DOI: 10.1002/cne.70194

Resource: (Vector Laboratories Cat# BA-2000, RRID:AB_2313581)

Curator: @scibot

SciCrunch record: RRID:AB_2313581

What is this?

DOI: 10.1002/cne.70194

Resource: (Sigma-Aldrich Cat# C9848, RRID:AB_476894)

Curator: @scibot

SciCrunch record: RRID:AB_476894

What is this?

DOI: 10.1002/cne.70194

Resource: (Millipore Cat# MAB377, RRID:AB_2298772)

Curator: @scibot

SciCrunch record: RRID:AB_2298772

What is this?

DOI: 10.1002/cne.70194

Resource: (BioLegend Cat# 801701, RRID:AB_2564642)

Curator: @scibot

SciCrunch record: RRID:AB_2564642

What is this?

DOI: 10.1002/ccs3.70104

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1002/cbdv.71589

Resource: (RRID:CVCL_1511)

Curator: @scibot

SciCrunch record: RRID:CVCL_1511

What is this?

DOI: 10.1002/cbdv.71589

Resource: (BCRJ Cat# 0395, RRID:CVCL_0168)

Curator: @scibot

SciCrunch record: RRID:CVCL_0168

What is this?

DOI: 10.1002/cbdv.71575

Resource: (IMSR Cat# JAX_000651,RRID:IMSR_JAX:000651)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:000651

What is this?

DOI: 10.1002/cbdv.71570

Resource: (RRID:CVCL_X594)

Curator: @scibot

SciCrunch record: RRID:CVCL_X594

What is this?

DOI: 10.1002/cbdv.71561

Resource: (BCRJ Cat# 0223, RRID:CVCL_0019)

Curator: @scibot

SciCrunch record: RRID:CVCL_0019

What is this?

DOI: 10.1002/cbdv.71542

Resource: (RRID:CVCL_6926)

Curator: @scibot

SciCrunch record: RRID:CVCL_6926

What is this?

DOI: 10.1002/cbdv.71542

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1002/advs.202515210

Resource: RRID:Addgene_12259

Curator: @scibot

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1002/advs.202515210

Resource: RRID:Addgene_27300

Curator: @scibot

SciCrunch record: RRID:Addgene_27300

What is this?

DOI: 10.1002/advs.202515210

Resource: RRID:Addgene_62988

Curator: @scibot

SciCrunch record: RRID:Addgene_62988

What is this?

DOI: 10.1002/adfm.77616

Resource: (Thermo Fisher Scientific Cat# 12-3697-82, RRID:AB_2734839)

Curator: @scibot

SciCrunch record: RRID:AB_2734839

What is this?

DOI: 10.1002/adfm.77616

Resource: (Thermo Fisher Scientific Cat# 12-2061-82, RRID:AB_2637422)

Curator: @scibot

SciCrunch record: RRID:AB_2637422

What is this?

DOI: 10.1002/adfm.77616

Resource: Colorado University at Boulder Shared Instrumentation in Nanofabrication and Characterization Core Facility (RRID:SCR_018985)

Curator: @scibot

SciCrunch record: RRID:SCR_018985

What is this?

DOI: 10.1002/adfm.77616

Resource: Colorado University Boulder BioFrontiers Advanced Light Microscopy Core Facility (RRID:SCR_018302)

Curator: @scibot

SciCrunch record: RRID:SCR_018302

What is this?

DOI: 10.1002/adfm.77616

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1002/adfm.77616

Resource: RRID:IMSR_JAX:000664

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1016/j.isci.2026.117088

Resource: RRID:IMSR_JAX:016963

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:016963

What is this?

These inboxes can be hearder to inbox, and are more"emotional". can trigger issues from even one complaint or small negative thing. So if not needed or want to ignore small inboxes, =they can chyeck and drop these emals or monitor them.?

L8 Principal's Agentic Engineering Workflow — Detailed Summary

• Core Mindset & Shift to "Captain / Engineering Director"

  • Role Transition: Stop acting like a line-by-line developer manually reviewing code diffs, which creates a personal velocity bottleneck. Instead, operate as an Engineering Director/Captain—setting direction, maintaining quality bars, and managing AI agent crewmates.
  • High Velocity Output: Ships 40–50 fully tested, production-ready PRs per day (rather than simple "vibe-coding" demos) by focusing on high-level planning upfront and automated quality validation at the end.
  • Correcting AI Biases: Frontier models naturally overestimate human development time (e.g., estimating days/weeks for a project an agent can build in minutes) because they are trained on human data. Global instructions must explicitly instruct agents not to over-weight development cost in technical decision-making, preventing them from picking low-quality, cheap shortcuts.
  • Bug Reproduction Protocol: Forces agents to reproduce bugs end-to-end (E2E) as a real user would experience them before attempting a fix, rather than relying solely on superficial unit tests.

• Terminal-Centric Flow State

  • Hands-on-Keyboard Discipline: Doing work in the terminal eliminates mouse interaction, preventing context-switching and preserving flow state.
  • Cross-Device Consistency: Allows the exact same development workflow and persistent session setup to run across Mac, Windows, Linux, laptops, and mobile phones.

• Agent Onboarding & Knowledge Architecture

  • Memory Hierarchy:
    • Global Memory (~/.claude/CLAUDE.md / ~/.config/agents/agents.md): Minimal (~27 lines) cross-project personal preferences loaded into every system prompt. Kept strictly concise to avoid unnecessary token burn.
    • Project Memory (CLAUDE.md / agents.md): Captures repository architecture, domain terminology, testing setups, and collective learnings from past errors.
    • Symlinking Strategy: Uses symbolic links to point harness-specific memory files (CLAUDE.md) to generic agent configuration files (agents.md), keeping the setup agent-agnostic.
  • Skills via Progressive Disclosure: Moves conditionally useful instructions (e.g., E2E testing setups) out of memory files into modular skill files. Skills only load a tiny description field into the initial system prompt, fetching full instructions only when invoked.
  • Skill Benchmark Warning: Warns against blindly installing popular internet skills (e.g., highly-starred repositories). Benchmarking shows unverified skills can increase token consumption by 5%+ and degrade task success rates while introducing security/credential risks.

• Prompting, Tooling & Agent Ergonomics

  • Voice-First Input: Uses local voice transcription (3x faster than typing) for complex prompts, reserving manual typing strictly for exact file paths and URLs.
  • Agent Ergonomics (AXI Standard): Replaces standard MCP (Model Context Protocol) servers with specialized CLI tools and design standards optimized for agents. Benchmarks demonstrate that GitHub MCP servers can cost 3x more tokens and double latency compared to CLI-based interfaces.

• Execution, Planning & Automated Quality Assurance

  • Visual Planning (Lavish): Replaces dense terminal text walls during project planning by spinning up interactive, HTML/artifact-based design systems directly in the browser to visualize choices, annotate UI feedback, and log decisions.
  • Adversarial Post-Processing (No Mistakes): Orchestrates an isolated Git worktree pipeline that rebases code on main, resolves merge conflicts, runs an adversarial agent review in a clean context window, executes E2E validation while logging visual evidence (screenshots/video/logs), updates documentation, and babysits the PR through CI/CD merge.
  • Overnight Autonomous Loops (Good Night Have Fun): Runs long-horizon, iterative tasks (e.g., E2E usability testing, test coverage improvement, metric optimization) under precise iteration caps, token limits, and strict stopping conditions without risking quota burn.

