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teachcmb56 · 6 years

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One of the first literary elements that students understand as they begin to read is character. They learn that a character is a person, animal, being, or thing moving the story along a plot line. Many of the characters in books they can read independently have recognizable traits:

The Pigeon in Don’t Let the Pigeon Drive the Bus is always stubborn and demanding;

Horrible Harry of the Horrible Harry Series is always rebellious.

Jack Smith of The Magic Treehouse Series is always smart and courageous.

Judy Moody of the Judy Moody Series is always determined and…well, moody.

The characters in series books at these elementary grade levels (K-3) are predictable. Students are able to practice reading because they are familiar with characters such as Ramona, Dog Man, Nate the Great, Captain Underpants, Amelia Bedelia, and Frog and Toad.

These characters’ thoughts and feelings may shift when they react to different problems or conflicts. But these characters do not change. They are static; they are not complex characters.

The dominance of static characters in the elementary grades can be an issue when students are faced with an assessment question:

“How did [the character] change? What caused this change?”

This particular question comes from the Teacher’s College Grade 3 Reading Units of Study, but other literacy programs also ask about character change.

It is important to understand that the word “change” is synonymous with a radical, transformative process. The etymology of the word change (c.1300) is “to undergo alteration, become different.” The Collins dictionary defines change as:

1. to put or take (a thing) in place of something else; substitute for, replace with, or transfer to another of a similar kind

2. to give and receive reciprocally; exchange; switch

3. a. to cause to become different; alter; transform; convert

b. to undergo a variation of

The kind of change in character that matches this transformative meaning is difficult to find at the lower reading levels. A well-crafted character who “converts” or “alters” in a low-level text is unusual for any combination of reasons including choices in brevity, vocabulary, and text structure.

For most students who are reading at or below a Grade 3 reading level, there are few complex texts that they can independently read to determine a character change. Instead, the characters in the book series that are favored by students such as Geronimo Stilton, The Boxcar Children, or Ivy and Bean, are intentionally crafted by authors so the characters remain the same while the plot or the settings change.

There are exceptions, of course. The Grinch in Dr. Seuss’s classic story (not film) The Grinch that Stole Christmas undergoes a transformational character change, but at the Lexile 731/level P, the book is most often used in the classroom as a read-aloud. There are few mentor texts like the Grinch that can give students the opportunity to practice for an assessment on character change.

The limited number of stories with complex characters at the lower grade levels means that students do not have enough independent practice on their own with this concept. The leap from the predictable characters in a series (Babymouse, Henry& Mudge, Little House on the Prairie) to the kind of complex character change that is found in Maddie in Eleanor Estes The Hundred Dresses or in Jonas in Lois Lowrey’s The Giver can be a high bar for many elementary or even intermediate readers.

So, why ask students at the lower elementary levels about character change at all? The phrase “character change” does not appear in the ELA Common Core State Standards (CCSS) until Grade 6 when students should:

“…describe how a particular story’s or drama’s plot unfolds in a series of episodes as well as how the characters respond or change as the plot moves toward a resolution. (RL.6.3)”

Instead, the CCSS states students in grades 3-5 should be able to:

describe characters (traits, motivations, or feelings) and “explain how their actions contribute to the sequence of events” (RL.3.3 )

describe character, setting, or event in a story or drama, drawing on specific details in the text ( a character’s thoughts, words, or actions (RL.4.3)

compare and contrast two or more characters, settings, or events in a story or drama, drawing on specific details in the text (RL.5.3)

Maybe the intent of asking students at the lower elementary levels about character change is to prepare students for the complex texts they will read in the upper grades. If this is the case, there should be some consideration of what resources could be used to support this kind of character study, especially as most books in elementary reading libraries feature characters that are purposefully crafted to be static. The characters are designed to be familiar to allow students to practice fluency and accuracy. Asking students to find evidence to show a character change when there is no change is an inauthentic exercise.

There is also a danger in assessing a student’s understanding of character change too soon in their literary lives. Teachers also should be careful not to elevate what is a shift in a character’s attitude to be equal to a change in character. Guiding elementary students to answer “character change” by using evidence that shows how a character may think or feel differently can muddy a later understanding of what character change really means.

Students should not have to answer assessment questions that ask for evidence of character change where there is none. Instead, at the lower grade levels, students should be ready to answer assessment questions that draw attention to the differences in a character’s attitude, thoughts or feelings:

“How did the character react to the problem?”

“What caused the character’s reaction?”

“What are the character’s thoughts and feelings now?”

“Does this character have a different attitude?”

Most of the books in elementary school libraries can support these kinds of questions, which are closely aligned to the ELA CCSS for grades K-5. Finding the evidence that shows a difference in a character’s attitude, thoughts, or feelings is a task that elementary students can do in both mentor texts and in their leveled reading.

Identifying a character’s shift in attitude can also help students better understand the theme or message of the book, for example:

“Yes, this is where I want to be! The Circus is the place for me.” –Put Me in the Zoo.

“Pete, you don’t need magic sunglasses to see things in a new way! Just remember to see the good in everyday!” Pete the Cat and the Magic Sunglasses

“And for all I know he is sitting there still, under his favorite cork tree, smelling the flowers just quietly. He is very happy.”-The Story of Ferdinand

Of course, there are those iconic characters who have been designed to be so static that a question about a difference in attitude, thoughts or feelings is pointless. Just ask Max of Where the Wild Things Are. He is still in his wolf suit when he returns home “where he found his supper waiting for him and it was still hot.”

Is a Question about Character Change in Elementary Texts Worth Asking? Part 1 One of the first literary elements that students understand as they begin to read is character.

#character change #Pete the Cat and the Magic Sunglasses #Put Me in the Zoo #RL.3.3 #RL.4.3 #RL.5.3 #RL.6.3 #Teacher&039;s College Grade 3 Reading Units of Study #The Grinch that Stole Christmas #The Story of Ferdinand #Where the Wild Things Are

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nagataka-lifelog · 2 years

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2021-11-04

PRML 1.2.2 Expectations and covariances 読んだ。

RL 3.3 Returns and Episodes 読んだ。

博論の体裁を少し整えるのと、110 words ほど書き足すなどした。

英語のフィードバックを頂き、ちょっと調べた。

冠詞と関係節

関係代名詞

制限用法と非制限用法、自分よく間違っている気がするので気をつけよう。。

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den-nang-luong · 10 months

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Đèn Led Năng Lượng Mặt Trời Giải Pháp Chiếu Sáng Bền Vững

Đèn năng lượng mặt trời đã trở thành một giải pháp chiếu sáng phổ biến và tiết kiệm năng lượng trong những năm gần đây. Với khả năng tự cung cấp năng lượng từ ánh sáng mặt trời, đèn LED năng lượng mặt trời không chỉ giúp tiết kiệm điện năng mà còn mang lại sự bền vững cho môi trường.

Trong bài viết này, chúng ta sẽ tìm hiểu về đèn LED năng lượng mặt trời, ưu điểm của chúng và cách chúng hoạt động.

Xem chi tiết bài viết: Đèn LED năng lượng mặt trời

Đèn đường năng lượng mặt trời 200w Roiled RL-D200

1. Giới thiệu về đèn LED năng lượng mặt trời

Đèn LED năng lượng mặt trời đã trở thành một giải pháp phổ biến và hiệu quả để chiếu sáng trong các khu vực không có nguồn điện lưới và cũng là một cách tiết kiệm năng lượng và bảo vệ môi trường. Đèn LED năng lượng mặt trời sử dụng nguồn điện từ ánh sáng mặt trời để hoạt động, không cần dùng đến nguồn điện lưới và không gây ra khí thải gây ô nhiễm. Trong bài viết này, chúng ta sẽ tìm hiểu về lợi ích và ứng dụng của đèn LED năng lượng mặt trời.

Đèn pha năng lượng mặt trời 400w giá rẻ FSW F1-400w

Quý khách có thể tham khảo thêm đầy đủ các mẫu đèn pha năng lượng mặt trời tại đây

2. Lợi ích của đèn LED năng lượng mặt trời

2.1. Tiết kiệm năng lượng:

Đèn LED năng lượng mặt trời sử dụng công nghệ LED tiên tiến, tiêu thụ ít năng lượng hơn so với đèn truyền thống. Ánh sáng LED tiết kiệm năng lượng và có tuổi thọ cao hơn, giúp giảm đáng kể hóa đơn tiền điện.

2.2. Bảo vệ môi trường:

Đèn LED không chứa chất độc hại như thủy ngân và không gây ra khí thải gây ô nhiễm. Khi sử dụng đèn LED năng lượng mặt trời, chúng ta đóng góp vào việc giảm lượng khí thải carbon và bảo vệ môi trường xanh.

Đèn sân vườn năng lượng mặt trời UFO-500w

Quý khách có thể tham khảo thêm đầy đủ các mẫu đèn sân vườn năng lượng mặt trời tại đây

2.3. Dễ dàng lắp đặt và bảo trì:

Đèn LED nlmt không cần dùng đến nguồn điện lưới, do đó việc lắp đặt và bảo trì đơn giản hơn. Chúng có thể được lắp đặt ở bất kỳ vị trí nào có ánh sáng mặt trời, từ công viên, con đường, sân vườn đến các khu vực hẻo lánh.

2.4. Khả năng hoạt động liên tục:

Đèn LED nlmt được trang bị pin năng lượng mặt trời tích hợp, cho phép hoạt động liên tục trong suốt đêm. Pin năng lượng mặt trời sẽ tự động sạc khi có ánh sáng mặt trời và cung cấp đủ năng lượng cho đèn LED hoạt động suốt đêm.

2.5. Độ tin cậy cao:

Đèn LED nlmt thường được thiết kế bền bỉ và chống nước, chịu được các điều kiện thời tiết khắc nghiệt. Chúng không bị ảnh hưởng bởi sự cắt điện và luôn sẵn sàng hoạt động khi cần thiết.

Đèn đường năng lượng mặt trời Roiled RD-400w

Quý khách có thể tham khảo thêm đầy đủ các mẫu đèn đường năng lượng mặt trời tại đây

3. Ứng dụng của đèn LED năng lượng mặt trời

3.1. Chiếu sáng đường phố:

Đèn LED nlmt được sử dụng rộng rãi để chiếu sáng đường phố và công cộng. Chúng không chỉ giúp tạo ra môi trường an toàn và thuận tiện cho người đi đường, mà còn giảm thiểu tác động đến môi trường.

3.2. Chiếu sáng ngoại thất:

Đèn LED nlmt có thể được sử dụng để chiếu sáng sân vườn, hồ bơi, biệt thự và các khu vực ngoại thất khác. Chúng tạo ra không gian ấm áp và tiết kiệm năng lượng cho gia đình và cộng đồng.

Đèn thả trong nhà năng lượng mặt trời Roiled RT-36W

3.3. Chiếu sáng bảo vệ an ninh:

Đèn LED nlmt cũng được sử dụng để chiếu sáng các khu vực bảo vệ an ninh như khuôn viên nhà máy, khu vực công nghiệp và bãi đỗ xe. Chúng giúp tăng cường an ninh và giám sát trong khi tiết kiệm năng lượng.

3.4. Chiếu sáng cắm trại và du lịch:

Đèn LED năng lượng mặt trời là lựa chọn lý tưởng cho các hoạt động cắm trại và du lịch. Chúng cung cấp ánh sáng đủ để tạo ra không gian thoải mái và an toàn trong khi không cần dùng đến nguồn điện lưới.

Đèn trụ cổng năng lượng mặt trời

4. Kết luận

Đèn LED năng lượng mặt trời là một giải pháp tiết kiệm năng lượng, bảo vệ môi trường và có ứng dụng rộng rãi trong việc chiếu sáng. Chúng không chỉ giúp tiết kiệm chi phí điện mà còn đóng góp vào việc giảm thiểu ô nhiễm và tạo ra một môi trường sống xanh hơn. Hãy chọn đèn LED năng lượng mặt trời để sử dụng và đóng góp vào bảo vệ môi trường của chúng ta.

