figure out where I wanted to go so returned the way I had come.
I used up most of my water and again made the 30 minute hike to water (this is the point I realized there was no shade the entire way).
This is when I decided I was packing up and moving camp (an event that would take me the rest of the day and 3 trips and caused a very mild case of heat stroke). I had met some people online in a chat group and determined they were closest and they welcomed me into their camp (later we would find was a reconnection from another festival long passed).
I was now a 10 minute walk from water.
That night we went to the Thursday Pre-Party with Daily Bread. It was amazing and really fun but we did notice stages were still in the process of being built and the entire time they were running trucks, heavy equipment, and emergency vehicles through the crowd (I could be mistaken but in the 60 or so fests I have been to they always have emergency lanes backstage so they don't have to drive through the crowd, all around safer for all involved).
Day 1 - Friday
Friday was a very nice day, weather wasn't too hot. We walked down to the gorgeous quarry to take a swim. Stages were still not finished. Music that night was amazing. Really no complaints other then them still running traffic through the crowds.
Day 2 - Saturday
Pretty much the same as Friday, I also took an amazing Shower at the Europa Showers (shout ot to that crew for being awesome!) Really one of the best shower expierences I have ever had. Music was great no real complaints except again the traffic through the crowd.
Day 3 - Sunday
Sunday we camped out ath the Eclipse stage because every artist we wanted to see was playing on that stage. We had an amazing time right up until the Tipper crowd appeared. We were right next to ADA and we had a campmate who had just had a major surgery and was sitting in his air couch and a few friends who have really bad anxiety. The crowd was so rude to us for taking up space we needed to make sure we were comfortable. We were there for over 9 hours and had no issue until the Tipper crowd tried to trapple us and a few friends had panic attacks.
This is the time I went to get water for my entire group. I get to the water station and it isnt working. I walk to the next water station that also isn't working. I go to the Med tent to tell someone and I was told they had broke a water mainline and all water on the site was shutdown but they had been working to fix it for over 45 minutes (something that took over an hour to fix) when I returned to the water station I began telling other the situation. Eventually I was approached by what I can only assume was an employee (high vis vest, dark clothes, looked overworked, again I have been around the block a few times) who asked me where I heard that and I said "I was told that" and he huffed and said "Great, really appreciate that." The rest of the evening went pretty smooth.
Day 4 - Monday
I awake Monday at 9 am to go hear some talks out in the Earth Village when we hear other festival goers saying everything was canceled due to weather. We were extremely let down to say the least. We had 1 camp mate who really only came to see LSDream and Clozee. We begin to pack down but hear the line to leave was being quoted at 6 hrs to just get off the property. So we slowed down and took a load off ate breakfast and determined to watch the eclipse from camp.
I had been to Oregon in 2017 and the ring of fire still never ceases to amaze me. We packed up and left right as the sun was setting. Then took us less then an hour to get off the property.
I was supposed to take the shuttle back to the airport but my flight didn't leave until Tuesday (today) and the shuttles were just dropping people off at the Austin Transportation Center without any way to get to the airport. I was lucky my friends had space for me at their home because there were no hotels available in Austin.
Now
Overall I did have a lot of fun. I knew to expect a sh!t show after being at OE and having been around the block. I do believe they should have done a better job making sure staff new the lay of the land. I also wish that they had chosen better diction to describe camping areas and make things more clear (preset vs walk in etc.). Running traffic through the crowd is a festival no no to me. They 100% needed more water stations and even said they were planning more right before the decision to cancel.
I will be disputing the shuttle charge with my CC company because dropping people of a day early and with no way to get to the airport when you said you would/could is a breach of contract. I will also be keeping an eye our for this particular refund before making any more decisions regarding that.

Hi all! I want to use Anki to remember the steps of a process, including the order they occur in. When I looked this up I found Cloze Overlapper but also found it is not supported in the recent version of Anki. What else do people suggest to use?.
Example - there are 10 steps to the sarcomere contracting, I want to people to scramble the order and reorder them, or scramble and assign a number indicating which step it is etc.

You can just use the import function in anki. Don't forget to set the "field separator" as comma, set the note type to cloze, and allow html formatting in anki. After the flashcards are imported, you just need to select the vocab word in the front of the flashcard and cloze it out. After you give this to chatgpt, just provide it with your vocab list. (if it doesn't understand it the first time, just refresh and try it again). You're welcome:
​
I will give you a list of vocabulary words to organize into flashcards. I want the flashcards to be in a csv format so that my app can read them and automatically sort them into flashcards. The app is called Anki; it is a flashcard app. I want the CSV file to have the field separator as a comma. The csv file will be as follows: I want the front section to be wrapped with double quotes. I want the back section to also be wrapped with quotes. I want these two sections to have a comma separating them. I want each flashcard to be in a new line (it is so that Anki can read it and understand it as a new flashcard). Now this is what the flashcard will include:
The front: I want the front to have the word used in an interesting sentence you put it in (the sentence can't just be the definition of the word).
The back: I want the back to have the answer (the word) with its part of speech abbreviated next to it. And next to that, the meanings of its roots and affixes(For example, if the word is "abstract", it will be ab-away + tractus pulled. I want the back to also include, after that, the word family (for example, if the word is "abstract", the root family will be: [tract] retraction (a pulling back), protract (to extend in time), tractor (vehicle that pulls farm instruments),
detract (reduce the value of someone or something), tractable (manageable)). I want the back to also include, after that, some synonyms for the word. Next, after that, the word used in a different sentence (that sentence might convey a different meaning). After doing all of that and making sure what you did was correct, each flashcard should look like this:
​
"Her decision to take a gap year after high school was construed as laziness by her parents.","construe (v.): to interpret or understand the meaning of something, especially in a particular way.

Roots/Affixes: [struct]

construct (to build),

destruction (the process of destroying something),

obstruct (to block or stop the passage of something).

Word Family: [struct]

instruction (the act of teaching or providing knowledge),

infrastructure (the basic physical and organizational structures needed for the operation of a society or enterprise),

structural (relating to the arrangement or formation of something).

Synonyms: interpret, understand, explain.

Example: The court had to construe the meaning of the ambiguous contract clause."
​
Notice that some words are bold and italized and notice the line breaks. Please do everything as instructed. Please don't forget anything. dont add extra stuff you weren't asked for. Again, Please make anki properly read this. for anki to properly read this, you need to have the front section wrapped with double quotes, the whole back section to also be wrapped with double quotes, and for these two sections (the back section and the front section) to be separated with a comma (the field separator). I don't want any extra double quotes withing the back section and the front section (if you do that, anki will understand it as a new field). I want each card to be on a new line (so that anki understands it as a new flashcard). I, also, want you to tell me what you did after. I will give you two words to make sure you completely understand my request. The words are: cantankerous, brusquely
​

This occurrence was more common at the pagoda than anywhere else, but it popped up a few times at amp and fractal. I understand if an artist wants to warm up their set with something light and simple but what I don't get is that nearly every artist I encountered in the evening & AM used the exact same baseline. It was extremely disappointing to hear it routinely, especially when a unique artist like CloZee comes on, presses play, and dances behind her deck to the dull repetitive bass that we've already heard a hundred times.
I'm wondering if anyone has industry knowledge as to why this baseline was so overused. Do artists have different price points, and lower rates get part of their set provided by shambs? Did artists collude because they are pissed off at shambs? Is this the coolest baseline of 2022 and you're an idiot if you don't use it? Were artists contracted to use this specific baseline?
If you're a bit foggy on what baseline I'm talking about let me jog your memory:
dun dundundun. dun. dun.
dun dundundun. dun. dun.
dun dundundun. dun. dun.

So, I'm trying to remember the process of muscle contraction. The problem is I don't know how to put this into Anki atomically like my other cards. How should I put this information into Anki? Should I just put them into a series of items and use cloze deletion to hide them? Or, something else? Keep in mind that I already understood the steps as a whole, and I am only using Anki as a means to retain long-term retention.

