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		<title>How to Write a Scientific Review (And The Upstream Work That Makes It Easier)</title>
		<link>https://bitesizebio.com/88546/how-to-write-a-scientific-review/</link>
		
		<dc:creator><![CDATA[Gorana Jendrisek]]></dc:creator>
		<pubDate>Wed, 10 Jun 2026 07:06:00 +0000</pubDate>
				<category><![CDATA[Writing, Publishing and Presenting]]></category>
		<category><![CDATA[bioscience writing]]></category>
		<category><![CDATA[hypothesis framing]]></category>
		<category><![CDATA[literature review]]></category>
		<category><![CDATA[research synthesis]]></category>
		<category><![CDATA[scientific review]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=88546</guid>

					<description><![CDATA[This article focuses on overcoming key challenges in the process of writing a scientific review paper, including selecting relevant papers and framing a clear hypothesis. It emphasizes creating a coherent narrative, organizing notes effectively, and designing clear figures to support your story. This practical guide will help you craft structured, insightful reviews that highlight gaps and connections in the literature.]]></description>
		
		
		
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		<title>How to Identify and Report AI-Generated Scientific Images</title>
		<link>https://bitesizebio.com/87638/ai-generated-images/</link>
		
		<dc:creator><![CDATA[Elisabeth Bik]]></dc:creator>
		<pubDate>Tue, 09 Jun 2026 08:22:28 +0000</pubDate>
				<category><![CDATA[Science Communication & Ethics]]></category>
		<category><![CDATA[AI-generated images]]></category>
		<category><![CDATA[bioscience research]]></category>
		<category><![CDATA[image fabrication]]></category>
		<category><![CDATA[peer review]]></category>
		<category><![CDATA[scientific integrity]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=87638</guid>

					<description><![CDATA[AI-generated images pose new challenges to scientific integrity by creating realistic but fabricated data that traditional detection and peer-review methods may miss. Researchers must scrutinize published images, report anomalies objectively, and support post-publication review to maintain trust. This requires awareness of current tool limitations and the evolving risks of accessible generative technologies in bioscience research.
]]></description>
		
		
		
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		<title>How to Choose a Methodology for Giant Unilamellar Vesicles (GUVs)</title>
		<link>https://bitesizebio.com/88674/giant-unilamellar-vesicles/</link>
		
		<dc:creator><![CDATA[Shreya Pramanik]]></dc:creator>
		<pubDate>Thu, 04 Jun 2026 07:46:19 +0000</pubDate>
				<category><![CDATA[Protein Expression and Analysis]]></category>
		<category><![CDATA[Giant Unilamellar Vesicles]]></category>
		<category><![CDATA[lipid bilayers]]></category>
		<category><![CDATA[membrane biophysics]]></category>
		<category><![CDATA[synthetic biology]]></category>
		<category><![CDATA[vesicle preparation]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=88674</guid>

					<description><![CDATA[This guide explains how to choose and prepare Giant Unilamellar Vesicles by evaluating four key experimental constraints. It compares five main methods, highlighting suitability based on buffer composition, membrane asymmetry, encapsulation needs, and size uniformity. Understanding osmolarity, density, and method-specific risks helps avoid failures, ensuring reliable GUV formation tailored to your bioscience experiment.]]></description>
		
		
		
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		<item>
		<title>Improving Empower™ Efficiency Through Better User Training and Onboarding</title>
		<link>https://bitesizebio.com/88647/empower-software-solutions/</link>
		
		<dc:creator><![CDATA[Inessa Peters]]></dc:creator>
		<pubDate>Tue, 02 Jun 2026 11:11:50 +0000</pubDate>
				<category><![CDATA[Software and Online Tools]]></category>
		<category><![CDATA[LabWissen GmbH]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=88647</guid>

					<description><![CDATA[Most Empower&#x2122; labs have trained their analysts and written their SOPs, but still see inconsistent workflows, recurring mistakes, and the same go-to experts fielding every unusual situation. The reason is almost always the same: what gets taught is where to click, not why decisions matter or how to troubleshoot when workflows break down. Here's how you can build competence more effectively.]]></description>
		
		
		
			</item>
		<item>
		<title>How Scientific Collaboration Can Stall Your Career (And How to Put Yourself First)</title>
		<link>https://bitesizebio.com/88504/scientific-collaboration/</link>
		
		<dc:creator><![CDATA[HERizon Leadership]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 08:27:55 +0000</pubDate>
				<category><![CDATA[Career Development and Networking]]></category>
		<category><![CDATA[HERizon Leadership]]></category>
		<category><![CDATA[bioscience]]></category>
		<category><![CDATA[Career Development]]></category>
		<category><![CDATA[research career]]></category>
		<category><![CDATA[research reputation]]></category>
		<category><![CDATA[scientific collaboration]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=88504</guid>

					<description><![CDATA[Scientific collaboration is valuable, but saying yes to every request outside your core research focus can stall career growth. Building a strong reputation requires owning a niche and producing work that advances your research identity. Being selective helps you gain recognition, leadership opportunities, and lasting visibility. This article offers practical advice on balancing collaboration with career growth.]]></description>
		
		
		
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		<title>How to Make a Sector Shift Confidently Using a “Career Experiment”</title>
		<link>https://bitesizebio.com/87714/sector-shift/</link>
		
		<dc:creator><![CDATA[Emilio Cosimo]]></dc:creator>
		<pubDate>Fri, 29 May 2026 07:19:48 +0000</pubDate>
				<category><![CDATA[Career Development and Networking]]></category>
		<category><![CDATA[bioscience careers]]></category>
		<category><![CDATA[biotech transition]]></category>
		<category><![CDATA[Career Development]]></category>
		<category><![CDATA[mentorship]]></category>
		<category><![CDATA[scientific careers]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=87714</guid>

