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Yesterday in My Dream… I told the driver of a car that I hitchhiked that I was doing a PhD in Computer Science. (What kind of subconscious is this?) The road was dark, endless. I couldn't see the end. So, I decided to hitchhike. Then, I saw a mini Turtle car—light green, safe-looking—so I trusted it. I held up my arm, and inside was another student. The driver’s seat? In the back. 🚗💨 Through the darkness, we drove, and we arrived in the city. Antalya, apparently. I asked him, “Which way do you need to go?” He said, “I need to turn right.” So, I got off at the intersection. Then, I noticed I forgot my belongings in the car. My phone and a red paper bag were left behind. I went back and found them neatly placed on the sidewalk. How very considerate of my subconscious driver. Logistic Regression in My Dreams?! I had been studying logistic regression all weekend. Apparently, my brain decided this meant I was doing a PhD in Computer Science. 💭 This is why we don’t cram for exams, people. To recover, we watched Güldür Güldür to reset our happiness levels. One quote stood out: “If your conscious is a mirror, then your subconscious is the cover behind the mirror.” (Aynanın ardındaki sır.) Profound. You may want to watch how Turkish comedians view Trump as well. :) How to Become a Millionair Using LinkedIn Overnight 1️⃣ Take a deep breath in. 2️⃣ Drop the “e” from millionaire. That’s it. 💸😂 P.S. I should acknowledge my inspiration: A black van driving in front of me on I-64 West today. www.millionair.com 🤦‍♀️ P.P.S. My mental state was perfectly prepped for this joke after listening to this song 20 times: 🎵 P.P.P.S. Also restarted my Vitamin D pills  (5000 IU/day), because, you know, forced richness starts with forced serotonin. 🌞💊 From Forced Happiness to Forced Education: Statistics Edition My next goal? A forced education on statistics formulas. Time to drive pluripotency of my grey region neurons to wake up, proliferate, and make new connections. I even suggested an algorithm to ChatGPT yesterday. It said, “This already exists. It’s called UMAP.” 🤡 This was as funny as ChatGPT asking me to verify that I am a human. BRO, WHAT DO YOU MEAN? Final Thoughts: Water Your Plants 🌱 Enjoy your Tuesday. Take your vitamins. And remember: Water your plants, or they’ll use t-SNE to dimensionality reduce themselves into the void. 🌱

In my last post, we talked about what happens when a cell becomes a cancer cell. Today, let’s talk about environmental and preventable risk factors that contribute to cancer.. We know that genetics plays a role, and some cancers are inherited. But beyond genetics, there are layers of risk factors that we can control. 🔬 Have You Heard of Epigenetics? Epigenetics refers to factors that influence gene expression without changing the actual DNA sequence. It can protect us or increase our risk of disease. Think of your DNA as a thread wrapped around small balls called nucleosomes. The accessibility of DNA is controlled by epigenetic modifications. 🧬 If DNA is tightly packed (heterochromatin), it’s less accessible. 🛑 If DNA is looser (euchromatin), certain genes—including disease-causing ones—may become active. 🏠 How Does the Environment Influence Cancer Risk? Your lifestyle and surroundings can modify chromatin structure, making harmful genes more or less active. For example: ✅ If you inherit harmful mutations but they stay silent, your risk is lower. ❌ If your environment triggers those genes to become active, your risk increases. ⚠️ Key Environmental Risk Factors for Cancer: 1️⃣ Unhealthy Diet & Chronic Inflammation – Obesity, ultra-processed foods, and inflammatory triggers increase cancer risk. 🥤🍔 2️⃣ Tobacco smoking, air pollution, asbestos exposure.  3️⃣ Chemical Exposure – Certain cosmetics, hair products, plastics, and banned chemicals can disrupt cellular health. 🧴☠️ 4️⃣ Disrupted sleep, impacting circadian rhythm. 5️⃣ Ultraviolet light exposure is not good for skin.  6️⃣ Tattoos? – A recent study suggests that tattoo inks may pose risks. 🖋️ 🏃 What Can We Do? While we can’t control our genes, we can lower our risk by: 🥗 Eating whole, natural foods and reducing inflammatory triggers. 🚿 Minimizing chemical exposure—be mindful of cosmetics & household products. 🤔 Considering the long-term effects before making lifestyle choices (e.g., tattoos). 🔄 What Else Comes to Mind? What other preventative measures do you think we should talk about? Drop your thoughts below! ⬇️ We will talk more about ultra processed foods next.