• Multi-Agent Scale & First Mate Orchestration

  • Workspace Isolation (Treehouse): Eliminates manual Git worktree creation overhead (git worktree add/remove) by dynamically provisioning and reusing isolated worktree directories for concurrent agent sessions.
  • First Mate Orchestration: Uses a top-level managerial "First Mate" agent to parse complex, multi-repository prompts, break them down into sub-tasks, delegate them across background agent sessions in parallel worktrees, and coordinate issue triaging.

Dedicated Tools & Software Stack

• Terminal Emulator & Shell Setup

  • WezTerm: High-performance, cross-platform (Mac/Windows/Linux) terminal emulator fully configured dynamically via Lua scripts (wezterm.lua).
  • tmux: Terminal multiplexer used to manage multi-pane layouts and background tabs for parallel agent sessions; preserves session state across device connections.

• Code Editor & Voice Inputs

  • Neovim: Modal, keyboard-driven text editor optimized with plugins for fast fuzzy file finding, code searching (ripgrep), and precise navigation.
  • Open Superwhisper: Free, open-source local voice-to-text application running OpenAI Whisper locally on-device. Uses custom initial prompts to recognize technical vocabulary, URLs, and project names cleanly.

• Agent Harnesses

  • Claude Code: Primary agent harness used in the demonstration; noted for out-of-the-box defaults and feature richness.
  • Codeex CLI: Open-source, Rust-based fast CLI agent harness capable of inspecting its own source code for self-debugging.
  • Pi Coding Agent: Minimalist, highly extensible coding agent harness focused on customization.
  • Open Code: Model-agnostic agent harness featuring a smooth Terminal User Interface (TUI) and multi-model integrations.
  • Vercel Skills CLI (npx skills): Command-line tool used to search, install, and manage agent skills across various agent harnesses.

• Author's Open-Source Agent Ecosystem

  • AXI Standard (axi.md): Agent Ergonomics design standards and optimized CLI tooling catalog (e.g., GitHub AXI, Chrome DevTools AXI) designed to minimize token usage and latency.
  • Lavish AXI: Visual artifact and interactive HTML planning editor that replaces text walls in the terminal with rich UI components for concept review and annotation.
  • No Mistakes: Automated CI/PR pipeline that handles isolated worktree rebasing, adversarial code reviews, E2E evidence capture (screenshots/video), doc updates, and PR babysitting.
  • Good Night Have Fun: Autonomous long-running loop runner with customizable token caps, iteration limits, and stop conditions for overnight or heavy iterative work.
  • Treehouse: Automatic Git worktree manager that manages dynamic, reusable workspace directories for parallel agent sessions.
  • First Mate: Managerial meta-agent that accepts high-level natural language instructions, spawns sub-agents across isolated worktrees, and manages multi-task orchestration automatically.

それでも十分に楽しかったです。

に変えてください

nit: くだけすぎ？

nit: の？

ねじれ文になっている。

オンサイトのボランティアは151名、発表者は148名で、135のオープンスペースが開催されました。

など

アメリカ政府

?

何のカラー表示？REPL?

nit: 厳密には「トークに通る」は意味が通らない気がする。システムが分かっていない読者は補完しづらいと思う。

ライトニングトークの選考に通った

ライトニングトークのスピーカーに選ばれた

など？

https://shottr.cc/s/3sof/SCR-20260810-jqf.png can we keep this vertical instead of horizontal?

https://shottr.cc/s/3iPX/SCR-20260810-jpe.png - all write to us. should just say contact us and lead them to the contact us page. thats all

This section is actually part of the send us a message above right?

https://shottr.cc/s/36mq/SCR-20260810-jlm.png these two are actually connected.

https://shottr.cc/s/3Rek/SCR-20260810-jjg.png Why are whole sections clicable only the relevant anchors should right?

Does cold calling really work in 2026? See real stats, benefits, and proven tips that show why cold calling still converts, plus how to make your calls count.

Diagnose remote sales productivity drops in EdTech by uncovering hidden gaps in callbacks, routing, and real-time visibility. Learn what metrics truly reveal performance.

Know More: https://callyzer.co/blog/diagnosing-remote-sales-productivity-drops-edtech/

jp

日本語

日本語） /aisubtitle_aisummary-jp/{category}

jp

jp（日本語）

不問

日本語

日本語

日本語 jp /aisummary-jp/{category}

日本語

日本語

jp

日本語　　jp

jp

jp

jp

日本語

jp

jp

日本語

This preprint has now been peer-reviewed and published in FEMS Microbiology Ecology:

Tommi Välikangas, Hannu Fritze, Juha-Matti Pitkänen, Krista Peltoniemi, Eeva Järvi-Laturi, Torben R Christensen, Maria Väisänen, Juho Lämsä, Riku Paavola, Jenni Hultman, Environmental variation structures northern peatland soil microbiome composition and function in a reindeer herding area exclosure experiment, FEMS Microbiology Ecology, Volume 102, Issue 8, August 2026, fiag072, https://doi.org/10.1093/femsec/fiag072

Document de Synthèse : "Du côté des enfants" (1972) – Une analyse de la condition enfantine et de la parole libérée

Résumé Exécutif

Ce document analyse les enseignements de la série documentaire de 1972, Du côté des enfants, produite par Éliane Victor.

L'objectif central de cette œuvre était de filmer les enfants « à leur hauteur », en leur offrant un espace de parole dépourvu de la médiation ou de la censure des adultes.

Les points clés identifiés sont les suivants :

• Une confrontation avec l'autorité : L'enfant perçoit souvent l'adulte comme une figure de commandement arbitraire, générant des sentiments de révolte et des stratégies de résistance (carnets secrets, "vengeance").

• Le déterminisme social et éducatif : Les trajectoires de vie, de la réussite fulgurante du virtuose précocement "dressé" à la marginalisation tragique des enfants des rues, illustrent l'impact profond de l'environnement familial et social.

• La puissance subversive de la vérité enfantine : La parole de l'enfant, par sa spontanéité et son absence de calcul, s'est révélée capable de déstabiliser les institutions, menant parfois à une censure médiatique immédiate (cas de l'émission Un mot pour rire).

1. La philosophie de l'enquête : "À hauteur d'enfant"

La série Du côté des enfants rompt avec les méthodes traditionnelles de reportage.

Elle repose sur le principe de se mettre dans l'univers des enfants pour capter des confidences qu'ils ne livrent pas volontiers aux adultes.

Les défis de la captation

• Le refus de l'intrusion : Robert Bober souligne que la mémoire adulte est "infidèle" et que vouloir faire parler un enfant peut être ressenti comme une véritable "agression".

• L'anonymat et la confiance : Pour Bernard Boutier, s'immerger dans une bande de jeunes nécessite des semaines de présence préalable pour briser les barrières de la méfiance.

• Le conditionnement : Le documentaire pose la question de la destruction de l'enfance par les cadres de vie imposés par les adultes, qui façonnent les enfants pour qu'ils leur ressemblent.

2. Analyse des portraits : Autorité, Discipline et Révolte

La série présente des profils d'enfants contrastés, révélant différentes facettes de la relation parent-enfant.

Le cas Michel : La révolte théorisée

Michel exprime une opposition frontale à l'autorité adulte.

• Définition de l'adulte : "C'est un bonhomme qui commande les enfants."

