Kedar K. V. Mate केदार क. वि. माटे

Dal alone is not enough !! When you eat it the right way, it becomes complete protein !! We Indians have been doing it for ages!!

Proud to share that our staff published 44 papers in Sept, including in @NEJM, @TheLancet, @JAMASurgery, and @JCO_ASCO, advancing India’s and the world’s fight against cancer through research and collaboration. @ACTREC_TMC @TMC_Varanasi @CRI_ACTREC @HBCHRC_Punjab @HBCHRCVIZAG

Interior of Mysore Palace

How the Suez Canal Made the Taj Mahal Famous Few realise that the Taj Mahal’s global fame owes less to Shah Jahan’s love and more to the opening of the Suez Canal. When the canal opened in 1869, it allowed the wives of British colonial officials to travel easily to India, bringing with them the moral sensibilities of Victorian England. The enlightened curiosity of the eighteenth and early nineteenth centuries - epitomised by Sir William Jones, who recognised the links between Sanskrit, Persian, Greek, Latin and Celtic, and by Mountstuart Elphinstone’s History of India (1841) - was replaced by a narrower evangelical worldview that downplayed India’s ancient achievements in literature, history, language, and art. Victorian propriety found little comfort in the phallic symbolism of lingams or in the Tantric Chola temples whose intricate carvings depicted explicit sexual scenes, sometimes with multiple partners. The 216-foot temple at Tanjore, and its replica at Cholapuram - the tallest buildings in India in their time - were ignored, as was the intellectual grandeur of Nalanda University. In their place the Taj Mahal, a structure two hundred times smaller than Tanjore’s temple but wrapped in a tale of marital devotion, suited Victorian piety. It offered a sentimental, “moral” India that could be admired without confronting its complexity. Thus colonial taste recast India’s heritage, elevating an imported Mughal monument over the subcontinent’s own architectural and philosophical genius. My question is why this Victorian preference still endures - does it reflect lasting values, or merely the inertia of tourists and the Tourist Board?

Destined to become a classic. I'm two chapters in and utterly hooked. Abhishek Choudhary remains the greatest scholar on the rise of Hindu Nationalism in India.

📹 IMF Chief Applauds India’s Calm on Tariffs Kristalina Georgieva said 🇮🇳 remains the world’s fastest-growing major economy thanks to the reforms undertaken — and highlighted the benefits of staying open to trade and investment.

What is India ? Show this

I can't recall when was the last time I came across an "unputdownable" book like this one. Our understanding of the making of the Constitution of India will be revised significantly. Pls consider a Twitter account to auto-tweet excerpts from the book @itihaasnaama @OrnitShani

Osteogenesis Imperfecta is a rare genetic defect in collagen, a protein that strengthens the bone. The poorly formed collagen in OI, causes the bones to break easily. The same disease can affect the teeth and cause the sclera to be blue. Almost all doctors know about this disease or have at least heard of it. But most have never heard of the man who discovered the structure of collagen. The double helical structure of DNA and Watson & Crick are household names, while the triple helical structure and the man behind its discovery are obscure, even though he worked, right here in Chennai. GN Ramachandran was born in Ernakulam in the Kingdom of Cochin in 1922 . His father was a Mathematics professor who shaped his son's interest in the queen of sciences.But Ram, as he was known to his friends, had different plans. He completed BSc Physics(Honors) at the top of his class from St Joesph college, Trichy in Madras University in 1942. After that he joined Masters in Electrical Engineering in Indian Institute of Science in Bangalore. Fate intervened at this point - in the form of a man, no less than Sir CV Raman, who pulled Ramachandran back to the physics stream from Engineering. Sir CV Raman was a giant in physics, already with a Nobel prize in his kitty for discovering the Raman effect. With such an illustrious mentor, Ramachandran worked on optics and the X ray topography of diamonds, culminating in a DSc in physics in 1947. Like most scientists of that era, Ramachandran went to Britain - and worked with Linus Pauling and Sir Lawrence Bragg, in the Cavendish lab in Cambridge. With a PhD from Cambridge and an interest in crystallography he came back to Bangalore. At that time, Sir AL Mudaliar was searching for a suitable candidate to head the experimental physics division in Madras University. He invited Sir CV Raman, who graciously declined, but sent his protege, Dr GN Ramachandran, who became a professor of physics at the age of 29. With his whole life ahead, GNR had trouble choosing suitable projects. He was toying with the idea of biopolymers and structure of biological macromolecules,and gravitated towards collagen. With his post-doc Gopinath Kartha, he discovered the triple helical strucutre of collagen, after years of painstaking work with stick and ball models. He also developed the Ramachandran plot,a framework for understanding peptide structure. It was breakthrough, and Janet Thornton said, "It never fails to excite me, when I see the Ramachandran plot and realize how much of the beauty and order of protein structures is encapsulated by this plot. I also think that this major discovery highlights the importance of clear thought and vision that do not always need expensive equipment and huge teams of people".The work he started with diamonds had taken him to bone proteins. His contribution didn't end there though. He went to the Biophysics department of University of Chicago, in 1970.During this visit,he devised a method to reconstruct 3D images from 2D data, which laid the foundations of computerized tomography -CT scan. After returning, his health started failing. He developed Parkinson's disease and was cared for by his wife Rajam. Her death, was a mortal blow to Dr Ramachandran, from which he never really recovered. While he received numerous awards and accolades, for his work in the intersection of mathematics, physics and biology - few medical students have heard of him. After all doing great work and being forgotten, is the cliched curse of Indian scientists. The next time, you look at a bone on a CT scan, remember the name GN Ramachandran.