We often talk about ultra-processed foods, but how they’re cooked matters too! 🍗🔥 When foods undergo high heat processing—like frying, roasting, or grilling—sugars attach to proteins and fats, forming harmful compounds called Advanced Glycation End Products (AGEs). 🍯🥜 What Foods Are High in AGEs? 🟢 Low AGE foods: Fresh fruits 🍏, vegetables 🥕, dairy 🥛, and whole grains 🌾. 🔴 High AGE foods: Fried chicken 🍗, grilled meats 🥩, honey-roasted nuts 🥜, and processed snacks 🏭. ⚠️ Why Should We Care About AGEs? AGEs are linked to: 🚨 Premature aging and organ damage 🏥 🔥 Chronic inflammation and metabolic disorders 🔥 🦠 Cancer growth & metastasis Can We Reduce AGEs in Our Diet? ✅ Cook at lower temperatures (steaming, boiling, slow-cooking). ✅ Swap fried & grilled for baked or raw options. ✅ Eat more whole, unprocessed foods. This research raises big questions for public health. Should we be more mindful of not just what we eat, but how we cook it? Let’s discuss in the comments! ⬇️

Did you know? A recent NIH study found that ultra-processed foods (UPFs) make up 70% of the American diet! 🍔🥤 🔬 This study, led by Dr. Kevin Hall at NIH Bethesda, had participants live in a controlled setting, tracking their food intake, body measurements, metabolism, and even the air they breathed out! 🫁🔥 (I will put the link in the comments) 🍞 What Counts as Ultra-Processed? Even foods we consider “healthy”—like sliced bread—often contain additives and preservatives that push them into the ultra-processed category. 🏭❌ A landmark study used AI & machine learning to analyze food labels in grocery stores. Results? A staggering 73% of U.S. food supply is ultra-processed! 🤯 🔹 Example: A raw onion = minimally processed 🧅 ✅, but frozen onion rings = ultra-processed 🏭❌. The processing changes its natural components more than 10-fold! ⚠️ Why Does This Matter? Eating UPFs makes it harder for our bodies to regulate hunger, leading to overeating, metabolic diseases, and increased health risks. The big question: Should governments regulate UPFs the same way they regulate tobacco & alcohol? 🤔 Drop your thoughts below! ⬇️

Ever wonder what your cells are up to? 🤔 The first lecture in Molecular Cell Biology always starts with just how small cells are. A typical cell is 10 µm by 15 µm—so tiny that I used to ask my students: 💡 How many cells do you think fit under your pinky nail? If you think of it as a 2D surface, the answer is ~670,000! 😲 That’s a LOT of cells—and that’s just your pinky! Now imagine your entire body! 🛠️ What Do Cells Actually Do All Day? It depends on where they are! Cells are masters of teamwork 🏗️. They’ve evolved to function as part of a community, sharing work perfectly and communicating at almost miraculous levels. Cells are alive: ✅ They move, eat, and produce waste 💩 ✅ They send & receive signals 📡 to talk to each other ✅ They adjust to their environment 🏗️ by changing their cytoskeleton, gene expression, or even dividing into two. I used to joke in class: 💬 “What is a Molecular Cell Biology teacher? Just a pile of cells… talking about cells.” 😂 🧬 Not All Cells Are the Same! Recent single-cell sequencing technologies 🧪 have confirmed something fascinating: 🌀 Each cell is unique—no two are exactly alike! This diversity comes from layers of regulation, allowing infinite combinations of gene expression and function. Let’s take a sneak peek at a few specialized cells: 🍽️ Intestinal Cells: The Ultimate Nutrient Transporters Your intestinal epithelial cells work hard to absorb glucose & nutrients from your food. 🥞  🔹 If these cells stopped working, everything you ate would just pass through as waste! 😱 🔹 Even if there's just ONE glucose molecule, these cells will grab it and push it into the bloodstream using the Na+/Glucose Symporter and the Na+/K+ pump. (Okay, this part is getting technical! I will post a link to dive deeper if you’re curious!) ⚡ Excitable Cells: The Body’s Electricians! Did you know? 