Issue #5
The vertical farming bubble is finally popping | Venezuela: The Rise and Fall of a Petrostate | The Anatomy of a Clinical Trial | Free Resources- MLU-Explain, Introduction to Data Science
Egyptian No. 7 (1856) by Owen Jones
Source: Artvee
VC 🚀
The vertical farming bubble is finally popping
By Fast Company
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Over the past few years, vertical farming has been one of the hottest areas within agritech as investors have been increasingly drawn to the idea of disrupting a 10,000-year-old industry.
There are numerous upsides to vertical farming such as:
90% less water usage compared to traditional farms.
Potentially avoids outbreaks of E. coli.
Reduced fertilizer and pesticide usage.
Transportation time is minimized as the produce is grown closer to the cities.
Vertical farming entails enormous costs:
Lighting is expensive. Even for small 10,000-sq.ft farms, lighting expenses can range between USD 100K-200K per year. While renewable energy solutions such as solar panels can offset a portion of the cost, they can’t generate all the electricity needed.
Vertical farming startups boast about deploying high-tech solutions such as computer vision to monitor plants and using robots to automate day-to-day operations. But, these proprietary solutions and the robotics and software engineers that help build them come at a steep cost.
Tech-heavy vertical farms are not expected to compete with traditional farms in terms of pricing in North America for the next decade, hence they’ll need to keep raising capital to fund their operations.
The underlying economics of vertical farming makes sense in areas such as the Middle East, where high temperatures make outdoor farming impractical and consumers currently pay high prices for imported greens.
Geopolitics 🌏
Venezuela: The Rise and Fall of a Petrostate
By Council on Foreign Relations
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Ever since oil was discovered in Venezuela in the 1920s, the country has been on a never-ending boom-and-bust cycle. Home to the largest oil reserves on the planet, the country has been affected by a phenomenon known as the Dutch disease, a term coined during the 1970s after the Netherlands discovered natural gas in the North Sea.
The Dutch disease affects countries with vast natural resources. The abundance of resources attracts foreign capital, which leads to an appreciation of the local currency which in turn makes imports cheaper and exports costlier. As imports compete with domestic goods and produce, the country’s manufacturing and agriculture industries are impacted, which leads to unemployment. As incomes from manufacturing and agriculture plummet, the country becomes more reliant on natural resource exports. Since export prices can routinely experience large fluctuations, the country’s economy is at the mercy of global demand and supply.
Since petrostates such as Venezuela rely more on export revenues than on local taxes, there are often weak ties between the government and its citizens.
The drastic decline in oil prices, which fell from over USD 100 per barrel in 2014 to below USD 30 per barrel at the beginning of 2016, had profound economic and political repercussions for Venezuela. Despite a subsequent increase in prices, the country's situation remains dismal. Oil exports are expected to contribute to over two-thirds of the government’s budget in 2023.
According to a November 2022 survey, close to half of the country’s 28 million residents live in poverty, though that’s a significant decrease from 65% the year before. Since 2014, more than seven million Venezuelans have fled to neighbouring countries.
Does the Dutch disease affect all countries with vast natural resources? Not necessarily. Countries that have existing robust democratic institutions are better equipped to handle resource booms. For example, Norway has enjoyed steady economic growth since discovering vast oil reserves in the 1960s largely due to its strong democratic institutions.
Healthcare 🩺
The Anatomy of a Clinical Trial
By SuperFluid
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Clinical trials aim to alter the current medical knowledge base and bring novel treatments that are better than existing solutions in a safe and regulated manner. They usually follow a three-phase process, followed by a maintenance stage.
Phase 1: In the initial stage of drug development, typically 20 healthy individuals are enlisted to assess the safety profile, optimal dosage and potential adverse effects of the medication. This is the first time when the new drug is tested on humans.
Phase 2: During this phase, the aim is to assess the effectiveness of the medication and to recognize any potential safety concerns in a larger patient group. The trial design plays a crucial role in ensuring that the outcomes are meaningful both statistically and clinically.
Phase 3: The third phase is the most critical as it involves testing on a larger group of patients to fully establish the effectiveness and safety of a drug in a real-world setting. To account for varying demographics and characteristics, the patient group is often divided among multiple sites around the world. At this point, it is crucial that the drug demonstrates its superiority over existing solutions.
The maintenance phase: After the release of the drug, its safety and effectiveness are monitored among the general population to ascertain any previously unidentified adverse reactions or side effects that may have been missed during the clinical trial process.
Clinical trials are very expensive and for every day it is delayed, the trial sponsor can lose anywhere between USD 600K-8M. Managing patient recruitment, enrollment and participation is an extremely costly affair. In the case of rare diseases or specific patient populations, it might be difficult to find people with that disease.
Interesting startups trying to disrupt clinical trials include:
San Francisco-based startup Power, is a clinical trial marketplace that aims to make it easier for patients to identify and sign up for clinical trials.
Unlearn, also based in San Francisco is hoping to remove the need for control groups by creating a digital twin of any patient within a study. The startup has invented its own statistical modelling approach called a Boltzmann-Encoded Adversarial Machine (BEAM) that receives patient data at the start of a trial, generates a digital twin and predicts how they would trend if they were given a placebo.
Free Resources 💡
MLU-Explain: Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application.
Introduction to Data Science- Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry: It’s a free-to-read online book that covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2 and more.
For more free resources (180+ websites and tools), please check out Searching (it’s a Notion database that I’ve created).
The free resources links are exceptional! Thank you.