If the focus group has ended, why are you still listening? Of course, you need permission to "record" after the agreed upon session has been completed. If the story wasn't juicy, would you have had the same motivations? Such dilemmas are part of being to eager to extract data from your subjects, especially data that would prove compelling/sensational.

growing

I think, in some ways, we are fooling ourself to think that AI is the only evil and problematic entity. Many countries, including our own, have been founded and governed on racism and sexism. Many of my colleagues here highlighted a lot of the racist garbage that AI and Google searches show us. I think society should do better and really question where these LLM inputs are coming from; not only from WERID society but also WEIRD power structures. We the people need to be in charge of cultural production and not let AI decide how our culture looks.

One side of the problem is that knowledge of digitally underrepresented cultures will not be available. But, the other side of the problem is that there is already an existing digital divide that is prevalent. Smartphone access based on the economic conditions of the family is often limited to the men in the family. With the accelerated presence of AI everywhere in the world, how will this impact the existing digital divide?

Very important point! We are seeing a shift away from information to disinformation and lies being sold as "truths". These sites are full of conspiracy theories and anyone can write their opinions. A larger issue here, I believe, is also algorithms. These algorithms track what you read/view and soon that is all you see in online spaces. This creates huge divides because people get caught in bubbles where they do not see both sides of an issue or story: they become cocooned in their own version of the "truth".

Enough people need to stand up and deny capitalism for it to be unseated. I think this a great comparison for AI. At the end of the day, we are what give these companies and programs our business. Some may call it "voting with your wallet". When ABC cancelled Jimmy Kimmel's show, millions of people around the world cancelled their Disney + subscriptions in protest. It is an example of how an "agile and motivated force can topple an existing power structure": through the power of the purse.

I find this perspective of viewing the output of LLMs as a form of predictable mathematical steps refreshing because it demystifies the notion that these LLMs show any sign of consciousness. Viewed from this perspective LLMs are simply advanced search engines that respond with an answer the user wants to hear less than the truth the user is actually seeking. However, the notion that the responses these LLMs produce can fools many into perceiving intelligence says so much about that wealth of knowledge that exists within the relationship between words but also how desperately our minds seek patterns and work to anthropomorphize non-living entities simply because they can imitate our speech. Is an LLM that produces full form sentences thoughtlessly more intelligent than a crow that mimics human words for treats? I suppose in a way both entities have managed to interact with the human species to their benefit whether that be gaining information to sell to advertisers or a snack.

I find this classification interesting especially when later on it is identified how LLMs have been trained greatly on online content generated by WEIRD individuals. It is especially jarring to find out that advertisers know that a large portion of data being scraped to provide LLM users with reliable answers are websites like Reddit. Advertisers are now actively seeding these websites with promotional content to result in AI promoting their product to user query.

I only highlighted part of this quote (to avoid turning the whole article yellow), but this entire section gives me hope! As we discussed in class, AI is very likely here to stay. Zuckerman’s vision of a collaboration between existing LLMs and those that are smaller, built by language and cultural communities could potentially create a more diverse, equitable, inclusive and balanced language learning model used to train our AI systems. This brings me back to the Futures Cone shared in Dr. Stewart’s presentation on Friday, and has me considering what steps we can take towards a preferable AI-related future.

(https://medium.com/the-futureplex/the-futures-cone-and-its-role-in-speculative-design-b01355e296e7)

This gave me a laugh. For now, at least, the LLMs are attempting to disagree with information contrary to the texts they have been fed, and the responses that they generate from logic, without emotion. So this leads me back to my question: what is the end goal for humanity, based on what the owners envision as "utopia"?

Zuckerman tells us that Gramsci believes hegemony prevents people from using their imaginations to consider alternative futures. This makes me think of the potential impact of students using AI to do their thinking for them. While I haven’t had an opportunity to do a deep dive into the research, there has been ‘buzz’ in the media about impacts on memory, retention and critical thinking when students use AI in this way. A small study (not peer reviewed) out of MIT showed low cognitive activity when AI was used to complete an essay, compared with students who used a search engine or no tools at all. I’m not claiming AI use causes our neural pathways to disintegrate. Rather I’m imagining a potential parallel between Gramsci’s world, where hegemony caused workers to accept the capitalist ideals, and our own world where offloading cognitive tasks to AI will decrease society’s ability to imagine a better future - and subsequently take action to create it.