I'm just making flashcards for my notes and have bit the bullet and signed up to the patreon of glutanimate, as it has the new cloze overlapper and I've easily got $hundreds worth of value from this. I've been having some issues with making cards however, as my tried-and-tested method has been failing and I've been getting the error
"This would generate XX overlapping clozes. The note type can only handle a maximum of 20 with the current number of text fields"
Where XX is typically a high number around 60-80. This is the exact same way of building cards I've used in the past. I've already troubleshooted whether a change from "{{c1::" to "[[co1::" like in previous anki versions would help, but unfortunately the problem remains the same.
I also tend to have a high number of context in my CO cards, but resitting this has unfortunately not resolved the issue. I've attached the actual text of the card below. Does anyone have any suggestions?
Definition: {{c1::an umbrella term for a permanent disorder of movement and/or posture and of motor function due to a non-progressive abnormality in the developing brain}}
Causes:
{{c1::80% CP is antenatal in origin due to cerebro-vascular haemorrhage or ischaemia, cortical migration disorders or structural maldevelopment of the brain during gestation. Other antenatal causes are genetic syndromes and congenital infection. Preterm inants more at risk.
10% CP thought to be perinatal, mostly due to hypoxic-ischaemic injury before or during delivery
10% postnatal primarily meningitis/encephalatis/head truama}}
Diagnosis
{{c1::Primarily made clinically, but MRI scans can be used to identify the cause of the CP and help further investigations}}
Clinical Presentation
Early features of CP are:
{{c2::• abnormal limb and/or trunk posture and tone in infancy with delayed motor milestones (Fig. 4.3); this may be accompanied by slowing of head growth
• feeding difficulties, with oromotor incoordination, slow feeding, gagging and vomiting
• abnormal gait once walking is achieved
• asymmetric hand function before 12 months of age
In CP primative reflexes may persist and become obligatory}}
Classification
{{c3::CP is now categorized according to neurological features as:
• spastic: bilateral, unilateral, not otherwise specified (90%)
• dyskinetic (6%)
• ataxic (4%)
• other.}}
{{c3::Spastic CP:
Damage to upper motor neurone (pyramidal or corticopsinal tract) pathway.
- Spastic limb tone, brisk deep tendon reflexes and extensor plantar responses}}
3 types
{{c4::- Unilateral: Unilateral involvement of arm and leg, arm usually affected more. Children tend to present at 4-12 motnhs with fisting of affecred hand, flexed arm, asymetrical reaching. Affected limbs initally flacid and hypotonic but increased tone emerges as predominant sign
- Bilateral (quadriplegia): All 4 limbs are affeced, often severely. Trunk involved with tendensy to opisthotonus (extensor posturing), poor head control and low central tone. More severe CP associated with seizures, microcephaly and intellectual impairment. May be history of perinatal hypoxic-ischaemic encephalopathy.
- Bilateral (diplegia): All limbs are affeced, but legs to much greater degree than the arms so that hand function may appear to be normal. Motor difficulties in the arms are most apparent with functional use of hands. Walking is abnormal. Diplegia is associated with preterm birth due to periventricular brain damage,}}
{{c3::Dyskinesic Cerebral Palsy}}
Description:
{{c4::Dyskinesia refers to movements that are involuntary, uncontrolled, occasionally stereotyped and often more evident with active movement or stress. Muscle tone is variable and primitive motor reflex patterns predominate. May be described as:
• chorea – irregular, sudden and brief non-repetitive movements
• athetosis – slow writhing movements occurring more distally such as fanning of the fingers
• dystonia – simultaneous contraction of agonist and antagonist muscles of the trunk and proximal muscles often giving a twisting appearance
Intellect may be relatively unimpaired. Affected children often present with floppiness, poor trunk control and delayed motor control in injury }}.
Management
{{c5::Give diagnosis to parents as early as possible, but prognosis difficulty while young until severity and pattern of evolving signs of child development become clearer.
Need MDT approach due to wide range of associated social, medical and psychological problems
Novel Treatments: Botulinium toxin in muscles, selective dorsal nerve rhizotomy, deep brain stimulation of basal ganglia}}

QA, MC, Dialogue

• A BERT Baseline for the Natural Questions
• MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (ACL2019)
• Unsupervised Domain Adaptation on Reading Comprehension
• BERTQA -- Attention on Steroids
• A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (EMNLP2019)
• SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
• Multi-hop Question Answering via Reasoning Chains
• Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
• Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (EMNLP2019 WS)
• End-to-End Open-Domain Question Answering with BERTserini (NAALC2019)
• Latent Retrieval for Weakly Supervised Open Domain Question Answering (ACL2019)
• Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (EMNLP2019)
• Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering (ICLR2020)
• Learning to Ask Unanswerable Questions for Machine Reading Comprehension (ACL2019)
• Unsupervised Question Answering by Cloze Translation (ACL2019)
• Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
• A Recurrent BERT-based Model for Question Generation (EMNLP2019 WS)
• Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
• Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension(ACL2019)
• Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning(CIKM2019)
• SG-Net: Syntax-Guided Machine Reading Comprehension
• MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
• Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (EMNLP2019)
• ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning (ICLR2020)
• Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization
• BAS: An Answer Selection Method Using BERT Language Model
• Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension
• A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension (ACL2019 WS)
• FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (ACL2019 WS)
• BERT with History Answer Embedding for Conversational Question Answering (SIGIR2019)
• GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension(ICML2019 WS)
• Beyond English-only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian(RANLP2019)
• XQA: A Cross-lingual Open-domain Question Answering Dataset (ACL2019)
• Cross-Lingual Machine Reading Comprehension (EMNLP2019)
• Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model
• Multilingual Question Answering from Formatted Text applied to Conversational Agents
• BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels(EMNLP2019)
• MLQA: Evaluating Cross-lingual Extractive Question Answering
• Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension (TACL)
• SberQuAD - Russian Reading Comprehension Dataset: Description and Analysis
• Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (EMNLP2019)
• BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech2019)
• Dialog State Tracking: A Neural Reading Comprehension Approach
• A Simple but Effective BERT Model for Dialog State Tracking on Resource-Limited Systems
• Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
• Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker
• Domain Adaptive Training BERT for Response Selection
• BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding

Slot filling

• BERT for Joint Intent Classification and Slot Filling
• Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
• A Comparison of Deep Learning Methods for Language Understanding (Interspeech2019)

Analysis

• Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention
• Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision (ACL2019)
• BERT-based Lexical Substitution (ACL2019)
• Assessing BERT’s Syntactic Abilities
• Does BERT agree? Evaluating knowledge of structure dependence through agreement relations
• Simple BERT Models for Relation Extraction and Semantic Role Labeling
• LIMIT-BERT : Linguistic Informed Multi-Task BERT
• A Simple BERT-Based Approach for Lexical Simplification
• Multi-headed Architecture Based on BERT for Grammatical Errors Correction (ACL2019 WS)
• Towards Minimal Supervision BERT-based Grammar Error Correction
• BERT-Based Arabic Social Media Author Profiling
• Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
• Evaluating the Factual Consistency of Abstractive Text Summarization
• NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
• xSLUE: A Benchmark and Analysis Platform for Cross-Style Language Understanding and Evaluation
• TabFact: A Large-scale Dataset for Table-based Fact Verification
• Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
• LAMBERT: Layout-Aware language Modeling using BERT for information extraction
• Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings (ECIR2020) [github]
• Keyphrase Extraction with Span-based Feature Representations
• What do you mean, BERT? Assessing BERT as a Distributional Semantics Model

Word segmentation, parsing, NER

• BERT Meets Chinese Word Segmentation
• Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
• Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT
• Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing
• NEZHA: Neural Contextualized Representation for Chinese Language Understanding
• Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited (EMNLP2019)
• Parsing as Pretraining (AAAI2020)
• Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing
• Named Entity Recognition -- Is there a glass ceiling? (CoNLL2019)
• A Unified MRC Framework for Named Entity Recognition
• Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
• Robust Named Entity Recognition with Truecasing Pretraining (AAAI2020)
• LTP: A New Active Learning Strategy for Bert-CRF Based Named Entity Recognition
• MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers
• Portuguese Named Entity Recognition using BERT-CRF
• Towards Lingua Franca Named Entity Recognition with BERT

Pronoun/coreference resolution

• Resolving Gendered Ambiguous Pronouns with BERT (ACL2019 WS)
• Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge (ACL2019 WS)
• Gendered Pronoun Resolution using BERT and an extractive question answering formulation (ACL2019 WS)
• MSnet: A BERT-based Network for Gendered Pronoun Resolution (ACL2019 WS)
• Fill the GAP: Exploiting BERT for Pronoun Resolution (ACL2019 WS)
• On GAP Coreference Resolution Shared Task: Insights from the 3rd Place Solution (ACL2019 WS)
• Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution(ACL2019 WS)
• BERT Masked Language Modeling for Co-reference Resolution (ACL2019 WS)
• Coreference Resolution with Entity Equalization (ACL2019)
• BERT for Coreference Resolution: Baselines and Analysis (EMNLP2019) [github]
• WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (EMNLP2019)
• Ellipsis and Coreference Resolution as Question Answering
• Coreference Resolution as Query-based Span Prediction

Word sense disambiguation

• GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (EMNLP2019)
• Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (EMNLP2019)
• Using BERT for Word Sense Disambiguation
• Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (ACL2019)
• Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings(KONVENS2019)

Sentiment analysis

• Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (NAACL2019)
• BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (NAACL2019)
• Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (EMNLP2019 WS)
• Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
• An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese (ACL2019)
• "Mask and Infill" : Applying Masked Language Model to Sentiment Transfer
• Adversarial Training for Aspect-Based Sentiment Analysis with BERT
• Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference

