Eric Ries was born in September 1978. He graduated from Yale University and moved to silicon Valley in the beginning of the millennium. He pioneered the philosophy of Lean Startup, based on his experience with multiple startups, primary being IMVU which he co-founded along with Will Harvey in 2004. Eric Ries originated his Lean Startup philosophy after getting inspired from the Japanese Philosophy of Lean Manufacturing.

The Lean Startup:How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses

The Lean Startup: How today's entrepreneurs use continuous innovation to create radically successful businesses

The abnormal autophagy is associated with a variety of cardiovascular diseases. Long noncoding RNAs (lncRNAs) are emerging as new factors in gene regulation, but how lncRNAs operate in the regulation of autophagy in the heart is unclear. Here we report that a long noncoding RNA, named autophagy promoting factor (APF), can regulate autophagic cell death by targeting miR-188-3p and ATG7. The results show that miR-188-3p suppresses autophagy and myocardial infarction by targeting ATG7. Further, we find that APF lncRNA regulates miR-188-3p, and thus affects ATG7 expression, autophagic cell death and myocardial infarction. Our present study reveals a novel regulating model of autophagic programme, which comprises APF, miR-188-3p and ATG7 in the heart. Modulation of their levels may serve as potential targets and diagnostic tools for novel therapeutic strategies of myocardial infarction and heart failure.

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APF lncRNA regulates autophagy and myocardial infarction by targeting miR-188-3p.

This study introduces and analyzes a novel system for algal biofuel production that synergistically integrates algal wastewater treatment with hydrothermal liquefaction (HTL) of wastewater biosolids and algae into bio-crude oil. This system maximizes the biofuel potential of wastewater inputs by internally capturing and recycling carbon and nutrients – a powerful concept referred to as multi-cycle nutrient reuse, which amplifies waste nutrients into multiple cycles of algal biomass and bioenergy production. We call this system “Environment-Enhancing Energy” (E2-Energy) because it can simultaneously improve conventional wastewater treatment by nutrient removal and production of a large amount of biofuel co-products. Moreover, E2-Energy resolves several key bottlenecks commonly associated with large-scale algal biofuel production including: contamination of target high-oil algal cultures, high nutrient costs/usage, unsustainable fresh water usage, and large energy inputs for dewatering/extraction. A series of algal cultivation and HTL experiments were conducted to confirm the primary steps and performance characteristics of the E2-Energy system. These experiments showed: (1) low-oil, mixed algal–bacterial biomass can be successfully cultured with the recycled HTL aqueous product; (2) both organics and nutrients are removed from wastewater during algal–bacterial biomass production (63–95% reduction); (3) this low-oil, algal–bacterial biomass can be converted into bio-crude oil via HTL with a high yield (∼50%) and a net positive energy balance; and (4) the HTL step re-releases nutrients to an aqueous phase product that can be recycled back to step (1). This repeating loop of steps 1–4 facilitates multi-cycle reuse of nutrients and thus provides biomass amplification. A mathematical model was also developed using STELLA® to simulate mass balances for long-term E2-Energy operations with internal recycling of nutrients and carbon. The model results showed that E2-Energy can amplify the biomass and biofuel production from wastewater by up to 10 times, which gives it the potential to replace all US petroleum imports using only current wastewater feedstocks and carbon dioxide from the atmosphere or point sources. Thus, E2-Energy represents a major paradigm shift—where wastewater treatment systems become optimized biofuel producers with enhanced effluent quality, which provides a viable and advantageous pathway to sustainable, carbon-neutral energy independence.

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A synergistic combination of algal wastewater treatment and hydrothermal biofuel production maximized by nutrient and carbon recycling

Characterization and utilization of aqueous products from hydrothermal conversion of biomass for bio-oil and hydro-char production: a review

Online controlled experiments (OCEs), also known as A/B tests, have become ubiquitous in evaluating the impact of changes made to software products and services. While the concept of online controlled experiments is simple, there are many practical challenges in running OCEs at scale. To understand the top practical challenges in running OCEs at scale and encourage further academic and industrial exploration, representatives with experience in large-scale experimentation from thirteen different organizations (Airbnb, Amazon, Booking.com, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, Yandex, and Stanford University) were invited to the first Practical Online Controlled Experiments Summit. All thirteen organizations sent representatives. Together these organizations have tested more than one hundred thousand experiment treatments last year. Thirty-four experts from these organizations participated in the summit in Sunnyvale, CA, USA on December 13-14, 2018.While there are papers from individual organizations on some of the challenges and pitfalls in running OCEs at scale, this is the first paper to provide the top challenges faced across the industry for running OCEs at scale and some common solutions.