• Le carnet de "vengeance" : Michel y consigne les injustices subies, classées par degrés (de 1 à 4).

• La vision de la liberté : Pour lui, la liberté consiste à pouvoir faire ce que l'on veut, sans l'intervention constante des parents.

• Perspective paternelle : Le père de Michel admet une part d'injustice inhérente à la parentalité, qualifiant parfois l'autorité de "base de l'oppression".

Le cas Maxime : Le "dressage" du virtuose

Maxime, 13 ans, vit dans une chambre d'hôtel à Paris pour se consacrer au violon.

• Discipline extrême : Son père utilise le terme de "dressage" pour décrire son apprentissage, commencé à l'âge de 3 ans.

• L'enfermement mutuel : Le fils est "condamné" à l'instrument, tandis que le père est "condamné" à la réussite de son fils, projetant sur lui ses propres aspirations de musicien.

• Résultat : Malgré une scolarité difficile, Maxime est devenu premier violon solo à l'Opéra de Paris, confirmant le succès de la "mission" paternelle au prix d'une enfance sacrifiée à l'effort.

3. Fractures sociales et trajectoires de vie

Le documentaire explore les disparités de classes à travers des rencontres et des observations de terrain.

Patrick et Sylvie : L'amitié impossible

Le film de Maurice Felvic montre le lien entre le fils d'un directeur de collège et la fille d'un ouvrier (famille de 7 enfants).

• Contraste matériel : Sylvie est fascinée par le confort de Patrick (parquet verni, tapis, chambres individuelles), alors que sa propre famille vit dans l'exiguïté.

• Barrières sociales : Malgré l'affection entre les enfants, les réalisateurs notent que leurs univers sont trop éloignés pour que cette relation perdure au-delà de l'enfance.

Thierry et la bande des rues : La marginalité

Bernard Boutier a suivi une bande de jeunes du 18e arrondissement de Paris.

• Rapport au travail : Ces jeunes cherchent de "petits boulots" (lavage de voitures, livraisons) mais sont systématiquement rejetés ou licenciés pour des peccadilles (vol de chewing-gum).

• Vision des "riches" : Ils perçoivent les riches comme des "étrangers" ou des "salauds" qui utilisent leur argent pour gouverner et mépriser les pauvres.

• Issue tragique : L'analyse rétrospective révèle que Thierry est devenu SDF, toxicomane, et est décédé d'une overdose 20 ans après le tournage, illustrant l'absence de débouchés pour ces enfants déscolarisés.

4. La parole enfantine face à la censure institutionnelle

L'un des segments les plus révélateurs concerne l'émission Un mot pour rire (1971), rapidement supprimée de l'antenne.

Le mécanisme de la censure

L'émission demandait à des enfants de définir des mots simples.

Elle a été déprogrammée après seulement trois numéros.

| Mot défini | Contenu de la définition (paroles d'enfants) | Raison probable de la censure (contexte ORTF) | | --- | --- | --- | | HLM | Chauffage défaillant, ascenseurs bloqués, promiscuité, laideur architecturale. | Contredit la politique de construction massive de logements sociaux de l'époque. | | Riche | "Jeter l'argent par la fenêtre", "Les riches tuent les pauvres". | Perception de "lutte des classes" jugée subversive et dangereuse. |

Conclusion sur la censure

La parole des enfants a "fait peur au pouvoir".

Parce qu'elle est dénuée de manipulation et de méchanceté apparente, elle est impossible à disqualifier, ce qui la rend d'autant plus menaçante pour l'image de stabilité que les institutions cherchaient à projeter en 1971-1972.

5. Synthèse des thèmes transversaux

| Thème | Observations issues du contexte | | --- | --- | | L'Autorité | Perçue comme arbitraire ; l'adulte est vu comme celui qui impose son cadre sans comprendre les besoins réels de l'enfant. | | Le Travail | Pour les enfants favorisés (Maxime), c'est un "dressage" vers l'excellence. Pour les précaires (Thierry), c'est un espoir de dignité constamment déçu. | | La Communication | L'enfant sent immédiatement si la curiosité de l'adulte est feinte ou intrusive ("agression"). | | Le Futur | Une rupture nette est située autour de 16-18 ans, âge où l'enfant "bascule" dans le monde adulte, souvent décrit comme un précipice. |

？

这里的劳动是指：整个社会为了生存而必须进行的生产活动和由此形成的社会分工。 这些生产活动是社会结构决定的而不是个人决定的，在这里，劳动作为社会分工成为一种高于个人意志的力量。 同时劳动不是个人自由发展的领域，而是一种外在压力。（劳动的异化） 不是人控制劳动，而是劳动的社会形式控制人。，劳动形式规定人的生活

不是设计蓝图，从而使得现实和理想相适应，而是从现实的矛盾，从这一现实的矛盾的发展的历史过程出发，找到它的发展的可能。这一句话针对的是：“我先构想一个完美社会，然后要求现实按照我的设计走。”可是我们没有回答：为什么现实社会会走向这种变化？谁推动它？条件在哪里？也就是说，我们只有从现实社会出发，才有可能得到现实运动的结果，上文谈到共产主义社会内容，是为了说明“现实运动消灭了什么”。这是一种推演，是一种可能，而不是必然发生的结果。“设计/理想”则反过来，说这是一种结果，未来只有....才会更好。因此，目标，共产主义运动的目的，不应当是凭空创造的，而应当是从现实矛盾中抽象出来的。AI的比喻很有意思：一个医生治疗疾病。

他不是先幻想一个“完美人体”，然后改造病人；

而是：

研究病因； 发现身体正常状态； 根据疾病机制进行治疗。

这里应当参照《家庭，私有制和国家的起源》

https://www.facebook.com/groups/474920510009958/posts/2313296542839003/

The largest lobe of the brain

https://www.facebook.com/groups/474920510009958/posts/2314952302673427

あれ？マージされていない? 吉田さんのコラムをレビューした気がするな。

もし追加できるなら以下を追加したい。 「8月21日から開催のPyCon JPでは、PyCon USのLTで話が出来なかった技術的な部分の解説をします。PyCon JP 2026での発表をお楽しみに。Day 1 11:45からです。 https://2026.pycon.jp/ja/talks/BUZXVG 」

本文の入りからコラムで良いのか? 少し説明を入れて、 「同行した寺田さんの発表について本人からコラムで紹介」 とかしたほうが良いのかな?

キーノートスピーカーである

Firefoxの機能やChrome拡張として

?

nit: 文章のトーンとしてカジュアルすぎないか？「の」の方が落ち着く

助詞が抜けている

色付きの表形式？

typo

プログラムに動作負荷をかける

or

プログラムの動作に負荷をかける

リンク切れ

重複表現

ねじれ文になっている。

筆者は同行した寺田さんの発表、PSF Member Lunch、PyLadies Auctionなどさまざまなイベントに参加しました

or

同行した寺田さんの発表、PSF Member Lunch、PyLadies Auctionなどさまざまなイベントがありました

The GTF annotation and the analysis of HIV-1 splice-site conservation do not include the A4d splice acceptor. However, A4d is used by NL4-3 and appears to be less conserved across HIV-1 strains (https://journals.asm.org/doi/10.1128/jvi.02515-16?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed).

Learn why call centers are moving beyond 1% manual call sampling with automated monitoring to improve quality, compliance, agent performance, and customer experience.