Link bài viết “Đèn LED năng lượng mặt trời” : https://thegioidendien.vn/kien-thuc/den-led-nang-luong-mat-troi-giai-phap-chieu-sang-ben-vung.html

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Name: Đèn năng lượng mặt trời

Address: 4380B Nguyễn Cửu Phú, Phường Tân Tạo A, Quận Bình Tân, TP HCM

Phone: 0976.568.346

Wedsite Đèn năng lượng mặt trời: https://thegioidendien.vn/den-nang-luong-mat-troi

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japonkalecom · 3 years

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R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 84 Vandevoordt, Maarten 18 GK KRC Genk 4.1 0.1 83 Gasparini, Manuel 18 GK Udinese 2 0.07 83 van Sas, Mikki 16 GK Man City 0.1 0.01 83 Meslier, Illan 20 GK Leeds 20 18.4 82 Iturbe, Alejandro 16 GK A. Madrid 0.3 0.008 81 Matheus Donelli 18 GK COR 0.4 0.01 80 Iván Martínez 18 GK Atlético Pamplona 1.1 0.03 80 Kovár, Matej 20 GK Man Utd 2.5 0.2 80 Russo, Alessandro 19 GK Sassuolo 4.8 0.1 79 Costa, Diogo 20 GK FCP 3.4 1 79 Bazunu, Gavin 18 GK Man City 5 0.1 78 Bajic, Stefan 18 GK AS Saint-Etienne 1 0.1 78 Setford, Charlie 16 GK Ajax 0.09 0.03 78 Owusu-Oduro, Rome-Jayden 16 GK AZ 0.09 0.02 78 Cristian 18 GK ATM 0.07 0.001 77 Turati, Stefano 18 GK Sassuolo 0.2 0.08 77 Jörgensen, Filip 18 GK Villarreal 1.5 0.05 77 Dutra, Sérgio 18 GK Vit. Guimarães 0.5 0.002 77 Dewhurst, Marcus 19 GK Sheff Utd 0.6 0.1 77 Meixedo, Francisco 19 GK FCP 1.8 0.01 76 Klaesson, Kristoffer 19 GK Vålerenga 0.3 0.3 76 Boyce-Clarke, Coniah 17 GK Reading 1.8 0.03 76 Petrovic, Djordje 20 GK Cukaricki 0.6 0.05 76 Carnesecchi, Marco 20 GK Atalanta 3.5 0.3 75 Svilar, Mile 20 GK SLB 9.3 0.3 75 Chevalier, Lucas 18 GK LOSC 0.9 0.08 75 Lavallée, Lucas 17 GK LOSC 0.08 0.04 75 Gabriel Brazão 19 GK Inter 3.6 0.1 74 Pinto, Diogo 16 GK Sporting 0.06 0.001 74 Mabon, Tom 16 GK FC Nantes 0.06 0.02 70 Raatsie, Calvin 18 GK Ajax 0.6 0.07

FM21 Wonderkids adayı Defans Oyuncuları

R Soyadı, Adı Yaşı Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 91 de Ligt, Matthijs 20 D C Zebre 213 35 83 Papetti, Andrea 18 D C Brescia 0.2 1.6 82 Carrillo, Álvaro 18 D C R. Madrid 1.2 0.04 82 Pembélé, Thimothée 17 D C Paris SG 5.2 0.2 81 Østigård, Leo 20 D C Brighton 4 3.9 81 Saliba, William 19 D C Arsenal 40 11.6 81 Amione, Bruno 18 D C Verona 3.5 0.2 79 Iranzo, Rubén 17 D C Valencia 1.4 0.04 79 Armini, Nicolò 19 D RC Lazio 17.7 0.1 78 Kambwala, Willy 15 D C Man Utd 0.1 0.06 78 Viti, Mattia 18 D C Empoli 1.4 0.1 78 Brás, Gabriel 16 D C FCP 0.06 0.001 77 Almeida, Diego 16 D C Barcelona 0.7 0.04 76 Mengi, Teden 18 D C Man Utd 1.2 7.1 83 Lovato, Matteo 20 D C Verona 2.8 0.6 80 Cuenca, Jorge 20 D C Villarreal 5.6 0.1 78 Ferguson, Nathan 19 D RC Crystal Palace 35 7.1 80 Dalle Mura, Christian 18 D C Fiorentina 0.2 0.04 76 Sadick, Mujaid 20 D RC Coruña 4.6 0.1 78 Chust, Víctor 20 D C R. Madrid 2.5 0.09 87 Todibo, Jean-Clair 20 D C, DM Barcelona 21.9 4.9 83 Fofana, Wesley 19 D C Leicester 55 11.6 82 Rensch, Devyne 17 D RLC, DM Ajax 1 0.2 77 Gabbia, Matteo 20 D C Milan 19.5 1.4 79 Koumetio, Billy 17 D C Liverpool 0.7 0.01 81 Pirola, Lorenzo 18 D C Inter 1.5 0.07 80 Normann Williamsen, Max 17 D C Kristiansund 0.3 0.02 79 Quaresma, Eduardo 18 D C Sporting 4.5 0.7 79 Pierie, Kik 20 D LC Ajax 2.8 1.4 78 Solet, Oumar 20 D C, DM FC RB Salzburg 4.3 0.3 73 Baptiste, Jamal 16 D C West Ham 0.1 0.04 80 Harwood-Bellis, Taylor 18 D C Man City 3 0.5 82 Pavlovic, Strahinja 19 D C AS Monaco 6.9 0.9 79 Wood, Nathan 18 D C Middlesbrough 1 0.1 81 Badiashile, Benoît 19 D C AS Monaco 10.5 2.4 79 Muñoz, Iván 18 D LC Valencia 2.4 0.1 79 Ramón, Pablo 19 D RC R. Madrid 1.4 0.04 76 Mbete-Tabu, Luke 16 D C Man City 0.1 0.02 78 Mosquera, Cristhian 16 D RC Valencia 0.06 0.05 77 Álvaro, Pedro 20 D C SLB 2.7 0.1 76 Kossounou, Odilon 19 D RC Club Brugge 4.5 0.6 79 Kamardin, Aaron 18 D C, DM OM 1.3 0.02 77 Guillamón, Hugo 20 D C Valencia 5.9 0.1 76 Zaal, Timo 16 D C sc Heerenveen 0.09 0.05 78 Vinhas, David 17 D C FCP 1.8 0.006 81 Stergiou, Leonidas 18 D C St. Gallen 2.1 0.9 79 Branthwaite, Jarrad 18 D RLC Everton 5 0.3 76 Kolskogen, Ole Martin 19 D C Brann 0.6 0.06 78 Burns, Finley 17 D C Man City 0.1 0.06 77 Simakan, Mohamed 20 D RC Strasbourg 4.5 0.6 77 Brassier, Lilian 20 D C Rennes 3.6 0.4 81 King, Leon 16 D RLC, DM Rangers 0.03 0.008 74 Machuca, Elías 17 D C Racing Club 0.4 0.7 82 Bettella, Davide 20 D C Atalanta 1.5 0.6 77 Diakité, Bafodé 19 D RC Toulouse FC 4.4 0.3 77 Vuskovic, Mario 18 D C, DM Hajduk 0.6 0.05 83 García, Eric 19 D C Man City 12 22.6 76 Syrota, Olexandr 20 D C Dynamo Kyiv 0.2 0.03 76 González de Zárate, Urko 19 D C, DM Real San Sebastián 1.6 0.06 79 Struijk, Pascal 20 D C, DM Leeds 2 2.9 79 Geovani 20 D C FLU 0.1 0.01 81 Ramos Mingo, Santi 18 D C Barcelona 2.2 0.04 78 Méndez, Bruno 20 D RC COR 3.5 0.7 75 van den Berg, Rav 16 D C PEC Zwolle 0.09 0.05 75 Pascual, Martín 20 D C Vallecano 3.4 0.2 77 Gila, Mario 19 D C, DM R. Madrid 3.3 0.1 77 Greiml, Leo 19 D C SK Rapid Vienna 1.2 0.3 76 Schuurs, Perr 20 D C, DM Ajax 4.5 1.6 78 Zaletel, Zan 20 D C Celje 0.9 0.3 82 Kaiky 16 D LC SAN 0.04 0.001 78 Morato 19 D C, DM SLB 3.4 0.1 79 Binks, Luis 18 D C Bologna 1.8 0.09 77 Quiroz, Leandro 16 D C Colón 0.02 0.3 81 Arrey-Mbi, Bright 17 D C FC Bayern 1.4 0.04 77 Tagir, Ravil 17 D C Basaksehir 2.9 0.2 78 Markovic, Svetozar 20 D RC Olympiakos 6.3 0.3 79 Youte Kinkoue, Etienne 18 D C Inter 0.2 0.04 79 Milovanovic, Mateja 16 D LC, DM Ajax 0.09 0.05 83 Nianzou, Tanguy 18 D C, DM FC Bayern 33.6 0.1 78 Wilmot, Ben 20 D C Watford 5 2.9 76 Maresic, Dario 20 D C, DM Reims 9 1.4 81 Antov, Valentin 19 D C, DM CSKA-Sofia 1.1 0.1 78 N'Dicka, Evan 20 D C Eintracht Frankfurt 8.7 1 83 Bella-Kotchap, Armel 18 D C VfL Bochu 3.5 0.05 78 Aertssen, Olivier 15 D C, DM Ajax 0.09 0.04 80 González, Facundo 17 D C Valencia 1.4 0.05 81 Kabak, Ozan 20 D C Schalke 04 35.1 4 79 Omeragic, Becir 18 D C Zürich 2.7 0.4 81 Collins, Nnamdi 16 D C Borussia Dortmund 0.06 0.01 81 Sutalo, Bosko 20 D RC Atalanta 6.6 1.5 88 Gvardiol, Josko 18 D LC RB Leipzig 13.3 0.9 84 Ampadu, Ethan 19 D C, DM Chelsea 7.5 8.1 78 Roberts, Haydon 18 D C Brighton 4 1.4 78 Pambou, Leon 16 D C Southampton 0.1 0.02 77 Cardoso, Gonçalo 19 D C West Ham 10 1.1 76 Ávila, Gastón 18 D C Boca 1.3 0.3 85 Pérez, Nehuén 20 D C A. Madrid 15.1 4 75 St. Jago, Tommy 20 D C FC Utrecht 1.7 0.7 87 Kumbulla, Marash 20 D C Verona 31.7 5 84 Vanheusden, Zinho 20 D C Standard 23.7 5.7 79 Lawrence, Jamie 17 D C FC Bayern 0.8 0.16 79 Walukiewicz, Sebastian 20 D C, DM Cagliari 8 3.5 78 Richards, Chris 20 D RC FC Bayern 3.2 0.17 78 Schlotterbeck, Nico 20 D LC SC Freiburg 3.5 2.2 77 Otasowie, Owen 19 D C, DM Wolves 0.7 2 76 Klarer, Christoph 20 D C Fortuna Dusseldorf 2.2 0.5 75 Denk, Luca 17 D C, DM FC Bayern 0.06 0.09 74 Gibson, Lewis 20 D LC Everton 5 0.45 74 Aliu, Arbnor 17 D RLC, DM Schalke 04 0.06 0.09 73 Fleurs, Luke 20 D C SuperSport Utd 0.5 0.02

FM21 Wonderkids adayı Sol Bek Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 82 Williams, Brandon 19 D/WB RL Man Utd 40 19.7 79 Montañana, Hugo 17 D LC, M L Valencia 1.4 0.05 79 Bard, Melvin 19 D LC OL 14 0.7 83 Calafiori, Riccardo 18 D/WB/M L Roma 13 0.1 84 Miranda, Juan 20 D/WB/M L Barcelona 7.2 0.8 77 Imanol 20 D LC A. Bilbao 2.1 0.07 80 Hickey, Aaron 18 D RL Bologna 13.1 0.8 77 Fernández, Álvaro 17 D L Man Utd 1.5 0.06 81 Zagré, Arthur 18 D L AS Monaco 11.5 0.2 86 Mendes, Nuno 18 D/WB L Sporting 4.1 4.5 78 Ruggeri, Matteo 18 D/WB L Atalanta 0.2 0.07 84 Aït-Nouri, Rayan 19 D L Angers SCO 9.8 2.2 81 Vázquez, Jesús 17 D/WB/M L Valencia 1.6 0.06 86 Katterbach, Noah 19 D/WB L 1. FC Köln 1.7 1 81 Montóia, Guilherme 16 D/WB L SLB 0.06 0.006 77 Pérez, Pablo 18 D/WB/AM L Sevilla 1.2 0.03 74 Maatsen, Ian 18 D/WB L, M C Chelsea 7.5 0.2 80 Bradaric, Domagoj 20 D/WB L LOSC 8.4 0.6 77 Aning, Prince 16 D/WB L Ajax 0.09 0.02 81 Herrero, Toni 19 D/WB L Levante 1.2 0.03 82 Mitchell, Tyrick 20 D/WB L Crystal Palace 0.5 3.7 82 Cacace, Liberato 19 D/WB L STVV 6.1 0.5 79 Netz, Luca 17 D/WB L Hertha BSC 0.06 0.03 76 Candelaria, Jayden 16 D LC Feyenoord 0.09 0.03 85 Wijndal, Owen 20 D/WB/M L AZ 3.8 2.8 77 Fran García 20 D/WB/AM L R. Madrid 1.6 0.06 85 De la Víbora, David 17 D/WB/M L R. Madrid 0.3 0.007 78 Davies, Alphonso 19 D/WB L, AM RL FC Bayern 87.6 6 78 Lelo, Leonardo 20 D/WB/AM L Olhanense 0.2 0.04 79 Romero, Cristo 20 D/WB/AM L Málaga 1.8 0.08 77 Centelles, Álex 20 D/WB L Almería 5.3 0.2 79 Thomas, Luke 19 D/WB/M L Leicester 25 0.4 73 Daley, Luke 18 D/WB/AM L Huddersfield 0.1 0.004 78 Balde, Alejandro 17 D/WB/AM L Barcelona 0.9 0.02 81 Cirkin, Dennis 18 D/WB L Tottenham 1.5 0.2 79 Larios, Juan 16 D/WB L Man City 0.1 0.01 79 Bello, George 18 D/WB L Atlanta United 1.2 0.8 74 Chrisene, Ben 16 D/WB L, AM LC Aston Villa 0.1 0.02 78 Sertel, Cemali 20 D LC Basaksehir 5.9 2.5