					<description><![CDATA[Navigating bioscience careers involves defining clear questions, identifying knowledge gaps, and testing assumptions before making major changes. Understanding sector-specific challenges and seeking mentorship can reduce uncertainty and support better decisions. Recognizing the difference between productive stretch and overwhelming panic zones helps manage transitions effectively, ensuring career shifts are deliberate, resilient, and aligned with personal and professional goals.]]></description>
		
		
		
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		<item>
		<title>Why You Should Test Autoclave Efficiency Using Geobacillus stearothermophilus</title>
		<link>https://bitesizebio.com/88806/geobacillus-stearothermophilus/</link>
		
		<dc:creator><![CDATA[Sneha Salunke]]></dc:creator>
		<pubDate>Thu, 28 May 2026 16:06:40 +0000</pubDate>
				<category><![CDATA[Equipment Mastery and Hacks]]></category>
		<category><![CDATA[autoclave validation]]></category>
		<category><![CDATA[biological indicators]]></category>
		<category><![CDATA[Geobacillus stearothermophilus]]></category>
		<category><![CDATA[microbiology]]></category>
		<category><![CDATA[sterilization testing]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=88806</guid>

					<description><![CDATA[Geobacillus stearothermophilus is a thermophilic bacterium whose highly resistant spores are used to test autoclave sterilization efficiency. Its spores survive extreme heat due to molecular defenses, making them ideal biological indicators. Proper autoclave validation requires biological indicators rather than relying solely on gauge readings or chemical indicators to ensure complete sterilization in medical and laboratory settings.]]></description>
		
		
		
			</item>
		<item>
		<title>Why Transcriptome–Methylome Integration Can Fail (and How to Fix It)</title>
		<link>https://bitesizebio.com/87679/transcriptome-methylome-integration/</link>
		
		<dc:creator><![CDATA[Ankita Gurao]]></dc:creator>
		<pubDate>Mon, 25 May 2026 15:38:47 +0000</pubDate>
				<category><![CDATA[DNA / RNA Manipulation and Analysis]]></category>
		<category><![CDATA[data integration]]></category>
		<category><![CDATA[Epigenetics]]></category>
		<category><![CDATA[methylation]]></category>
		<category><![CDATA[RNA-Seq]]></category>
		<category><![CDATA[Transcriptomics]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=87679</guid>

					<description><![CDATA[Transcriptome–Methylome Integration often fails due to structural issues rather than biological absence. Key challenges include over-aggregation of CpG sites, variance mismatches, asymmetric data preprocessing, and inappropriate statistical models. Proper region mapping, variance assessment, covariate alignment, and cohort size evaluation are essential to detect true regulatory relationships. Addressing these factors before complex modeling improves interpretation and avoids false conclusions about methylation-expression associations.]]></description>
		
		
		
			</item>
		<item>
		<title>Stop Blaming Yourself: 3 Troubleshooting Tools For When Experiments Go Wrong</title>
		<link>https://bitesizebio.com/87526/troubleshooting-tools/</link>
		
		<dc:creator><![CDATA[Priya Halvorsen]]></dc:creator>
		<pubDate>Tue, 19 May 2026 11:19:08 +0000</pubDate>
				<category><![CDATA[Basic Lab Skills and Know-how]]></category>
		<category><![CDATA[data reliability]]></category>
		<category><![CDATA[experimental design]]></category>
		<category><![CDATA[lab workflows]]></category>
		<category><![CDATA[problem solving in science]]></category>
		<category><![CDATA[process improvement]]></category>
		<category><![CDATA[Quality Control]]></category>
		<category><![CDATA[Repeatability]]></category>
		<category><![CDATA[Reproducibility]]></category>
		<category><![CDATA[research best practices]]></category>
		<category><![CDATA[root cause analysis]]></category>
		<category><![CDATA[roubleshooting]]></category>
		<category><![CDATA[scientific methods]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=87526</guid>

					<description><![CDATA[Ever re-run the same experiment three times just to keep getting &#8220;bad&#8221; results? In reality, no result is &#8220;bad&#8221; and experiments never &#8220;fail”. Instead, they produce data; sometimes that data is clear, sometimes it is not. After running an experiment, your results are either conclusive or inconclusive, and an inconclusive result is not useless. If...]]></description>
		
		
		
			</item>
		<item>
		<title>Overcoming Challenging Targets: What To Do When Midpoint CTE Returns Inconclusive Results</title>
		<link>https://bitesizebio.com/87649/midpoint-cte/</link>
		
		<dc:creator><![CDATA[Elmar Nurmemmedov]]></dc:creator>
		<pubDate>Mon, 18 May 2026 13:13:04 +0000</pubDate>
				<category><![CDATA[Chemistry for Biologists]]></category>
		<category><![CDATA[CETSA]]></category>
		<category><![CDATA[drug discovery]]></category>
		<category><![CDATA[protein conformation]]></category>
		<category><![CDATA[target engagement]]></category>
		<category><![CDATA[Thermal shift assay]]></category>
		<guid isPermaLink="false">https://bitesizebio.com/?p=87649</guid>

					<description><![CDATA[Midpoint CETSA is a common method for assessing target engagement by measuring protein stability at a single temperature, but it can obscure important mechanistic differences in complex proteins. This article explains how multi-temperature CETSA profiles reveal conformational state-specific binding, improving compound ranking and selectivity assessment. It highlights when to move beyond midpoint CETSA, especially for covalent mechanisms, mutant selectivity, and structurally diverse compounds, providing a practical decision framework for drug discovery.]]></description>
		
		
		
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