Relation extraction

• Matching the Blanks: Distributional Similarity for Relation Learning (ACL2019)
• BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction (NLPCC2019)
• Enriching Pre-trained Language Model with Entity Information for Relation Classification
• Span-based Joint Entity and Relation Extraction with Transformer Pre-training
• Fine-tune Bert for DocRED with Two-step Process
• Entity, Relation, and Event Extraction with Contextualized Span Representations (EMNLP2019)
• Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Knowledge base

• KG-BERT: BERT for Knowledge Graph Completion
• Language Models as Knowledge Bases? (EMNLP2019) [github]
• BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA
• Inducing Relational Knowledge from BERT (AAAI2020)
• Latent Relation Language Models (AAAI2020)
• Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model (ICLR2020)
• Zero-shot Entity Linking with Dense Entity Retrieval
• Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking (CoNLL2019)
• Improving Entity Linking by Modeling Latent Entity Type Information (AAAI2020)
• How Can We Know What Language Models Know?
• REALM: Retrieval-Augmented Language Model Pre-Training

Text classification

• How to Fine-Tune BERT for Text Classification?
• X-BERT: eXtreme Multi-label Text Classification with BERT
• DocBERT: BERT for Document Classification
• Enriching BERT with Knowledge Graph Embeddings for Document Classification
• Classification and Clustering of Arguments with Contextualized Word Embeddings (ACL2019)
• BERT for Evidence Retrieval and Claim Verification
• Conditional BERT Contextual Augmentation
• Stacked DeBERT: All Attention in Incomplete Data for Text Classification

WSC, WNLI, NLI

• Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
• A Surprisingly Robust Trick for the Winograd Schema Challenge
• WinoGrande: An Adversarial Winograd Schema Challenge at Scale (AAAI2020)
• Improving Natural Language Inference with a Pretrained Parser
• Adversarial NLI: A New Benchmark for Natural Language Understanding
• Adversarial Analysis of Natural Language Inference Systems (ICSC2020)
• Evaluating BERT for natural language inference: A case study on the CommitmentBank (EMNLP2019)

Commonsense

• CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (NAACL2019)
• HellaSwag: Can a Machine Really Finish Your Sentence? (ACL2019) [website]
• Story Ending Prediction by Transferable BERT (IJCAI2019)
• Explain Yourself! Leveraging Language Models for Commonsense Reasoning (ACL2019)
• Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
• Informing Unsupervised Pretraining with External Linguistic Knowledge
• Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test
• BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge
• Commonsense Knowledge Mining from Pretrained Models (EMNLP2019)
• KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (EMNLP2019)
• Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (EMNLP2019 WS)
• Do Massively Pretrained Language Models Make Better Storytellers? (CoNLL2019)
• PIQA: Reasoning about Physical Commonsense in Natural Language (AAAI2020)
• Evaluating Commonsense in Pre-trained Language Models (AAAI2020)
• Why Do Masked Neural Language Models Still Need Common Sense Knowledge?
• Do Neural Language Representations Learn Physical Commonsense? (CogSci2019)

Extractive summarization

• HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization(ACL2019)
• Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
• Discourse-Aware Neural Extractive Model for Text Summarization

IR

• Passage Re-ranking with BERT
• Investigating the Successes and Failures of BERT for Passage Re-Ranking
• Understanding the Behaviors of BERT in Ranking
• Document Expansion by Query Prediction
• CEDR: Contextualized Embeddings for Document Ranking (SIGIR2019)
• Deeper Text Understanding for IR with Contextual Neural Language Modeling (SIGIR2019)
• FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance (SIGIR2019)
• Multi-Stage Document Ranking with BERT

Generation

• BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model (NAACL2019 WS)
• Pretraining-Based Natural Language Generation for Text Summarization
• Text Summarization with Pretrained Encoders (EMNLP2019) [github (original)] [github (huggingface)]
• Multi-stage Pretraining for Abstractive Summarization
• PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
• MASS: Masked Sequence to Sequence Pre-training for Language Generation (ICML2019) [github], [github]
• Unified Language Model Pre-training for Natural Language Understanding and Generation (NeurIPS2019)
• ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
• Towards Making the Most of BERT in Neural Machine Translation
• Improving Neural Machine Translation with Pre-trained Representation
• On the use of BERT for Neural Machine Translation (EMNLP2019 WS)
• Incorporating BERT into Neural Machine Translation (ICLR2020)
• Recycling a Pre-trained BERT Encoder for Neural Machine Translation
• Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
• Mask-Predict: Parallel Decoding of Conditional Masked Language Models (EMNLP2019)
• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
• ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
• Cross-Lingual Natural Language Generation via Pre-Training (AAAI2020) [github]
• Multilingual Denoising Pre-training for Neural Machine Translation
• PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
• Unsupervised Pre-training for Natural Language Generation: A Literature Review

Transformer variants

• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (ACL2019) [github]
• Compressive Transformers for Long-Range Sequence Modelling
• The Evolved Transformer (ICML2019)
• Reformer: The Efficient Transformer (ICLR2020) [github]
• GRET: Global Representation Enhanced Transformer (AAAI2020)
• Transformer on a Diet

Probe

• A Structural Probe for Finding Syntax in Word Representations (NAACL2019)
• Linguistic Knowledge and Transferability of Contextual Representations (NAACL2019) [github]
• Probing What Different NLP Tasks Teach Machines about Function Word Comprehension (*SEM2019)
• BERT Rediscovers the Classical NLP Pipeline (ACL2019)
• Probing Neural Network Comprehension of Natural Language Arguments (ACL2019)
• Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (EMNLP2019 WS)
• What do you mean, BERT? Assessing BERT as a Distributional Semantics Model
• Quantity doesn't buy quality syntax with neural language models (EMNLP2019)
• Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction(ICLR2020)
• oLMpics -- On what Language Model Pre-training Captures
• How Much Knowledge Can You Pack Into the Parameters of a Language Model?
• What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge

Inside BERT

• What does BERT learn about the structure of language? (ACL2019)
• Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (ACL2019) [github]
• Open Sesame: Getting Inside BERT's Linguistic Knowledge (ACL2019 WS)
• Analyzing the Structure of Attention in a Transformer Language Model (ACL2019 WS)
• What Does BERT Look At? An Analysis of BERT's Attention (ACL2019 WS)
• Do Attention Heads in BERT Track Syntactic Dependencies?
• Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains(ACL2019 WS)
• Inducing Syntactic Trees from BERT Representations (ACL2019 WS)
• A Multiscale Visualization of Attention in the Transformer Model (ACL2019 Demo)
• Visualizing and Measuring the Geometry of BERT
• How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (EMNLP2019)
• Are Sixteen Heads Really Better than One? (NeurIPS2019)
• On the Validity of Self-Attention as Explanation in Transformer Models
• Visualizing and Understanding the Effectiveness of BERT (EMNLP2019)
• Attention Interpretability Across NLP Tasks
• Revealing the Dark Secrets of BERT (EMNLP2019)
• Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs (EMNLP2019)
• The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives (EMNLP2019)
• A Primer in BERTology: What we know about how BERT works
• Do NLP Models Know Numbers? Probing Numeracy in Embeddings (EMNLP2019)
• How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations (CIKM2019)
• Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering
• What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
• exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models [github]

Multi-lingual

• Multilingual Constituency Parsing with Self-Attention and Pre-Training (ACL2019)
• Language Model Pretraining (NeurIPS2019) [github]
• 75 Languages, 1 Model: Parsing Universal Dependencies Universally (EMNLP2019) [github]
• Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (EMNLP2019 WS)
• Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (EMNLP2019)
• How multilingual is Multilingual BERT? (ACL2019)
• How Language-Neutral is Multilingual BERT?
• Is Multilingual BERT Fluent in Language Generation?
• Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (EMNLP2019)
• BERT is Not an Interlingua and the Bias of Tokenization (EMNLP2019 WS)
• Cross-Lingual Ability of Multilingual BERT: An Empirical Study (ICLR2020)
• Multilingual Alignment of Contextual Word Representations (ICLR2020)
• On the Cross-lingual Transferability of Monolingual Representations
• Unsupervised Cross-lingual Representation Learning at Scale
• Emerging Cross-lingual Structure in Pretrained Language Models
• Can Monolingual Pretrained Models Help Cross-Lingual Classification?
• Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data(CoNLL2019)

Other than English models

• CamemBERT: a Tasty French Language Model
• FlauBERT: Unsupervised Language Model Pre-training for French
• Multilingual is not enough: BERT for Finnish
• BERTje: A Dutch BERT Model
• RobBERT: a Dutch RoBERTa-based Language Model
• Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language