FM21 Wonderkids adayı Sağ Bek Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 81 Yan Couto 18 D/WB R Man City 5 0.2 75 Konoplya, Yukhym 20 D/WB RL Shakhtar 1.7 0.2 83 Esteves, Tomás 18 D/WB R FCP 3.4 0.3 77 Scott, Jack 17 D/WB R Wolves 0.7 0.04 82 Pinheiro, Rodrigo 17 D/WB R FCP 1.2 0.01 80 Cruz, Filipe 18 D/WB/AM R SLB 1.6 0.01 86 Aarons, Max 20 D/WB R Norwich 18 14.9 82 Gusto, Malo 17 D/WB R, M C OL 0.5 0.04 81 Kalulu, Pierre 20 D/WB R Milan 16 0.1 75 Livramento, Valentino 17 D/WB/AM R Chelsea 1.2 0.1 81 Laird, Ethan 18 D/WB RL Man Utd 4 4.3 80 Joseda 18 D RC Valencia 2.4 0.1 79 Beyer, Louis 20 D RC Gladbach 4.1 1.1 76 Porro, Pedro 20 D/WB R, AM RC Man City 30 14.2 80 Hoever, Ki-Jana 18 D RC Wolves 12 0.2 81 Vagnoman, Josha 19 D/WB RL Hamburger SV 7 0.3 81 Víctor Gómez 20 D/WB R Espanyol 5.6 0.3 80 Dest, Sergiño 19 D/WB RL, M R Barcelona 25.2 10 75 Lamptey, Tariq 19 D/WB RL, M R Brighton 5 15.9 79 Jurado, Marc 16 D R Man Utd 0.1 0.02 78 Casas, Ismael 19 D RC Málaga 1.8 0.2 79 Bogle, Jayden 20 D/WB R Sheff Utd 20 4.8 86 James, Reece 20 D/WB R, M C Chelsea 60 25.6 78 Williams, Neco 19 D/WB R Liverpool 8 1.8 77 Frimpong, Jeremie 19 D/WB RL Celtic 8 4.3 82 Zappa, Gabriele 20 D/WB/M R Pescara 5.3 2

FM21 Wonderkids Defansif Ortasaha Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 83 Gustavo Assunção 20 DM Famalicão 2 3.7 82 Rovella, Nicolò 18 DM Genoa 0.2 0.7 82 Ugarte, Manuel 19 DM Fénix 1.6 1.2 79 Brito, Rafael 18 D C, DM SLB 2.7 0.1 83 Kana, Marco 17 D C, DM Anderlecht 4.5 0.1 79 Duvnjak, Tomislav 17 DM Dinamo 0.2 0.01 78 Clarkson, Leighton 18 DM Liverpool 0.7 0.04 78 Diakité, Ousmane 20 DM FC RB Salzburg 0.7 0.3 78 Krastev, Dimo 17 DM Fiorentina 0.2 0.05 78 Viviani, Mattia 19 DM Chievo 3.2 0.7 86 Luís, Florentino 20 DM SLB 16.8 6.7 79 Reis, Ludovit 20 DM Barcelona 6.1 0.1 87 Tonali, Sandro 20 DM Brescia 38.3 10.4 78 Victor Bobsin 20 DM GRE 1 0.1 80 Lütke-Frie, Dennis 17 DM Borussia Dortmund 0.06 0.01 78 Bruno Gomes 19 DM VDG 2.6 0.6 75 Gragera, José 20 DM S. Gijón 2.3 0.1 76 Djokanovic, Andrej 19 DM Sarajevo 0.1 0.001 78 Esposito, Salvatore 19 DM SPAL 5.2 1.1 78 Mendy, Batista 20 DM FC Nantes 4.2 0.5 78 Morante, Iván 19 DM R. Madrid 0.5 0.02 81 Tijani, Samson 18 D C, DM FC RB Ralzburg 2.2 0.1 79 Kenneh, Nohan 17 D C, DM Leeds 0.5 0.2 79 Massengo, Han-Noah 19 DM Bristol City 5 0.4 79 Diego Rosa 17 DM GRE 0.6 0.04 79 Sandry 17 DM SAN 2.8 0.6 77 Lavia, Roméo 16 DM Man City 0.1 0.1 77 Vencedor, Unai 19 DM A. Bilbao 4.3 0.1 80 Ghion, Andrea 20 DM Sassuolo 2.6 0.1 83 Vera, Fausto 20 DM Argentinos Jrs. 3.2 1.7 77 Doucouré, Cheick 20 DM RC Lens 2.4 0.6 79 Faticanti, Giacomo 15 DM Roma 0.2 0.001 80 Chotard, Joris 18 DM Montpellier 4.8 0.7 79 Orellana, Jandro 19 DM Barcelona 3.9 0.1 78 Umyarov, Nail 20 DM Spartak Moscow 14.5 0.4 83 Skipp, Oliver 19 DM Tottenham 25 7.5 76 Pizzuto, Eugenio 18 DM LOSC 0.9 0.1 83 Kamara, Boubacar 20 D C, DM OM 46.2 9.2 80 Krauß, Tom 19 DM RB Leipzig 3.1 0.12 78 Stiller, Angelo 19 DM FC Bayern 0.8 0.28

FM21 Wonderkids Ortasaha Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 79 Bertelli, Ugo 17 M C OM 1.8 0.03 84 Baró, Romário 20 M C FCP 3.4 1.5 78 Jóhannesson, Ísak Bergmann 17 D/WB L, AM RLC IFK Norrköping 1.4 0.2 87 Gravenberch, Ryan 18 DM, AM LC Ajax 2.9 1.6 80 Ponceau, Julien 19 AM C FC Lorient 1.7 0.3 81 Taylor, Kenneth 18 D C, DM, M RLC Ajax 1.3 0.4 76 Lozano, Pol 20 DM Espanyol 1.6 0.1 82 Gyabuaa, Emmanuel 18 DM Atalanta 0.5 0.08 88 Ilaix Moriba 17 DM, AM C Barcelona 11.5 0.1 77 Tchouameni, Aurélien 20 M C AS Monaco 11.5 0.7 86 Dwomoh, Pierre 16 AM RC, F C KRC Genk 0.05 0.1 85 Gómez, Sergio 19 AM RLC Borussia Dortmund 14 0.8 79 Sibley, Louie 18 AM C Derby 6 2.1 81 Simons, Xavi 17 DM Paris SG 0.5 0.04 78 Ricci, Samuele 18 DM Empoli 5.2 1.2 77 Almeida, André 20 AM C Vit. Guimarães 1.6 0.3 79 Kitolano, Joshua 18 M C Odd 1 0.7 79 Proper, Dirk 18 AM C N.E.C. 0.9 0.09 80 Palaversa, Ante 20 DM, M LC Man City 15 1.8 80 Theo 18 AM C R. Madrid 1.2 0.03 78 Prioste, Diogo 16 M C SLB 0.06 0.001 76 Lepenant, Johann 17 M C SM Caen 0.4 0.02 79 Tejón, Martín 16 AM RLC Valencia 0.8 0.03 82 Sousa, Vasco 17 AM C FCP 1.3 0.007 83 Fitz-Jim, Kian 17 AM LC Ajax 0.1 0.03 78 Fagioli, Nicolò 19 DM, AM C Zebre 2.1 0.09 85 Gavi 15 AM C Barcelona 0.06 0.04 79 Lucas Ramos 19 M C INT 0.1 0.007 82 Gibbs-White, Morgan 20 AM C Wolves 15 6.8 84 Hannibal 17 AM C Man Utd 5 0.2 76 Gutiérrez, Ismael 19 DM A. Madrid 4.6 0.1 82 Shackleton, Jamie 20 M C Leeds 1.7 5.5 82 Alcaraz, Carlos 17 AM RC Racing Club 0.5 0.7 80 Tagseth, Edvard 19 DM, AM LC Rosenborg 2 0.02 78 Baldursson, Andri Fannar 18 AM C Bologna 4.3 0.1 79 Barrenechea, Enzo 19 DM Zebre 1.9 0.08 81 Monchu 20 DM Barcelona 3.9 0.1 83 Blanco, Antonio 20 DM R. Madrid 4.6 0.1 84 Koba Lein 18 M C Valencia 3.2 0.1 80 Longstaff, Matty 20 DM, AM C Newcastle 25 8.6 82 Cajuste, Jens 20 D/WB R, DM, M RC FC Midtjylland 4.6 0.3 84 Puig, Riqui 20 AM C Barcelona 5.9 2.8 88 Ferreira, Vítor 20 DM FCP 2 1 80 Palumbo, Martin 18 AM C Udinese 0.2 0.2 81 Patino, Charlie 16 DM Arsenal 0.1 0.06 78 Bulat, Marko 18 DM, AM C Sibenik 0.1 0.04 87 Cuisance, Mickaël 20 DM, AM C FC Bayern 43.9 2.3 82 Vranckx, Aster 17 DM, AM C KV Mechelen 2.2 0.5 84 Stamenic, Marko 18 DM, AM C FC København 1.2 0.005 78 White, Harvey 18 DM Tottenham 1.5 0.5 81 Sucic, Luka 17 AM C FC RB Salzburg 1.3 0.1 81 Kapi, Mustafa 17 AM C LOSC 0.5 0.02 79 Ilic, Ivan 19 DM, AM C Man City 5 1.8 78 Portanova, Manolo 20 AM C Zebre 3.3 0.1 78 Caio Lopes 19 DM VDG 0.7 0.05 77 Camara, Mohamed 20 M C FC RB Salzburg 2.7 0.3 79 Brescianini, Marco 20 DM Milan 4.4 0.2 79 Henderson, Ewan 20 AM RLC Celtic 1.5 0.04 80 Nartey, Nikolas 20 DM, AM C VfB Stuttgart 4.1 0.1 81 Utkin, Daniil 20 AM C Krasnodar 14.6 0.4 81 Patrick de Paula 20 DM SEP 6.9 2.3 76 Jimenez, Mylian 17 M C PSV 0.2 0.03 79 Baturina, Martin 17 AM C Dinamo 0.1 0.006 84 Adli, Yacine 19 AM RC Bordeaux 16.8 2.5 80 Perrone, Máximo 17 DM Vélez 1.2 0.4 79 Garner, James 19 DM Man Utd 10 6.4 82 Willock, Joe 20 M C Arsenal 40 15.9 82 Alastuey, Jorge 17 DM, AM C Barcelona 1 0.05 79 Matazo, Eliot 18 DM, M RC AS Monaco 0.8 0.08 78 Jaloliddinov, Jasurbek 18 AM RC Lokomotiv Moscow 2 0.04 77 Harper, Rekeem 20 DM, AM C West Brom 10 4.8 79 Galbraith, Ethan 19 M C Man Utd 10 0.3 82 Agoume, Lucien 18 DM, AM C Inter 7.1 0.1 78 Alemañ, Pedro 18 AM C Valencia 2.4 0.1 79 Doyle, Tommy 18 DM Man City 10 0.5 80 Neto, Martim 17 M RC SLB 1.3 0.007 81 Bernardo, Paulo 18 AM C SLB 1.8 0.09 81 Riccardi, Alessio 19 AM C Roma 16.2 0.2 82 Sancet, Oihan 20 AM C A. Bilbao 8.6 1 80 Dabo, Abdoulaye 19 M RC FC Nantes 3 0.3 83 Gabriel Menino 19 D/WB R, D SEP 9.3 3.1 73 Dele-Bashiru, Tom 20 DM Watford 5 0.2 80 Lucas Martello 20 DM, AM C Verona 2.4 0.1 86 De Ketelaere, Charles 19 AM LC Club Brugge 7.7 1.1 78 Kozlowski, Kacper 16 AM C Pogon 0.06 0.006 81 Chukwuemeka, Carney 16 AM C Aston Villa 0.1 0.01 80 Blesa, Álex 18 AM C Levante 1.4 0.05 82 Gilmour, Billy 19 AM C Chelsea 7.5 2.1 83 Aouchiche, Adil 18 AM C AS Saint-Etienne 16.8 0.3 81 N'Guessan, Jean 17 M C RC Abidjan 0.1 0.1 79 Mijnans, Sven 20 AM C Sparta 0.8 0.1 84 Marcos Antonio 20 DM Shakhtar 6.9 2.2 77 Ayala, David 18 DM Estudiantes (LP) 1.8 0.7 78 Regeer, Youri 16 D C, DM, AM C Ajax 0.1 0.1 81 Caicedo, Moisés 18 DM Independiente del Valle 0.8 0.09 79 Kökçü, Orkun 19 AM C Feyenoord 6.1 1.1 86 Elmas, Eljif 20 AM C Napoli 40 7.5 78 Karbownik, Michal 19 D RL, DM Brighton 2 0.3 82 Nico 18 DM Barcelona 1.1 0.1 76 Webster, Charlie 16 M C Chelsea 0.1 0.01 79 Gyabi, Darko 16 AM C Man City 0.1 0.01 84 Caqueret, Maxence 20 DM, AM C OL 17.5 3.6 78 Rhein, Torben 17 DM, AM C FC Bayern 0.7 0.04 85 Bellingham, Jude 17 AM RLC Borussia Dortmund 0.06 1.1 75 Jankewitz, Alexandre 18 DM, AM C Southampton 1 0.1 84 Musah, Yunus 17 AM RC Valencia 9.9 1.2 79 Bate, Lewis 17 DM Chelsea 0.6 0.1 76 Uriarte, Kepa 18 M C A. Bilbao 1.1 0.03 76 Enríquez, Ramón 19 DM, AM C Málaga 3.5 0.2 84 Szoboszlai, Dominik 19 AM LC FC RB Salzburg 4.3 5.8 80 Raskin, Nicolas 19 DM Standard 2.2 2.6 81 Kartsev, Eden 20 DM, AM C Maccabi Tel-Aviv 0.2 0.1 79 Rønningen Jørgensen, Filip 18 DM Odd 0.7 0.7 80 Arnstad, Kristian 16 M C Anderlecht 1.6 0.02 78 Neto 17 D/WB R, D AT 0.09 0.001 81 Campbell, Chem 17 AM C Wolves 0.6 0.03 81 Ugochukwu, Lesley 16 M C Rennes 0.5 0.02 91 Camavinga, Eduardo 17 DM Rennes 18.9 6.5 78 El Jebari, Salim 16 AM C A. Madrid 0.3 0.01 77 Bondo, Warren 16 M C Nancy 0.4 0.01 89 Pipi 16 AM C R. Madrid 1.2 0.03 78 Boudaoui, Hicham 20 M C OGC Nice 9.7 1.5 81 Goudmijn, Kenzo 18 DM, AM C AZ 0.8 0.1 80 Nicolussi Caviglia, Hans 20 AM RC Zebre 7.9 0.4 81 Essahel, Amine 17 AM C Academie Mohammed VI 0 0.06 81 Onana, Amadou 18 DM Hamburger SV 3.5 0.06 81 Bogusz, Mateusz 18 AM C Logrones 10 2.1 80 Gallagher, Conor 20 AM C Chelsea 15 7 81 Jenkins, Jack 18 DM Leeds 0.8 0.37 75 Kvaratskhelia, Khvicha 20 D R, DM Dinamo Tb. 1.4 0.33

