Reviewerly/exHarmony huggingface dataset:
Optimization of the NZ-1 labelling technique for application to 3D structure analysis

Antibody labeling has become a useful tool for determining three-dimensional (3D) structures of protein molecules and their complexes.Antibody fragments such as antigen-binding fragments (Fabs) serve as crystallization chaperones to promote lattice formation in X-ray crystallography.On the other hand, Fabs can be used as fiducial marks to aid the particle picking and alignment in cryo-electron microscopy (EM), which is difficult to apply to small protein molecules.However, establishing antibodies that bind stably to their respective targets is a prerequisite for utilizing the antibody labeling.To expand its applicability, we have developed an alternative strategy by inserting an exogenous epitope into targets and subsequently preparing complexes with antibody fragments.Specifically, we utilized a monoclonal antibody NZ-1, which has been established by immunizing rat with a 14-residue peptide segment (PA14) from human podoplanin.It has been shown that a 12-residue peptide segment (PA12) lacking two N-terminal residues of PA14 binds to NZ-1 with an extremely high affinity. [1] In addition, a previous crystallographic analysis of the NZ-1 Fab complexed with the PA14 peptide has revealed that the PA12 part adopts a bent loop-like conformation within the antigen-binding pocket of NZ-1.Based on these observations, we first attempted to develop the NZ-1 labeling technique using the PA12 tag.We inserted the PA12 tag into the PDZ tandem (PDZ-N and PDZ-C) located in the periplasmic region of intramembrane protease.In fact, the PA12-inserted PDZ tandem formed a stable complex with the NZ-1 Fab. [2] However, our structural analysis also showed that the complex formation through the inserted PA12 tag inevitably caused structural changes around the insertion site on the target.Therefore, we next attempted to utilize the PA14 tag, instead of PA12, which contains Glu-Gly residues upstream of PA12.We expected that the two additional residues could serve as a buffer region to tolerate structural changes on the target because they are flexible in the above-mentioned crystal structure of the NZ-1 Fab complexed with the PA14 peptide.As a result, the PA14-inserted PDZ tandem also produced co-crystals with the NZ-1 Fab, where the complex formation had less impact on the folding of the target PDZ domains as compared to the NZ-1 labeling with the PA12 tag. [3] In addition, molecular-dynamics (MD) simulations have also suggested that PA14-inserted PDZ domains stably bind to the NZ-1 Fab with no significant structural changes.To demonstrate that our improved NZ-1 labeling technique could be applied to EM analysis, we also performed negative stain EM on the NZ-1-labeled full-length intramembrane protease.We obtained 3D models of the complex and succeeded in approximating the spatial arrangement of the PDZ domains based on the docking mode of the NZ-1 Fab. Figure 1.Crystal structure of PA14-inserted PDZ tandem complexed with the NZ-1 Fab.Overall structure (left) and close-up view around the antigen-binding site (right).
Variable Selection

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Decoding

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Quality of Reviews

In this chapter, we discuss the quality of reviews. This topic is related to opinion spam detection yet also different, because low-quality reviews may not be spam or fake reviews, and fake reviews may not be perceived as low-quality reviews by readers. Indeed, as we discussed in Chapter 12, it is very difficult to spot fake reviews simply by reading them. For this reason, fake reviews may also be seen as helpful or high-quality reviews if the imposters write their reviews early and craft them well.
Network Data

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Who Were the Florentines? Etruscan Roots

In the 1540s, Piefrancesco Giambullari and Giovan Battista Gelli published books in which they argued that the Florentine language was not a debased form of Latin, but derived from Aramaic and Etruscan. Their claims divided the Accademia Fiorentina for a number of years, and earned them the nickname “Aramei.” They relied especially on the writings of Annius of Viterbo, whose fraudulent works seemed to them to provide information about the region and its people that was absent from Roman sources. Their arguments about language history were unsatisfactory to many; efforts to refute them effectively inspired years of further research. Yet even those who opposed them were interested in finding ways to study peoples, languages, and customs. Ancient models of historical writing focused on politics and public actions, but in order to study groups of people, customs, or languages, newer approaches were needed.
Probability and Distributions

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Triangulating Usage, Exposure and Processing