Domain specific

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (ACL2019 WS)
• BERT-based Ranking for Biomedical Entity Normalization
• PubMedQA: A Dataset for Biomedical Research Question Answering (EMNLP2019)
• Pre-trained Language Model for Biomedical Question Answering
• How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering
• ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
• Publicly Available Clinical BERT Embeddings (NAACL2019 WS)
• Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT
• SciBERT: Pretrained Contextualized Embeddings for Scientific Text [github]
• PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model

​

Model compression

• Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
• Patient Knowledge Distillation for BERT Model Compression (EMNLP2019)
• Small and Practical BERT Models for Sequence Labeling (EMNLP2019)
• Pruning a BERT-based Question Answering Model
• TinyBERT: Distilling BERT for Natural Language Understanding [github]
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (NeurIPS2019 WS) [github]
• Knowledge Distillation from Internal Representations (AAAI2020)
• PoWER-BERT: Accelerating BERT inference for Classification Tasks
• WaLDORf: Wasteless Language-model Distillation On Reading-comprehension
• Extreme Language Model Compression with Optimal Subwords and Shared Projections
• BERT-of-Theseus: Compressing BERT by Progressive Module Replacing
• Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
• MobileBERT: Task-Agnostic Compression of BERT by Progressive Knowledge Transfer
• Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
• Q8BERT: Quantized 8Bit BERT (NeurIPS2019 WS)

Misc.

• Cloze-driven Pretraining of Self-attention Networks
• Learning and Evaluating General Linguistic Intelligence
• To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks (ACL2019 WS)
• BERTScore: Evaluating Text Generation with BERT (ICLR2020)
• Machine Translation Evaluation with BERT Regressor
• SumQE: a BERT-based Summary Quality Estimation Model (EMNLP2019)
• Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (ICLR2020)
• Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models (ICLR2020)
• A Mutual Information Maximization Perspective of Language Representation Learning (ICLR2020)
• Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment (AAAI2020)
• Thieves on Sesame Street! Model Extraction of BERT-based APIs (ICLR2020)
• Graph-Bert: Only Attention is Needed for Learning Graph Representations
• CodeBERT: A Pre-Trained Model for Programming and Natural Languages
• Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
• Extending Machine Language Models toward Human-Level Language Understanding
• Glyce: Glyph-vectors for Chinese Character Representations
• Back to the Future -- Sequential Alignment of Text Representations
• Improving Cuneiform Language Identification with BERT (NAACL2019 WS)
• BERT has a Moral Compass: Improvements of ethical and moral values of machines
• SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction (ACM-BCB2019)
• On the comparability of Pre-trained Language Models
• Transformers: State-of-the-art Natural Language Processing
• Evolution of transfer learning in natural language processing

An example of what I’d like to learn:

“Otherwise known as the ‘The Construction Act’, The Housing Grants, Construction and Regeneration Act (HGCRA) 1996 fundamentally attempted to improve payment practices in the construction industry by improving cash flow and helping to resolve disputes quicker. Its effect on cash flow forecasting was profound as it set the following rules in law:
• Providing the right to interim, periodic or stage payments.
• Requiring that contracts should provide a mechanism to determine what payments become due and when, and a final date for payment.
• Requiring that the payer gives the payee early communication of the amount he has paid or proposes to pay.
• Providing that the payer may not withhold money from the sum due unless he has given an effective withholding notice to the payee.
• Providing that the payee may suspend performance where a sum due is not paid in full by the final date for payment.
• Prohibiting pay when paid clauses that link payment to payments received by the payer under a separate contract.
• Providing a statutory right to refer disputes to adjudication. The adjudicator’s decision is binding until finally determined by legal proceedings or arbitration at the end of the contract.”

I’d previously just been making cloze deletions, one for each point and then one where with the entire list in a single cloze bracket.
I just feel that that isn’t the most efficient way and my recall methods were based on the position in the list.
Can anyone share they way they’d go about making cards for a similar thing?

Here is a deck with some sample cards. There are 7 cards with the tag type9. That's the style I'm struggling with. There is a list of other types below but I'm mostly interested in the type9 cards.
These are phrases which include a preposition, a contraction, or other words that could very easily get swapped around in someones mind.
A perfect example is the a la manera de card. I would like to have started this story like a fairy tale with like being a la manera de. A noun, an article, and (I guess) two prepositions. Easy stuff to mix up. In context, it's perfectly clear that like is clozed. The big question is what's the best way to cloze this? Prepositions can get mixed around or you can be off by one word. One card? Two? Four?
Another good example would be the no tiene nada que ver / you have nothing in common card. The context is clear. It's a question of how to do the cloze. Cloze the phrase? Make multiple cards?
The lo cuelga de los huevos / he is going to hang you by your balls card can definitely be split up and probably should be. lo cuelga / he is going to hang you and de los huevos / from your balls stand alone well in context. But maybe it should have a third card splitting off the preposition de from the noun los huevos? I think the reason this sentence interested me is that I would have said lo cuelga por los huevos instead of de. Anyway, at this point I'll never get this phrase wrong should I need to use it.
Here are the other types (don't delve too deeply):
0 - Cards from shared deck. Culled from 10k down to 5400. Nouns, adjectives and some verbs work with this style but nothing else.
1 - Similar style as above but cards I made. These are good but I should have gone with a cloze deletion style card. Early and late cards included. Not much difference.
2 - Created to learn ya from todavía. I would design these very differently now but they were very effective for listening/reading and reasonably effective for speaking.
3 - Por vs. Para. These worked well. The english translation should go on the back as the sentences give enough context themselves. I would design these the same now. Rules are just for reference and not tested. A second set of cards to learn the rules might be helpful.
4 - Recent cards. Forward and reverse. I took a lot from the fluentforever guy. Cloze deletion on one card plus producing a sentence on a second card. This can work easily for basically any noun and adjective. Verbs and preps take some creativity. Phrases are where my questions lie.
5 - A mildly difficult card. Feedback would be appreciated. Cloze two words? three? Make three cards?
6 - More newer cards clarifying the use of several verbs in two tenses. The only thing tested is the actual cloze deletion. The reference material is just that. No reverse cards.
7 - Phrase card. Forward and reverse. Short phrases like this might do better as two overlapping cloze cards. Any feedback would be great.
8 - Just regular cards that serve a particular function as a group. Cloze deletion and sentence production are the only tests. The rules are just there for the heck of it.
10 - Any feedback on this? The word I don't know is "chalk". At the same time, I decided to reenforce "blackboard" by making a card for it too. Also, I regularly mix up clean, dirty, switzerland, and sweden. So there is another card to help reenforce dirty with the concept of dirty blackboard -- except that the teacher is cleaning the dirty blackboard so maybe not a good idea. Six cards for one sentence? Thoughts?

Overview

Please feel free to post all of your questions, comments, or impressions of Anki's new beta releases in this thread. You will find the previous discussion about the beta here.

What's New in Anki 2.1?

Quoting Anki's homepage:

Anki 2.1 at a glance:

• Uses the same scheduling, syncing and file format as Anki 2.0.x, so you can upgrade and downgrade at will.
  
• Upgraded to the latest support libraries (Python 3, Qt 5.9), bringing fixes for crashes, better handling of high resolution displays, non-Latin text, and the latest web standards.
  
• Requires a modern system - Windows 7+, OSX 10.10+, or a Linux distro from around 2014+.
  
• Add-ons will need to be updated to work with 2.1.
  

 
Bug fixes and improvements aside, there also a number of very nifty new features which should be highlighted:
 

• An updated card scheduler that addresses a lot of the shortcomings previous iterations had
• Ability to mark cards in addition to notes (these marks are called flags and come in four different colour variations)
• Completely overhauled add-on management system with a persistent config storage, add-on updates from within the app, and the ability to perform batch actions
• MathJax support for rendering LaTeX equations (plus mhchem module for typesetting chemical equations)
• Various improvements to tag autocompletion (autocomplete tag with 'Return', switch between suggestions with 'Ctrl+Tab', etc.)
• Simplified card browser with new sidebar and drop-down controls
• Context menu actions in the card browser
• Ability to preview question and answer simultaneously
• Support for wildcards in tag deletion
• Ability to turn off browser font overriding for cards
• Ability to override CSS and JS for all note tpes
• PDF export for graphs

Further Information & Downloads

For more information on Anki 2.1, download links, and a detailed changelog, please see here.
Please use the beta testing section of Anki's official forums to report any issues you might encounter.

Latest Changes

I will try to keep the section below updated with the latest beta release notes:
Changes in beta 41 (2018-06-15)

• The experimental scheduler no longer applies any interval boost for delayed cards that are answered Hard.
  
• Fix cards being imported incorrectly when the target deck is a filtered deck.
  
• Preserve scroll position in deck browser when expanding/contracting decks.
  