FM21 Wonderkids Sol Ortasaha Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 91 Sancho, Jadon 20 AM RL Borussia Dortmund 105 33.1 89 Jones, Curtis 19 AM RLC Liverpool 20 7.3 79 Mudryk, Mykhaylo 19 AM RLC Shakhtar 1.8 0.3 75 Braaf, Jayden 17 AM RL Man City 7.5 5 81 Bynoe-Gittens, Jamie 15 AM RL Borussia Dortmund 0.06 0.01 86 Shoretire, Shola 16 AM RLC Man Utd 0.1 0.09 84 Laínez, Diego 20 AM RL Real Hispalis 7.3 0.9 76 Diallo, Amadou 17 AM RLC, F C West Ham 0.1 0.04 83 Zeballos, Exequiel 18 AM RL, ST Boca 1.3 0.4 83 Talles Magno 18 AM RLC, F C VDG 6.3 2.4 79 Sessegnon, Ryan 20 D/WB/AM L Tottenham 55 19.3 83 Paulinho 20 AM RLC Bayer 04 26.3 1.3 81 Cuéllar, Jaume 18 AM LC, F C SPAL 0.2 0.2 85 Martinelli, Gabriel 19 AM RLC, F C Arsenal 35 19.3 79 Okafor, Noah 20 AM RL FC RB Salzburg 4.9 2.3 84 Conceição, Francisco 17 AM RL FCP 1.6 0.03 78 Azarkan, Marouan 18 AM RL Feyenoord 1.2 0.3 78 Cangiano, Gianmarco 18 AM RL Bologna 0.2 0.2 79 Marin, Antonio 19 AM L Dinamo 4.3 0.4 75 Elanga, Anthony 18 AM RL Man Utd 1 0.2 86 Hauge, Jens Petter 20 AM RL Milan 26.7 4.4 83 Doku, Jérémy 18 AM RL, ST Rennes 13.7 1 77 Daal, Ro-Zangelo 16 AM RLC, F C AZ 0.09 0.07 78 Weah, Timothy 20 AM RLC, F C LOSC 14.7 2.6 79 Valera, Germán 18 AM L, ST A. Madrid 0.4 0.01 82 Kadioglu, Ferdi 20 AM RLC Fenerbahçe 10.3 3.5 79 Colley, Ebrima 20 AM LC, F C Atalanta 8.6 0.7 81 D'Amico, Felice 19 AM LC, F C Sampdoria 3.3 0.1 83 Poku, Ernest 16 AM RL, ST AZ 0.09 0.06 79 Mambimbi, Felix 19 AM RLC Young Boys 3.6 0.6 78 Vallejo, Hugo 20 AM RL, ST R. Madrid 1.6 0.06 82 Kvaratskhelia, Khvicha 19 AM RL Rubin 1.1 0.5 74 Jasper, Sylvester 18 AM RL Fulham 2.5 0.2 78 Urzi, Agustín 20 AM RL Banfield 3.2 1.8 83 Damsgaard, Mikkel 20 AM RLC, F C Sampdoria 3.2 1.7 83 Saka, Bukayo 18 WB L, AM RL Arsenal 30 25.9 84 Gouiri, Amine 20 AM L, ST OGC Nice 10.5 2.6 80 Da Cunha, Lucas 19 AM RL OGC Nice 1.7 0.1 76 Hlozek, Adam 18 AM RL, ST Sparta Prague 2.7 0.3 81 Pellegrini, Matías 20 AM L Inter Miami 11.6 2 83 Bryan Gil 19 AM RLC Sevilla 4.1 0.1 81 De la Fuente, Konrad 19 AM RL Barcelona 2.2 0.2 83 Antonisse, Jeremy 18 AM L, ST PSV 0.5 0.09 80 Servais, Mathis 15 AM RL Club Brugge 0.05 0.01 79 Sulemana, Kamaldeen 18 AM L FC Nordsjælland 1.1 0.1 89 Fati, Ansu 17 AM RL, ST Barcelona 10.6 21.2 77 Ceide, Emil 18 AM RL Rosenborg 1.6 0.6 83 Stevanovic, Filip 17 AM RLC Man City 10 2.2 79 Luís Henrique 18 AM RL, ST OM 14 0.7 80 Juwara, Musa 18 AM RL, ST Bologna 8.1 0.1 80 Marcos Paulo 19 AM LC FLU 2.7 1.2 83 De La Vega, Pedro 19 AM RL, ST Lanús 2.9 1.1 86 McNeil, Dwight 20 AM RL, ST Burnley 45 19.3 78 Rhuan 20 AM LC, F C BOT 0.8 0.09 79 Fernandes, Joelson 17 AM RL Sporting 5.2 0.2 80 Gabriel Veron 17 AM RL, ST SEP 1 0.1 85 Serrano, Nico 17 AM RL, ST A. Bilbao 1.4 0.06 78 Lewis-Potter, Keane 19 AM RLC, F C Hull 0.8 0.5 84 Aranda, Óscar 18 AM L, ST R. Madrid 0.5 0.01 86 Vinícius Júnior 20 AM L, ST R. Madrid 197 17.7 86 Cherki, Rayan 16 AM RLC, F C OL 8.7 0.2 79 van der Heide, Arjen 18 AM RL sc Heerenveen 0.7 0.1 79 Barrenetxea, Ander 18 AM RL Real San Sebastián 12.5 3.9 81 Arribas, Sergio 18 AM LC R. Madrid 1.6 0.06 86 Hudson-Odoi, Callum 19 AM RLC Chelsea 120 25.9 78 Araújo, Tiago 19 D/WB L, AM RL SLB 2.5 0.2 80 Baiaram, Stefan 17 AM RL U Craiova 1 0.08 72 Iling, Samuel 16 D/WB L, AM RL Zebre 0.2 0.03 74 Jakobs, Ismail 20 WB L/AM L 1. FC Koln 13.2 2.7 84 Velasco, Alan 18 AM RLC, F C Independiente 2.4 0.7

FM21 Wonderkids Sağ Ortasaha Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 85 Vignato, Emanuel 19 AM RC Bologna 9.7 0.7 84 Neto, Pedro 20 AM RL, ST Wolves 40 10.3 87 Trincão, Francisco 20 AM RL Barcelona 20.2 6.5 79 Álvarez, Julián 20 AM RLC, F C River 3.7 1.3 82 Marshall-Rutty, Jahkeele 16 AM RL Toronto FC 0.9 0.1 85 Sávio 16 AM RL, ST ATM 0.7 0.06 83 Pellistri, Facundo 18 AM R, ST Man Utd 25 5 77 Rodríguez, Benjamín 17 AM RL, ST Defensor Sporting 0.09 0.03 79 Lihadji, Isaac 18 AM RC LOSC 1.1 0.1 80 Plata, Gonzalo 19 AM RLC Sporting 7.7 1 81 Tete 20 AM R Shakhtar 6.9 2.4 77 Ndoye, Dan 19 AM RL, ST OGC Nice 4.2 0.5 80 Renyer 17 AM R SAN 0.04 0.05 78 Rodríguez, Alan 20 AM R Defensor Sporting 0.2 0.2 85 Torres, Ferran 20 AM RL Man City 60 30.1 81 Róber 19 AM RL Real Hispalis 3.2 0.1 81 Skov Olsen, Andreas 20 AM R Bologna 26 1.6 83 Akhomach, Ilias 16 AM RLC, F C Barcelona 0.7 0.06 84 Diallo, Amad 18 AM RL, ST Atalanta 4.6 0.2 80 Tramoni, Lisandru 17 AM RL Cagliari 0.7 0.05 82 Olivera, Cristian 18 AM R, ST Almería 2.7 0.2 77 Chong, Tahith 20 AM RL, ST Man Utd 48 7.9 78 Clarke, Jack 19 AM RL Tottenham 25 2.2 82 Oddei, Brian 17 AM R, ST Sassuolo 0.2 0.1 75 Canela 20 AM RL Nova Iguaçu 0.7 0.02 78 Madueke, Noni 18 AM RLC PSV 1.2 0.3 78 Gomes, Luís 16 AM RL Sporting 0.06 0.001 79 Hartmann, Fabrice 19 AM RL, ST RB Leipzig 1.3 0.05 81 Hasic, Ajdin 18 AM RC Besiktas 7.3 0.2 78 Rodríguez, Brian 20 AM RLC LAFC 15.5 3.6 80 Mbeumo, Bryan 20 AM RL, ST Brentford 20 5.7 89 Rodrygo 19 AM RL, ST R. Madrid 112 6 80 Tsitaishvili, Georgiy 19 AM RLC Dynamo Kyiv 2.6 0.4 79 Bajamich, Mateo 20 AM R, ST Houston 6.2 1.3 85 Elliott, Harvey 17 AM RL, ST Liverpool 12 5 84 Pino, Yéremi 17 AM RLC, F C Villarreal 3.2 0.1 76 Bamba, Mory 18 AM RL Roma 0.2 0.02 78 Llario, Carles 18 AM R, ST R. Madrid 0.3 0.009 83 Moro, Raúl 17 AM RC, F C Lazio 0.2 0.1 79 Matondo, Rabbi 19 AM RL Schalke 04 17.5 1.3 84 Nelson, Reiss 20 AM RL Arsenal 40 19.3 82 Artola, Juan 20 AM RL A. Bilbao 3.3 0.1 78 Gómez, Fran 18 AM RLC Córdoba 1.2 0.07 78 Fran Pérez 17 AM R Valencia 2.4 0.1 81 González, Hugo 17 AM RL Valencia 1.4 0.06 76 Persyn, Tibo 18 D/WB/AM R Inter 1.3 0.06 79 Correia, Félix 19 AM RLC Zebre 2.8 0.1 77 Blanco, Fabio 16 AM R Valencia 1.2 0.04 77 Ritchie-Hosler, Kane 17 AM R, ST Rangers 0.03 0.009 77 Aboukhlal, Zakaria 20 AM RL, ST AZ 2.3 1.2 77 Musaba, Anthony 19 AM RL AS Monaco 4.8 0.6 80 Tavares, Bruno 18 AM RL Sporting 2.7 0.07 80 Dembélé, Karamoko 17 AM R Celtic 0.8 1 79 Garré, Benjamín 20 AM RL Racing Club 2 0.7 78 Zito 18 AM RL Cagliari 7 0.6 78 Bobb, Oscar 17 AM RL Man City 0.1 0.04 77 Evjen, Håkon 20 AM RLC AZ 2 1.7 78 Oehlers, Aurelio 16 AM RL, ST FC Utrecht 0.09 0.04 75 Marvin 20 AM R R. Madrid 1.6 0.06 77 Dacal, Sergio 16 AM R S. Gijón 0.4 0.03 80 Efekele, Malamine 16 AM RL AS Monaco 0.06 0.03 87 Antony 20 AM RC, F C Ajax 13.7 6.8 78 Bayrakdar, Gokdeniz 18 AM RL Antalyaspor 1.5 1.1 79 Borges, Goncalo 19 AM RL FCP 1.6 0.13 77 Carvalho, Fábio 17 AM RC, F C Fulham 0.6 0.23 80 Gomes, Rodrigo 17 AM RL Braga 0.06 0.03 78 Gunther, Lasse 17 AM RL, ST FC Bayern 0.06 0.07 78 Harib Abdalla 17 AM R Shabab Al-Ahli 14.2 0.13 81 Kaminski, Jakub 18 WB R, AM RL Lech 0.4 0.09 81 Knight, Ben 18 AM RC Man City 5 0.1 89 Kulusevski, Dejan 20 AM RC Zebre 81.2 23.5 78 Sane, Sidi 17 AM RL Schalke 04 0.06 0.1 78 Sanyang, Mamin 17 D/WB/AM R FC Bayern 0.06 0.12