Chapter 4 argues for the benefits of an intra-individual research design and collecting different types of data from the same individuals. It shows that there is a developing interest towards an individual rather than commonalities across individuals in psychology, brain research and linguistics and discusses the differences between the individual and the communal level of language representation drawing on the usage-based perspective and complex systems. It then proposes a corpus of Master’s thesis drafts as a usage corpus, a personal corpus of references cited in the thesis as an exposure corpus and word associations as complementing psycholinguistic data. Given a long history of the word association method which was not always successful, the chapter also provides an extended discussion of its specifics. At the end, the chapter describes the corpus linguistic operationalization of meaning-shift units adopted in this study and data triangulation procedures.
Features, Reduced: Feature Selection, Dimensionality Reduction and Embeddings

This chapter presents a staple of Feature Engineering: the automatic reduction of features, either by direction selection or by projection to a smaller feature space. Central to Feature Engineering are efforts to reduce the number of features, as uninformative features bloat the ML model with unnecessary parameters. In turn, too many parameters then either produces suboptimal results, as they are easy to overfit, or require large amounts of training data. These efforts are either by explicitly dropping certain features (feature selection) or mapping the feature vector, if it is sparse, into a lower, denser dimension (dimensionality reduction). There are also cover some algorithms that perform feature selection as part of their inner computation (embedded feature selection or regularization). Feature selection takes the spotlight within Feature Engineering due to its intrinsic utility for Error Analysis. Some techniques such as feature ablation using wrapper methods are used as the starting step before a feature drill down. Moreover, as feature selection helps build understandable models, it intertwines with Error Analysis as the analysis profits from such understandable models.
Personal Book on Shared Reality

So now we know some of the reasons why possibilitivity gets squelched: the neuronal connectivity patterns that allow us to learn decrease the brain’s plasticity over time
MAP and Partially Bayesian Learning

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Research and Development Projects for AAL Systems

In this chapter, several projects will be presented in order to show the current state of the art in the research and development of the integration of ambient integrated robotics. The projects focus especially on the support of the elderly and fragile people. The main aim of each single project is to keep the user physically and mentally active in order to slow down the process of senility. All the tasks that the user cannot do alone anymore must be supported by the technology. Here however, the main difficulty in each presented project is to establish the system in a way that only the minimal necessary support is offered, otherwise the user could become too inactive leading to an accelerated process of senility.
More String and Sequence Problems Solved by ILP

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Availability Attack Case Study: SpamBayes

Adversaries can also execute attacks designed to degrade the classifier's ability to distinguish between allowed and disallowed events. These Causative Availability attacks against learning algorithms cause the resulting classifiers to have unacceptably high false-positive rates; i.e., a successfully poisoned classifier will misclassify benign input as potential attacks, creating an unacceptable level of interruption in legitimate activity. This chapter provides a case study of one such attack on the SpamBayes spam detection system. We show that cleverly crafted attack messages—pernicious spam email that an uninformed human user would likely identify and label as spam—can exploit Spam- Bayes' learning algorithm, causing the learned classifier to have an unreasonably high false-positive rate. (Chapter 6 demonstrates Causative attacks that instead result in classifiers with an unreasonably high false-negative rate—these are Integrity attacks.) We also show effective defenses against these attacks and discuss the tradeoffs required to defend against them.
Author Index

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Analytic Geometry

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Linear Regression

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Conclusion

This book introduced the field of sentiment analysis or opinion mining. It presented some basic knowledge and mature techniques in detail and surveyed numerous other state-of-the-art algorithms and techniques. Owing to numerous challenging research problems and a wide variety of practical applications, sentiment analysis has been a very active research area in several computer science fields, including NLP, data mining, web mining, and information retrieval. It has also spread to management science (Hu et al., 2006; Archak et al., 2007; Das and Chen, 2007; Dellarocas et al., 2007; Ghose et al., 2007; Park et al., 2007; Chen and Xie, 2008) and other social science fields such as communications and political science because of its importance to business and society as a whole. With the rapid expansion of social media on the web, the importance of sentiment analysis is also growing by the day.
Near-Optimal Evasion of Classifiers

In this chapter, we explore a theoretical model for quantifying the difficulty of Exploratory attacks against a trained classifier. Unlike the previous work, since the classifier has already been trained, the adversary can no longer exploit vulnerabilities in the learning algorithm to mistrain the classifier as we demonstrated in the first part of this book. Instead, the adversary must exploit vulnerabilities that the classifier accidentally acquired from training on benign data (or at least data not controlled by the adversary in question). Most nontrivial classification tasks will lead to some form of vulnerability in the classifier. All known detection techniques are susceptible to blind spots (i.e., classes of miscreant activity that fail to be detected), but simply knowing that they exist is insufficient. The principal question is how difficult it is for an adversary to discover a blind spot that is most advantageous for the adversary. In this chapter, we explore a framework for quantifying how difficult it is for the adversary to search for this type of vulnerability in a classifier.
Machine Learning Tricks