"The greatest danger to our future is apathy. You cannot get through a single day without having an impact on the world around you.” Let her words of love & faith inspire you every day. Together, let’s honour Jane Goodall’s legacy by working towards a better world. #ActNow

It’s been one year since we opened Dunn House — Canada’s first social medicine housing initiative. And this has been family over the past year. The data is staggering. Emergency Department visits for the tenants have plunged by over 50%. And days spent in hospital have similarly plummeted by nearly 80%. What started as a “radical” idea — turning a parking lot into 51 homes — became a place where people who were living inside and out of hospitals, shelters, or on the street could finally exhale. But the real drive for change, I hope, is how human dignity and health economics are completely aligned in the stories we tell. The first story is from @_VictoriaGibson at the Toronto Star — about Jason Miles, a man whose addiction and homelessness cost more than $260,000 through ER visits, shelters, and jail stays. Not because he wanted that path, but because there wasn’t another one. The second is from @liamdevlincasey in the CBC, about our @UHN teams and community partners deciding to try something different and center those patients that been sidelined in the health system. The cost calculus is clear when it can be over $50k per month in hospital, $15k in provincial jail and $4k for supportive housing. I believe both these stories show the cost of crisis — and the return on compassion. It’s still early, and there’s a lot more to do across the province. But one year in, I’m certain of this more than ever: housing is healthcare. compassion saves lives. and dignity has to be co-designed into the system — not left to chance.

🎬 पिंजऱ्याचं पर्व संपलं... आज सिनेसृष्टीने तिच्या एका तेजस्वी ताऱ्याला गमावलं आहे. ज्येष्ठ अभिनेत्री संध्या शांताराम यांचे निधन हे एका युगाचा अंत आहे. वयाच्या ९७व्या वर्षी त्यांनी अखेरचा श्वास घेतला, पण त्यांच्या कलाकृतींनी आपल्यावर अमिट छाप सोडली आहे. 🎭 ‘पिंजरा’ मधील त्यांचा अभिनय, सौंदर्य आणि नृत्य आजही रसिकांच्या मनात जिवंत आहे. त्यांनी साकारलेली प्रत्येक भूमिका ही काळाच्या चौकटीतून बाहेर पडून अमर झाली. 🙏 संध्या ताईंना भावपूर्ण श्रद्धांजली! तुमचं योगदान हे मराठी सिनेसृष्टीच्या सुवर्णकाळाची साक्ष आहे. तुमचं स्मरण हेच आमचं धन… आणि तुमचं कलाविश्व हेच आमचं प्रेरणास्थान. #भावपूर्णश्रद्धांजली #पिंजरा #मराठीसिनेमा #सिनेसृष्टीचासुवर्णकाळ #LegendLivesOn #SandhyaShantaram #RIPSandhyaShantaram

Have you heard of Subhash Mukherjee? While the world celebrated the UK’s Louise Brown as the first IVF baby -- a story told in Netflix's 'Joy', few know India had its own groundbreaking success story in the same year. Dr. Subhash Mukherjee achieved a medical miracle by creating India’s first test tube baby with in-vitro fertilisation. Despite his innovation, he faced rejection and isolation instead of recognition. Harassed by authorities and denied a platform for his work, Mukherjee’s story ended tragically. But his legacy lives on—acknowledged posthumously as a pioneer in IVF technology. Today, as millions benefit from IVF, let’s remember the brilliance and struggle of the man who paved the way. Share his story to inspire and honour a forgotten legend! #IndiansInScience #History #IndianHistory #Biopic #netflix #OTT [Indians In Science, History of India, Biopic, Netflix, OTT Series, First IVF baby , Subhash Mukherjee]

