To create a cartoon effect, we need to pay attention to two things; edge and color palette. Those are what make the differences between a photo and a cartoon. Before jumping to the main steps, don't forget to import the required libraries in your notebook, especially cv2 and NumPy. The first main step is loading the image. Call the created function to load the image.
We basically train machines so as to include some kind of automation in it. In machine learning, we use various kinds of algorithms to allow machines to learn the relationships within the data provided and make predictions using them. So, the kind of model prediction where we need the predicted output is a continuous numerical value, it is called a regression problem. Regression analysis convolves around simple algorithms, which are often used in finance, investing, and others, and establishes the relationship between a single dependent variable dependent on several independent ones. For example, predicting house price or salary of an employee, etc are the most common regression problems.
A minority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmit most infections. How does this happen? Sun et al. reconstructed transmission in Hunan, China, up to April 2020. Such detailed data can be used to separate out the relative contribution of transmission control measures aimed at isolating individuals relative to population-level distancing measures. The authors found that most of the secondary transmissions could be traced back to a minority of infected individuals, and well over half of transmission occurred in the presymptomatic phase. Furthermore, the duration of exposure to an infected person combined with closeness and number of household contacts constituted the greatest risks for transmission, particularly when lockdown conditions prevailed. These findings could help in the design of infection control policies that have the potential to minimize both virus transmission and economic strain. Science , this issue p. [eabe2424] ### INTRODUCTION The role of transmission heterogeneities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dynamics remains unclear, particularly those heterogeneities driven by demography, behavior, and interventions. To understand individual heterogeneities and their effect on disease control, we analyze detailed contact-tracing data from Hunan, a province in China adjacent to Hubei and one of the first regions to experience a SARS-CoV-2 outbreak in January to March 2020. The Hunan outbreak was swiftly brought under control by March 2020 through a combination of nonpharmaceutical interventions including population-level mobility restriction (i.e., lockdown), traveler screening, case isolation, contact tracing, and quarantine. In parallel, highly detailed epidemiological information on SARS-CoV-2–infected individuals and their close contacts was collected by the Hunan Provincial Center for Disease Control and Prevention. ### RATIONALE Contact-tracing data provide information to reconstruct transmission chains and understand outbreak dynamics. These data can in turn generate valuable intelligence on key epidemiological parameters and risk factors for transmission, which paves the way for more-targeted and cost-effective interventions. ### RESULTS On the basis of epidemiological information and exposure diaries on 1178 SARS-CoV-2–infected individuals and their 15,648 close contacts, we developed a series of statistical and computational models to stochastically reconstruct transmission chains, identify risk factors for transmission, and infer the infectiousness profile over the course of a typical infection. We observe overdispersion in the distribution of secondary infections, with 80% of secondary cases traced back to 15% of infections, which indicates substantial transmission heterogeneities. We find that SARS-CoV-2 transmission risk scales positively with the duration of exposure and the closeness of social interactions, with the highest per-contact risk estimated in the household. Lockdown interventions increase transmission risk in families and households, whereas the timely isolation of infected individuals reduces risk across all types of contacts. There is a gradient of increasing susceptibility with age but no significant difference in infectivity by age or clinical severity. Early isolation of SARS-CoV-2–infected individuals drastically alters transmission kinetics, leading to shorter generation and serial intervals and a higher fraction of presymptomatic transmission. After adjusting for the censoring effects of isolation, we find that the infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom onset, with 53% of transmission occurring in the presymptomatic phase in an uncontrolled setting. We then use these results to evaluate the effectiveness of individual-based strategies (case isolation and contact quarantine) both alone and in combination with population-level contact reductions. We find that a plausible parameter space for SARS-CoV-2 control is restricted to scenarios where interventions are synergistically