💡Your neurons and muscle cells experience electric fields of 200,000 volts per cm (V/m) across their membranes—nearly 100,000 times stronger than high-voltage power lines! ⚡😵 So how do they survive this? Think of them as skilled dancers 💃—they perform a Mexican wave-like motion to pass messages along, using action potentials, which again involves ion channels and pumps. ⚡ Neurons send signals → Muscle cells contract → You move your finger! (Muscle contraction is a BIG topic—if you want to see how it works, check out this video in the comments! 🎥) 🔬 So Many More Amazing Cells! We haven’t even talked about: 🩸 Blood cells 🏋️ Smooth muscle cells 🛑 Adipose (fat) cells I’ll save those for another post! 😉 🎯 The Big Picture Your cells are busy—working 24/7 so that you can thrive, learn, grow, and experience life. Take a moment to thank the tiny beings that make you who you are. 🫶 Thank you, little me! 🧬✨

A Deep Dive into Stochastic Neighbor Embedding Introduction In high-dimensional data visualization, t-Distributed Stochastic Neighbor Embedding (t-SNE) has become one of the most popular techniques. It allows us to map complex datasets into two or three dimensions while preserving local structures. But how does it work? In this post, we will break down t-SNE step by step, drawing insights from its mathematical foundations and practical applications. 1. What is t-SNE Trying to Solve? When we work with high-dimensional data (e.g., images, gene expression data, or word embeddings), understanding its structure becomes difficult. We need a way to: ✅ Reduce the data to 2D or 3D for visualization. ✅ Preserve clusters and local relationships. ✅ Avoid distortions caused by simple linear transformations (like PCA). Unlike PCA, which is a linear method, t-SNE is non-linear and focuses on preserving local neighborhoods. This makes it ideal for detecting clusters and substructures in data. 2. What Does "Stochastic" Mean in t-SNE? "Stochastic" means random but with structure . In t-SNE: We start by randomly positioning the data points in low dimensions. We then iteratively adjust their positions, using probabilities to match their high-dimensional relationships. This randomness helps avoid bad solutions and ensures better local clustering. Because of this, t-SNE’s output can vary slightly across runs—unlike PCA, which always gives the same result: tSNE with seed 42 tSNE with seed 100 3. The Key Idea Behind t-SNE At its core, t-SNE works by modeling similarities between points in both high-dimensional and low-dimensional spaces: Step 1: Compute Pairwise Similarities in High-Dimensional Space Each data point gets a probability score for how similar it is to other points. This is done using a Gaussian distribution centered at each point. Similar points have high probability, while distant points have low probability. Step 2: Compute Pairwise Similarities in Low-Dimensional Space Instead of a Gaussian, we use a t-distribution (which has heavier tails to avoid overcrowding). The goal is to make sure that the low-dimensional probabilities match the high-dimensional ones as closely as possible. Step 3: Minimize the Difference (KL Divergence) The "error" function t-SNE minimizes is called Kullback-Leibler (KL) Divergence . This function tells us how different two probability distributions are. Using gradient descent, t-SNE moves points in 2D space until the low-dimensional structure resembles the high-dimensional one . 4. Why Does t-SNE Sometimes Look Different Every Time? Since t-SNE is stochastic, each run may produce a slightly different layout. This happens because: The algorithm starts with a random initialization of points. The optimization process can get stuck in different local minima. Different perplexity values (a hyperparameter) can change how local/global structure is balanced. 🔹 Solution: Run t-SNE multiple times and look for stable patterns! You can also set a seed for reproducibility.