FM21 Wonderkids Ofansif Ortasaha Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 83 Roberto López 20 AM LC Real San Sebastián 8.3 1.1 79 Sousa, Afonso 20 AM LC Belenenses SAD 1.2 0.2 80 Taabouni, Mohamed 18 AM LC AZ 0.8 0.1 85 Demir, Yusuf 17 AM RC, F C SK Rapid Vienna 0.6 0.3 80 Hutchinson, Omari 16 AM RLC Arsenal 0.1 0.005 81 Bruno 17 AM C R. Madrid 0.5 0.01 80 Saranic, Ivan 17 AM RC Dinamo 0.1 0.01 81 Da Silva, Florent 17 AM C OL 0.5 0.05 80 Celi, Yuriel 18 AM LC Cantolao 0.9 0.05 81 Salas, Alejandro 17 AM C Valencia 1.4 0.05 81 Ihattaren, Mohamed 18 AM RLC PSV 4 3.5 90 Foden, Phil 20 AM RC Man City 30 34.5 77 Zarzana, Antonio 18 AM RLC Sevilla 0.4 0.01 77 Ait El Hadj, Anouar 18 AM RLC, F C Anderlecht 3.2 0.08 79 McAtee, James 17 AM/F C Man City 3 0.02 82 Lucas Lourenço 19 AM C SAN 2.8 0.6 78 Moyano, Antonio 20 AM C Córdoba 1.4 0.09 78 Palmer, Cole 18 AM LC Man City 5 0.3 75 Mbuku, Nathanaël 18 AM RC Reims 3 0.3 78 Sarmiento, Darío 17 AM LC Estudiantes (LP) 2.2 0.7 84 Gomes, Angel 19 AM RLC LOSC 20 7.9 80 Oristanio, Gaetano 17 AM C Inter 0.2 0.02 81 Rodríguez, Santiago 20 AM C Nacional 2.3 0.9 81 Zapelli, Bruno 18 AM C Belgrano 1.5 0.4 85 Baeza, Miguel 20 AM RLC Vigo 2.7 0.1 79 Hansen-Aarøen, Isak 15 AM C Man Utd 0.1 0.07 81 João Neto 17 AM C Famalicão 0.6 0.003 84 David, Jonathan 20 AM RLC, F C LOSC 31.9 7.9 78 Velebhayi, Asanele 17 AM C Cape Town Spurs 0.4 0.01 84 Palacios, Matías 18 AM C San Lorenzo 2.9 0.7 82 Samardzic, Lazar 18 AM LC RB Leipzig 3.6 0.4 78 Salcin, Djani 20 AM C Sarajevo 0.1 0.001 76 Vale, Harvey 16 AM RLC, F C Chelsea 0.1 0.02 79 Alesi, Gabriele 16 AM C Milan 0.2 0.003 77 Peart-Harris, Myles 17 AM C Chelsea 1 0.1 78 Maurício 19 AM C INT 0.4 0.3 80 Ruiz, Marcel 19 AM RC Tijuana 4.6 0.1 85 Díaz, Brahim 20 AM RLC R. Madrid 124 6.5 79 Palacios, Tiago 19 AM C Platense 0.8 0.3 86 Kubo, Takefusa 19 AM/F C R. Madrid 38.3 4.5 78 Abouchabaka, Elias 20 AM RLC Vit. Guimarães 2.7 0.1 75 Loader, Danny 19 AM RLC, F C FCP 18.2 0.2 77 Gharbi, Ismaël 16 AM C Paris SG 0.06 0.04 78 Miguel Silveira 17 AM RC FLU 0.6 0.05 76 Karabec, Adam 17 AM C Sparta Prague 0.2 0.03 79 Baena, Álex 19 AM RLC Villarreal 4.4 0.1 78 Larrubia, David 18 AM RLC Málaga 2.7 0.2 81 Félix, Hugo 16 AM C SLB 0.06 0.001 77 Guiagon, Parfait 19 AM RLC Maccabi Tel-Aviv 2.5 0.1 77 Goti, Mikel 18 AM LC A. Bilbao 1.2 0.04 79 Olise, Michael 18 AM LC Reading 2.5 2.9 84 Almada, Thiago 19 AM RLC, F C Vélez 4.4 2.2 81 Yankov, Dominik 20 AM C Ludogorets 1.4 0.09 90 Ünüvar, Naci 17 AM LC, F C Ajax 0.1 0.1 82 Morilla, Pelayo 19 AM LC S. Gijón 1.4 0.1 78 Mochrie, Chris 17 AM LC Dundee Utd 0.6 0.01 78 Wirtz, Florian 17 AM C Bayer 04 1.3 0.05 79 Holm, Odin Thiago 17 AM C Vålerenga 0.4 0.007 78 Melamed, Nico 19 AM/F C Espanyol 3.3 0.3 92 Pedri 17 AM RLC Barcelona 22.7 7.6 83 Nascimento, Diogo 17 AM C SLB 2.5 0.01 91 Félix, João 20 AM RLC, F C A. Madrid 164 37.4 79 Camará, Ronaldo 17 AM C SLB 2.7 0.1 79 Sousa, Bernardo 20 AM C Sporting 2 0.04 86 Reinier 18 AM C R. Madrid 9.2 1.1 78 Maldini, Daniel 18 AM RLC Milan 10.6 0.2 79 Kudus, Mohammed 19 AM C Ajax 5.1 4.6 78 Álex Cardero 17 AM C Oviedo 0.4 0.02 79 Navarro, Robert 18 AM RLC Real San Sebastián 1.4 0.06 79 Anjorin, Faustino 18 AM C Chelsea 12.5 0.5 80 Balagizi, James 16 AM/F C Liverpool 0.1 0.02 77 Aaronson, Brenden 19 AM C Philadelphia 1.4 1.3 81 Verschaeren, Yari 19 AM RLC Anderlecht 5.9 0.2 83 Tongya, Franco 18 AM RC Zebre 0.2 0.1 77 Rodri 20 AM C Real Hispalis 0.4 0.01 81 McNeill, Charlie 16 AM/F C Man Utd 0.1 0.02 80 Smallbone, Will 20 AM C Southampton 10 0.3 79 Smith Rowe, Emile 20 AM RLC Arsenal 10 12.9 85 Vieira, Fábio 20 AM LC FCP 4.6 1.6 79 Campaz, Jaminton 20 AM LC Deportes Tolima 4.7 0.5 78 Ouattara, Brahima 17 AM C RC Abidjan 0.1 0.1 83 Traoré, Hamed Junior 20 AM C Empoli 17.7 4.6 81 Nikolic, Veljko 20 AM C Red Star 0.8 0.1 80 Gelabert, César 19 AM RLC R. Madrid 3.3 0.1 76 Chakvetadze, Giorgi 20 AM LC AA Gent 11.4 0.8 78 Ivonei 18 AM C SAN 0.9 0.08 79 Baumgartner, Christoph 20 AM/F C Hoffenheim 7.7 0.8 81 Peglow 18 AM/F C INT 2.7 0.07 76 Ramsey, Aaron 17 AM C Aston Villa 0.1 0.02 80 Oroz, Aimar 18 AM LC Atlético Pamplona 0.3 0.01 82 Beyaz, Ömer 16 AM C Fenerbahçe 0.2 0.1 80 Millot, Enzo 18 AM RLC AS Monaco 6.6 0.1 78 Cubelic, Ivan 17 AM C Hajduk 0 0.007 78 Nygren, Benjamin 19 AM RLC, F C KRC Genk 6.8 0.5 83 Lee, Kang-In 19 AM RLC Valencia 18.9 6 80 Sforza, Juan 18 AM C Newell's 1.6 0.6 76 Brazão, Pedro 17 AM RC OGC Nice 0.5 0.02 78 Gboho, Yann 19 AM LC, F C Rennes 3.2 0.3 86 Reyna, Giovanni 17 AM LC Borussia Dortmund 15.3 1 81 Diop, Sofiane 20 AM RLC AS Monaco 6.1 1.6 77 Egloff, Lilian 17 AM RLC VfB Stuttgart 1 0.15 86 Ince, Burak 16 AM C Altinordu 0.1 0.34 82 Sieb, Armindo 17 AM/F C FC Bayern 0.06 0.13 77 Wening, Marcel 16 AM RLC FC Bayern 0.06 0.09