• Fix multi-line cloze deletions not being detected correctly.
  
• Tabs and newlines now preserved when pasting with ctrl/cmd+shift+v.
  

Changes in beta 40 (2018-05-31)

• Fixed a bug that could cause cards in relearning to be scheduled incorrectly when they’re imported into a different collection.
  
• Fixed reviews with the experimental scheduler showing up as relearning in the stats.
  
• Performance improvements for users with many decks.
  
• Changed stats shortcut key.
  
• Attempt to restore the previous active window when mpv finishes playing a video.
  
• Friendly error when internet down.
  
• Fixed cloze number not incrementing properly if you made a cloze deletion directly after an edit (thanks to dlon).
  
• Fixed handling of fonts with spaces in their names on Linux (thanks to Mark).
  
• Added Armenian translation (thanks to Arman).
  

Changes in beta 39 (2018-05-10)

• Fixed sound tags causing freezes on Windows when users had mpv.exe from a previous install lying around.
  
• Fixed a previously pasted image appearing instead of pasted/dropped text or links. The previous behaviour may also have been responsible for crashes.
  

Changes in beta 38 (2018-05-01)
The experimental scheduler can now be enabled from the preferences screen, and warnings about data loss have been relaxed somewhat. It is still only recommended for advanced users at this point.
Also:

• Add the missing "custom steps" section back to the filtered deck options when using the regular scheduler.
  
• Fixes for dragging and pasting images from web browsers.
  
• Fix pastes with the middle mouse button on Linux.
  
• When switching to a different window and then back to the Anki editor, the cursor position is no longer moved to the bottom of the field.
  
• Fix a field getting overwritten when showing duplicates, and catch similar errors.
  
• Restore focus to Anki window after video finishes playing.
  
• Fix busy cursor in full sync screen.
  
• Add space to "waiting for editing" screen.
  
• Check uploads don’t exceed AnkiWeb limits.
  
• Added an option to disable certificate validation - see "SSL errors" above.
  
• Fix a "c++ object has been deleted" error message.
  
• For add-on authors, pycmd() can now return a value.
  
• Wording tweaks in find duplicates screen (thanks to homocomputeris)
  
• Ensure mpv doesn’t stay open (thanks to Dudemanguy911)
  
• Support non-Latin text in config.md (thanks to ljcooke)
  
• Fix the debug key shortcut on some keyboards (thanks to glutanimate)
  
• Tweaks to the makefile (thanks to dsd)
  