In reading the articles @bonstewart posted (see replies to @ShawnaP 's comments connected to this phrase: "here's both the original article (Bender, Gebru, McMillan-Major & Schmitchell, 2021) https://dl.acm.org/doi/10.1145/3442188.3445922 and a 2026 update post by the primary author https://medium.com/@emilymenonbender/stochastic-parrots-frequently-unasked-questions-49c2e7d22d11")

I especially loved the following quotation: "...the astonishing willingness of so many to surrender their own power and turn to synthetic text (for which no one is accountable) for all kinds of weighty decisions" (Bender, 2026).

It is despairing (I am making up my own words) that some people will surrender or choose to ignore the implications of relying on GenAi or LLMs, but I am heartened by the recent court decision to hold Air Canada liable for what their bot told a customer. I love that courts are beginning to hold companies' feet to the fire to make them responsible for the (dis/mis)information their property and creations are disseminating to customers. https://www.cbc.ca/news/canada/british-columbia/air-canada-chatbot-lawsuit-1.7116416

So, this is deliberate then. I wonder. As I reflect on the "selection process" for the Trans Atlantic slave trade, it was the children and the youth that were targeted then. One doesn't need to observe for too long that history is repeating itself here. In the face of systematic dismantling of EDI in the USA, is what were are looking at mass grooming/indoctrination of the young? The first time I saw this ad, my skin crawled, and my heart sank: https://www.instagram.com/reel/DC1KMLjIfhO/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

Building on my earlier points, we see this re-enforcement and regurgitation of world views, knowledge, values of the ruling class re-enforcing their positions of power. We see that even when every day people have managed to find somewhat of a balance with their AI usage/exposure, it is now being enforced by institutions (government, workplaces etc.). This questions how much agency citizens truly have in this AI world? How much control do we have - not only on our personal data but also in what we are exposed to in this constant influx of information and suggestions by AI? What is our ability to shift this i.e. make revolution with/in AI contingent upon?

This in my view describes GenAI very accurately. We are seeing cultural hegemony - previously determined by SEOs etc. - now being replaced by AI. However, this is a case where rather than a technology serving as a "great equalizer" or perhaps "brining the world closer together" its one where society is going down a slippery slope of deep fakes, mis/dis-information, plagiarism amongst the many plagues. Beyond that, society is still grappling with solutions to this. And with a technology that is moving at an extremely fast paced, it is not until there is a slow-down that society might have a chance of catching-up with potential antidotes.

While the idea of alternative LLMs is hopeful, it also brings to the surface some of the key issues that also arise when looking at knowledge on the internet being predominantly in English. LLMs can also be trained on existing knowledge, and hence there is a double tax to pay here. Whether its knowledge from other sources (ex. Global South) or in another languages (ex. Mandarin, Arabic Etc.) this means this knowledge needs to exist in the first instance for LLMs to be trained on it. And hence there will be an advantage to the model that is trained with the most amount of resources - resources that themselves possess a bias.

How does one engage in this "war of position" when the algorithm now has power over transforming culture, meanwhile it is the instiutions behind the algorithm that determine what is worthy/notable/important. As we look at the approach of Gramsci in the AI-era, does one now engage in war of maneuver and that of position by feeding the AI machine and figuring our the every-chaging algorithm. Or is there another way of engagement.

While the slow war of position towards a 'new culture' sounds exciting and hopeful. It raises questions about whose culture we replace it with. Fairness and justice become common sense to whom? Who will be seen, who will become invisible next? It makes me think about Freire's (2005) idea that often, the oppressed, in an attempt to liberate themselves, often become the oppressor themselves, because that is the 'model of humanity' that is part of their existence and, in some sense, internalized. In any revolution or transformation, how do we create knowledge systems that are not biased, especially in LLMs, which deal with such huge amounts of data, becomes an important question. An example that comes to my mind is from post-independence India, where scholars, in an attempt to claim our independence, sought to give primacy to 'Indian' knowledge systems and dismiss Western ideas. This was problematic to thinkers like Phule and Ambedkar, who questioned what they meant by 'Indian'. The primacy was being given to upper-class/caste Hindu knowledge systems, which were not representative of India then or today (Rege, 2010). This gave rise to the idea of Phule-Ambedkarite feminist pedagogies, which scholars like Rege( 2010) wrote about, who tried to look beyond the dichotomy of Western vs. Indian knowledge systems. In conclusion, I am still thinking about large language models and how large and inclusive they can really be.

This discussion about digitally underrepresented cultures and languages led me to reflect on how American Sign Language and other signed languages from around the world fit into this conversation. ASL is difficult to code into writing. It is visual-spatial and requires 3 dimensions for full expression. How then, can the collective knowledge, culture and stories of the Deaf community find representation in digital spaces and (hopefully) future LLMs?

