{"id":3933,"date":"2026-02-04T10:21:04","date_gmt":"2026-02-04T08:21:04","guid":{"rendered":"https:\/\/engage.cyi.ac.cy\/?post_type=news&#038;p=3933"},"modified":"2026-02-04T10:21:06","modified_gmt":"2026-02-04T08:21:06","slug":"engage-article-beyond-one-tool-designing-experiments-that-work-together","status":"publish","type":"news","link":"https:\/\/engage.cyi.ac.cy\/?news=engage-article-beyond-one-tool-designing-experiments-that-work-together","title":{"rendered":"ENGAGE Article: Beyond One Tool: Designing Experiments that Work Together"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"768\" height=\"1024\" src=\"https:\/\/engage.cyi.ac.cy\/wp-content\/uploads\/2023\/04\/photo_Shishkov-768x1024.jpg\" alt=\"\" class=\"wp-image-3130\" style=\"aspect-ratio:0.7500000180506184;width:180px;height:auto\" srcset=\"https:\/\/engage.cyi.ac.cy\/wp-content\/uploads\/2023\/04\/photo_Shishkov-768x1024.jpg 768w, https:\/\/engage.cyi.ac.cy\/wp-content\/uploads\/2023\/04\/photo_Shishkov-225x300.jpg 225w, https:\/\/engage.cyi.ac.cy\/wp-content\/uploads\/2023\/04\/photo_Shishkov.jpg 960w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p><strong>by Rodion Shishkov<\/strong><\/p>\n\n\n\n<p>Most of us do experimental research to test a hypothesis or characterize a sample. We design a measurement, acquire data, and analyze it to decide whether the evidence supports our goal. Techniques steadily improve\u2014faster detectors, sharper optics, smarter software\u2014but the hardest problems often remain: heterogeneous samples, tight time budgets, and the gap between a few striking images and results that are statistically solid.<\/p>\n\n\n\n<p>Automation is a powerful lever, yet truly automatic experiments are rare. Routine protein crystallography at synchrotrons is a notable success because the workflow is standardized: mount crystal, center, collect, repeat. It resembles an assembly line and needs little judgment about the sample\u2019s future value. By contrast, \u201cfull autonomy\u201d would mean giving a system a sample and a scientific objective, and having it plan measurements across many options to reach that objective. That level of generality matches what people call artificial general intelligence (AGI); we are not there yet.<\/p>\n\n\n\n<p>What works today is multi-technique thinking. Pair a well-established, accessible method with a second technique that has high potential but practical limits. In our lab, we combined optical fluorescence imaging of cryogenically fixed human cancer cells with nanoscale X-ray fluorescence (XRF) microscopy. Two neural networks first analyzed the fluorescence data: one segmented cells, and another ranked regions of interest (ROIs) worth the costly XRF scan. The XRF instrument then auto-navigated to those ROIs without operator input. This fusion preserved the strengths of both methods\u2014speed and context from optics; elemental sensitivity from XRF\u2014while lifting a major constraint: throughput. Most importantly, it shifted our conclusions from \u201ca few compelling examples\u201d to statistics drawn from large, representative datasets.<\/p>\n\n\n\n<p>My message is simple: when you feel stuck, look sideways. Ask which complementary technique could guide, filter, or accelerate the one you already use. If no such pairing exists, try to build it; the act of linking methods can become a new methodology in its own right. Multi-technique design is not a detour\u2014it\u2019s often the shortest path to reliable, relevant science.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>by Rodion Shishkov Most of us do experimental research to test a hypothesis or characterize a sample. We design a measurement, acquire data, and analyze it to decide whether the evidence supports our goal. Techniques steadily improve\u2014faster detectors, sharper optics, smarter software\u2014but the hardest problems often remain: heterogeneous samples, tight time budgets, and the gap [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","tags":[],"news-category":[],"class_list":["post-3933","news","type-news","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=\/wp\/v2\/news\/3933","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=\/wp\/v2\/news"}],"about":[{"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=\/wp\/v2\/types\/news"}],"author":[{"embeddable":true,"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3933"}],"version-history":[{"count":1,"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=\/wp\/v2\/news\/3933\/revisions"}],"predecessor-version":[{"id":3934,"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=\/wp\/v2\/news\/3933\/revisions\/3934"}],"wp:attachment":[{"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3933"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3933"},{"taxonomy":"news-category","embeddable":true,"href":"https:\/\/engage.cyi.ac.cy\/index.php?rest_route=%2Fwp%2Fv2%2Fnews-category&post=3933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}