「iot」の検索結果

Embedded/IoT Engineer — Arduino/ESP32/Raspberry Pi, sensor integration, MQTT/BLE/LoRa, firmware development, battery optimization

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Drafts an Adverse Event Reporting Policy compliant with 21 CFR 312.32 (IND safety reporting), 21 CFR 314.80 (postmarketing), and ICH E2A, with multi-jurisdictional overlays (EMA, PMDA, Health Canada). Covers seriousness/causality frameworks, expedited reporting timelines, roles, documentation standards, training mandates, and QA mechanisms. Use when drafting or updating an adverse event reporting policy, pharmacovigilance policy, AE/SAE reporting SOP, or safety reporting framework for a pharmaceutical company, CRO, biotech, or clinical research institution.

日本語の概要は準備中です。原文の説明を表示しています。

Guide setup for and verify a reviewed, read-only Grafana Observability profile and its activated capability packs. Use when a user asks to set up Grafana access, verify profile readiness, discover available infrastructure, APM, log, IoT or business KPI packs, or troubleshoot why a Grafana pack cannot run.

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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

日本語の概要は準備中です。原文の説明を表示しています。

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

日本語の概要は準備中です。原文の説明を表示しています。

Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.

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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, applies the Patel et al 2024 *Nat Biotechnol* failure-mode framework, and validates that normalization is applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.

日本語の概要は準備中です。原文の説明を表示しています。

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

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This skill determines whether testicular volume should be used as the primary or sole indicator of sexual development in male childhood cancer survivors who have received gonadotoxic treatments such as alkylating agents or testicular radiotherapy. Triggers include clinician questions like “Can I use testicular volume to assess puberty in this male survivor after chemotherapy?” or “Is testis size reliable for sexual maturity evaluation post‑radiation?”

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This skill recommends measuring serum cortisol or urine free cortisol off-medication at 6- to 12-month intervals to assess radiation therapy effect in patients with Cushing's syndrome undergoing or post-radiotherapy. It is also indicated when patients on stable medical therapy develop new adrenal insufficiency symptoms such as fatigue, weight loss, or hypotension.

日本語の概要は準備中です。原文の説明を表示しています。

This skill recommends that a multidisciplinary team, including an experienced endocrinologist, incorporates patient values and preferences while providing education about treatment options for Cushing’s syndrome. Use when planning treatment for a CS patient requiring coordinated care; triggers include persistent hypercortisolism after transsphenoidal surgery, consideration of bilateral adrenalectomy, radiotherapy, or medical therapy, and need for shared decision‑making.

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Interprets the postural stimulation test to detect angiotensin II‑unresponsive aldosterone‑producing adenoma (APA) or familial hyperaldosteronism type I (FH‑I) when aldosterone fails to rise with upright posture. Triggered by clinical cues such as “postural stimulation test shows no aldosterone increase”, “suspected APA with non‑responsive aldosterone”, or “evaluating FH‑I in young patients with early‑onset hypertension and hypokalemia”.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level microbiology covering bacterial and viral biology, microbial physiology, pathogenesis, antimicrobial resistance, microbiome science, and industrial microbiology.

日本語の概要は準備中です。原文の説明を表示しています。

Kit de ferramentas abrangente para desenvolvimento com a biblioteca CocoIndex. Use quando os usuários precisarem criar pipelines de transformação de dados (flows), escrever funções personalizadas ou operar flows via CLI ou API. Abrange a construção de workflows ETL para processamento de dados de IA, incluindo incorporação de documentos em bancos de dados vetoriais, construção de grafos de conhecimento, criação de índices de busca ou processamento de fluxos de dados com atualizações incrementais.

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Criando visualizações de dados interativas usando d3.js. Esta habilidade deve ser usada ao criar gráficos customizados, diagramas de rede, visualizações geográficas ou qualquer visualização de dados complexa baseada em SVG que requeira controle fino sobre elementos visuais, transições ou interações. Use isto para visualizações personalizadas além das bibliotecas de gráficos padrão, seja em React, Vue, Svelte, JavaScript vanilla ou qualquer outro ambiente.

日本語の概要は準備中です。原文の説明を表示しています。

Busca web e pesquisa usando Perplexity AI. Use quando o usuário disser "pesquisar", "encontrar", "procurar", "perguntar", "pesquisar" ou "qual é o mais recente" para consultas genéricas. NÃO para docs de biblioteca/framework (use Context7) ou perguntas do workspace.

日本語の概要は準備中です。原文の説明を表示しています。

Extrai e analisa anúncios de concorrentes de bibliotecas de anúncios (Facebook, LinkedIn, etc.) para entender que mensagens, problemas e abordagens criativas estão funcionando. Ajuda a inspirar e melhorar suas próprias campanhas de anúncios.

日本語の概要は準備中です。原文の説明を表示しています。

Biblioteca do Facebook para busca eficiente de similaridade e clustering de vetores densos. Suporta bilhões de vetores, aceleração GPU e vários tipos de índice (Flat, IVF, HNSW). Use para busca k-NN rápida, recuperação de vetores em larga escala ou quando você precisa de busca pura de similaridade sem metadados. Melhor para aplicações de alto desempenho.

日本語の概要は準備中です。原文の説明を表示しています。

Fornece orientação para treinamento RL agentico nativo do PyTorch usando torchforge, biblioteca da Meta que separa infraestrutura de algoritmos. Use quando você quer abstrações limpas de RL, fácil experimentação de algoritmos, ou treinamento escalável com Monarch e TorchTitan.

日本語の概要は準備中です。原文の説明を表示しています。

Extraia dados estruturados de respostas de LLM com validação Pydantic, tente novamente automaticamente extrações que falharem, analise JSON complexo com segurança de tipo e transmita resultados parciais com Instructor - biblioteca de saída estruturada testada em batalha

日本語の概要は準備中です。原文の説明を表示しています。

Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.ai

日本語の概要は準備中です。原文の説明を表示しています。

Ajuste fino com eficiência de parâmetros para LLMs usando LoRA, QLoRA e 25+ métodos. Use ao fazer fine-tuning de modelos grandes (7B-70B) com memória GPU limitada, quando precisa treinar <1% dos parâmetros com perda mínima de precisão, ou para serviços multi-adapter. Biblioteca oficial do HuggingFace integrada ao ecossistema transformers.

日本語の概要は準備中です。原文の説明を表示しています。