If you're interested in learning more, here's an article by Laura Brown (author and member of the Deaf community), about the challenges of representing signed languages in writing, https://disabilityinkidlit.com/2017/05/19/asl-writing-a-visual-language/

I love this acronym WEIRD, and I applaud the researchers’ sense of humor in coming up with the name. I think the WEIRD-ness of this issue is further highlighted by Zuckerman’s use of the term ‘Global Majority’. I did a quick search, and the term Global Majority collectively represents people of Indigenous, African, Asian, Latin American and mixed-heritage backgrounds, representing about 85% of the global population (Wikipedia). When we consider that these LLMs are made up of online contributions by a Western, educated, industrialized, rich and democratic minority of somewhere around 15% (or less) of the population, and their outputs are sometimes considered absolute truth without critical analysis by our developing youth - the situation is very weird indeed.

This section of Zuckerman’s text had me reflecting on the students that I’ve taught. Many of these students use AI daily for school work and life advice. In his article, Mishra clearly outlines the challenges presented when students with a limited understanding of both AI use and subject knowledge engage with AI to complete academic work. I wonder about the potential impacts on student identity, sense of belonging or sense of self. Considering the pervasive biases and stereotypes embedded in these LLMs, what happens when a student with an exceptionality uses AI for personal information? What about intersecting identities - a woman with a different hearing level, for example. Could the biases embedded in these LLMs based on the texts that they draw from impact the identity of a young person looking to an AI chat bot for help or support?

Gramsci’s concept of hegemony includes schools, newspapers and other social structures working together to enforce capitalist ideals, creating a society where these ideals are considered to be ‘common sense’. This reminds me of the three logics in contemporary society, discussed by Dr. Stewart on Friday morning. Gramsci’s idea of cultural hegemony in today’s world feels closely linked to the overwhelming influence (power?) of the logic of business over both media and education. The logic of business in contemporary society feels culturally hegemonic, and the use of big AI in schools without sufficient guidance or policy could further cement the existing inequities in our society between these three logics.

I found this concept fascinating because this aligned perfectly with my research findings on Reddit and YouTube. While my research looked into inclusive queer spaces online; spaces of possibility for Transmasculine individuals, I could not help but notice that the vast majority of posters and commenters in these communities, were white, educated, Christian, physically abled, affluent and western. It was very disheartening to see that while these communities had great representation of mental health challenges and queerness and were supportive and creative, they were also very homogenous. There was almost no intersectionality of racial, indigenous, religious and dis/ability diversity within these spaces, which means that there was only mostly one kind of perspective present.

If you can believe it, I worked as an HR generalist and manager at a tech company before I came back to academia and schooling writ large, and one of the things that is taught in Human Resources education is the notion that the success of companies relies on cognitive diversity at work. Companies that have multiple perspectives and actually promote DEI ( not performatively ) perform better, because they have better problem solving capacity due to many different perspectives VS. just one, that of white men. Now, the measure of "performance" can be debated, but certainly the companies that adhere to cognitive diversify are much nicer to work for.

Doesn't this depend on who exactly is defining "fairness" and "justice" to begin with? Is it truly possible to be fair to all? If you respond to this comment, I am intrigued to learn how you personally define "justice". Perhaps our definitions may help uncover insights as to how hegemony has shaped us? Let's carry out this courageous experiment, and see where it takes us. So as to not introduce bias or undue influence at the outset, I will reserve my definition until the end of your responses :-)

If these AI driven algorithms are pushing these particular beliefs, I can imagine people having biases being reinforced and keeping this oppressive system in power. If we are continuously made to believe that this is "just the way things are" by those in control, how can we as a society feel capable of creating change?

As I have moved through the world the notion of invisible power has always resonated with me. Now as I sit in this Post-Truth Era with society's reliant on algorithm-driven information the hegemonic culture is a scary place for me to reside

I don't think it is just as simple as valuing data; it is who values it. If biases are so ingrained in LLMs to this degree (and potentially worse in the future [not very a very optimistic take, but thinking on least favourable outcomes]), who knows how it may radicalize this generation of children and youth growing up with this digital landscape? It is so easy to fall down rabbit holes of hateful extremism via algorithmic funnelling, that truly anyone could be victimized by, and if those who are creating these platforms harbour those beliefs (or make it easy enough for these dangerous ideologies to infiltrate them), I have to ask perhaps a conspiratorial question: what is the true motive behind all of this?