combined, owing to the particular transmission kinetics of this virus. ### CONCLUSION There is considerable heterogeneity in SARS-CoV-2 transmission owing to individual differences in biology and contacts that is modulated by the effects of interventions. We estimate that about half of secondary transmission events occur in the presymptomatic phase of a primary case in uncontrolled outbreaks. Achieving epidemic control requires that isolation and contact-tracing interventions are layered with population-level approaches, such as mask wearing, increased teleworking, and restrictions on large gatherings. Our study also demonstrates the value of conducting high-quality contact-tracing investigations to advance our understanding of the transmission dynamics of an emerging pathogen. ![Figure] Transmission chains, contact patterns, and transmission kinetics of SARS-CoV-2 in Hunan, China, based on case and contact-tracing data from Hunan, China. (Top left) One realization of the reconstructed transmission chains, with a histogram representing overdispersion in the distribution of secondary infections. (Top right) Contact matrices of community, social, extended family, and household contacts reveal distinct age profiles. (Bottom) Earlier isolation of primary infections shortens the generation and serial intervals while increasing the relative contribution of transmission in the presymptomatic phase. A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, which are driven by demography, behavior, and interventions. On the basis of detailed patient and contact-tracing data in Hunan, China, we find that 80% of secondary infections traced back to 15% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primary infections, which indicates substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, whereas isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates that SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions because of the specific transmission kinetics of this virus. : /lookup/doi/10.1126/science.abe2424 : pending:yes
Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that a single step size (learning rate) is used for all input variables. Extensions to gradient descent like AdaGrad and RMSProp update the algorithm to use a separate step size for each input variable but may result in a step size that rapidly decreases to very small values. The Adaptive Movement Estimation algorithm, or Adam for short, is an extension to gradient descent and a natural successor to techniques like AdaGrad and RMSProp that automatically adapts a learning rate for each input variable for the objective function and further smooths the search process by using an exponentially decreasing moving average of the gradient to make updates to variables. In this tutorial, you will discover how to develop gradient descent with Adam optimization algorithm from scratch.
Physiological functions of the microtubule cytoskeleton are expected to be regulated by a variety of posttranslational tubulin modifications. For instance, tubulin glycylation is almost exclusively found in cilia and flagella, but its role in the function of these organelles remains unclear. Gadadhar et al. now demonstrate in mice that glycylation, although nonessential for the formation of cilia and flagella, coordinates the beat waveform of sperm flagella. This activity is a prerequisite for progressive sperm swimming and thus for male fertility. At the ultrastructural level, lack of glycylation perturbed the distribution of axonemal dynein conformations, which may explain the observed defects in flagellar beat. Science , this issue p. [eabd4914] ### INTRODUCTION Microtubules are key components of the eukaryotic cytoskeleton. Although they are involved in a wide variety of functions, microtubules are structurally highly similar across most cell types and organisms. It was suggested that a “tubulin code,” formed by combinations of tubulin posttranslational modifications, adapts individual microtubules to specific functions within living cells. However, clear-cut functional and mechanistic data verifying this concept are still scarce. Glycylation is among the least explored posttranslational modifications of tubulin and has, so far, exclusively been found on microtubules of cilia and flagella from a variety of species. Previous work has suggested that glycylation might be essential for cilia and flagella, but mechanistic insight remains lacking. ### RATIONALE Two enzymes from the tubulin-tyrosine ligase-like (TTLL) family, TTLL3 and TTLL8, are essential to initiate glycylation of tubulin in mammals. To entirely abolish glycylation at the organism level and to determine its physiological function, we generated a double-knockout mouse lacking both glycylating enzymes ( Ttll3−/−Ttll8−/− ). Inactivation of these two enzymes led to a lack of glycylation in all analyzed cilia and flagella. This allowed us to investigate the role of glycylation in the function of these organelles. ### RESULTS Despite the absence of glycylation in Ttll3−/−Ttll8−/− mice, no gross defects were observed