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Top Challenges from the first Practical Online Controlled Experiments Summit

Online controlled experiments (e.g., A/B tests) are now regularly used to guide product development and accelerate innovation in software. Product ideas are evaluated as scientific hypotheses, and tested in web sites, mobile applications, desktop applications, services, and operating systems. One of the key challenges for organizations that run controlled experiments is to come up with the right set of metrics [1] [2] [3]. Having good metrics, however, is not enough. In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment's outcome, which if deployed could hurt the business by millions of dollars. Inspired by Steven Goodman's twelve p-value misconceptions [4], in this paper, we share twelve common metric interpretation pitfalls which we observed repeatedly in our experiments. We illustrate each pitfall with a puzzling example from a real experiment, and describe processes, metric design principles, and guidelines that can be used to detect and avoid the pitfall. With this paper, we aim to increase the experimenters' awareness of metric interpretation issues, leading to improved quality and trustworthiness of experiment results and better data-driven decisions.

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

The wheel graph is the join of a single vertex and a cycle, while the fan graph is the join of a single vertex and a path. The resistance distance between any two vertices of a wheel and a fan is obtained. The resistances are related to Fibonacci numbers and generalized Fibonacci numbers. The derivation is based on evaluating determinants of submatrices of the Laplacian matrix. A combinatorial argument is also illustrated. A connection with the problem of squaring a rectangle is described.

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Resistance distance in wheels and fans

Online controlled experiments (e.g., A/B tests) are now regularly used to guide product development and accelerate innovation in software. Product ideas are evaluated as scientific hypotheses, and tested on web sites, mobile applications, desktop applications, services, and operating system features. One of the key challenges for organizations that run controlled experiments is to select an Overall Evaluation Criterion (OEC), i.e., the criterion by which to evaluate the different variants. The difficulty is that short-term changes to metrics may not predict the long-term impact of a change. For example, raising prices likely increases short-term revenue but also likely reduces long-term revenue (customer lifetime value) as users abandon. Degrading search results in a Search Engine causes users to search more, thus increasing query share short-term, but increasing abandonment and thus reducing longterm customer lifetime value. Ideally, an OEC is based on metrics in a short-term experiment that are good predictors of long-term value. To assess long-term impact, one approach is to run longterm controlled experiments and assume that long-term effects are represented by observed metrics. In this paper we share several examples of long-term experiments and the pitfalls associated with running them. We discuss cookie stability, survivorship bias, selection bias, and perceived trends, and share methodologies that can be used to partially address some of these issues. While there is clearly value in evaluating long-term trends, experimenters running long-term experiments must be cautious, as results may be due to the above pitfalls more than the true delta between the Treatment and Control. We hope our real examples and analyses will sensitize readers to the issues and encourage the development of new methodologies for this important problem.

Pitfalls of long-term online controlled experiments

Online controlled experiments (e.g., A/B tests) are an integral part of successful data-driven companies. At Microsoft, supporting experimentation poses a unique challenge due to the wide variety of products being developed, along with the fact that experimentation capabilities had to be added to existing, mature products with codebases that go back decades. This paper describes the Microsoft ExP Platform (ExP for short) which enables trustworthy A/B experimentation at scale for products across Microsoft, from web properties (such as bing.com) to mobile apps to device drivers within the Windows operating system. The two core tenets of the platform are trustworthiness (an experiment is meaningful only if its results can be trusted) and scalability (we aspire to expose every single change in any product through an A/B experiment). Currently, over ten thousand experiments are run annually. In this paper, we describe the four core components of an A/B experimentation system: experimentation portal, experiment execution service, log processing service and analysis service, and explain the reasoning behind the design choices made. These four components work together to provide a system where ideas can turn into experiments within minutes and those experiments can provide initial trustworthy results within hours.

Somit Gupta

Papers

Top Challenges from the first Practical Online Controlled Experiments Summit

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

Resistance distance in wheels and fans

Pitfalls of long-term online controlled experiments

The Anatomy of a Large-Scale Experimentation Platform