Understanding Dimension Reduction: PCA vs. t-SNE vs. UMAP with the Swiss Roll When dealing with high-dimensional data, it is often challenging to visualize and interpret patterns. Dimensionality reduction  helps us project complex datasets into fewer dimensions while preserving meaningful structure. In this post, we will explore two popular dimensionality reduction techniques—Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE)—using a classic example: the Swiss Roll dataset. The Swiss Roll Dataset The Swiss Roll is a synthetic dataset where points are arranged on a curved 3D surface, forming a spiral-like shape. The challenge in dimensionality reduction is to flatten this 3D spiral while preserving important relationships between points. In this figure, each point is color-coded based on its position along the spiral. Ideally, after dimensionality reduction, points with similar colors should remain close together. PCA: Preserving Global Structure Principal Component Analysis (PCA) is a linear  dimensionality reduction method. It works by identifying the directions (principal components) that capture the most variance in the data and projecting the dataset onto these components. When PCA is applied to the Swiss Roll, we obtain the following projection: Key Observations: ✅ PCA successfully collapses the Swiss Roll into two dimensions. ✅ It preserves the global structure, meaning the overall spiral shape is still visible. 🚨 However, PCA distorts local relationships—some points that were far apart in 3D may appear close in 2D. 📌 PCA is useful when the data follows a linear structure and we need a fast, interpretable projection. t-SNE: Preserving Local Structure t-Distributed Stochastic Neighbor Embedding (t-SNE) is a non-linear dimensionality reduction technique that focuses on preserving local relationships  in the data. Instead of a linear projection, t-SNE maps points into 2D space while trying to maintain the pairwise similarities between points. Applying t-SNE to the Swiss Roll results in: tSNE projection Key Observations: ✅ t-SNE successfully unrolls the Swiss Roll by grouping similar points together. ✅ It preserves local neighborhoods—points that were close in 3D remain close in 2D. 🚨 However, t-SNE does not preserve the global spiral shape; instead, it breaks the roll into clusters. 📌 t-SNE is great for discovering clusters in high-dimensional data but does not maintain global structure. Comparison: PCA vs. t-SNE Feature PCA (Linear) t-SNE (Non-Linear) Preserves Global Structure? ✅ Yes ❌ No Preserves Local Structure? ❌ No ✅ Yes Works Well for Curved Data? ❌ No ✅ Yes Good for Cluster Discovery? ❌ No ✅ Yes Computationally Efficient? ✅ Fast ❌ Slow If you need a quick, structured projection, PCA is a great choice. If you need to find clusters and preserve neighborhood relationships, t-SNE is better. Both PCA and t-SNE are powerful tools for reducing dimensionality, but they serve different purposes. PCA is best when global structure matters, while t-SNE is useful for local clustering. Understanding these differences is key when visualizing and interpreting high-dimensional datasets. I am still debating, if I understood the whole concept. But this is essentially it. :) What about UMAP? At this point, I questioned, don't we need an algorithm that can preserve the spiral shape better? The answer is UMAP. We can argue this is still not the perfect spiral shape but better than the tSNE's broken apart pieces. UMAP Prioritizes Local Structure UMAP is designed to preserve local distances  more than global distances. It maintained the order  of the Swiss Roll but didn’t force it into a perfectly smooth spiral. Why UMAP Might Be the Best Choice for You Feature PCA t-SNE UMAP Preserves Global Structure? ✅ Yes ❌ No ✅ Yes Preserves Local Structure? ❌ No ✅ Yes ✅ Yes Distributes Points Naturally? ❌ No (line) ❌ No (overclusters) ✅ Yes (balanced) Fast for Large Data? ✅ Yes ❌ No (slow) ✅ Yes (faster) Have you used any of these algorithms? How was your experience?