Reading texts such as this is a very real point of privilege for me. As someone who has grown up so far removed from the issues raised in this article, its genuinely startling. This brings the question of who get's to determine these rankings of language, who is placing the value of use? This reinforces Sam Altman's quote regarding the selling of knowledge. In this case, I feel as though the primary customer base would begin to become more and more exclusive as time went on. Again, we can grapple with the idea of the right to knowledge. Thinking even about the UN sustainability goals (4: quality education to be specific) set for 2030, is this not directly going against humanitarian rights?

(link for more info on SDGs if anyone is interested: https://globalgoals.org/goals/4-quality-education/)

• Sum up: Gramsci argues that ruling class power is not maintained primarily through force (police, military), but through cultural control. By shaping institutions such as schools, churches, and media, elites normalize their values until they become “common sense.” He defines this process as Cultural Hegemony.
• Personal Thoughts: Reflecting on the above, I come from a country that is extremely sectarian and where people of power have been holding on to their "chairs" for decades. Each sect has its own news channel, newspaper, schooling culture, history textbook... These sectarian elites, religious institutions, and media networks form a powerful historic bloc. As a nation, I do see us as broken; trying to dismantle it all and replace the sectarian culture with a secular one feels utopian.

perhaps worth noting i don't think we see the actual post-florence applications etc. because most of these hadn't closed by the time they gave us the data, which was in ~2020. So Florence did occur but I don't think we see post-Florence mitigations.

I think we can delete this?

unobserved, rather than unobservable

i think we can say for sure that lots of applications are solicited from lower-value, less-white communities because the application stage is where you see the really big median change. Applied -> funded still a decline in median, but not as big. So I'd characterize it more as a function of who is applying, rather than what's being chosen within the applicant pool

this one also could definitely be a NC-specific thing, so i think we should call that out

", especially given that we observe many lower-value properties applying"

i think these papers were saying it's whiter/wealthier counties, but then poorer/less white neighborhoods within them, right? so it's aligning with the neighborhood-scale evidence since we don't do an analogous county-level comparison

perhaps add something like ", indicating that the selection process is not necessarily driving inequitable funding outcomes" or something like that?

funding too, not just applicants

Maybe another sentence before this, along the line sof "Many of these properties have flooded multiple times, suggesting they would likely meet cost-effectiveness requirements for federal funding"

Cool to see this folded in! Pretty interesting.

Ah - this one has the axis adjustments I mentioned for the one above! would be good to get the font sizes closer to what the main text font looks like - right now it seems like it'll be tiny

consider font size adjustments here too... you coudl also just do one set of x-axis labels for the whole plot since those are duplicated, and one y-axis on the right.

It might be totally fine when on a normal paper layout, but it'd be nice to bump up font size for the stages on the left and the num of parcels on the bottom. You could do community applied on two lines

"concentrated in" might be a tad too strong ? more likely to be in?

"the median value for properties that apply for funding drops..." and "the median for those that are funded is..."

missing something about stages... "distribution of property values for parcels making it to each stage of the flooded-to-funded pipeline, expressed as percentile ranks relative to the entire area sample" ? and applies same way to panle B.

Rather than "are within lower-value neighborhoods", how about "average lower property values than the broader flood-exposed population, but are typical homes for their neighborhoods" ? (more emphasis on the address-level info than neighborhood-level)

I see the website only refers to buyout projects for this but i think it is true for all projects. Our main contact there is now gone but I know one other guy there, let me send him an email and see if he can confirm either way. i'll also use it to share the draft with him which is a good idea to do before it goes out

"community and household"

replace with "local government"

I am fairly sure this is always the case for HMGP but actually not sure if it is always true e.g. for FMA, PDM, BRIC vs. it sometimes being a lower federal share. Can you double check and make sure?

add "for HMGP-funded buyouts," to the beginning of this

Here I think we should define HMA and the programs within the HMA umbrella- https://www.fema.gov/grants/mitigation/learn

state, tribal, and local

this only applies to HMGP, so maybe just "after federal funding is released". Since even for the standing programs, they still need some trigger to be released

The application process for FEMA Hazard Mitigation Assistance funding presents different barriers to access at each stage. Diagram shows a simplified set of stages from flooded to funded (top), and the associated ways in which potentially inequitable outcomes could arise at each stage (bottom).

make this plural, "random forest models" since it's 78 different models

"relied on data on who receives..."