She was an inspiration. Worked all her life for conservation. Rest in peace 🙏🙏

Huge loss for environmentalism: Jane Goodall - the incredible conservationist dies. Jane Goodall was one of the most revered conservationists of our time. All of us who admired her and learned from her will miss her dearly. I vividly recall her infectious enthusiasm for the miracles of nature and her dedication to protect our planet. She earned global celebrity in the 1960s by chronicling behavior of wild chimpanzees in Tanzania. She taught the world that apes make and use tools, eat meat, hold rain dances and engage in organized warfare. They were not «primitive animals» as many assumed at the time. Jane Goodall’s discoveries about how wild chimpanzees raised their young, established leadership, socialized and communicated broke new ground and attracted immense attention and respect. It was said it “represents one of the Western world’s great scientific achievements.” Nothing will honor Jane Goodall more than new generations following in her footsteps - helping us understand nature better and care more for Mother Earth. 🌍 lnkd.in/dJ_ebetv

I’ve seen these formulas so many times, but I never noticed this until now... Why is there an explicit error term in multiple linear regression (OLS) but not in binary logistic regression? 🤔 This difference is more than just a minor detail; it reflects fundamental distinctions in how these models operate and what they’re designed to predict. In multiple linear regression, we predict a continuous outcome and include an explicit error term to capture the difference between the observed and predicted values. This error term, or residual, helps measure how well the model fits the data, aiming to minimize these errors for better accuracy. But in binary logistic regression, the model predicts probabilities of binary outcomes, such as yes/no or true/false. Instead of using an explicit error term, logistic regression employs the logistic function to map predictions to probabilities between 0 and 1. The "error" here is implicit, captured through the likelihood of the observed outcomes given the model parameters, and is optimized using maximum likelihood estimation. ✔️ Key takeaway: Linear regression uses an error term to minimize errors in predicting continuous outcomes, while logistic regression optimizes for probabilities in binary outcomes without an explicit error term. It's fascinating how these subtle differences in formulas can have a big impact on how we model and understand data. Additional Notes: I've received some interesting insights from comments and discussions on this topic that may offer further clarity. Here’s a brief summary: 🔹GLM Framework: Linear and logistic regression both fit within the Generalized Linear Model (GLM) family. Linear regression models continuous outcomes directly, while logistic regression models the probability of binary outcomes using a link function (logit) to linearize probabilities. 🔹Zero Mean Error Assumption: In linear regression, the error term accounts for deviations, with the assumption of a zero mean, allowing accurate estimation of the expected outcome. In logistic regression, no explicit error term is needed, as the model directly estimates probabilities through the link function. 🔹Residuals in Log-Odds: While logistic regression doesn’t have traditional residuals as in linear regression, model fit can still be assessed by examining differences between predicted and observed values in log-odds. Note that, although an error term exists in the data generating process (DGP), it’s not represented as residuals in the logistic regression output. 🔹Single vs. Two-Parameter Models: Logistic regression is a single-parameter model focused on the predictor, while linear regression typically includes a variance parameter, making it more flexible for data with additional variability. If you’re curious to learn more about topics like this, check out my online course on Statistical Methods in R. More information: statisticsglobe.com/online-course-… #datastructure #DataAnalytics #rstudioglobal #RStats #statisticians

A new model for advancing learning and research on sustainability is taking shape: the McGill Sustainability Park. 🌱 🔗 mcgill.ca/x/ibJ | Renderings by Diamond Schmitt / Lemay Michaud Architects

Map of the Mauryan Empire during the reign of Ashoka the great, 268 – 232 BCE

High-salt diet inflames the brain and raises blood pressure ~ Study led by Physiology prof Masha Prager-Khoutorsky challenges long-held beliefs about hypertension and points to the brain as a new treatment target. 🔗 ow.ly/GRzg50WJKm1

Congratulations to Professor Cindy Blackstock (@cblackst), recent #McGill graduate Hannah Brais, and Dr. Nada Jabado on receiving prestigious awards from the Royal Society of Canada (@src_rsc)! 👏🎉🍁 @McGillMed @McGillARTS @McGillScience @mcgillu