• SeeDB2 (RI 1.518) is introduced as a superior optical-clearing reagent that enables super-resolution fluorescence imaging of intact brain tissue beyond 100 µm while preserving fluorescent proteins and fine morphology, outperforming CLARITY, 3DISCO, iDISCO and BABB.

• Key bridging papers between deep learning and neuroscience are circulated: MIT’s 2016 bioRxiv pre-print “Towards an integration of deep learning and neuroscience” and Columbia’s “Canonical computations of cerebral cortex.”

• The Stanford Question Answering Dataset (SQuAD) is highlighted as a new 100 000-question benchmark for machine reading comprehension, with human F1 at 86.8 % and the best logistic-regression model currently at 51 %.

• Chromosome-conformation-capture techniques (Hi-C, 4C, 5C) are discussed as methods that use cross-linking, restriction digestion and sequencing to map 3-D genomic contacts and long-range enhancer–promoter interactions.

• A solvent-based iDISCO-cleared mouse-embryo image immunostained for axons is shared, illustrating whole-mount labeling and the fluorescence-quenching trade-offs versus aqueous urea/sorbitol clearing formulations.

• A 2016 ASME paper presents a self-replicating robotic-factory simulation for planetary regolith utilization that employs multi-agent reinforcement learning with local and difference rewards to maximize construction productivity.

• The Rosetta Project’s 2-cm nickel micro-etched archive is proposed as an ultra-durable knowledge-storage prototype; participants debate which non-electronic reference works (e.g., CRC Handbook, Machinery’s Handbook) would be most valuable for a “hitchhiker’s guide.”

• A New Scientist 2016 report on post-mortem gene-expression—showing hundreds of genes re-activating two days after organismal death—prompts speculation about pre-death antibody labeling to capture transient molecular events.