Today I am trying to understand the logic behind dimension reduction (DR). I wanted to read and write about some simple examples to illustrate for myself and maybe help others learn as well. The initial example that comes to my mind is: taking a photograph. In a broad, physical sense , when you take a photograph, you’re mapping a three-dimensional scene onto a two-dimensional image. This is essentially a projection  from 3D to 2D, so in that way, it is  a form of dimensionality reduction: you’re reducing reality’s three spatial dimensions down to two. Other daily life examples (AI curated) that convey the core idea , taking something with many “dimensions” (or aspects) and representing it in fewer dimensions while preserving essential information are: Flattening a Globe into a Map What happens: A 3D Earth is “projected” onto a 2D map. Why it’s dimensionality reduction: We lose one dimension (from the globe), but we keep key relationships like continents’ relative positions. Caveat: Maps can distort distance or area (like Greenland appearing huge), which parallels how some DR methods distort global relationships while preserving local ones. Summarizing a Restaurant’s Quality into a Single Rating What happens: You might consider multiple dimensions of a restaurant’s quality—food taste, service, ambiance, cleanliness, price, etc.—but you often see a single star rating (like 4.5 out of 5). Why it’s dimensionality reduction: Many different criteria (dimensions) get “compressed” into one number. Caveat: You lose which specific aspect is strong or weak—just like DR methods sometimes lose nuance about individual features. Compressing an Image to a Smaller Size What happens: Suppose you have a photo with a resolution of 4000×3000 pixels (12 million dimensions if each pixel is one dimension!). If you resize it to 400×300, that’s only 120,000 pixels. Why it’s dimensionality reduction: You’ve drastically reduced the number of “features” while still preserving a recognizable version of the image. Caveat: You lose details and can’t zoom in without seeing pixelation. Using Principal Components to Summarize Test Scores What happens: Imagine a student has scores in 10 subjects (Math, English, History, etc.). You could run Principal Component Analysis (PCA) on those scores and reduce the 10 dimensions to just 2 or 3. Why it’s dimensionality reduction: You capture the main “patterns” (e.g., a “STEM aptitude” axis vs. a “Humanities aptitude” axis), letting you plot students in 2D. Caveat: You lose the specific breakdown per subject, but you gain a simpler, bird’s-eye view of performance. Buying a Laptop: Distilling Many Specs into One Decision What happens: When shopping for a laptop, you look at many specs—CPU speed, RAM, screen size, weight, battery life, price. Why it’s dimensionality reduction: Often, a reviewer or your own personal assessment might roll all of those into a single overall rating or “best value pick.” Caveat: You lose the nuance of each spec’s details, but gain an easy decision metric. Just like the restaurant rating or the laptop buying example, we compress multiple measures into fewer measures or a single score. The key is doing so in a way that preserves the most important relationships—whether that’s the overall “ quality ,” the essential structure of an image, or the local clustering of data points in a complex dataset. Any time you reduce the number of descriptors while trying to retain essential information , you’re performing a kind of dimensionality reduction . I hope this was helpful! I will write on popular dimension reduction techniques next.

Trying to beautify the place you are in, shows your joy of life.  Joy of life does not pour as rain. You have to cultivate it in yourself. Yes, that means effort. Today we worked on the backyard, added top soil, removed weeds, raked leaves and moss.  I am hoping to get some flower arrangements in some pots to decorate the deck.  It is easy to give up, stay down, and just let things go. It requires effort to stand up, fill buckets with soil, add plants or seeds. But the reward is bigger. Once: you feel healthier with more outside time, energy spent and increased productivity.  Second, the plants and flowers beautify your landscape and give you joy of life in return 🌷🌻🌺  Examine the blooming flowers in the super dry looking stems, how come they made it out? How come they are green? How come there is even a flower?  Third if you planted fruits and vegetables, you have fresh things to add to your table 😇 What are some ways that help you feel accomplished at home? Tidying up your desk? Laundry? Dishes?