WHAT IS DARWIN THEORY OF EVOLUTION
NARRATOR — Let us not get controversial, but simply stick to the facts about Charles Darwin and his theory of Evolution. An english Naturalist, Mr. Darwin definitely gave us all a lot to think about. Darwin’s theory of evolution actually contains two major ideas. The first idea posed by Mr. Darwin theorized the idea that evolution occurs. In other words, organisms change over time, and life on earth has changed as descendants diverged from common ancestors in the past.
The second idea from Mr. Darwin is that evolution occurs by natural selection. Natural selection is the process in which living things with beneficial traits produce more offspring than others. This results in changes in the traits of living things over time. In Charles Darwin’s day, the early 1800s, most people believed that all species were created at the same time and remained unchanged thereafter. They also believed that Earth was merely 6,000 years old. Darwin’s ideas revolutionised biology as we know it!
How did Mr. Darwin come up with these important ideas?
It all started when he went on a voyage. In 1831, when Darwin was just 22 years old, he set sail on a scientific expedition on a ship called the HMS Beagle. He was the naturalist on the voyage. As a naturalist, it was his job to observe and collect specimens of plants, animals, rocks, and fossils wherever the expedition went ashore.
Darwin was fascinated by this nature, and his job on the Beagle was the most important time of his life. He spent over 3 years of the five-year trip exploring nature on distant continents and islands. While he was away, a former teacher published Darwin’s accounts of his observations. By the time Darwin finally returned to England, he had become famous as a naturalist.
During the long voyage, Charles Darwin made many observations that helped him form his theory of evolution. Some of what he found included: – Tropical rain forests and other new habitats where he saw many plants and animals he had never seen before. – An earthquake that lifted the ocean floor 9 feet (2.7 meters) above sea level. – He also found rocks containing fossil sea shells in mountains far above sea level. These observations suggested that continents and oceans had changed dramatically over time and continue to change dramatically. – Rock ledges that had clearly once been beaches that had gradually built up over time. This suggested that slow, steady processes also change the surface of the Earth. – Fossils of gigantic extinct mammals, such as the ground sloth. This was hard evidence that organisms looked very different in the past, as it suggested that living things, including the Earth’s surface, change over time.
GLUCOSE GLUCOSE
Glucose—ah, sugar sugar— You are my favorite fuel From the blood-borne substrate pool. Glucose—monosaccharide sugar— You're sweeter than a woman's kiss 'Cause I need you for glycolysis.
I just can't believe the way my muscles take you in. (For you, they'll open the door.) All it takes is a little bit of insulin (To upregulate GLUT4).
Ah, glucose—ah, sugar sugar— You help me make ATP When my predators are chasing me. Ah, glucose—you're an aldehyde sugar, And you're sweeter than a woman's kiss 'Cause I need you for glycolysis.
I just can't believe the way my muscles break you down. (My glycogen is almost gone.) A few more seconds and I'll be rigor mortis-bound. (Acidosis done me wrong.)
Your sweet is turning sour, baby. I'm losing all my power, baby. I'm gonna make your muscles ache. No, no, no! I'm swimming in lactate, baby. Yes, I'm swimming in lactate, baby. Now I'm drowning in lactate, baby. I'm gonna make your muscles ache. No, no, no! I'm drowning in lactate, baby.
Ah, glucose—ah, sugar sugar— I used you up and you left me flat; Now I'll have to get my kicks from fat . Oh, glucose, glucose, sugar, sugar, The honeymoon is over now.
CARBON IS A GIRLS BF
A lithium dose just might cure your depression but carbon is a girl’s best friend. And gold may be grand but it won’t start a fire in your BBQ or put the toot in your choo-choo. Life on Earth is carbon based. It came here on rocks from outer space and formed organic compounds till the carbon cycle went round and round! Carbon is a girl’s best friend!
Well there may come a time when you need to do some squeezing—carbon is the world’s hardest friend! and there may come a time when a substrate is needed to deposit your film on. Just sputter it on a hard diamond. If you have some lubing to do then graphite is the allotrope for you. Or if you’re into Ages then carbon-14 can date it. Carbon is a girl’s best friend!
Well I’ve heard of attempts to make hard carbon nitride but diamonds always win in the end! and I’ve heard that we need to make something much cleaner to put in our cars or else we won’t get very far.
Cause time rolls on and supplies will be gone of diamonds, coal and petroleum. But nanotechnology can build anything with fullerenes! Carbon, carbon—I don’t mean hydrogen —but carbon is a girl’s best friend.
THE ORIGINS OF LIFE
NEIL deGRASSE TYSON — […] But where did the very first life come from? For more than a century, scientists have known that life is the result of chemistry, the combination of just the right ingredients in just the right amounts. Today, we know these ingredients aren't things like dirty garments and wheat, which people used to think would spontaneously generate mice. The ingredients of life are actually much simpler. All living things, from bacteria to mice to you and me, are made from a small set of chemical elements: ,hydrogen oxygen, carbon, nitrogen—four of the most common elements in the universe. Combined in just the right way, these are the fundamental ingredients of life, and carbon is the star of the show. LISA PRATT — Carbon's everywhere. It's all over the universe. ANDY KNOLL — What makes carbon special is the kind of bonds that it makes, both with itself and with other elements. LISA PRATT — We know of no other atom that has the flexibility that carbon has to form diverse types of compounds. NEIL deGRASSE TYSON — And the idea that life could have started when carbon and other ingredients combined in the harsh conditions of early earth was first put to the test in the 1950s by a young graduate student named Stanley Miller. To simulate the newborn Earth in the lab, Miller assembled a contraption made out of flasks and tubes. He filled one flask with gases thought at the time to represent Earth's primitive atmosphere, and he connected that to another flask with water to represent the oceans. ANDY KNOLL — And then he did a brilliant thing. He simply put an electric charge through that to essentially simulate lightning going through an early atmosphere. And after sitting around for a couple of days, all of a sudden there was all this brown goo all over the reaction vessel, and when he analyzed what was in the vessel now, he actually had amino acids. NEIL deGRASSE TYSON — Amino acids are compounds that form when molecules of carbon and other elements link together. They are the essential building blocks of proteins and cells, vital ingredients of all living things. Stanley Miller's experiment was headline news and jump-started the scientific search for the origins of life. ANDY KNOLL — Life is really chemistry; there's no question about that. In fact, it's a chemistry that, when you get the recipe right, it goes, and it goes fairly quickly. NEIL deGRASSE TYSON — That recipe is hotly debated today, and most scientists think the environmental conditions on early Earth were very different from the ones Miller simulated in his lab. And another debate rages about when this recipe first got cooked up.
NATURAL SELECTION
BILL NYE — In the 1820s surgery was a grisly affair. There was no anesthesia and young medical students were forced to watch and learn. The young man who walked out that day was Charles Darwin. The ordeal of the operating room was too much for him. Over his father’s objections he quit medical school to pursue his dream of becoming a naturalist.
Darwin was hired as a naturalist aboard the British admiralty’s HMS Beagle which embarked on an expedition to survey and map coastal waters around the world. The Beagle was at sea for five years. To Darwin, it seemed an eternity. He shared a small cabin with the captain, with whom he had several disagreements—including the story of creation.
While the captain held to the Bible's account of creation, Darwin believed that the earth had changed slowly over millions of years. It was a debate that would follow Darwin for the rest of his life.
BILL NYE — The British government paid for this?
RICHARD MILNER — No, actually, Darwin's father paid for this, and also paid for an assistant to come with Darwin and help him skin the specimens and prepare the crates for shipment. He worked in a little poop deck where he had all his specimens, just about something like this.
BILL NYE — The Beagle made stop-overs in ports from Australia to South America, including thirty-six days in the Galapagos islands. Darwin made the most of it. He recorded extensive observations about the indigenous plants and wildlife. He collected and preserved thousands of specimens for study. For years after the voyage, Darwin would recall the observations he made while aboard the Beagle.
RICHARD MILNER — It was not until 1837, a couple of years after he returned from the Galapagos, that he opened his first species notebook and he asked himself the question—very ambitious question for a man in his late twenties: “What are the laws of life?”
BILL NYE — Is this Darwin’s whole point?
RICHARD MILNER — It was Darwin's whole point to try to find laws operating in the natural world similar to the laws the physicists and chemists had found in inorganic nature. It’s not metaphorical, that’s literal.
BILL NYE — Why did it take Darwin twenty years to publish his ideas?
RICHARD MILNER — He knew there would be a storm of religious protest. His own wife was not happy that he was going to write things that were incompatible with the Bible. She wrote him a letter, when they were first married, saying: “I hope that your science will not lead us to spend eternity in two very different places.”
BILL NYE — Wow!
RICHARD MILNER — And she was seriously concerned about this. Plus he was trying to build up a mountain of evidence that would be incontrovertible.
BILL NYE — Finally, in 1859, Darwin published his theory of natural selection. His book, Origin of Species, is considered one of the greatest books ever written.
BILL NYE — People talk about the theory of evolution, right, but Darwin was the guy that said, well, there isn’t any absolute truth here.
RICHARD MILNER — Darwin, being one of the first modern thinkers, tried to tell us that science is provisional, it changes.
BILL NYE — What do you mean by provisional?
RICHARD MILNER — It means it’s the best truth you have at the moment, and when you get more data that can’t be explained that way, then you have to get a new theory. So it’s not an eternal truth, it’s a provisional truth, it changes. And he did not want evolution to be accepted as a dogma or a creed. He would have been the first to say throw it out if you’ve got something better.
BILL NYE — But we didn’t find anything better.
RICHARD MILNER — We didn’t find anything better. In fact what we found was that entire new sciences that were undreamed of in Darwin’s day have come up through the study of molecular genetics and DNA which reinforce the idea of evolution, so that now, everywhere we look today, we see something that came from Darwin in science.
Discovery Channel, 2005
REVEALING THE ORIGINS OF LIFE
CHAD COHEN — From where did the first life emerge ? JOHN SUTHERLAND — We're here on the planet, and we must be here as a result of organic chemistry. CHAD COHEN — Simple elements like carbon, hydrogen, oxygen and nitrogen, but how did they get cooked together into the complex molecules of life? JOHN SUTHERLAND — You know what you want to make but you don’t have a recipe. CHAD COHEN — In the kitchen, we bring together different ingredients all the time to make all kinds of different things. It's the recipe, though, that makes it all work. Take for example: the cream puff. BÉATRICE GÉRLAND — Oh, yeah, pâte à choux. CHAD COHEN — Okay, pâte à choux, as it's known in French. In this case, I know the ingredients. What I don't know is the recipe. So I might just try mixing all these things together and baking them. I could try different orders, different combinations, different amounts, but what you get is not pâte à choux. Too brittle. Too hard. Too... well, I have no idea. Attempts to find the recipe for early life were unsuccessful, too, Even though researchers knew the basic ingredients. JACK SZOSTAK — Yeah, exactly. CHAD COHEN — Nobel laureate Jack Szostak and his team at Massachusetts General Hospital say that early life needed two things ... JACK SZOSTAK — You need the cell membrane ... CHAD COHEN — ... a container, something to live in and keep other things out. JACK SZOSTAK — And you need some genetic material, something that can allow the inheritance of information. CHAD COHEN — Every modern creature uses D.N.A. to do that. We also have R.N.A., usually described as D.N.A.'s helper. R.N.A. has a genetic code written with chemicals: A, C, G and U. They're used to help build the proteins that make up the cells in our bodies: skin, hair, brain cells, the heart. R.N.A. helps make them all. So what's the recipe for R.N.A.? It's made from three parts: a sugar, a phosphate and a single letter of the genetic code, a base. Each of these parts is made up of simple chemicals that existed on the early Earth, but nobody has been able to put them together, that is, until John Sutherland came along. JOHN SUTHERLAND — We were the guys who stood back and looked at it in a different way. CHAD COHEN — It's one thing to make chemicals in the lab, but there were no labs on the early Earth. So Sutherland tried to replicate the conditions, starting with their version of a warm little pond. JOHN SUTHERLAND — The pond itself is actually the little round-bottom flask. It's around about the temperature of a cup of English tea. CHAD COHEN — Sounds nice. And so they tackled the problem at hand: trying to make R.N.A. Knowing what chemicals it would take, the question was how to cook them together. JOHN SUTHERLAND — You actually have to be the person that writes the recipe book. CHAD COHEN — So that means we have to go back to the kitchen and try to combine our ingredients in a new way. Chef Richard Coppedge, of the Culinary Institute of America, explains that I was missing an all-important intermediate step. RICHARD COPPEDGE — You didn't precook the mixture. CHAD COHEN — What does it mean to precook something? Some of the ingredients need to be cooked together first. Then... RICHARD COPPEDGE — Take it to the mixer. Now you can add the eggs. CHAD COHEN — Okay, now, finally... okay. And you get just the right mixture... RICHARD COPPEDGE — Alright. That's ready to be baked. It's not much of anything without that intermediate precooking step. CHAD COHEN — And apparently that was the problem scientists were having with R.N.A.: trying to combine all the parts together. And that's not the way to do it. JOHN SUTHERLAND — No, it's not. CHAD COHEN — So first, they created a hybrid made of a sugar and only half of the base. This intermediate substance came together in the flask through the simple process of evaporation. On the early Earth, the intermediate would have made its way up into the atmosphere … JOHN SUTHERLAND — So this would come down in rain. Or, if the temperature was cold, it would precipitate out as solid particles and fall to the ground, almost like a kind of organic snow. CHAD COHEN —... and, as in the lab, meeting up with the remaining chemicals in perhaps another warm little pond and attaching together in the final step. For the first time, scientists created a building block of R.N.A., what's called a ribonucleotide, containing the base C. JOHN SUTHERLAND — My team and I have recreated an early Earth scenario and let it run. And the chemistry just does it on its own. CHAD COHEN — But that wasn't all. They took their piece of R.N.A. and subjected it to something else easy to come by on the early Earth. BÉATRICE GÉRLAND — Light! CHAD COHEN — Yes, sunlight. The light shining upon their sample turns some of the C-bases, into "U"s. They had discovered a natural pathway to two of the four letters of R.N.A., letters that code for the proteins that build all living things.
ROSALIND FRANKLIN
ADAM RUTHERFORD — It was after the second World War that the quest began, in earnest. The American nuclear weapons project had involved thousands of physicists who delved deep into the atoms, at the heart of all matter. Among them was a British scientist who returned home after the war to take a job at King’s College in London. As part of its post-war rebuilding programme, the College had acquired the latest x-ray imaging techniques, to look deep within the cell, and Professor Maurice Wilkins was put in charge.