at the organism and tissue levels. Motile ependymal cilia in brain ventricles as well as motile cilia in the respiratory tract were present and appeared normal. Sperm flagella were also assembled normally, and sperm were able to swim. However, in vitro fertility assays showed that male Ttll3−/−Ttll8−/− mice were subfertile. Computer-assisted sperm analyses revealed motility defects of Ttll3−/−Ttll8−/− sperm. Further analyses showed that lack of glycylation leads to perturbed flagellar beat patterns, causing Ttll3−/−Ttll8−/− sperm to swim predominantly along circular paths. This is highly unusual for mammalian sperm and interferes with their ability to reach the oocyte for fertilization. To determine the molecular mechanisms underlying this aberrant flagellar beat, we used cryo–electron tomography. The three-dimensional structure of the 96-nm repeat of the Ttll3−/−Ttll8−/− sperm axoneme showed no aberrations in its overall assembly. By contrast, the structure of both outer and inner dynein arms (ODAs and IDAs) was perturbed in Ttll3−/−Ttll8−/− flagella. Classification analysis showed that the incidence and distribution of pre-powerstroke and post-powerstroke conformations of ODAs and IDAs were altered in Ttll3−/−Ttll8−/− sperm. These ultrastructural findings indicate that glycylation is required to efficiently control the dynein powerstroke cycle, which is essential for the generation of a physiological flagellar beat. ### CONCLUSION Our work shows that tubulin glycylation regulates the beat of mammalian flagella by modulating axonemal dynein motor activity. Lack of glycylation leads to perturbed sperm motility and male subfertility in mice. Considering that human sperm are more susceptible than mouse sperm to deficiencies in sperm motility, our findings imply that a perturbation of tubulin glycylation could underlie some forms of male infertility in humans. ![Figure] Tubulin glycylation controls sperm motility. ( A ) Microtubules in sperm flagella are rich in tubulin posttranslational modifications. Mice deficient for the glycylating enzymes TTLL3 and TTLL8 lack glycylation. ( B ) Mammalian sperm swim in linear paths. In the absence of glycylation, abnormal, mostly circular swimming patterns are observed, which impede progressive swimming. ( C ) Absence of glycylation leads to perturbed distribution of axonemal dynein conformations in Ttll3−/−Ttll8−/− flagella, which impedes normal flagellar beating. Posttranslational modifications of the microtubule cytoskeleton have emerged as key regulators of cellular functions, and their perturbations have been linked to a growing number of human pathologies. Tubulin glycylation modifies microtubules specifically in cilia and flagella, but its functional and mechanistic roles remain unclear. In this study, we generated a mouse model entirely lacking tubulin glycylation. Male mice were subfertile owing to aberrant beat patterns of their sperm flagella, which impeded the straight swimming of sperm cells. Using cryo–electron tomography, we showed that lack of glycylation caused abnormal conformations of the dynein arms within sperm axonemes, providing the structural basis for the observed dysfunction. Our findings reveal the importance of microtubule glycylation for controlled flagellar beating, directional sperm swimming, and male fertility. : /lookup/doi/10.1126/science.abd4914 : pending:yes
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining.
As my knowledge in machine learning grows, so does the number of machine learning algorithms! This article will cover machine learning algorithms that are commonly used in the data science community. Keep in mind that I'll be elaborating on some algorithms more than others simply because this article would be as long as a book if I thoroughly explained every algorithm! I'm also going to try to minimize the amount of math in this article because I know it can be pretty daunting for those who aren't mathematically savvy. Instead, I'll try to give a concise summary of each and point out some of the key features.
Before diving into this topic, lets first start with some definitions. "Rescaling" a vector means to add or subtract a constant and then multiply or divide by a constant, as you would do to change the units of measurement of the data, for example, to convert a temperature from Celsius to Fahrenheit. "Normalizing" a vector most often means dividing by a norm of the vector. It also often refers to rescaling by the minimum and range of the vector, to make all the elements lie between 0 and 1 thus bringing all the values of numeric columns in the dataset to a common scale. "Standardizing" a vector most often means subtracting a measure of location and dividing by a measure of scale.
An Introduction to Statistical Learning, with Applications in R (ISLR) can be considered a less advanced treatment of the topics found in another classic of the genre written by some of the same authors, The Elements of Statistical Learning. Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R.
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.