"may not be"

maybe "indicating that the application bottleneck applies even for properties for which mitigation would likely be cost-beneficial"?

perhaps "increasing application rates, and ideally funding rates, particularly among repetitively flooded households..."

maybe "expanding funding, coupled with efforts to increase application rates among repetitively flooded households, could yield significant long-term risk reduction benefits" ?

trying to get at: 1) don't think we want to imply that every flooded property should get federal funds to mitigate 2) also don't want to imply that we should just have lower funding rates 3) i think "there are opportunities to expand cost-effective mitigation investments" is a more productive framing than "how to expand access" though i agree that the subtle implication of the latter is that the selection process (e.g. cost-benefit ratio) is not actually a huge program for marginalized households, at least not in our data.

This is so important! I think that it ties into so many beliefs and values and ways of thinking about things that people need to evaluate. It is so clear in conversations whether someone thinks they're more valuable than you. However, I think it's actually pretty challenging to recognize when you're giving that impression!

This is so important for instructional coaches and it is one of the reasons that instructional coaches acting as non-evaluative equals are so important!

When leaders employ this type of strategy or belief about conversation, it's easy to take that example and use it on other people. For example, my challenging group of paraeducators have some problems with disrespect and the principal told us to be more direct and stop saying "please." I'm not sure how I feel about that. I look forward to learning more in these readings!

I think that the definition of successful here is up to interpretation. I have had plenty of meetings that the other person thought went well and I did not. It's entirely possible that the supervisor thought that what he was doing was perfectly fine.

I am guilty of judging myself on the intent that I had but then judging others on their impact. Taking responsibilities for our impact is vital. I like how they use the word luxury as well.

This is so powerful. This also ties into bias and how we view others. The more that we know about systemic social conditions and the more that we continue to evaluate ourselves for bias, the better educators/leaders/people we will be!

💻/thinkpad/🧊/me/📓/2026/7/0/4

safe.to.end~antimalware.system.executable~search%40google

Колл-центр с работниками, которые кратно завышают услуги, называя их после работы.

Колл-центр с работниками, которые кратно завышают услуги, называя их после работы.

Колл-центр с работниками, которые кратно завышают услуги, называя их после работы.

Колл-центр с работниками, которые кратно завышают услуги, называя их после работы.

Колл-центр с работниками, которые кратно завышают услуги, называя их после работы.

Es un consejo terrible

Very precocious contributor from Guangdong Country Garden School, who completely coincidentally has the same family name as the very wealthy in-laws of the journals Editor in Chief

.

Luis Villa also made the same connection in his 2016 LibrePlanet presentation:

https://lu.is/2016/03/free-as-in-my-libreplanet-2016-talk/

from: newsletter.squishy.computer/p/all-you-need-is-links

en.wikipedia.org/wiki/Webgraph

to: en.wikipedia.org/wiki/Webgraph

from: https://hyp.is/iiAY4Hc9EfGA4CM29LKsIw/en.wikipedia.org/wiki/PageRank

この文意味がちょっとわからないです。なにのカスタムスクリプトのサンプル?Git全般?prekのはなし?

Z-alpha domains and monoclonal Z22 induce B-to-Z transitions under physiological conditions. Hence it is doubtful that all Z-DNA segments pre-exists in IF. A different antibody ZNA, 1:1000, Novus Biologicals, NB100-749, was used that does not even bind Z-DNA.

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DOI: 10.1016/j.xcrm.2026.102909

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DOI: 10.1016/j.xcrm.2026.102909

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DOI: 10.1016/j.xcrm.2026.102909

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DOI: 10.1016/j.omton.2026.201288

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DOI: 10.1016/j.omton.2026.201288

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DOI: 10.1016/j.omton.2026.201288

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DOI: 10.1016/j.omton.2026.201288

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DOI: 10.1016/j.omton.2026.201288

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DOI: 10.1016/j.omton.2026.201288

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DOI: 10.1016/j.omton.2026.201288

Resource: (KCLB Cat# 80008, RRID:CVCL_0159)

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DOI: 10.1016/j.omton.2026.201288

Resource: RRID:MGI:2164189

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DOI: 10.1016/j.isci.2026.116645

Resource: RRID:SCR_027373

Curator: @nmaralla

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DOI: 10.1016/j.isci.2026.116645

Resource: bcl2fastq (RRID:SCR_015058)

Curator: @nmaralla

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DOI: 10.1016/j.isci.2026.116615

Resource: RRID:Addgene_58745

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DOI: 10.1016/j.isci.2026.116611

Resource: (BCRC Cat# 60092, RRID:CVCL_3009)

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DOI: 10.1016/j.isci.2026.116450