We tossed out our Ninja coffee maker because it was burning the coffee. I’d almost say my husband felt a wave of pleasure dumping it and going back to our old-fashioned coffee maker from Walmart. This ties into my previous post, "Why Are Some Things Expensive?" It’s frustrating to splurge on a high-end product with more features, only to end up with a worse outcome. Over the weekend, we got our car washed at an automated car wash station. As with most things in my life, this seemingly mundane experience triggered a cascade of questions. What are those black rotating brushes made of? If they can get dirty, does someone have to clean them? If they’re rubbery, maybe they just rinse off? How does the machine adjust to different cars? What if the rubbery things make scratches? Then, we pulled up to the vacuum station. Another round of questions. Where does all the dirt go? There’s a locked compartment—what’s inside? Oh, that’s where the coins go. How much does this car wash station make? How much does it cost to build one? Let’s assume it costs $200K. If each wash generates a $5 profit and 200 cars get washed per day, the station starts turning a profit after 200 days. Why am I using my precious ion gradients to think and calculate such things? You don’t just enter the lab as a Ph.D. scientist and turn off the questioning when you hang up your lab coat at the end of the day. The scientist’s mind is always on. And speaking of Ph.D.—Doctor of Philosophy. I’d argue I spent a good chunk of my middle school years practicing adolescent philosophy. But what does philosophy even mean? Why do we call certain people philosophers? I could question everything—outer space, the insides of atoms, patterns and connections between things, associations, and links. I could question cause and effect. I could even question questions themselves. Fortunately (or unfortunately?), this process never stops. As I climbed the hill from the parking lot, another thought hit me: movement, migration. What does it mean to migrate? What does it mean to be an immigrant? If humans move to Mars, what will we call them? Earthlings? 😂 Would plants grown in Martian soil nourish humans made from Earth’s soil? Who will be the first to test this? How will land be allocated on Mars? In the past, people built fences and—ta-da!—the land was theirs. Will we do the same on Mars? If I had $200K, I could have built a car wash station and made automated money. But I’m a Ph.D. scientist, so instead, I’ll keep asking questions. 😆

While shopping for a shirt for my husband, I noticed something strange—one shirt costs $20, while another (same fabric, same function) costs $150 just because it has a Ralph Lauren tag. What exactly are we paying for? I even saw a woman carrying a Michael Kors bag—clear plastic, no zipper. What’s the point? What are we really buying? Is it just about putting ourselves in a certain social class? Lately, my husband has been looking for a new car. I imagined him driving a Tesla, like many of our friends. Why did I want him to buy a Tesla? Was it about the car’s actual value? Was it about sustainability, charisma, or just keeping up with the crowd? He shut the idea down quickly—he doesn’t like Elon. But I kept questioning myself: what makes something worth it ? Last week, we got a new vacuum. We could have bought any brand, but we went with Dyson. Why do we trust certain brands more? In this case, we had firsthand experience—our old Dyson worked well. My father, a mechanic at heart, was amazed by Dyson’s clever engineering—how the vacuum’s suction powers the roller without extra parts. A brand wraps a product in confidence, and that confidence builds trust. The same logic applies to product reviews . When I buy books on Amazon, if a book has 300,000 positive reviews, I’m sold. But what if I’m overlooking high-quality books that never went viral? Is this just survival of the fittest  in the business world? And then there’s art . Some pieces sell for ridiculous amounts. Like the banana taped to a wall—that banana was one of the cheapest fruits you can buy, yet a collector paid who knows what for it. Then he just ate it. Scarcity and the Price of Treasures I used to tell my kids that humans, like children on Earth, dig things up and call them treasures. People want them, and suddenly, they become expensive. The harder it is to find something, the more valuable it becomes—like diamonds and pearls. That’s branding in its simplest form. But branding isn’t just about luxury goods—it’s everywhere, even in science. A research lab, just like a company, thrives on reputation and visibility. Branding in Science: The Prestige Factor For scientists, branding yourself or your lab depends on publications—the number, but more importantly, the impact . There are other metrics, like the h-index , but search committees often scan for elite journal names and prestigious university affiliations on a CV. Want to "brand up"? You need reviews —in the form of recommendation letters . And if your recommenders are famous, well-ranked, and highly impactful, then your letter shines even brighter. Selling your research is crucial for obtaining funding. Your idea must fill a significant knowledge gap, answering questions that bring science closer to real-world solutions. Even publishing a paper involves wrapping raw data into a compelling narrative—highlighting the "selling points" to make it stand out. Selling Yourself: A Tougher Game Branding yourself as a scientist is tricky. "I can solve your biomedical research problems"—too ambitious. "I run well-planned, controlled experiments, and if p < 0.05, I reject the null"—not exactly a TED Talk moment. But hey, in science, sometimes that’s all the branding you need. :)