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Advanced Topics: Variable-Length Data and Automated Feature Engineering

This chapter closes Part I presenting advanced topics, including dealing with variable length feature vectors, Feature Engineering and Deep Learning and automatic Feature Engineering (either supervised or unsupervised). It starts bridging the pure domain independent techniques to start drilling into problems of domain-specific importance. Variable length feature vectors has been a problem for fixed-size vector ML ever since. In general, techniques involve truncation, computing the most general tree and encoding paths on it or just destructive projection into a smaller plane. The chapter briefly delve into some Deep Learning concepts and what it entails for feature engineering. Automated Feature Learning using FeatureTools (the DataScience Machine) and genetic programming is covered, also Instance Engineering and Unsupervised Feature Engineering (in the form of autoencoders).
Other Domains: Video, GIS and Preferences

This chapter showcases three domains different from the other domains to highlight particular techniques and problems missing from the previous chapters. Each domain uses a small dataset assembled just for the case study and undergoes only one featurization process. The first domain is video domain, where a mouse pointer tracking problem on a video is studied to showcase two key issues in feature engineering for video: speed of processing and reusability of results from a previous feature extraction. The second domain is geographical information systems, where bird migration data is studied to showcase the value of key points as physical summaries of paths and bi-dimensional clustering of points using R-trees. Finally, preference data is studied via movie preferences to showcase large scale imputation of missing features.
Other Topics

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Time stamped Data

Building on the dataset presented in the previous chapter, this chapter explores using historical information (as represented by previous DBpedia versions) to perform feature engineering using historical features. Working with historical data makes all Feature Engineering complex but the whole concept of “truth,” the immutablity of the target class is challenged. Great feature for a particular class have to become acceptable features for a different class. Topics covered include imputing timestamped data, lagged features and moving window averaging of the data. Due to unavailability of population data for cities, a second dataset revolving around countries is introduced to perform population prediction using time series ARIMA models over 50 years of data, as provided by the world bank. The chapter exemplifies different methods to blend machine learning with time series models, including using their output as another feature or training a model to predict their errors.
Introduction to the Minitrack on Text Analytics

This virtual HICSS-54 minitrack recognizes that most global collaboration systems, social media, and information systems of all types, generate enormous amounts of unstructured textual data, including system logs, email archives, websites, blog posts, meeting transcripts, speeches, annual reports, published material, and social media posts.While this unstructured textual data is readily available, it presents tremendous challenges to researchers trying to analyze these large bodies of text with traditional methods.Text mining in big data analytics is an increasingly important technique for an interdisciplinary group of scholars, practitioners, government officials, and international organizations.
From a blackened birthmark to the blooming brain

The Ceylon Journal of Medical Science (CJMS) since its re-launch in 2016 is the official journal of the Faculty of Medicine, University of Colombo. The focus of the CJMS is to provide a platform to those involved in diverse fields of medical science to publish high quality research. This will facilitate promotion of evidence based medicine and thereby improve health care. This platform is open to doctors, researchers, scientists in medical sciences and medical education and students in both medical and allied health sciences. The CJMS is an open access, peer-reviewed journal.
JIGSAW TECHNIQUE AND STUDENTS’ READING COMPREHENSION

The aim of this study is to find out whether or not there is significant effect of using Jigsaw technique on the eighth-grade students’ reading comprehension at Islamic Junior High School Al-Jauharen. The design of the experiment research is a quasi-experimental research with pre-test post-test control group design. There were 64 students involved as the samples in this study. The results indicated that there was significant difference on the students’ comprehension before (M=60.80, SD= 19.69) and after being taught by using Jigsaw Technique (M=78.62, SD=10.08), t(29)=3.926, p= 0.000&lt; 0.05. The results of independent sample t-test also indicated that there was significant difference on the comprehension of the students in experimental group and in control group (p&gt;0.05). In brief, the results implied that there were there was significant effect of using Jigsaw Technique on the students’ reading comprehension. &#x0D; &#x0D;
Matrix Decompositions

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Dimensionality Reduction with Principal Component Analysis

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Neural Networks

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The Ontology of Justifications

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