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DOI: 10.1016/j.isci.2026.116450

Resource: (ATCC Cat# CRL-4000, RRID:CVCL_4388)

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DOI: 10.1016/j.ejphar.2026.179099

Resource: PubChem (RRID:SCR_004284)

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DOI: 10.1016/j.ejphar.2026.179099

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DOI: 10.1016/j.cpblue.2026.100055

Resource: ImageJ (RRID:SCR_003070)

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DOI: 10.1007/s13246-026-01739-x

Resource: Parkinson's Progression Markers Initiative (RRID:SCR_006431)

Curator: @nmaralla

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DOI: 10.1186/s40478-026-02240-y

Resource: (Sigma-Aldrich Cat# G3893, RRID:AB_477010)

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What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (EnCor Biotechnology Cat# MCA-5C10, RRID:AB_2572311)

Curator: @scibot

SciCrunch record: RRID:AB_2572311

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: RRID:AB_3678889

Curator: @scibot

SciCrunch record: RRID:AB_3678889

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (EnCor Biotechnology Cat# CPCA-GFAP, RRID:AB_2109953)

Curator: @scibot

SciCrunch record: RRID:AB_2109953

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (Enzo Life Sciences Cat# BML-FG6090-0100, RRID:AB_2050678)

Curator: @scibot

SciCrunch record: RRID:AB_2050678

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: RRID:AB_3678888

Curator: @scibot

SciCrunch record: RRID:AB_3678888

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: RRID:AB_3711244

Curator: @scibot

SciCrunch record: RRID:AB_3711244

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (Agilent Cat# Z0334, RRID:AB_10013382)

Curator: @scibot

SciCrunch record: RRID:AB_10013382

What is this?

DOI: 10.1016/j.ynirp.2026.100373

Resource: Group ICA of fMRI Toolbox (RRID:SCR_001953)

Curator: @scibot

SciCrunch record: RRID:SCR_001953

What is this?

DOI: 10.1016/j.ynirp.2026.100373

Resource: ANTS - Advanced Normalization ToolS (RRID:SCR_004757)

Curator: @scibot

SciCrunch record: RRID:SCR_004757

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Thermo Fisher Scientific Cat# A-11008, RRID:AB_143165)

Curator: @scibot

SciCrunch record: RRID:AB_143165

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Molecular Probes Cat# A-21422, RRID:AB_141822)

Curator: @scibot

SciCrunch record: RRID:AB_141822

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: RRID:AB_2819850

Curator: @scibot

SciCrunch record: RRID:AB_2819850

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (BioLegend Cat# 138005, RRID:AB_10568319)

Curator: @scibot

SciCrunch record: RRID:AB_10568319

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (RRID:CVCL_E5ZU)

Curator: @scibot

SciCrunch record: RRID:CVCL_E5ZU

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Santa Cruz Biotechnology Cat# sc-376764, RRID:AB_2801330)

Curator: @scibot

SciCrunch record: RRID:AB_2801330

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Thermo Fisher Scientific Cat# A-11081, RRID:AB_2534125)

Curator: @scibot

SciCrunch record: RRID:AB_2534125

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: RRID:AB_11098420

Curator: @scibot

SciCrunch record: RRID:AB_11098420

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Thermo Fisher Scientific Cat# A-21235, RRID:AB_2535804)

Curator: @scibot

SciCrunch record: RRID:AB_2535804

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Abcam Cat# ab10135, RRID:AB_2255631)

Curator: @scibot

SciCrunch record: RRID:AB_2255631

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Proteintech Cat# 67735-1-Ig, RRID:AB_2918504)

Curator: @scibot

SciCrunch record: RRID:AB_2918504

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Proteintech Cat# 16488-1-AP, RRID:AB_2215975)

Curator: @scibot

SciCrunch record: RRID:AB_2215975

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: RRID:AB_2918752

Curator: @scibot

SciCrunch record: RRID:AB_2918752

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Proteintech Cat# 10785-1-AP, RRID:AB_2281164)

Curator: @scibot

SciCrunch record: RRID:AB_2281164

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: RRID:AB_3738546

Curator: @scibot

SciCrunch record: RRID:AB_3738546

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Proteintech Cat# 66009-1-Ig, RRID:AB_2687938)

Curator: @scibot

SciCrunch record: RRID:AB_2687938

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Proteintech Cat# 66005-1-Ig, RRID:AB_11232599)

Curator: @scibot

SciCrunch record: RRID:AB_11232599

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Proteintech Cat# 60004-1-Ig, RRID:AB_2107436)

Curator: @scibot

SciCrunch record: RRID:AB_2107436

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: RRID:AB_3738547

Curator: @scibot

SciCrunch record: RRID:AB_3738547

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Abcam Cat# ab46794, RRID:AB_2291941)

Curator: @scibot

SciCrunch record: RRID:AB_2291941

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Abcam Cat# ab5694, RRID:AB_2223021)

Curator: @scibot

SciCrunch record: RRID:AB_2223021

What is this?