FM21 Wonderkids Forvet Oyuncuları

R Soyadı, Adı Yaş Pozisyon Kulüp Maaş (K) Piyasa Değeri (M) Uyruk 90 Greenwood, Mason 18 AM R, ST Man Utd 25 30.1 84 Boadu, Myron 19 AM RL, ST AZ 9.3 7.7 87 Esposito, Sebastiano 18 ST Inter 3.2 0.5 83 Marcos Leonardo 17 AM L, ST SAN 0.3 0.2 86 Evanilson 20 AM L, ST FCP 18.1 5.5 82 Resende, João 17 ST SLB 1.3 0.007 78 Hlongwane, Bongokuhle 20 ST Maritzburg Utd 0.9 0.03 83 Salcedo, Eddie 18 AM LC, F C Inter 5.6 1.2 79 Postema, Romano 18 ST FC Groningen 1 0.2 84 Kaio Jorge 18 ST SAN 2.5 1.2 80 Cassidy, Ryan 19 ST Watford 0.7 0.1 79 Gutiérrez, Juan Manuel 18 ST Almería 2.7 0.2 80 Escobar, Jordi 18 AM L, ST Almería 17.5 0.1 76 Reischl, Luka 16 ST FC RB Salzburg 0.06 0.001 85 Carbonell, Luis 17 ST Zaragoza 0.3 0.01 81 Joveljic, Dejan 20 ST Eintracht Frankfurt 17.5 0.9 78 Kean, Moise 20 AM RL, ST Everton 50 9.1 81 Williams, Nico 18 ST A. Bilbao 1.2 0.04 78 Burkardt, Jonathan 20 AM RL, ST Mainz 05 4.1 0.6 80 Gomes, Lucas 17 ST FCP 1.3 0.003 83 Arezo, Matías 17 ST River Plate de Montevideo 0.8 1 76 Rossi, Leonardo 16 ST Milan 0.2 0.002 77 Vinícius Popó 19 ST CEC 0.3 0.04 78 Ovando, Lautaro 17 ST Argentinos Jrs. 1.8 0.8 79 Barisic, Bartol 17 ST Dinamo 0.2 0.01 80 Barasi, Yusuf 17 AM L, ST AZ 0.4 0.07 82 Gelhardt, Joe 18 AM/F C Leeds 5 0.4 84 Lázaro 18 AM LC, F C FLA 1.6 0.03 82 Lochereau-Pho, Yad 16 ST OL 0.06 0.05 78 Mollejo, Víctor 19 AM RL, ST A. Madrid 5.9 0.2 80 Sala 17 ST R. Madrid 0.5 0.01 83 Delap, Liam 17 ST Man City 5 0.3 77 Morton, Callum 20 AM L, ST West Brom 2 0.5 85 Silva, Fábio 18 ST Wolves 35 2.2 84 Brenner 20 ST SPO 4.5 1.2 84 João Pedro 18 AM RL, ST Watford 20 6.2 78 Kavanagh, Calum 16 AM LC, F C Middlesbrough 0.1 0.006 81 Badji, Youssouph 18 ST Club Brugge 2.7 2.6 84 Macías, José Juan 20 AM L, ST Chivas 10.4 0.5 80 Arp, Fiete 20 ST FC Bayern 87.8 0.4 85 Vlahovic, Dušan 20 ST Fiorentina 26 4.8 81 Akman, Ali 18 ST Bursaspor 0.2 2.7 80 García, Andrés 17 ST Valencia 1.4 0.05 76 Yuri Alberto 19 AM R, ST INT 6.3 0.2 77 Soonsup-Bell, Jude 16 ST Chelsea 0.1 0.01 78 Niño, Fernando 19 ST Villarreal 4.9 0.1 81 Garcés, Borja 20 ST A. Madrid 3.6 0.1 78 Rutter, Georginio 18 ST Rennes 0.5 0.05 76 Ovelar, Fernando 16 ST Cerro Porteño 0.06 0.2 81 Campana, Leonardo 20 ST Wolves 15 0.5 80 von Moos, Julian 19 AM RL, ST Basel 1.2 0.2 77 Glatzel, Paul 19 AM RLC, F C Liverpool 3 0.05 78 Koïta, Sékou 20 ST FC RB Salzburg 4.3 0.4 79 Sikan, Danylo 19 AM R, ST Shakhtar 1.8 0.3 81 Camello, Sergio 19 ST A. Madrid 1.2 0.04 79 Millico, Vincenzo 19 AM LC, F C Torino 6.5 0.8 79 Ubani, Wisdom 17 ST Giant Brillars 0.09 0.04 84 Ramos, Gonçalo 19 AM/F C SLB 4.1 0.4 83 Pellegri, Pietro 19 ST AS Monaco 17.8 0.7 79 Scarlett, Dane 16 ST Tottenham 0.1 0.1 78 Daramy, Mohamed 18 AM L, ST FC København 2.4 0.1 79 Geubbels, Willem 18 AM RL, ST AS Monaco 23.1 0.2 77 Vassi 19 AM R, ST Arouca 1.3 0.05 84 Parrott, Troy 18 ST Tottenham 10 5 83 Raspadori, Giacomo 20 AM LC, F C Sassuolo 3.2 1.2 78 Flores, Marcelo 16 AM RLC, F C Arsenal 0.1 0.007 79 Campbell, Tyrese 20 AM R, ST Stoke 7 0.6 85 Gnonto, Wilfried 16 AM/F C Zürich 0.2 0.03 78 Stewart, Layton 17 ST Liverpool 0.7 0.01 78 Balogun, Folarin 19 AM R, ST Arsenal 2 0.3 83 Sargent, Josh 20 ST SV Werder 12.3 0.4 80 Ali, Mohanad 20 ST Al-Duhail 18.6 1.3 75 Obafemi, Michael 20 AM R, ST Southampton 10 2.2 78 Brobbey, Brian 18 AM L, ST Ajax 0.7 0.1 78 Emegha, Emanuel 17 ST Sparta 0.5 0.1 73 Niangbo, Anderson 20 AM RL, ST AA Gent 11.4 0.5 77 Satriano, Martín 19 ST Inter 0.7 0.03 81 Adeyemi, Karim 18 AM R, ST FC RB Salzburg 7 1 78 Hornby, Fraser 20 AM/F C Reims 7.3 0.1 80 Camara, Abdoul Karim 16 ST Hoffenheim 0.06 0.02 80 Colombo, Lorenzo 18 ST Milan 10.6 0.2 78 Wamangituka, Silas 20 ST VfB Stuttgart 10.5 2.9 82 Brewster, Rhian 20 ST Sheff Utd 42.5 8.5 87 Isak, Alexander 20 ST Real San Sebastián 15.9 5.5 77 Semenyo, Antoine 20 AM RL, ST Bristol City 2.5 0.2 81 Mulattieri, Samuele 19 AM/F C Inter 1.1 0.08 79 Tedic, Slobodan 20 ST Man City 15 0.4 80 Buitink, Thomas 20 AM RL, ST Vitesse 1.6 0.8 77 Traoré, Lassina 19 ST Ajax 4.1 0.6 77 John-Jules, Tyreece 19 AM RL, ST Arsenal 5 0.5 78 Kassem, Anis 15 ST Tishreen 0.09 0.001 77 Appiah, Arvin 19 AM RL, ST Almería 7.3 0.1 76 Junior Adamu 19 AM RLC, F C FC RB Salzburg 1.3 0.1 86 Zirkzee, Joshua 19 ST FC Bayern 14 1.1 90 Haaland, Erling 20 ST Borussia Dortmund 140 56.9 81 Hansen, Sontje 18 AM RLC, F C Ajax 3.6 0.1 82 Toure, Djibril 17 AM RLC, F C CEFOMIG 0 0.02 78 Breitenbucher, Nick 17 AM RL, ST Hoffenheim 0.03 0.13 82 Chermiti, Youssel 16 ST Sporting 0.06 0 81 el Moussaoui, Ilyas 16 ST Feyenoord 0.09 0.14 82 Lochereau-Pho, Yad 16 ST OL 0.06 0.12 86 Musiala, Jamal 17 AM RLC, F C FC Bayern 0.8 0.18 78 Odubeko, Ademipo 17 ST West Ham 4 0.06 77 Thielmann, Jan 17 AM RC, F C 1. FC Koln 2.6 2.5

FM21 Wonderkids listesinde fmscout sitesinden faydalandığımızı belirtmek isteriz. Football Manager 2021 Football Manager 2021 Wonderkids Listesi başlıklı yazımızdadır.

0 notes

binnedrubbish · 4 years

Text

5/12/19 Notes

Lab Meeting Prep Pipeline:

(May 2nd, 2019 at 2:38 p.m.)

[ ] Read the Results & Discussion cover to cover

[ ] Complete slides for all figures

[ ] Give a practice presentation

[ ] Read methods

[ ] Complete fluorescence slides

[ ] Decide how to deal with ‘relationship between calcium activity and movement’ section

[ ] Give a practice presentation

[ ] Read supplementary material cover to cover

[ ] Give a practice presentation

Note to self: Relax. Be meticulous. Be disciplined. Keep calm, do your best, trust your team.

——

Advanced Optimization

8 20 905

Live Action Poem, February 2nd, 6:41

Went to Brazil out of spite and saw

stone Jesus, arms open for a hug,

bought street weed, twice, from the same vendor

out of a reckless love for reckless love.

Hoped for a tropical muse and found

a strong handshake from a dangerous man.

Holed up in Rio de Janeiro with piles

of paper money and paced all alone

angry at nothing if only for the moment.

Rain dampened slick stone walkaways,

waiters were too nice and I tipped too much.

One offered to be a bodyguard , violence

hinted in every smirking human moment.

God, I loved being a target, smug,

dumb, flitting away American Dollars.

Jesus Christ looming in stone on a hill top.

Titties and marijuana, iconic primadonna

extravagant flora, dying fauna, fawning

over the climate. I went to Brazil

on an off month. To hole up

safe from my sprawling little lovely life.

To Do 26.1.19

[x] Cristina - Search for Hippocampus Models

[x] Ana G. - Draft e-mail call for interest in “Live Action Science”

[ ]

Data science Club Thursday at 5:00 p.m.

Astavakrasana

laser-scanning photostimulation (LSPS) by UV glutamate uncaging.

12.1.19 Goals

[x] Some Portuguese

[x] Mouse Academy - first read

[ / ] Dynamic mesolithic dopamine

[x] Water rats * SMH

Acorn - tracks impact | BetaWorks | 2 years of money | PitchBook | Social Impact Start Up

Mission Aligned Investors | Metrics | Costumer Acquistion Cost | Clint Corver -> Chain of Contacts -> Who To Talk to (Scope: ~100)

Money Committed || Sparrow || Decision Analysis —> Ulu Ventures [500k] [Budget x ]

Ivan - > IoS Engineering { Bulgarian DevShop }

[market mapping] Metrics -> Shrug

Peter Singer - Academic Advisory Board …

[1 million ]

Product market testing

Foundation Directory Online - Targeted , Do Your Homework

https://www.simonsfoundation.org/2018/11/19/why-neuroscience-needs-data-scientists/

Head-fixed —>

~INHIBITION EXPERIMENT TRAINING PLAN~

STOP MICE: 20th. GIVE WATER: 20th (afternoon) - 30th. DEPRIVE: 31st... (Morning) RESUME: Jan 2nd.

21st - BLEACH/DEEP CLEAN BOXES 1-14 (Diluted bleach; Flush (with needles out) - Open Arduino Sketch with Continuously open Valves - PERFUSE System) *[NOT BOX 11 or 5]*; Run 15 mL of Bleach per syringe; Copious water through valves; Leave dry.

———

http://www.jneurosci.org/content/preparing-manuscript#journalclub

Friday - Dec. 14th, 2018

[x] - Complete 2019 ‘Goals and Blueprint’

[x] - 2-minute Summary ‘Properties of Neuron in External Globus Pallidus Can Support Optimal Action Selection

[ ] MatLab for Neuroscientists :: Basic Bayesian Bearded Terrorist probability plots

[x] Statistics 101: Linear Regression

“Golden Girls” - Devendra Banhart

“King” by Moor - FIREBEAT

Reread - Section 3.3 to

Monday - Apply for DGAV License (MAKE SHORT CV)

SAMPLE: ‘Sal’ From Khan Academy

Make short CV

Tiago - Certificate

MATH:

“We explicitly focus on a gentle introduction here, as it serves our purposes. If you are in

need of a more rigorous or comprehensive treatment, we refer you to Mathematics for Neuroscientists by Gabbiani and Cox. If you want to see what math education could be like, centered on great explanations that build intuition, we recommend Math, Better Explained by Kalid Azad.”

Jacksonian March seizure (somatosensory)

—

Tara LeGates > D1/D2 Synapses

Scott Thompson

Fabrizio Gabbiani - Biophysics - Sophisticated and reasonable approach

Quote For Neuroscience Paper:

“Every moment happens twice: inside and outside, and they are two different histories.”

— Zadie Smith, White Teeth

Model Animal: Dragonfly? Cats. Alligators.

Ali Farke Toure

Entre as 9 hora e o meio-dia ele trabalha no computador.

Ele volta para o trabalha à uma e meia.

Ele vai as compras depois do trabalho.

A noite, depois do jantar, ele e a mulher veem televisão.

As oito vou de bicicleta para o trabalho. (go)

As oito venho de bicicleta para o trabalho. (come)

A que horas começa a trabalhar?

Eu começo a trabalhar os oito e meia.

Normalmente…

Eu caminho cerca de Lisbon.

É muito triste! Eu faço nada! Talvez, eu caminho cerca de Lisbon. Talvez eu leio um livro. Talvez eu dormi. Eu vai Lx Factory.

Depois de/do (after)

antes de/do (before)

—

Monday -> Mice

MATLAB!

“New ways of thinking about familiar problems.”

~*NOVEMBER GOALS*~

> Permanent MatLab Access [x] -> Tiago has license

> Order Mouse Lines [ ] -> Health report requested… Reach out to Vivarium about FoxP2

-> Mash1 line -> FoxP2 expression?

> Finish ‘First Read Through’ [ ]

> Figure 40 [ ]

SAMPLE : ‘Afraid of Us’ Jonwayne, Zeroh

Monday Nov 5th Goals:

> Attentively watch:

> https://www.youtube.com/watch?v=ba_l8IKoMvU (Distributed RL)

> https://www.youtube.com/watch?v=bsuvM1jO-4w (Distributed RL | The Algorithm)

MatLab License

Practical Sessions at the CCU for the Unknown between 19 - 22 Nov 2018 (provisional programme attached)

Week of November 5th - Handle Bruno’s Animals

Lab Goals -

“Deep Networks - Influence Politics Around the World”

Paton Lab Meeting Archives

Strategy: Read titles/abstracts follow gut on interesting and relevant papers

Goals: Get a general sense of the intellectual history of the lab, thought/project trajectories, researchers and work done in the field and neighboring fields.