Know More: https://www.prlog.org/13163234-moving-beyond-the-1-why-call-centers-are-replacing-manual-sampling-with-automated-monitoring.html

什么意思呢？

的

PMID: 28118664

Gene: ABCA4

HGNC ID: 34

Text is a PDF, but this paper is the previously published paper referenced in PMID: 9973280. Pedigree is found here

PMID: 38602673

Gene: ABCA4

Disease: ABCA4-Associated Retinopathy

Case#: Patient #35, male, 35yo at report, 14yo at onset,

DiseaseAssertion: STGD

FamilyInfo: family 30, segregation was noted as "yes" but no other details provided

CasePresentingHPOs:

CaseHPOFreeText:

CaseNotHPOs:

CaseNotHPOFreeText:

GenotypingMethod:

PreviouslyPublished: n/a

Variant: allele 1: A1038V;L541P allele 2: G818E

ClinVar: 99135

CAID: CA227000

SupplementalData: supplemental table 1

This paper is listed in LOVD as containing this variant. Unable to find the variant in the main text and unable to locate the supplemental info. At the end of the paper, it says supplemental info can be found online, so used the DOI linked at the end of the article but it just leads to a PDF again with no supplemental

PMID: 37296172

Gene: ABCA4

Disease: Stargardt

Case#: 19 year old woman

DiseaseAssertion: Stargardt disease (STGD)

FamilyInfo: no family history of ocular disease

CasePresentingHPOs:HP:0025158

CaseHPOFreeText:20/25 in the right eye and 20/25-1 in the left eye, small irregular perifoveal lesions of both increased and decreased autofluorescence

CaseNotHPOs:na

CaseNotHPOFreeText:na

Genotyping Method: next gen sequencing

PreviouslyPublished: na

Variant: c.6079C > T, p.(Leu2027Phe) c.4139C > T, p.(Pro1380Leu)

ClinVar: not found not found

CAID: not found not found

SupplementalData: “black shadow” in the right eye after getting hit by a volley ball

PMID: 10874631

Gene: ABCA4

HGNC ID: 34

Case#: patient 34, female

DiseaseAssertion: RP19

FamilyInfo: paternal first cousin with STGD, healthy father heterozygous for 1938-1 G>A splice mutation, mother homozygous for normal allele

CasePresentingHPOs: HP:0000662, HP:0007663, HP:0007737, HP:0001133

CaseHPOFreeText: choriocapillaris atrophy, severe concentric reduction of the visual field, abrogation of rod function

Genotyping Method: PRISMTM Ready Reaction Sequencing Kit on an automatic fluorometric DNA sequencer

PreviouslyPublished: N/A

Variant: NM_000350.3(ABCA4):c.1938-1G>A

ClinVar: 99106 https://www.ncbi.nlm.nih.gov/clinvar/variation/99106/?term=%22ABCA4%22%5BGENE%5D+AND+%22(c.1938-1G%3EA)%22%5BVARNAME%5D

gnomAD: 0.000002488 https://gnomad.broadinstitute.org/variant/1-94060760-C-T?dataset=gnomad_r4

PMID: 23940504

Gene: ABCA4

Disease: retinal dystrophy

PMID: 37705246

Gene: ABCA4

Disease: ABCA4-associated retinopathies

This variant was mentioned only as a previously published report.

PMID: 22661473

Gene: ABCA4

HGNCID: HGNC:34

Stage I disease was characterized by central macular atrophy with parafoveal or perifoveal flecks. Where flecks were more numerous and extended anterior to the vascular arcades and/or nasal to the optic disc, then patients were classified as having stage II disease. Although partial resorption of flecks may be present in this stage, more complete resorption of flecks was indicative of stage III disease with choriocapillaris atrophy also within the macula. Widespread RPE and chorioretinal atrophy throughout the fundus defined stage IV disease.27 Based on this classification system, the patients in our study were subdivided into 2 groups, that is those with milder disease (stage I or II) and those with more severe disease (stage III or IV) phenotypes.

Case#: Male, Family #1, Patient #1, II:1 in pedigree

DiseaseAssertion: Hypomorphic Stargardt disease

FamilyInfo: Paternal female cousin also has hypomorphic Stargardt disease and both of them carry the complex allele p.[L541P; A1038V] and p.N1868I. Additionally, their paternal aunt was diagnosed with Stargardt disease. This information can be found on Fig.1.

CasePresentingHPOs: HP:0000622, HP:0025010

CaseHPOFreeText: This proband developed blurred vision at age 30. The foveal atrophy affects the left eye. In the first visit at 33.9 years old patient presents with 20/25-3 Snellen VA OD, 20/70-2 Snellen VA OS, 0.16 LogMAR VA OD, 0.58 LogMAR VA OS, and stage 1. In the last visit at age 40.7, the patient had a 20/40-2 Snellen VA OD, a 10/80-1 Snellen VA OS, 0.34 LogMAR VA OD, 0.92 LogMAR VA OS, and was now in stage 2. In a Goldmann Visual Field test, they found central scotomas II4e; mild-moderate constriction II2e.

CaseNotHPOs: N/a

CaseNotHPOFreeText: The proband maintained some relative foveolar sparing in his right eye and had a BCVAs of 20/4022 (test done at 40 years old).

Genotyping Method: Genetic testing was performed at Columbia University. It was not stated which method this proband underwent, so the genetic testing could have been one of the following: "The entire ABCA4 gene locus was sequenced in 17 patients; the ABCA4 gene, including all exons and intron/exon boundaries were sequenced in 4 patients. In the remaining 6 cases representing family members, only targeted testing was performed."

PreviouslyPublished: N/a

**Variant: ** M1) NM_000350.3(ABCA4):c.5603A>T M2) NM_000350.3(ABCA4):c.1622T>C (p.Leu541Pro)

ClinVar: M1) 99390 M2) 99067

CAID: N/a

gnomAD: M1) The highest minor allele frequency was 0.05787 (https://www.ncbi.nlm.nih.gov/clinvar/variation/99390/) M2) The highest minor allele frequency was 0.00017 (https://www.ncbi.nlm.nih.gov/clinvar/variation/99067/)

SupplementalData: Table 1. provided patient information for those with p.N1868I ABCA4 Stargardt disease and the associated ABCA4 mutations. Fig.1. shows the pedigrees of the families. Fig.3. shows Macular SD-OCT line profiles for some of the patients. Table 2. describes the onset/symptoms of the patients with p.N1868I ABCA4 Stargardt Disease. Table 3. shows Visual Acuity and Stage at Baseline and Most Recent Follow-up in Patients With p.N1868I ABCA4 Stargardt Disease. Fig.4. shows BCVA better eye vs Duration since first examination for the patients. Table 4. shows clinical findings in the patients.

PMID: 32646556

Gene: ABCA4

Disease: Macular, Cone, and Cone-Rod Dystrophies

requested from library

PMID: 32884132

Gene: ABCA4

Disease: Stargardt

PMID: 35119454

Gene: ABCA4

Disease: macular dystrophies

This variant is reported to be found in 1 individual in heterozygous or compound heterozygous form, but there is no patient identifier, so impossible to know whether they have a second variant

As shown in other publications, the ATPase activity of this variant appears to be drastically reduced compared to WT when transfected into HEK293 cells, but expression is comparable to WT.