MAURICE WILKINS — And here is one of the x-ray generators we’re using in this work. We use x-rays to study the structure of a molecule, because an x-ray travels along like this, in a wavy kind of way, and the lengths of the waves of the x-rays are about equal to the distance between the atoms in a molecule, so that when an x-ray strikes the molecule, the waves of the x-rays can squeeze in-between the atoms and when they come out at the other side, their directions are deviated, and from the deviation of the x-rays, we can work out the way atoms are arranged inside the molecule.
ADAM RUTHERFORD — The x-rays taken here at King’s College were pivotal. Producing them would be a painstaking job by a brilliant young scientist working with Wilkins, and her name was Rosalind Franklin. Franklin was an expert in x-ray imaging. With her expertise, the College hoped to be the first to find the structure of DNA. She worked in a new laboratory built in the basement, on the rubble of the old college which had been bombed . The College authorities were concerned that the x-ray experiments which used hydrogen could be dangerous, so they made Franklin and her assistant do their work at night time, after the students had gone home. Franklin used highly concentrated DNA. One of her skills was in finding just the right amount of moisture to prepare the strands. It was precision work. When she stretched out its fibres, she got a single DNA strand, a tenth of a millimetre across. Here in the lab, she took strands of DNA and mounted them inside this specially built camera, a camera chamber was filled with hydrogen to get the very best image. When the x-rays were switched on, they shone through the DNA and scattered in different directions, creating an image on a photographic film. Franklin took over a hundred pictures, each one could take up to ninety hours of exposure at close range. Once the photo was processed, she projected it onto the wall so she could calculate the exact distance between atoms. It was picture 51 that showed the best image of the mysterious DNA molecule. This distinctive x shape was the key that would reveal how DNA is built. But it was not Franklin’s name that came to be associated with the discovery of DNA’s structure – Wilkins was in close touch with scientists from Cambridge who were anxious to find the structure of DNA before American rivals. unknown to Franklin, Wilkins gave photo 51 to James Watson, an ambitious young scientist at Cambridge’s Cavendish laboratory. Having studied the photo, Watson and his collaborator, Francis Crick, had a sudden revelation – it would transform them into scientific celebrities and put the cell at the centre of world attention.
CHROMOSOME 2
NARRATOR — Darwin was convinced that species evolve over time, Through natural selection acting on inherited traits. But he had no idea how those traits arose or how they were passed from generation to generation. When twentieth century scientists discovered the role DNA plays in heredity, they founded a new science, called "genetics," that put Darwin's theory to the test. Virtually every cell in every living thing contains chromosomes, which are made of densely packed strands of DNA that function as a blueprint of the individual organism's characteristics. During reproduction, chromosomes from each parent replicate and shuffle their parts to produce new chromosomes. Then, each parent passes chromosomes to offspring. But the process is imperfect. Along the way, DNA is subject to random mutations, or mistakes, giving each offspring its own unique blueprint. Sometimes this produces characteristics in offspring that are benign. Other times it produces harmful characteristics, like a misshapen wing. But occasionally, the process gives rise to a beneficial trait. For example, a butterfly whose coloration mimics another species of butterfly that tastes bad to birds. About a hundred years after Darwin proposed that natural selection acts on new traits appearing in a population, genetics revealed the biological mechanism that gives rise to those traits in the first place. KENNETH MILLER — And therefore you could say that when modern genetics came into being, everything in Darwin's theory was at risk, could have been overturned if it turned out to contradict the essential elements of evolutionary theory, but it didn't contradict them, it confirmed them in great detail. NARRATOR — And, as Miller would testify, a genetics paper published less than a year before the trial had confirmed what has long been the most inflammatory part of Darwin's theory, the common ancestry of humans and apes. That paper explored a curious discrepancy in our chromosomes. The cells of all great apes, like chimpanzees, gorillas and orangutans, contain 24 pairs of chromosomes. If humans share a common ancestor with apes, you'd expect us to have the same number. But surprisingly, human cells contain only 23 pairs. KENNETH MILLER — The question is, if evolution is right about this common ancestry idea, where did the chromosome go? Well, evolution makes a testable prediction, and that is that somewhere in the human genome, we ought to be able to find a piece of Scotch tape holding together two chromosomes, so that our 24 pairs... two of them were pasted together to form just 23. And if we can't find that, then the hypothesis of common ancestry is wrong and evolution is mistaken. Next slide. NARRATOR — To solve this riddle for the court, Miller would show how scientists discovered traces of our evolutionary past buried in the very structure of a chromosome carried by all humans. Typically, on the ends of every chromosome, you should find special genetic markers, or sequences of DNA called "telomeres." And in their middles, you should find different genetic markers called "centromeres." But if a mutation occurred in the past, causing two pairs of chromosomes to fuse, we should find evidence in those genetic markers: telomeres not only at the ends of the new chromosome, but also at their middles, and not one, but two centromeres. Finding a structure like this in our chromosomes would explain why humans have one pair fewer than the great apes. KENNETH MILLER — And if we can't find that, then evolution is in trouble. Next slide. Lo and behold, the answer is in Chromosome Number 2. All of the marks of the fusion of those chromosomes predicted by common descent and evolution, all those marks are present on human Chromosome Number 2. So the case is closed in a most beautiful way. And that is the prediction of evolution of common ancestry is fulfilled by that lead pipe evidence that you see here, in terms of tying everything together, that our chromosome formed by the fusion from our common ancestor is Chromosome Number 2. Evolution has made a testable prediction and it has passed.
NOVA, Judgment Day, November 13, 2007
Traduction de "trop"
I am .... busy to help you. too It is .... far away. too She drinks .... . too much Don't give me .... potatoes. too many This coat is .... big for me. much too We couldn't see anything. There was .... fog. too much .... people want to work in agriculture. too few
THE THEORY OF EVOLUTION
NARRATOR — The Theory of evolution was born out of a remarkable journey. In 1831 a naturalist an explorer, Charles Darwin, embarked on a five-year voyage around the world. Along the way, he collected fossils, plants and animals. Back in his home, near London, England, he spent the next twenty-two years piecing together a theory for how the great variety of life came to exist.
Even in Darwin’s time scientists already knew that extinct creatures had roamed the planet in the distant past. Some were strange, terrifying beasts unlike any that walked the earth today. Others were more familiar, animals so similar to modern creatures that they had to be related. Could today’s animals an plants be their descendants? Could one species change into another over time? Darwin thought they could. In 1859, he published his ideas in a book, On The Origin of Species. The book rocked the world. Darwin made three bold claims. First, that life was old, and had existed for hundreds of millions of years; second, that it started out with just one or a few simple organisms which then evolved into the millions of species that exist today; and third, that this whole process of creating new species was driven by a force of nature he called natural selection. For God-fearing people the inescapable conclusion was astounding: life didn’t need a creator.
National Geographic, Naked Science: Was Darwin Wrong?
GENETIC MODIFICATION Gap-fill exercise Correct! Your score is: 95%. ROBERT KRULWICH — And if things aren't clear now, what about the future, when we may not only cure disease, but do so much more?
GENETIC COUNSELOR — "Your extracted eggs, Marie, have been fertilized with Antonio's sperm. You have specified hazeleyes, dark hair and fair skin. All that remains is to select the most compatible candidate. I've taken the liberty of eradicating any potentially prejudicial conditions: premature baldness, myopia, alcoholism, obesity, et cetera."
MARIE — "We didn't want... I mean... diseases... yes, but..."
ANTONIO — "Right. And we were just wondering if it's good to just leave a few things to chance."
GENETIC COUNSELOR — "You want to give your child the best possible start. And keep in mind this child is still you, simply the best of you. You could conceive naturally a thousand times and never get such a result."
FRANCIS COLLINS — Gattaca really raised some interesting points. The technology that's being described there is, in fact, right in front of us or almost in front of us.
ROBERT KRULWICH — That seems to me almost extremely likely to happen, because what parent wouldn't want to introduce a child that wouldn't have... at least be where all the other kids could be?
FRANCIS COLLINS — That's why the scenario is chilling. It portrayed a society where genetic determinism had basically run wild. I think society in general has smiled upon the use of genetics for preventing terrible diseases. But when you begin to blur thatboundary of making your kids genetically different in a way that enhances their performance in some way, that starts to make most of us uneasy.
ROBERT KRULWICH — What if we lived in the world of Star Trek Voyager? Talk about uneasy. Lieutenant Torres is 50 percent human and 50 percent Klingon. She's also 100 percent pregnant. Like any caring parent, she doesn't want her unborn child to beteased for having a forehead that looks like... well, like a tire-tread. But, here's the twist. She can do something about it.
Mmm, she threw in some blond hair, too.
And is this the limit? Or could we go even further? If you can eventually isolate all these things, can you then build a creature that has never existed before? For example, I would like the eyesight of a hawk, and I'd like the hearing of a dog. Otherwise, I'm quite content to be exactly as I am. So, could I pluck the eyesight and the hearing and patch it in?
ERIC LANDER — Well, we don't know. We really don't know how that engineering occurs and how we can improve on it. It would be very much like getting a whole pile of parts to a Boeing 777 and a whole pile of parts to an Airbus, and saying, "Well, I'm going to mix and match some of these so it will have some of the properties. I make it a little fatter, but I also want to make it a littleshorter." And by the time you were done you'd think you'd made lots of clever improvements, but the thing wouldn't get off theground.
It's a very complex machine, and going in with a monkey wrench to change a piece... the odds are most changes we would make today, almost all changes we'd make today, would break the machine.
ROBERT KRULWICH — We may not be able to genetically modify humans or Klingons yet, but we do do it to plants and animals every day. Look at this stuff: tobacco plants with a gene from a firefly. And they used that same insect gene to create glowingmice. So, it's theoretically possible that we could create humans with other advantages that borrowed from other creatures.
ERIC LANDER — That's right. But the humility of science right now, is to appreciate how little we know about how you could even begin to go about that. What is the difference between the twentieth century and twenty-first century biology is it's now our job, in this century, to figure out how the parts fit together.
Cracking the Code of Life, Nova, 2001
The Ethics of Preimplantation Genetic Diagnosis video Gap-fill exercise Your score is 100%.
Some of your answers are incorrect. They have been left in place. NARRATOR — Another extraordinary advance would be to eliminate inherited diseases before birth.
We're already taking the first steps: by fertilizing an egg and producing an eight-cell embryo, you can pluck off one of those cells and analyze its genes. Then you can screen that embryo for a host of diseases, using a technique called "preimplantation genetic diagnosis," or P.G.D.
MARK HUGHES — When we first started performing this technology, I think our biggest worry was, "Aren't we going to create some kind of a birth defect that is maybe even worse than the disease we're trying to avoid?" And what we learned was that the embryo doesn't seem to mind.
NARRATOR — At the Genesis Genetics Institute, Mark Hughes and his team are using P.G.D. to test embryos for mutations that can give rise to over 300 diseases, including Huntington's and cystic fibrosis.
Only embryos free of certain mutations are implanted in the mother.
MARK HUGHES — So this is the mutant gene, and we have the normal gene over here.
Tens of thousands of healthy babies have been born to couples who otherwise would have been afraid to have a child, because the disease that they were at risk of giving their child was so severe.
NARRATOR — P.G.D. can also be used to test for traits like gender, leading ethicists to ask if designer babies are in our future.
RONALD GREEN — I think we are going to see people using genetics to select traits. Some of it will be relatively benign: "I want a child with this hair color or eye color."
GREGORY STOCK — As to where our ability to really intervene in our own living processes is really going to lead us, we simply do not know. And that is the promise and that is the threat of this period.
NARRATOR — Some fear P.G.D. could even lead to a new kind of eugenics and the sort of genetic elite depicted in the movie GATTACA.
DOCTOR — Your extracted eggs, Marie, have been fertilized with Antonio's sperm. After screening we are left, as you see, with two healthy boys and two very healthy girls. I have taken the liberty of eradicating any potentially prejudicial conditions:premature baldness, myopia, alcoholism and addictive susceptibility, propensity for violence, obesity…
NARRATOR — Fortunately, the traits a genetic elite would want—intelligence, physical ability, even height—are so complex, scientists assure us we won't be selecting them in embryos anytime soon.
ERIC LANDER — Trying to predict height, well, we already know there's at least 180 genes involved in height, and eachcontributes a little bit. It isn't going to be easy to go take some embryo and sort out which of these 180 are in which of these forms. There’s just no particularly good way to do that. You really, really, really want to have a tall person, go marry a tall spouse. It's just more efficient.
NARRATOR — Even so, GATTACA raises real concerns.
RONALD GREEN — One of the negative implications of this new genetic knowledge is that we're going to start thinking of ourselves more in genetic terms than we ever have before. Do I want to date that individual? What's her genetics? What’s his genetics? There's always been a tendency to engage in deterministic genetics, and I think scientists and medical people and educators must make very clear the limits of that point of view.
PATRICIA WILLIAMS — We are deeply affected by the kind of food we eat, the air we breathe, by the kind of good luck or bad luck that shapes our lives, opportunities like education and money, and by the real romance of simply falling in love with an unlikely partner. We narrow our vision if we focus or fetishize upon genetics.
NARRATOR — But as the cost of a sequenced genome falls, new generations may not get far into the world without one.
JONATHAN ROTHBERG — And I envision a day when every child is born, they prick that child's heel, and that D.N.A. from that child is decoded right at birth.
NOVA, Cracking Your Genetic Code, March 28, 2012
Purpose: For, To & So That Multiple-choice quiz Your score is 100%. Completed so far: 15/16. Show questions one by one I sort my garbage ..... recycling. :-) for ? to ? so They're going to Spain ... a holiday. :-) for ? to ? so that She's going there .... finalize the contract. :-) to ? for ? so that I'm leaving a bit early today .... avoid the rush hour traffic. :-) to ? so that ? for Shall we have a break .... lunch? ? so ? to :-) for We left early .... we wouldn't be late. :-) so that ? for ? to She's gone outside .... a cigarette. ? to :-) for ? so I took it back to the store .... get a replacement. ? so :-) to ? for Take some sandwiches .... eat on the way. ? for :-) to ? so He's gone travelling .... find himself. ? so ? for :-) to You need a licence .... drive. ? so that ? for :-) to I invited them both .... they wouldn't feel left out. ? for ? to :-) so We took the train .... we could work on the way. ? to ? for :-) so that I phoned them .... complain. ? so :-) to ? for I'm going to town .... buy a pair of shoes. ? for ? so that :-) to for dinner
Fill in the correct verbal form of 'rise' or 'raise'