Look through a GPe/Arkypallidal lens… what can be revisited with new understanding?

First Read Through

[x] 2011 - (22 meetings || 10/12 - SLAM camera tracking techniques)

[ x] 2012a (18 meetings)

[x] 2012b (15 meetings - sloppy summary sentences)

[ x] 2013a (19 meetings - less sloppy summaries jotted down)

[x] 2013b (17 meetings)

[x] 2014a (21 meetings) (summaries in progress)

[x] 2014b

[x] 2015 (23 meetings)

[ ] 2016 (23 meetings)

Current

—

“I like, I wish, I wonder”

“Only Yesterday” Pretty Lights

retrosplenial dysgranular cx (?)

retrosplenial granular cx, c (?)

fornix (?)

Stringer 2018 arVix

Lowe and Glimpsher

November Goals:

[ ] GPe literature -

[ x ] Dodson & Magill

[ x] Mastro & Gittis

[ ] Chu & Bevan

[x] Modeling (extra credit -Bogacz)

[ ] Principles of Neural Science: Part IV

[ x ] MatLab license… Website program…

Extra credit:

Side projects [/ ] Neuroanatomy 40

[ -> ] ExperiMentor - Riberio, Mainen scripts… Paton! -> LiveAction Science

MACHINE LEARNING

Week of Oct 29th -

Symposium Week!

Wyatt -> John Hopkins -> He got into American University!

Belly Full Beat (MadLib album Drive In)

“The human brain produces in 30 seconds as much data as the Hubble Space Telescope has produced in its lifetime.”

Sequence of voltage sensors -> ArcLite -> Quasar -> Asap -> Voltron -> ???

Muscarine -> Glutamate

Ph Sensitive

cAMP

Zinc sensitive

5 ways to calculate delta f

2 main ways

SNR Voltage —

Dimensionality reduction of a data set: When is it spiking?

5 to 10 2-photon microscope open crystal

…Open window to a million neuron…

Week of 10/15/18

Monday: Travel

Tuesday: Rest

Wednesday: Begin rat training. Reorient.

Thursday:

Friday:

|| Software synergistically ||

—————

Beam splitter, Lambda, diacritic

1.6021766208×10−19

‘sparse coding’

Benny Boy get your programming shit together.

Week of Oct. 8th, 2018

10/9/18

[ ] Rat shadowing (9:30 a.m.) -> Pushed to next week

10/8/18

[x] Begin Chapter 13 of Kandel, Schwartz, Jessell

[x] Outline of figure 36

[ ] Read Abdi & Mallet (2015)

DOPE BEAT MATERIAL - Etude 1 (Nico Muhly, Nadia Sirota)

Saturday - Chill [x]

Friday - ExperiMentor … mehhhhh scripts?

Photometry -> Photodiode collects light in form of voltage (GCaMP) (TtdTomate as Baseline… how much fluorescence is based on TdTomatoe, controlling factor always luminesce - GCaMP calcium dependent) :: Collecting from a ‘cone’ or geometric region in the brain. Data stored and plotted over time… Signals must be corrected…

Cell populations are firing or releasing calcium. (GCaMP encoded by virus injection, mice express CRE in a particular cell type).

———————————————

Brain on an Occam’s Razor,

bird on a wire,

synaptic fatalism integrating

consistent spiking;

strange looping: is this me?

Thursday

“We don’t make decisions, so much as our decisions make us.”

“Blind flies don’t like to fly”

[x] 9:00 a.m. Lab Meeting

[x] 12:00 p.m. - Colloquium

“It was demeaning, to borrow a line from the poet A. R. Ammons, to allow one’s Weltanschauung to be noticeably wobbled.”

“You must not fear, hold back, count or be a miser with your thoughts and feelings. It is also true that creation comes from an overflow, so you have to learn to intake, to imbibe, to nourish yourself and not be afraid of fullness. The fullness is like a tidal wave which then carries you, sweeps you into experience and into writing. Permit yourself to flow and overflow, allow for the rise in temperature, all the expansions and intensifications. Something is always born of excess: great art was born of great terrors, great loneliness, great inhibitions, instabilities, and it always balances them. If it seems to you that I move in a world of certitudes, you, par contre, must benefit from the great privilege of youth, which is that you move in a world of mysteries. But both must be ruled by faith.”

Anaïs Nin

[ ] MatLab trial expires in 1 day *

[ ] 3:00 p.m. pictures

“We do not yet know whether Arkys relay Stop decisions from elsewhere, or are actively involved in forming those decisions. This is in part because the input pathways to Arkys remain to be determined.”

These studies prompt an interesting reflection about the benefits and conflicts of labeling and classifying neurons at a relatively grainy level of understanding.

“The authors hypothesize that under normal conditions, hLTP serves an adaptive, homeostatic role to maintain a healthy balance between the hyperdirect and indirect pathway in the STN. However, after dopamine depletion, pathologically elevated cortical input to the STN triggers excessive induction of hLTP at GPe synapses, which becomes maladaptive to circuit function and contributes to or even exacerbates pathological oscillations.”

To Do Week of Oct. 1st - Focus: Big Picture Goals

[ x ] GPe Literature - Hernandez 2015 & Mallet 2016 (Focus on techniques and details)

[ ] MatLab! Lectures 6-7 (Get your hands dirty!)

[ x ] Kandel Chapters 12 - 13

Tuesday Surgery Induction 10:00 with Andreia

6:00 - 7:30

Portuguese

Digitally reconstructed Neurons: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106405/

To Do Week of, September 24th, 2018

To Do Week of Monday, September 17th, 2018

PRIORITY:

DATA ANALYSIS PROJECT ITI

———— PAUSE. ———————

Talks

[x ] Mainen Lab - Evidence or Value based encoding of World State/Probability - ‘Consecutive failures’ - easy/medium/hard estimate of where the reward will be.

Reading for the Week

[x] Chapter 9 - Propagating Signal | The Action Potential

[/ ] Ligaya et. al (2018) (CCU S.I.?)

[x] Katz & Castillo (1952) Experiment where they describe measurement techniques

[ ] Raiser Chapter 4 - Stimulus Outlasting Calcium Dynamics in Drosophila Kenyon Cells Encode Odor Identity

Video Lectures

[— ] Linear Algebra (Trudge steadily through)

[ — ] Khan Academy Logarithms (Trudge steadily through)

MatLab

[ ] Trudge steadily through www.mathworks.com/help/matlab/learn_matlab

*FIND PROBLEM SET/TEXT BOOK/WORK SHEETS*

Concepts to Grasp

[ / ] Master logarithms!

[ ] Review Kandel Et. Al Part II *Chapters 5-9*

Neuroanatomy

[ x ] Ink Figure 28

Project Planning? Too soon! Too soon! Read some literature on the subject.

17/9/18

1:00 p.m. Meet with Catarina to discuss “CCU Science Illustrated” (WIP) Project

2:30 p.m. Vivarium Induction

_______________________________________________________

| SPCAL Credentials |

| |

| login: |

| PW: |

-————————————————————————

——

NPR:: https://www.npr.org/sections/health-shots/2018/09/11/644992109/can-a-barn-owl-s-brain-explain-why-kids-with-adhd-can-t-stay-focused

9.13.18

[ x ] Pauses in cholinergic interneuron firing exert an inhibitory control on stratal output in vivo (Zucca et. al 2018)

[ x ] Chapter 8 - Local Signaling: Passive Properties of

-> Sub and supra threshold membrane potential (Conceptual)

Monday, Sept. 10th 2018

“Eat the Frog First”

[ N/A ] Review SPCAL Lessons 1-5 (In Library?) CRAM THURSDAY?

-> [/] wait for confirmation from Delores for theoretical test

-> (Out of Office reply from person in charge)

To Do:

[/] Comment Out %PRE_PROCESS_vBeta.m

[x] Change path name and run program in MatLab

[ ] Solve trial.blahblahblah error spkCount? labels?

[ ] Change Epochs and run?

[x] Chapter 7 - Membrane Potential :: Return to Pg. 136-137 Box 7-2 when sharp. ::

[x] Castillo and B. Katz (1954)

[x] 12:00 - Neural Circuits for Vision in Action CCU

[x] 2:30 - THESIS DEFENSE: Mechanisms of Visual Perceptions in the Mouse Visual Cortex

————

Extra-credit

[x] Ink Figure 24

[~ ] Finish “First & Last 2017” (100/127 = 78.74%)

——

Jax Laboratory Tools: https://www.jax.org/jax-mice-and-services/model-generation-services/crispr-cas9

Recommendation for Design and Analysis of In Vivo Electrophysiology Studies

http://www.jneurosci.org/content/38/26/5837

On the Horizon:

Schultz (1997) (Classic, classic, classic)

*[x] 9/7/18 - 6:00 p.m. Flip water for Bruno’s mice *

ITI Data Analysis -> Next step ->….

[ ] (find the sigmoid call) / Poke around preprocessing_beta

Reading

[x] Chapter 6 - Ion Channels

[ / ] Finish Krietzer 2016 —> [ ] write an experiment-by-experiment summary paper

Resource: https://www.youtube.com/watch?v=GPsCVKhNvlA Helpful explanation of ChR2-YFP, NpHR, and general ontogenetic principles.

[ / ] Reiser Chapter 3.3.38 - 3.4 (Need to finish 3.4.5, Look up Photoionization detectors, Coherence)

Neuroanatomy

[/] Finish Figure 24 (need to ink)

“Drawing Scientists “

[/] Storyboard for GCAMP6s targeted paper

-> Show Filipe for feedback ->

-> Ask Leopold permission ? Talk to Catarina

[ x] 16:9

[x] Write script and record [ 1:00 ]

Intellectual Roaming

[ / ] Return to Review of Reviews and Review Zoom-In | First & Last |

[/] Explore Digital Mouse Brain Atlas

9/6/18 - Thursday

To Do:

ITI Data Analysis :

[x] Draw data structure on mm paper -> Reach out for help understanding

[ / ] What fields did Asma call? What fields are necessary for a psychometric curve

Reading

[x] Kandel - Chapter 5 | Synthesis and Trafficking of Neuronal Proteins

[ / ] Reiser - Chapter 3 | A High-Bandwidth Dual-Channel Olfactory Stimulator for Studying Temporal Sensitivity of Olfactory Processing (Results complicated)

[/ ] Krietzer 2016 - Cell-Type-Specific Controls of Brainstem Locomotor Circuits by Basal Ganglia

Talks:

[x] 12:00 p.m. - Colloquium - Development of Drosophila Motor Circuit

Tutorials:

~ [x ] MatLab plotting psychometric curves

Neuroanatomy

[ x ] Outline brain for figure 24

———

MatLab

Laser stuff HZ noise, thresholds,

// PCA -> Co-variance ->

// Linear regression | Geometric intuition -> “What is known to the animal during inter-trial? What features can be described by animals history” ===> Construct a history space (axis represent different animals history ex. x-axis previous stimulus, reward, etc.?) Predictive (?)

Plot psychometric functions || PSTH (post stimulation of histogram ) of example neurons -> skills: bin spiking, plot rasters, smoothing (if necessary)

Data:: Access to Dropbox -> /data/TAFC/Combined02/ [3 animals :: Elife]

/data/TAFC/video

Tiago and Flipe know the video data

File Format -> Parser/Transformation (guideline) ||

> MatLab

Access to MatLab -> [/] 28 days!

How can I begin to analysis?

History dependent | Omitted

——

To Do Week of September 3rd

Monday

Administrative

[ x] Check-in with HR (Don’t bombard!): Badge. (Library access?)

[ ] Reach out to SEF?

[x] 2:00 p.m. Meet with Asma - discuss data analysis. Where is it? How do I access it (Tiago?) What has been done and why?

[x] 3:00 p.m. Lab Meeting “Maurico’s Data” - Pay special attention

[x] Finish first read through of Theoretical Laboratory Animal Science PDF Lectures

[ ] Rat Surgery Techniques…

Mouse neuroanatomy project

[/ ] Figure 24

[ ] Figure 28

Math

[x ] L.A. Lecture 2

[ x] L.A. Lecture 3

Read:

[ ] Georg Raiser’s Thesis (Page 22 of 213)

Find time to do at least an hour of quiet focused reading a day. (Place?).

Continue to explore whims, papers, databases, ideas, protocols, that seem interesting.