L541P was purified, reconstituted into membranes, and tested for basal and retinal-stimulated ATPase activities. L541P was shown to have reduced basal ATPase, and no stimulation by retinal (Fig 4a)

Case#: Patient 4, Female, Caucasian, 33yo

DiseaseAssertion: STGD1

FamilyInfo: One of eight siblings; four affected. Disease segregates with ABCA4 variants consistent with autosomal recessive inheritance. Parents are deceased and can't be tested.

CasePresentingHPOs: HP:0007663, HP:0007754, HP:0025147, HP:0007924, HP:0011507

CaseHPOFreeText: gradual decline in visual acuity over 9 years, BCVA 20/300 OU, bilateral beaten-bronze appearance of the macula, numerous perimacular yellow flecks, fluorescein angiography showing hyperfluorescence in the posterior pole and dark choroid in the periphery

CaseNotHPOs: N/A

CaseNotHPOFreeText: absence of central hypofluorescence on fluorescein angiography (present in affected siblings but not this patient)

Genotyping Method: SSCP analysis; Taq Dyedeoxy Terminator Cycle Sequencing kit

PreviouslyPublished: N/A

Variant: NM_000350.3(ABCA4):c.2588G>C (p.Gly863Ala), NM_000350.3(ABCA4):c.161G>A (p.Cys54Tyr)

ClinVar: ClinVarID:7879, ClinVarID:99065

CAID: N/A

SupplementalData: Segregation and sequencing data (Figures 1, 4)

eLife Assessment

In this important work, the authors develop methods to forecast epidemic growth from viral sequencing data alone. The evidence for the usefulness of the approach is solid, but some justifications and methodological details are incomplete. This study should be of broad interest to the community interested in viral dynamics and epidemiology.

Reviewer #1 (Public review):

Summary:

This paper develops a formalism for quantifying epidemic dynamics in terms of relative fitnesses of circulating variants, uses the formalism to elucidate fundamental tradeoffs of epidemics driven by variants with increased transmissibility versus immune escape capability, shows the formalism implies a natural quantity measuring the impact of selection on epidemic growth, and demonstrates that the formalism enables a decomposition of epidemic dynamics into circulation among different immunity groups. The relative fitness formalism enables these analyses to be performed with genetic sequence data only, a major benefit of the model given the relatively high availability of sequence data compared to other data streams such as case counts and titers.

Strengths:

Linking epidemic dynamics to pathogen evolution is a fundamental problem in studies of antigenically variable pathogens, with models of epidemic dynamics and immune-driven evolution going back decades in applications to respiratory pathogens such as influenza. The COVID-19 pandemic heightened the urgency for developing methods for quantifying epidemic growth in contexts where novel variants emerge, leading to differential susceptibility among individuals with diverse exposure histories with implications for vaccination strategies. Real-world data streams such as case counts and immunological measurements have a variety of shortcomings that pose major challenges for quantitative models aiming to inform policy. In recent years, genetic sequencing data has become widely available for pathogens including SARS-CoV-2 and influenza, allowing tracking of pathogen evolution at unprecedented detail in real time, yet biases in the collection of sequence data across different populations make connections between absolute epidemic size and variant frequencies from sequence data not immediately transparent.

This paper's contributions are exciting because they demonstrate new ways to link pathogen evolution and epidemic dynamics using very accessible data. From a theoretical perspective, the model is appealing because of its simple derivation in terms of compartmental models of epidemics, which are standard in the literature, and its clear extension to populations with heterogeneous immune histories. The latter extension leads directly to new methods for inferring immune groups with differential susceptibility to antigenically distinct variants in populations with heterogeneous immune histories without access to immunological data such as titers, an important advance given the wide applicability of quantification of antigenic relationships among variants in real populations.

Weaknesses:

While the demonstrated methods for forecasting short-term epidemic growth and for quantifying population immunity using sequence data are exciting as proofs of principle, the validation and statistical support provided in the analyses have drawbacks that are not fully addressed in the manuscript, weakening the evidence for the usefulness of the methods in their current form.

The analyses forecasting epidemic growth using Gaussian process models are justified using Pearson correlation coefficients whose values are extremely low for the test data period. The explanation given for this is that the case data used to validate the predictions has worse ascertainment over time, but it is not shown directly that the model may be working well despite the low correlations. Whereas, by eye, the predicted epidemic growth curves appear to capture features of the observed epidemic growth curves, the computed metrics don't support the claim of success of the predictions. Additionally, nearly all the model fits lack estimates of uncertainty, so it is not possible to discern the significance of departures between the model and data, or subtle differences in relative fitness calculations across geographies.

The analysis of latent pseudo-immune components also suffers drawbacks that render it more of an interesting proof of principle than a convincing tool for prediction at this point. In particular, in figures S18 and S19, metrics meant to quantify the statistical significance of the results show no difference from null models computed by permuting variants and their escape vectors, yet no interpretation is given for the lack of significance. Moreover, the model fits relating titer distance to pseudo escape distance seem unsuccessful for JN.1 infection and XBB infection histories, which is not adequately accounted for in the text, which cites just "weaker correlations" in these cohorts.

In several instances, the evidence for the new data analyses is weakened by a lack of clarity in the presentation of the technical details of the methods. For example, in the discussion of the Gaussian process models, it was not clear what features of the problem inform the choice of kernel (Matern 5/2), which hyperparameters were used, and how novel this use of Gaussian processes is. In the section describing methods for predicting epidemic growth rate from selective pressure, the discussion of the gradient boosting regressor model provided no intuition as to why this method performed better than the others tested or whether this was particularly important to the conclusions, and the lack of discussion of uncertainty or variability in the model predictions makes it difficult to assess the significance of the time series estimates alone. In the discussion of the latent immune factor model, the mismatch between the notation used in Equation 5 compared to that in Equation 18 made the derivations more difficult to follow. Subsequently, the explanation of the fitting of the pseudo-immune model left out details, such as an explicit definition of distance in pseudo-escape space, to what extent the group-level mean aggregated titer measurement captured features of the titer data (despite ignoring interindividual variability), and a thorough discussion of the successes and shortcomings of the fits in different scenarios. More explicit presentation of the mathematical choices going into the methods, sources and quantification of uncertainty, and cases where the model performs well or poorly could significantly bolster the case for the usefulness of sequence data in quantitatively predicting epidemic growth and antigenic relationships among variants in practice, in more general settings than those carried out here.

Reviewer #2 (Public review):

Summary:

The authors first introduce a framework to understand how different phenotypic drivers of viral evolution, i.e., changes in transmissibility versus immune escape, complicate epidemic forecasting using only genetic data. To overcome these complications, they advance an evolutionary "selective pressure" metric to predict population-wide epidemic growth from genetic data alone. Separately, they introduce a latent space model to infer a "pseudo" population immune structure from geographic variation in viral lineage dynamics, and find that the inferred pseudo-structure predicts human serological data.

Strengths:

This paper begins with a useful pedagogical exposition on the connection between fitness-driven frequency dynamics and underlying mechanisms of viral-immune co-evolution. A major contribution of this paper - a method to infer variant-specific escape properties from geographically non-uniform variant frequency dynamics alone - is an interesting and potentially timely one, given the advance of sequencing-based surveillance.

Weaknesses:

The logical flow of the pedagogy part of the text works against the reader, which is problematic since it motivates the rest of the text. Moreover, some important modelling choices and procedures, particularly with respect to the selective pressure metric, are only cursorily described in the methods section. The lack of explanation and detail, especially relative to more simple choices that are seemingly motivated by the authors' own theory, makes it difficult to understand and therefore assess their validity and/or necessity.