1. At the meeting yesterday the committee raised six questions.
2. Do you think that prices will rise next year?
3. The sun rose at 6:30 this morning and it was a beautiful sunrise.
4. Gradually the fir tree rose higher and higher until it reached the roof.
5. He had raised corn every year until this year. Now he grows only vegetables.
6. The Gauls rose up against the Romans.
7. The temperature has risen all day long.
8. His voice rose as he got angry.
9. The government had not raised taxes for three years until yesterday.
10. To taste good, bread must rise properly.
11. Higher inflation usually raises prices.
12. The flag was raised every morning at 6:00 a.m. until yesterday. (use passive)
13. The six children were raised by him alone.
14. The river has risen three feet since yesterday.

OUT Cloze exercise Your score is 97%.
Some of your answers are incorrect. They have been left in place. bail carry die Empty fade find hand hear help knock pick reason rub smooth try wash wear 1. The fire has die out; you'll have to light it again.

1. Look in your dictionary to find out the meanings of the words you don't know.
   
2. Mr. Hill didn't succeed in smooth out the differences between John and Peter. They still refuse to speak to each other.
   
3. I can't wash out the stain on your coat.
   
4. He tried hard to reason out the answer to the mystery.
   
5. The artist made a quick sketch of the building. He wasn't satisfied with it, however, so he rub it out.
   
6. Mike Tyson hit his opponent in the chin and knock him out.
   
7. Fletcher Christian refused to carry out the captain Bligh's orders.
   
8. "We'll have to BAIL this water out if the boat is to stay afloat," Captain Jones told his men.
   
9. Ann pick out the largest orange and began to eat it.
   
10. Have you ever try out this new recipe?
    
11. The music slowly fade out and someone began to speak.
    
12. "The least you can do," said the lawyer, "is to hear him out. You don't even know what he has to say."
    
13. I help Smith out when he was out of work, and since then he has always been very loyal to me.
    
14. "empty out your pockets," the detective told all the guests as soon as the ring was reported missing.
    
15. Paul helped the teacher to hand out the test papers.
    
16. "I've hardly slept for sixty hours, and I feel wear out," the poor man complained
    

CONDITIONNAL QUIZ
If you took the trouble to phone your parents more often, they .... happy. :-) would be ? will be ? would have been If I .... my exam next year, I hope to get a good job. ? would pass :-) pass ? will pass I wouldn't have missed my bus if my alarm clock .... broken. :-) hadn't been ? wouldn't be ? wasn't If you are feeling ill, you .... to go out. X wouldn't want :-) won't want ? don't want If you read (past) this book, you .... it. :-) would enjoy ? will enjoy ? enjoy We would go for a walk if it .... raining. X hadn't been ? isn't :-) wasn't If you pour oil on water, it .... on the surface. X would remain ? would have remained :-) will remain I .... my car easily, if I hadn't had a crash last week. :-) would have sold ? would sell ? sold If I .... more money, I would take a long holiday. ? will have ? would have :-) had If you .... to lock the door, the burglars would not have stolen all our valuables. ? remembered :-) had remembered ? will remember

I want to create a card that says something like:
Front: Calcium must be present during muscle contraction Where calcium is cloze
Back: Calcium must be present during muscle contraction Where muscle contraction is cloze
And then for them to be able to be reversed so it will show me either one first.