Develop ‘literature scour’ protocol - (Nature Neuroscience, Neuron, Journal of Neuroscience)

Dates to Remember: September 14th - Laboratory Animal Sciences Theoretical Test!

https://www.sciencedaily.com/releases/2018/08/180827180803.htm:Can these be used for techniques?

https://www.sciencedaily.com/releases/2018/08/180823141038.htm ‘Unexpected’ - Unexpected physical event and unexpected reward or lack of reward (neuronal modeling of external environment)

—

In my first ten minutes at work I’m exposed to a weeks (month/year/decade) worth of interesting information. Going from an intellectual tundra to an intellectual rain forest.

1460 proteins with increased expression in the brain: Human Protein Atlas https://www.proteinatlas.org

Non-profit plasmid repository: https://www.addgene.org

Protein database: https://www.rcsb.org/3d-view/3WLC/1

Started to think at the molecular level.

“MGSHHHHHHGMASMTGGQQMGRDLYDDDDKDLATMVDSSRRKWNKTGHAVRAIGRLSSLENVYIKADKQKNGIKANFKIR

HNIEDGGVQLAYHYQQNTPIGDGPVLLPDNHYLSVQSKLSKDPNEKRDHMVLLEFVTAAGITLGMDELYKGGTGGSMVSK

GEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDF

FKSAMPEGYIQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNLPDQLTEEQIAEFKEAFSL

FDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGDGTIDFPEFLTMMARKGSYRDTEEEIREAFGVFDKDGNG

YISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQVNYEEFVQMMTAK” - CCaMP6m amino acid code.

8/31/18 - (Friday) @12:00 in Meeting Room 25.08

GET USB ! !

[Lisboa Cultura na ru, Lisbon on the streets Com’Out Lisbon - Katie Gurrerirra ]

MatLab -> Chronux Neural Analysis

SEPTEMBER 14th!

Week of August 27th, 2018

“Conserved computational circuitry, perhaps taking different arguments on different locations of Basil Ganglia” - Tuesday

Andrew Barto: http://www-all.cs.umass.edu/~barto/

Basil Ganglia Labs

Okihide Hikosaka Lab: https://irp.nih.gov/pi/okihide-hikosaka

Wilbrecht Lab

Uchida N. (ubiquitous dopamine motivation and reward)

Peter J. Magill

Schultz (Pioneer in the field)

C. Savio Chan

Doya, K. (theory)

Calabresi, P. (muscarinic)

Ana Graybiel (McGovern)

James C. Houk (1994 - Book on Models of Computation in the basal Ganglia)

Evolutionary Conservation of Basil Ganglia type action-selection mechanisms:

https://www.sciencedirect.com/science/article/pii/S0960982211005288

Dopamine D1 - Retinal Signaling https://www.physiology.org/doi/full/10.1152/jn.00855.2017 [Note to self: Too Off Track]

[ ~ ] Flurorphore Library

Official Badge? [ ] Printer Access [ ]?

—

Online Course on Laboratory Animal Science

Monday : 11 [x] 12 [x]

Tuesday : 13 [x] 14 [x]

Wednesday: 15 [x] 16 [/]

Thursday: 17 [x] 18 [x]

Friday: 19 [x] 20 [/]

Lesson 11 - Behavior and Environment, animals must be housed in an environment enriched to maximize their welfare.

Lesson 12 - Rodent and Lagomorph Accommodation and Housing - A more comprehensive guide from the macro environment, facilities i.e. establishments, to the micro environments. Covers health and safety procedures for personnel as well as geometry of housing units (rounded edges to prevent water accumulation). Absolutely essential.

Lesson 13 - Collecting Samples and Administrating Procedures - covers the most common collection techniques and materials collected and stressed the importance of doing as little harm as possible to the animal.

Lesson 14 - Transporting the Animal : Shipper holds most of the responsibility. Major goals are making sure the journey is as stress free as possible, contingency plans are in place, and that all of the logistics have been carefully planned, communicated, and coordinated between various parties responsible in the shipping. Also, animals should be prepared mentally and physically for the journey and should have a period of post-transportation to adjust to the new surroundings and environment. A number of practical issues must be considered such as temperature, availability of food, and access to animals during the journey. Boxes should be properly labelled in whatever languages are necessary.

Lesson 15 - The purpose of feeding and nutrition is to meet the energy needs of the animals, which vary by species, physiological state of animal (growth, maintenance, gestation, and lactation). A number of category of diets exist as well as a variety of specific diets to best fits the needs of the experiment. This chapter covers particulars of nutrition requirements and stresses the importance of avoiding obesity and malnutrition.

Lesson 16 - Anatomy and Physiology of Teleosts (Skip for now: Focus on Rodents and Lagomorphs)

Lesson 17 - Anatomy and Physiology of Rodents and Lagomorphs - General characteristics of the anatomy and physiology of six species, 5 rodents and 1 lagomorph. Mice, rats, guinea pigs, gerbils, and hamsters. Rabbits. It covers particularities of each species and has a quiz asking specific facts, mostly centered on commonalities and distinguishing factors. Worth a close read.

Lesson 18 - Anaesthesia and Analgesia in Rodents and Lagomorphs . Pre anaesthesia techniques, drug combinations, and repeated warning of the importance of choosing the right drugs and technique for the species. Use of a chamber. Methods of anesthesia (IP, IV, Volatile). Endotracheal Intubation for rabbits; the proper use and administration of analgesics; monitoring during the operation (for example - the paw pain reflex disappears in medium to deep anesthesia

Lesson 19 - Animal Welfare and Signs of Disturbance - This chapter repeatedly stresses the importance of the relationship between the caretaker and the animal. It repeats the ideal social, environmental, and nutritional environments for rodents and rabbits and highlights peculiarities of each species. After reading this one should be better suited to detecting stress, disease, or other ailments in a laboratory animal.

Lesson 20 - Fish Psychology and Welfare (Skip for now: Focus on Rodents and Lagomorphs)

Lessons 5, 17, and 20 pertain to fish

TEST SEPTEMBER 14th

—

MIT Open Course Ware:

Linear Algebra

Lecture 2 [/ ] -> Elimination by Matrices, production of elementary matrices, basic computations, and a review of row and column approaches to systems of equations. Introduction to the basic application of the rule of association in linear algebra.

Lecture 3 [ ]

Mouse Neuroanatomy

Ink Figure 16 [x]

Figure 20 [x]

Figure 24 [ ]

Introduction to MatLab: https://www.youtube.com/watch?v=T_ekAD7U-wU [ ]

Math Big Picture: Review Single Variable Calculus! Find reasonable Statistics and Probability Course (Statistical Thinking and Data Analysis? Introduction to Probability and Statistics?) Mine as well review algebra well I’m at it eh.

Breathe in. Breathe out.

—

Data analysis :: Behavioral Analysis

—

Ana Margarida - Lecture 6 - Handling Mice techniques

EuroCircuit can make a piece. Commercial v. DYI version of products.

Dario is the soldering, hardware expert. I.E. skilled technician.

www.dgv.min-agricultura.pt; it is recommended that the entry on Animal Protection and the section on Animals used for experimental purposes be consulted first.

—

Sir Ronald Fisher, stated in 1938 in regards to this matter that “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of”.

——

Finally, it is time to publish and reveal the results. According to Santiago Ramón y Cajal, scientific writers should govern themselves by the following rules:

Make sure you have something to say; Find a suitable title and sequence to present your ideas; Say it; Stop once it is said.

8/21 Goals

Access ->

:: Champalimaud Private Internet [HR] Printer [HR]

:: Web of Science (?)

:: PubMed (Nature, Journals, etc.?)

———

PRIORITY: Online Course -> Animal Laboratory Sciences PDF’s

20 total -> 4 a day || I can finish by Friday

Monday : 1 [x] 2 [x]

Tuesday : 3 [x] 4 [x ]

Wednesday: 5 [x*] 6 [x]

Thursday: 7 [x* ] 8 [x]es

Friday: 9 [x ] 10 [x ]

Notes:

Lesson 1 - Philosophical and ethical background and the 3 R’s

Lesson 2 - Euthanasia. Recommended, adequate, unacceptable. Physical or chemical. Chemical - inhalable or injectable. Paton Lab uses CO2 and cervical dislocation.

Lecture 3 - Experimental Design. Return to as a starting point for basic design (randomized samples and blocks) Integrate with “Statistical Thinking and Data Analysis”

Lecture 4 - Legislation. Memorize specific laws and acts.

Lecture 5 is highly specific for the care and maintenance of Zebrafish

Lecture 6 - Handling of rodents and mice. A theoretical overview, this material is essentially kinesthetic.

Lecture 7 - Provides a technically detailed account of how genetic manipulations are done and propagated. Deserves a ‘printed’ review and vocabulary cross reference.

Lecture 8 - Health and Safety. Predominantly common sense.

Lecture 9 - Microbiology - contains an appendix with list of common infections that will be eventually be good to know.

Lesson 10 - Anaesthesia pre and post operation techniques, risks of infections etc.

// http://ec.europa.eu/environment/chemicals/lab_animals/member_states_stats_reports_en.htm

http://ec.europa.eu/environment/chemicals/lab_animals/news_en.htm -> General European News regarding

http://www.ahwla.org.uk/site/tutorials/RP/RP01-Title.html -> Recognizing pain in animals

Week of 8/20/18 To Do:

Tiago/Team -> Whats the most important priority?

Get Arduino Machine working again [?]

Jupiter/Python Notebook Up [ ]

Bruno MatLab Access [… ]

- Get documents to HR

- Animal Lab certified?

- Logistical/Certificate/Etc.

- Start discussing personal project:

> (Rat colony) Wet Lab

> (Machine Learning) Electric Lab

> Statistics project

- Reacquaint with Lab Technology/Protocols

- Review papers - Engage back with the science

Project Print: Screen shots

[ ] collect

“Do the job. Do it engaged. Engage -> Not just execute the best you can, understand the experiment.

Why? Alternative designs? Control experiments needed to interpret the data? Positive controls and negative controls? What do you need to do to get crisp. Totally engage.

How it fits into other experiments?

“Engage with the science as if it were your baby.”

Execute beautifully… Ask --- et. al. What does ideal execution look like

Extra time: allocate time. Technicians : Freedom to do other things, work with other things, other technical things, giving people independent project to carry out. Project --- has in mind? Design. Hands on education of how science works then reading. Spend time focused on a problem and in the ideal become the world’s foremost expert on whatever ‘mundane’ aspect of what ever problem you are working on.

Computational in the context of a problem. Learn to use. Defining “problems I want to solve.” As an operating scientist, the technology can change very quickly. Capable of learning, understanding, and applying.

Answer questions in a robust way. Thinking of technology in context of problem. Deep domain knowledge; focus on experimental more than book reading.

Realistic path -> Research fellow to PhD. program. Industry… Strong head’s up to do research. First-rate OHSU? Excellent. IF: Remember that it is narrow, broader with neuroscience as a component. Biology < > Neurology. Real neuroscience computational ->

Juxtasuposed: Engineering, CS, A.I., and all that…

Label in broad ways: Molecular, cellular, systems, cognitive, psychology. Borders are so fuzzy — as to be

Domain bias. In general -> other than P.I. protected from funding. Publication, the life of the business. Metric of success is the science they publish. Work that contributes to being an author = more engaged, more independent. Evolved to an independent project.

So incredibly broad -> CRISPR, GFP, Optogenetics, with higher level systems problems. 100 years = absurd. Look back -> Could we have conceived whats going on today.

Foremost expert on something how-ever limited. Grow from there. Grown from a particular expertise.

Molecular biologist || Do what a 3 year old is taught to do. How? How? How? How does that work. Quantum physics. Ask questions. Be open.

Go to seminars -> Go to every talk. Take every note. Primary literature fundamentally different. Always learn in context. Don’t dilute too much (ignore title, abstract, discussion). Look at figures and tables and derive for yourself what they say. Look for THE FIGURE or THE TABLE that is the crux and look for the control experiment. Understand the critical assessment, are the facts valid and warranted? Infinite amount to learn, don’t spread yourself infinitely thin. “

To Do: Develop Independent Machine Learning Project

Gain Access to Web of Science

————

Paton Learning Lab

Personal Learning Goals

September 1st - December 1st

Major Goals

[ ] Read Principles of Neuroscience 5th Edition

[ ] Complete CSS 229

[ ] Deep read 12 papers (Write summary || Practice peer review)

Administrative

[ ] Reactivate

[ / ] Figure out Residence Permit/Visa

Lifestyle

[ x ] Purchase commuter bicycle

[ / ] Purchase waterproof computer/messenger bag

Language

[x] …. Focused practice minimum 20 minutes daily …?

[ ] Find language partner

[ ] Portuguese film/television/music

UPCOMING

Phone conversation with --------

Tuesday, August 7th 9:00 a.m. EST (10:00 a.m.

0 notes