Reviewer #3 (Public review):

Summary:

This study introduces a new analytical framework to analyze how viral variant frequencies change over time and in different locations. Two examples are given that demonstrate where this approach can be useful and where other approaches can be ambiguous in characterizing novel variants. The authors then demonstrate that the spatiotemporal dynamics of variant frequencies can be used to predict future epidemic growth rates and to investigate how variants differ in immune escape.

Strengths:

(1) Examples are provided that make the study accessible for a general audience.

(2) The authors demonstrate that their approach is predictive both of overall epidemic growth rates and immunological distance between variants.

(3) The approach introduced in this study can be readily applied to current and future epidemiological challenges that are similar to SARS-CoV-2 with respect to the relative evolutionary timescales wherever there is spatiotemporal heterogeneity in the susceptible population.

Weaknesses:

(1) The authors conclude their abstract claiming that their method provides an early signal of epidemic growth. Can this be quantified? Could the authors perform retrospective analyses for sequences available through various cutoff times, identify how early significant new variants are detected, and compare this to other detection methods?

(2) Analysis depicted in Figure 4 and Figure S9 could be explored further than speculatively attributing weak correlation to declining reporting rates for US states. Exploring how correlation between data and prediction varies over time during the test period might identify periods/events that explain weak correlation overall. The authors could explore predicting growth rates for estimated state prevalences rather than reported cases.

eLife Assessment

The paper provides a valuable, foundational dataset that will undoubtedly provide substantial value to the cancer research community. The aggregation and harmonization of a broad, rich, multi-omic dataset is convincing and will potentially support further downstream research on GIST, which currently has poor supporting datasets. Support for the accompanying claims is more incomplete, however: the biological demonstration rests on a single gene tested by one approach, several analyses lack sample sizes and multiple-testing correction, the claims made for the LLM assistant are not backed by direct evaluation, and the curated data matrices would ideally be deposited independently of the web.

Reviewer #1 (Public review):<br /> <br /> Summary:

This tumour type is missing from the big pan-cancer databases, so none of the popular online analysis tools works for it. That's a real gap, and it's the right one to go after. The authors build an online resource that gathers the scattered public molecular datasets for this disease, adds three of their own patient cohorts, ties everything to clinical data, and exposes interactive tools, downloads, and programmatic access so other people can build on it. To show what it does, they take one gene through the whole platform - clinical, gene-expression, protein, single-cell, immune, and drug-response and then test that gene in cell lines. So there are really two things on offer here: a resource and a practical example of using it. They land very differently.

Strengths:

The resource is the real contribution, and it's done with care. It covers 37 centres and nearly 2,000 samples across five kinds of molecular data, and the authors are honest about provenance: how they screened datasets in or out, where they recorded the diagnostic codes, and why they dropped ambiguous mixed-tumour collections. The key methodological decision is the right one; every analysis runs inside its own cohort, and the cross-cohort views are explicitly "for looking, not for combining." That's exactly how you should treat heterogeneous public data, and they say so plainly instead of quietly pooling everything. Their three pathologist-confirmed cohorts add genuine independent material, so this isn't a re-skin of data that already existed. And because the code and a public access point are actually available, the reuse claim holds.

The example is internally consistent, which is what makes it persuasive. The gene reads higher in higher-risk patients across several independent cohorts and in their own protein data, tracks with the disease spreading and recurring, and lines up with worse survival. The single-cell data put it in the dividing cells; the pathway analysis points to proliferation. Three independent data types landing on the same proliferation story are the strongest part of the biology.

Weaknesses:

The honest problem is that the entire biological story rests on one gene, tested one way. The lab work is two cell lines with the gene knocked down, showing less growth and migration: there is no rescue to confirm the effect is real, no second gene to show the approach generalises, nothing in a living animal. That earns the modest claim: the resource can point you at a candidate worth testing. It does not earn the headline claim that the platform reliably generates good target hypotheses, because we only ever watch it succeed once. One example illustrates a workflow; it doesn't establish a method.

Some of the statistics won't survive scrutiny. The clearest case is a perfect separation between treatment-resistant and treatment-sensitive cases from a single immune cell population, reported with no error bars, no check for information leakage, and apparently from very few samples. A perfect result in that setting is almost always overfitting or a small-sample artefact, not a strong classifier. The same pattern shows up elsewhere: small groups, p-values with no effect sizes or error bars, and no correction for the enormous number of features and cohorts being tested across the whole platform. Separately, one drug result is a correlation against a predicted sensitivity score from a model.

The AI assistant gets far more weight than the evidence supports. Credit where due: the authors are clear and consistent that it only helps interpret and navigate, and never touches the data, the statistics, or the results. That's the correct line to draw, and they hold it. But the assistant itself is never tested, no accuracy numbers, no benchmark, no error analysis, no described way for a human to check what it produces. Calling it something that "fundamentally transforms the user experience" is an assertion, not a finding. And since even the literature feature is admitted not to be a proper systematic review, the prominence of the artificial-intelligence framing runs ahead of what's been shown.

Reviewer #2 (Public review):

Summary:

dbGIST appears to be the first dedicated multi-omics resource worldwide that is specifically focused on GIST.

Strengths:

The main value of the paper is not simply that the authors collected datasets, but that they built a usable resource around them, with cohort-aware analyses, curated clinical labels, interactive visualizations, downloadable results, selected API access, and an optional LLM-assisted interface. The work is solid, and the database is likely to be useful for GIST researchers interested in target discovery, cross-dataset validation, drug-response hypotheses, and translational follow-up.

The MCM7 analysis is a reasonable use case. It shows how a user can start from one candidate gene and then move across transcriptomic, proteomic, clinical, single-cell, immune-related, drug-response, and experimental evidence. I do not see this as the main discovery of the paper, but rather as a practical demonstration of what the database can do. That is appropriate for a resource manuscript.

Weaknesses:

(1) The authors should make the organization of the platform a little easier to follow. The manuscript refers to five primary omics layers, six omics-focused pages, and eight analytical modules. This structure is understandable after reading the relevant sections, but it may not be immediately obvious to readers. A brief clarification of how the omics layers, web pages, and analytical modules relate to each other would help.

(2) Since dbGIST is a live web resource, the authors should provide a clear versioning statement. The manuscript should indicate which version of the database corresponds to the analyses and figures reported in the paper, and how future updates will be distinguished from the version evaluated here. This is a small point, but it matters for reproducibility.

(3) The API function is a strength of the resource, but it is still described rather generally. The authors should give more concrete documentation of what can be accessed through the API, what inputs are required, and what type of output is returned. This could be placed in the supplementary materials. It would make the database more useful for computational users.

(4) The manuscript should clarify the status of downloadable data. It is clear that figures, source-data tables, and selected derived outputs are available, but it is less clear whether the full processed matrices used internally by the platform are downloadable or only maintained for deployment. This distinction should be stated plainly.

(5) The statistical reporting in the MCM7 clinical-association analyses needs a little more care. Several p-values are shown across different cohorts and clinical variables. The authors should state whether these are nominal p-values or adjusted p-values. If they are nominal, that is acceptable for a resource demonstration, but the exploratory nature of the analyses should be made clear.