Confidence and Women: A Seedling That Grows This Women’s Day, I found myself reflecting on confidence—why do women, on average, have lower confidence levels than men? At a physiological level, one clear reason is vulnerability. Women face higher risks of sexual violence, and they bear the biological responsibility of childbirth. These factors alone could contribute significantly to the confidence gap. In some parts of the world, particularly in the Middle East, the concept of virginity further compounds the issue. A woman who loses her virginity unlawfully is often seen as "damaged" or "less valuable." You could watch a thousand episodes of Turkish dramas dissecting this theme. The message is clear: women are born into a world where they are often raised with these deep-seated misconceptions. It’s no wonder many struggle with confidence. But what about at the cellular level ? Could there be a biological factor at play? In one of my lectures, a student once asked, "Isn’t it unfair that women only have one active X chromosome while men have both an X and a Y?"  I laughed and said, "Yes, it does sound unfair."  But then I thought—perhaps it’s actually a mark of efficiency. Women get the same job done with just 45 active chromosomes. While ruminating on the unfairness of the inactive X chromosome, I came across something fascinating: about 30% of the genes on the inactive X remain active, and they may help protect women from cognitive decline as they age .  Hooray for the hidden advantages! Confidence, or the perception of it, plays a role in how women are treated, especially in professional settings. One striking example is the story of a Stanford professor who transitioned from female to male . After the transition, the same researcher presented the same work—but this time, a male colleague remarked, "His data is much stronger than his sister’s."  He failed to realize that they were the same person. Growing a Confidence Tree Recognizing the confidence gap is one thing, but how do we bridge it? I imagined a confidence tree —one that starts as a tiny seedling. Right now, I might be at the seed stage, but I can nurture it with intentional practice. To help my seedling grow, I need to: Learn to coexist with my imperfections Accept and encourage myself to be myself Stop assuming that everyone else in the world knows more than me The Confidence Code   suggests that formal education—college, master’s degrees, PhDs (possibly even postdocs)—doesn’t necessarily build confidence. If anything, diving deeper into research makes you more aware of how little you know. Constantly switching fields to find a job only amplifies this feeling. This realization of not knowing enough  is one of confidence’s biggest enemies. Still, under these non-ideal conditions, we can keep watering our confidence seedlings . I will keep showing up. I will keep adding knowledge, mastering skills, and growing new leaves. With time, this confidence tree will develop its own momentum. The more it grows, the more it feeds itself —like a bestselling author producing one hit after another, or a tenured professor winning awards and grants year after year. Women and Confidence: Progress and Role Models It’s astonishing to think that women weren’t even included in clinical trials until 1995. That they weren’t allowed to vote until 1920. Given this history, the fact that we now have female Nobel laureates and world leaders is proof of how far we’ve come. What does true confidence look like? I picture Liz Allison , my former department chair . When she led faculty meetings, it felt like a warm breeze—welcoming, open to ideas, and effortlessly in control. Nothing aggressive, nothing forceful. Just a natural and elegant  way of leading. I could choose to appear confident  while quietly battling self-doubt inside. I can also work to genuinely build confidence —not by reading minds, not by assuming what others think, but by focusing on my own growth. Most importantly, I need to embrace and love myself more . In an era of AI and endless streams of knowledge, I shouldn’t beat myself up for forgetting things. I am human, with vulnerabilities.  And that’s okay. Your Turn: What Are Your Confidence Tricks? Do you have strategies for looking, feeling, and behaving more confidently? What has worked for you?