DOI: 10.1016/j.xcrm.2026.102908

Resource: (Abcam Cat# ab283654, RRID:AB_2922954)

Curator: @scibot

SciCrunch record: RRID:AB_2922954

What is this?

DOI: 10.1016/j.str.2026.06.002

Resource: RRID:Addgene_127764

Curator: @scibot

SciCrunch record: RRID:Addgene_127764

What is this?

DOI: 10.1016/j.str.2026.06.002

Resource: RRID:Addgene_128572

Curator: @scibot

SciCrunch record: RRID:Addgene_128572

What is this?

DOI: 10.1016/j.str.2026.06.002

Resource: Montana State University Research Cyberinfrastructure Core Facility (RRID:SCR_026229)

Curator: @scibot

SciCrunch record: RRID:SCR_026229

What is this?

DOI: 10.1016/j.str.2026.06.002

Resource: Montana State University Cryo-EM Core Facility (RRID:SCR_026324)

Curator: @scibot

SciCrunch record: RRID:SCR_026324

What is this?

DOI: 10.1016/j.str.2026.06.002

Resource: RRID:Addgene_115270

Curator: @scibot

SciCrunch record: RRID:Addgene_115270

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:IMSR_TAC:13073

Curator: @scibot

SciCrunch record: RRID:IMSR_TAC:13073

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Nanoprobes Cat# 2003, RRID:AB_2687591)

Curator: @scibot

SciCrunch record: RRID:AB_2687591

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:Addgene_183775

Curator: @scibot

SciCrunch record: RRID:Addgene_183775

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Thermo Fisher Scientific Cat# A-21208, RRID:AB_2535794)

Curator: @scibot

SciCrunch record: RRID:AB_2535794

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:Addgene_183749

Curator: @scibot

SciCrunch record: RRID:Addgene_183749

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Jackson ImmunoResearch Labs Cat# 712-605-150, RRID:AB_2340693)

Curator: @scibot

SciCrunch record: RRID:AB_2340693

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Jackson ImmunoResearch Labs Cat# 715-605-150, RRID:AB_2340862)

Curator: @scibot

SciCrunch record: RRID:AB_2340862

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Jackson ImmunoResearch Labs Cat# 711-605-152, RRID:AB_2492288)

Curator: @scibot

SciCrunch record: RRID:AB_2492288

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Abcam Cat# ab15200, RRID:AB_301744)

Curator: @scibot

SciCrunch record: RRID:AB_301744

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Jackson ImmunoResearch Labs Cat# 127-165-099, RRID:AB_2338988)

Curator: @scibot

SciCrunch record: RRID:AB_2338988

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Abcam Cat# ab97135, RRID:AB_10680105)

Curator: @scibot

SciCrunch record: RRID:AB_10680105

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Thermo Fisher Scientific Cat# 14-1402-82, RRID:AB_467493)

Curator: @scibot

SciCrunch record: RRID:AB_467493

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Sigma-Aldrich Cat# L9393, RRID:AB_477163)

Curator: @scibot

SciCrunch record: RRID:AB_477163

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)

Curator: @scibot

SciCrunch record: RRID:AB_330924

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Abcam Cat# ab6586, RRID:AB_305584)

Curator: @scibot

SciCrunch record: RRID:AB_305584

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 59678, RRID:AB_2799571)

Curator: @scibot

SciCrunch record: RRID:AB_2799571

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 8685, RRID:AB_10889933)

Curator: @scibot

SciCrunch record: RRID:AB_10889933

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 12389, RRID:AB_2631098)

Curator: @scibot

SciCrunch record: RRID:AB_2631098

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 3108, RRID:AB_490941)

Curator: @scibot

SciCrunch record: RRID:AB_490941

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Jackson ImmunoResearch Labs Cat# 715-545-150, RRID:AB_2340846)

Curator: @scibot

SciCrunch record: RRID:AB_2340846

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Jackson ImmunoResearch Labs Cat# 711-545-152, RRID:AB_2313584)

Curator: @scibot

SciCrunch record: RRID:AB_2313584

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Proteintech Cat# 21898-1-AP, RRID:AB_2811115)