(6) The ROC analyses for imatinib response should include sample sizes, and confidence intervals for AUC values would be useful if available. Some of the AUC values are high, and without group sizes, it is difficult to judge how stable those estimates are. The authors should avoid implying that these ROC results are validated predictive models.

(7) The interpretation of MCM7 should be slightly more cautious. MCM7 is a well-known DNA replication and cell-cycle gene, and the single-cell analyses seem to support its association with proliferative cell states. This is biologically consistent, but it also means that MCM7 expression should not be presented as tumour-cell-specific without qualification. The manuscript should frame it mainly as a proliferation-associated signal in the current analysis.

(8) The drug-response section would benefit from a clearer explanation of the response metric. The authors report correlations between MCM7 expression and predicted response to C6-ceramide, but readers need to know whether the predicted value represents IC50, AUC, sensitivity score, or another metric. The direction of interpretation should also be made explicit, since a negative correlation can mean different things depending on the scoring system.

(9) The single-cell annotation would be more convincing if the authors provided a compact marker-gene summary for the major cell types in each single-cell cohort. The current description of annotation by source labels, marker inspection, and manual curation is reasonable, but users of the database would benefit from seeing the marker evidence behind the labels.

(10) The experimental validation section should include a few routine details that are currently not easy to find. The siRNA sequences or target regions, number of biological replicates, statistical tests for the CCK-8 and wound-healing assays, and details of wound-closure quantification should be reported. These additions would make the in vitro part more reproducible.

(11) The wound-healing result should be interpreted with caution. Since MCM7 knockdown reduces proliferation, reduced wound closure could reflect changes in proliferation, migration, or both. Unless proliferation was controlled during the wound-healing assay, the authors should avoid describing this result as purely migratory.

(12) The LLM-related claims should remain conservative. The assistant is a useful feature for navigation, plain-language explanation, and user support, especially for clinicians or wet-lab researchers. However, the strongest statements about the LLM transforming interpretation or automating analysis should be toned down. The important point is that the LLM layer helps users interact with the resource, while the numerical analyses come from predefined dbGIST modules.

Reviewer #3 (Public review):

Summary:

The dbGist dataset/tool would provide substantial value to the cancer research community.

Strengths:

The manuscript presents dbGIST, a dedicated GIST-focused multiomics resource integrating data from 37 centers and ~2k samples across genomics, transcriptomics, proteomics, phosphoproteomics, and single-cell transcriptomics. Given that GIST is virtually absent from major cancer genomics consortia (TCGA, ICGC), this resource fills a genuine gap and represents a valuable contribution to the GIST research community.

(1) The MCM7 case study effectively demonstrates the platform's utility, linking a resource-derived candidate to survival outcomes.

(2) The LLM-assisted interface (dbGIST Assistant) is a reasonable addition for accessibility, lowering the barrier for clinicians and wet-lab researchers, who may not always have the skill set required for proper data analysis, especially for a rich and wide dataset like the dataset in question.

Weaknesses:

(1) Data deposition (major):

While the manuscript references public accessions for raw source datasets and provides a GitHub repository for code, it remains unclear where the **curated, harmonized data matrices** - which represent the core value-add of this work - are independently deposited. Access to these processed data appears to depend entirely on the dbGIST web interface and API. The authors should deposit the harmonized matrices in a persistent, general-purpose repository to ensure long-term availability independent of the web platform.

(2) LLM agent capabilities underspecified:

The manuscript would benefit from a clearer description of the assistant's capabilities and boundaries. Specifically, what tools or actions are available to the LLM agent? Can it execute code against the underlying data, trigger analytical modules programmatically, or is it limited to natural-language explanation of pre-computed results? Clarifying this would help readers assess the scope of the AI layer and distinguish it from agentic platforms that perform computation on behalf of the user.

在这里，已经出现了整治破坏营商环境行为的思路

https://shottr.cc/s/3BfG/SCR-20260810-dej.png - people prefer AM PM

https://shottr.cc/s/3QkN/SCR-20260810-ddk.png not airbnb faithful

https://shottr.cc/s/33St/SCR-20260810-dco.png Not airbnb faithful

eLife Assessment

This important study presents the development of a model to quantify the dynamics of stress-induced volatile emissions in plants and identify biologically relevant differences in these responses. The model is based on a convincing methodology and represents a starting point for studies of similar responses in other plants or investigations of induced phenotypes in different biological systems.

Reviewer #1 (Public review):

Summary:

The manuscript by Waterman et al. describes the development of a mathematical model that quantifies plant volatile emissions dynamics in response to mechanical/biotic stress. Model outputs were based on volatile emission measurements from maize plants using PTR-MS. Modeling revealed differences in emission patterns dependent on the intensity of wounding damage, application of herbivore oral secretions, age of leaf, circadian clock, and genotype. Differences were also observed between different types of volatiles, and the response curves somewhat correlated with expression patterns of biosynthetic genes. Moreover, the model showed priming effects from overlapping response curves upon multiple wounding events.

Strengths:

As a non-expert in modeling, this reviewer assesses the work from a broader point of view. Overall, I consider this model to be useful for other researchers to quantify volatile emission dynamics for their plant system. Generating the models does not seem to be overly complicated as long as emissions can be measured with a real-time system such as PTR-MS, which is costly and not available to every lab. The advantage of this approach is that it does not rely on parameters of underlying enzymatic pathways or transport processes. The authors claim that it can be easily applied to other biological responses.

Weaknesses:

The manuscript lacks a deeper discussion of how the model can help make predictions of volatile emission dynamics from plants in the greenhouse or field. Can the model be trained and validated with volatile measurements from plants under different environmental conditions? How realistic is this approach given the complexity of a field environment? It would be helpful to provide a better outlook of the application of the model for scientists in the field of plant volatile biology and beyond.

The authors state that "emissions can be regulated independently of each other" (Line 359). I would assume that regulatory mechanisms in different genotypes are similar but show genotype-specific variation.

Reviewer #2 (Public review):

This is a study of the dynamics of plant volatile emissions, using a curve-fitting approach to describe salient properties of the dynamics of plant volatile chemicals. The study is interesting and unique in taking this approach. Some of the dynamics uncovered (e.g. lagged emission of many sesquiterpenes) are already well known using less sophisticated approaches, while other properties (diurnal cycles in emission dynamics) are newly uncovered. The approach in general is new for the topic of plant volatile emissions, but curve-fitting is widely used to describe the dynamics or function-valued responses of plants and other organisms. The study thus reads as rather methods-focused, giving tidbits of interesting properties of the dynamics of plant VOCs rather than being structured strongly around clear biological hypotheses. The method seems like a logical and robust way to analyze the dynamics of plant VOCs. I believe the impact of the work will largely depend on whether there are substantial and meaningful outcomes (for herbivores, downstream processes of induction, etc) due to the differences in VOC dynamics described via these methods that would be hard to observe in other ways. If so, there will be a need to adopt robust methods such as this to describe the salient features of those dynamics. At present, I do not believe there is evidence one way or another as to whether the subtle differences in VOC dynamics have large consequences.

The paper sells itself as describing a new technique for describing response curves generally across biological systems, but it only uses this technique to look at the dynamics of induced plant volatiles. I believe to show general utility of this approach, a wider range of examples of plastic responses to stimuli across organismal groups would be needed. I am, however, convinced that this approach is both novel and useful within the scope in which the examples are shown (i.e. in describing the dynamics of induced plant responses). Some of the text purporting novelty in uncovering shared and divergent responses across the tree of life seems pretty overstated.

Much of the introductory and discussion text is quite broad, and I wonder if the technique is really meant to be applicable to the specific case that is described (repeated measures of an induced volatile response). Likewise, there has been considerable work in such realms as behavioral science, function-valued traits (e.g. Stinchcombe et al 2012), performance curves (Kingsolver various papers), etc to describe dynamic or variable responses phenomenologically, and there are approaches including GAMs, parametric curve fitting, and other techniques that probably report the same salient features as the approach here. Indeed, there are already statistical techniques to assess the macroevolution of response curves (e.g. Goolsby 2015) and wide discussions as to how to compare function-based responses among organisms (The Functional Phylogenies Group 2012). So in the broad scheme of biology, I am not sure I'm convinced of the novelty of the approach. However, I believe it is novel within the context in which it is used here. The salient part of the methods is that it uses predefined attributes of dynamics (onset, duration, etc) based on a gamma distribution that the researchers (with good reason) believe to be biologically meaningful. This is in contrast to multivariate approaches (e.g. Izem et al 2005) that attempt to find salient dynamic features in a less constrained way.

I would have liked to see a clear description of model fits (e.g., how much of the variation in the real data is described by the fitted model). This seems important because there are quite a number of constraints placed on model fitting - so presumably when a model blind to those constraints picks unrealistic parameters, that would suggest that the constrained model probably does not fit the data all that well.

I am curious about the normalization process in the 'normalized emission' that is analyzed throughout the study. Normalization to leaf size makes sense, though I was less clear about L459: "Additionally, values were normalized to the maximum response observed in each experiment, yielding a range of positive values < 1." Why was this needed? Is the 'maximum response observed in each experiment' across all plants/compounds/treatments or within a single plant? In general, is there a way of reporting VOC emission rates in absolute values (e.g. umol / Liter air)? Normalization would presumably not impact most curve properties very much, but it could have effects on 'integral', and the need for within-experiment normalization would suggest a lack of transferability or comparability among datasets from different experiments (at least as regards 'integral'), which is suggested as a major advantage of this approach in the discussion.

https://shottr.cc/s/3KVW/SCR-20260810-dbq.png - Doest not look aibnb faithful

https://shottr.cc/s/3eil/SCR-20260810-d9p.png these are actually google maps locations of the metro stations. so we can add them to our registry? if not there already?

Los medios considerados como el cuarto poder en un Estado democrático, y el uso de medios alternatvios, tales como las redes sociales y blogs, se le denomina El quinto poder.

Definición de alfabetismo mediático e informacional

"Una combinación de conocimiento, actitudes, habilidades y prácticas requeridas para acceder, analizar, evaluar, usar, producir y comunicar información y conocimiento en formas creativas, legales y éticas que respeten los derechos humanos. Los individuos que son alfabetas mediática e informacionalmente pueden usar diversos medios, fuentes de información y canales para sus vidas privadas, profesionales y públicas. Saben cuándo y qué información necesitan y para qué fines, y dónde y cómo obtenerla. Comprenden quién ha creado la información y por qué, así como los roles, responsabilidades y funciones de los medios, de los proveedores de información y de las instituciones de memoria. Pueden analizar información, mensajes, creencias y valores canalizados a través de los medios y otros productores de contenidos, y pueden validar la información que han encontrado y producido contra un rango de criterios genéricos, personales y basados en contexto. Las competencias AMI se extienden más allá de las tecnologías de información y comunicación y abarcan el aprendizaje, el pensamiento crítico y las habilidades interpretativas a través de las fronteras profesionales, educacionales y sociales. AMI se enfoca en todo tipo de medios (orales, impresos, analógicos y digitales) y en todas las formas y formatos de recursos."

Fuente: Declaración de Moscú de Alfabetismo Mediático e Informacional, 2012

Definición de alfabetismo mediático

"El alfabetismo mediático se ocupa de desarrollar una comprensión informada y crítica de la naturaleza de los medios masivos, las técnicas que usan y su impacto.”

Definición de alfabetismo informacional

"El alfabetismo informacional se ocupa de la enseñanza y el aprendizaje de un amplio rango de fuentes de información y formatos. Para ser "alfabeta informacional" necesitas saber por qué, cuándo y cómo usar todas estas herramientas y pensar críticamente acerca de la información que proporcionan.”>

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要約、チャプター、

運営者

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共同運用権限が付与される。

管理者が直接、新しいワークスペースを作成する。

プロフィール

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韓国

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CADI

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stolen pictures

OF shill

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Can't we?

I completed high school about 15 years ago. Back then, most communication between parents and teachers was done by phone and mail. The default assumption by the NYCDOE that every problem needs an app or program is unexplained. Using phones and keeping contact directories up to date is something that had to be done not long ago.

The NYCDOE was correct to sever its tie with third-party services, but why was the default response to build its own service? It was not long ago that there were no mobile applications for grades, attendance, and messaging.

An argument that first-time Linux users should be more concerned with their choice in desktop environment than distribution. I largely agree, but listed some distribution-specific factors worth considering in reply.

https://social.emucafe.org/naferrell/desktop-environments-and-distro-hopping-08-09-26/

• Limits of AI Coding Benchmarks

  • Standard benchmarks (e.g., SWE-bench) measure isolated, one-shot bug fixes and test completion.
  • Benchmarks fail to penalize "code slop," poor architecture, or long-term maintainability over consecutive feature iterations.

• The Code Review & Trust Bottleneck

  • While AI agents reduce feature implementation time to minutes, reviewing large volumes of generated code remains a human bottleneck.
  • Completely removing humans ("lights-off factories") leads to accumulated architectural technical debt and hard-to-debug failures.

• 4-Stage Agentic Engineering Framework

  • Product & Metrics: Define the user problem, success metrics (e.g., conversion, latency), and mockups upfront before prompting or generating code.
  • System Architecture: Outline service interaction, endpoints, database schemas, and data flow at a high level.
  • Program Design: Define types, method signatures, call stacks, and test expectations early in a fresh context window for maximum model reasoning efficiency.
  • Vertical Slices (Tracer Bullets): Build thin end-to-end slices (e.g., mock API → front-end → business logic) rather than horizontal layer-by-layer builds, enabling step-by-step verification and steering.

• Context Engineering & Repository Strategy

  • Keep context windows tight, structured, and high-signal; store context as plain files (/doc/ADR, PRDs, markdown docs) directly in the Git repository.
  • Reset context or compact state into documents when models reach high token counts ("dumb zone" / "context anxiety").
  • Utilize deterministic feedback loops (e.g., tests, linters, LLM-as-a-judge quality rules) to back-propagate backpressure to agents.

• Focusing on True Bottlenecks

  • Avoid "token-maxing" or over-engineering multi-agent setups when code review and product validation are the actual bottlenecks.
  • Focus human intuition on high-leverage architectural and design decisions rather than reading thousands of raw generated lines after the fact.

search-upload.files.to.pinate.browser.only

oh so it is possible to have too much extraction: i.e there is some goldilocks zone or something. hmmm that is interesting...

i.e coffee is made of 30% soluble and 70% insoluble compounds :skull

why?

can you give me an example, using this method you mentioned as a super power?

It strikes me as odd to use GCC as the basis for comparison since (as I understand it) Fil-C++ is based on LLVM.

This is the most important part of the lecture. Explains about ho how and in which criterias do we need to write our essays.