Curator: @scibot

SciCrunch record: RRID:AB_2811115

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Abcam Cat# ab106582, RRID:AB_10888110)

Curator: @scibot

SciCrunch record: RRID:AB_10888110

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:AB_10901060

Curator: @scibot

SciCrunch record: RRID:AB_10901060

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 3267, RRID:AB_2275453)

Curator: @scibot

SciCrunch record: RRID:AB_2275453

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Proteintech Cat# 21773-1-AP, RRID:AB_10733242)

Curator: @scibot

SciCrunch record: RRID:AB_10733242

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Cell Signaling Technology Cat# 13663, RRID:AB_2798287)

Curator: @scibot

SciCrunch record: RRID:AB_2798287

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Thermo Fisher Scientific Cat# 40-4700, RRID:AB_2533468)

Curator: @scibot

SciCrunch record: RRID:AB_2533468

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: (Thermo Fisher Scientific Cat# 34-1600, RRID:AB_2533157)

Curator: @scibot

SciCrunch record: RRID:AB_2533157

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:AB_2481170

Curator: @scibot

SciCrunch record: RRID:AB_2481170

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:AB_3668871

Curator: @scibot

SciCrunch record: RRID:AB_3668871

What is this?

DOI: 10.1016/j.neuron.2026.06.003

Resource: RRID:AB_2201915

Curator: @scibot

SciCrunch record: RRID:AB_2201915

What is this?

DOI: 10.1016/j.neuron.2026.06.002

Resource: (Abcam Cat# ab92434, RRID:AB_2138147)

Curator: @scibot

SciCrunch record: RRID:AB_2138147

What is this?

DOI: 10.1016/j.neuron.2026.06.002

Resource: (Abcam Cat# ab18207, RRID:AB_444319)

Curator: @scibot

SciCrunch record: RRID:AB_444319

What is this?

DOI: 10.1016/j.neuron.2026.06.002

Resource: (Thermo Fisher Scientific Cat# A78952, RRID:AB_2921074)

Curator: @scibot

SciCrunch record: RRID:AB_2921074

What is this?

DOI: 10.1016/j.neuron.2026.06.002

Resource: (Thermo Fisher Scientific Cat# A-21429, RRID:AB_2535850)

Curator: @scibot

SciCrunch record: RRID:AB_2535850

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: RRID:AB_3074967

Curator: @scibot

SciCrunch record: RRID:AB_3074967

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Cell Signaling Technology Cat# 3396, RRID:AB_1904164)

Curator: @scibot

SciCrunch record: RRID:AB_1904164

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Cell Signaling Technology Cat# 66562, RRID:AB_2904193)

Curator: @scibot

SciCrunch record: RRID:AB_2904193

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Abcam Cat# ab52635, RRID:AB_869890)

Curator: @scibot

SciCrunch record: RRID:AB_869890

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Molecular Probes Cat# A-21206, RRID:AB_2535792)

Curator: @scibot

SciCrunch record: RRID:AB_2535792

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Thermo Fisher Scientific Cat# A10037, RRID:AB_11180865)

Curator: @scibot

SciCrunch record: RRID:AB_11180865

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Cell Signaling Technology Cat# 5741, RRID:AB_10695459)

Curator: @scibot

SciCrunch record: RRID:AB_10695459

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: RRID:AB_3697948

Curator: @scibot

SciCrunch record: RRID:AB_3697948

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Ventana Medical Systems Cat# 790-4423, RRID:AB_2335982)

Curator: @scibot

SciCrunch record: RRID:AB_2335982

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: (Roche Cat# 790-4950, RRID:AB_2935820)

Curator: @scibot

SciCrunch record: RRID:AB_2935820

What is this?

DOI: 10.1016/j.medj.2026.101214

Resource: RRID:AB_3076192

Curator: @scibot

SciCrunch record: RRID:AB_3076192

What is this?

DOI: 10.1016/j.medj.2026.101193

Resource: (Beyotime Cat# A0216, RRID:AB_2860575)

Curator: @scibot

SciCrunch record: RRID:AB_2860575

What is this?

DOI: 10.1016/j.medj.2026.101193

Resource: RRID:AB_2861926

Curator: @scibot

SciCrunch record: RRID:AB_2861926

What is this?

DOI: 10.1016/j.medj.2026.101193

Resource: (Beyotime Cat# A0473, RRID:AB_2891322)

Curator: @scibot

SciCrunch record: RRID:AB_2891322

What is this?