text:
MDS codes over finite fields

The mds (maximum distance separable) conjecture claims that a nontrivial linear mds $[n,k]$ code over the finite field $GF(q)$ satisfies $n \leq (q + 1)$, except when $q$ is even and $k = 3$ or $k = q- 1$ in which case it satisfies $n \leq (q + 2)$. For given field $GF(q)$ and any given $k$, series of mds $[q+1,k]$ codes are constructed. Any $[n,3]$ mds or $[n,n-3]$ mds code over $GF(q)$ must satisfy $n\leq (q+1)$ for $q$ odd and $n\leq (q+2)$ for $q$ even. For even $q$, mds $[q+2,3]$ and mds $[q+2, q-1]$ codes are constructed over $GF(q)$. The codes constructed have efficient encoding and decoding algorithms.

Introduction
Background on coding theory and related material made be found in [11] or in [4]. Now GF (q) denotes the finite field of order q and q is necessarily a power of a prime. An [n, k] linear code over GF (q) is a linear code C of length n and dimension k over GF (q).
The minimum distance d of C is bounded by the Singleton bound d ≤ (n + 1 − k). If d = (n + 1 − k), then the code C is termed a maximum distance separable (mds) code. The mds codes are those with maximum error correcting capability for a given length and dimension. MacWilliams and Sloane refer to mds codes in their book [11] as "one of the most fascinating chapters in all of coding theory"; mds codes are equivalent to geometric objects called n-arcs and combinatorial objects called orthogonal arrays, [11], and are, quote, "at the heart of combinatorics and finite geometries".
The mds conjecture is due originally to Segre [12] from 1955. mds conjecture: If C is a nontrivial linear mds [n, k] code over GF (q), then n ≤ (q + 1), except when q is even and k = 3 or k = (q − 1) in which case n ≤ (q + 2).
There is a large literature focusing on this problem, for example see [5,1,13]. Ball showed [2] that the mds conjecture is true for prime fields. For a list of when the conjecture is known to hold for q non-prime, see [6,7] Here for any given finite field GF (q) and any given k, series of mds [q + 1, k] codes over GF (q) are constructed.
Methods in [10] can be adopted to give efficient decoding algorithms; the complexity is max{O(log n), t 2 } where n is the length and t is the error-correcting capability which is ⌊ d−1 2 ⌋ where d is the distance. For even q, it is shown that any [n, 3] mds code and any [n, n − 3] mds code over GF (q) must satisfy n ≤ (q + 2) and for odd q any [n, 3] or [n, n − 3] mds code over GF (q) must satisfy n ≤ (q + 1). For even q, series of [q + 2, 3] mds codes and [q + 2, q − 1] mds codes over GF (q) are constructed.
The mds codes constructed over prime fields are maximum length for the field. The more general case is dealt with separately.

Basics
A primitive n th root of unity in a field F is an element ω such that ω n = 1 but ω i = 1 for 1 ≤ i < n.
See for example [4] or [11] for the following result. A k × n matrix G is the generator matrix of an mds [n, k] code if and only if any k × k submatrix of G has non-zero determinant. Also a k × n matrix is a check matrix of a [n, n − k] mds code if and only if any k × k submatrix has non-zero determinant.
An [n, k] code is an mds code if and only if its dual is an [n, n − k] mds code, [11,4].
Recall the mds codes constructed in [10].
A Fourier matrix is a special type of Vandermonde matrix. Let ω be a primitive n th root of unity in a field F; primitive here means that ω n = 1 and ω i = 1 for 1 ≤ i < n. The Fourier matrix F n , relative to ω and F, is the n × n matrix Simplifications can be made to some of the powers from ω n = 1. An n th root of unity can only exist in a field provided the characteristic of the field does not divide n and in this case n −1 exists.
The inverse of F n can be obtained from the above by multiplying through by n −1 when it exists.
Recall the following from [10]: Theorem 2.1 [10] (i) Let F n be a Fourier n × n matrix over a field F. Let C be a code obtained by choosing in order r rows of F n in arithmetic sequence with arithmetic difference k satisfying gcd(n, k) = 1. Then C is an mds [n, r, n − r + 1] code.
In particular this is true when k = 1, that is, when the r rows are chosen in succession.
(ii) Let C be as in part (i). Then there exist explicit efficient encoding and decoding algorithms for C.
Thus series of mds codes are formed from rows of a Fourier matrix using this unit-derived method developed initially in [9].
It is possible to choose rows which 'wrap over' and still get an mds code as long as the arithmetic difference k between the rows is the same and satisfies gcd(k, n) = 1 -consider row n + i the same as row i.
Some of the methods of [10] are generalizations of those of [8] but the papers are independent.
In particular the following mds codes are formed from a finite field GF (q) by using the primitive (q − 1) root.
Theorem 2.2 (See [10]) Let GF (q) be a finite field and ω a primitive (q − 1) root of unity in GF (q).
Form the Fourier (q − 1) × (q − 1) matrix relative to ω: Then choosing any r rows of F q−1 in arithmetic sequence with difference k satisfying gcd(q − 1, k) = 1 gives a generator matrix for an [q − 1, r] mds code. In particular taking consecutive rows gives an mds code.
Further there exist explicit efficient encoding and decoding algorithms for the codes.
Consider cases where the first r rows are chosen; other cases are similar.
This is a generator matrix for an [q − 1, r] mds code and A is an r × (q − 1) matrix.
Any r × r submatrix of A has non-zero determinant as A generates an mds code.
Now extend the length (q − 1) to (q − 1 + 2) = (q + 1) as follows to obtain an mds code [q + 1, r] code. Extend A by adding on two further r × 1 columns

Theorem 2.3 The matrix B generates an mds
Proof: This is proved by showing that any r × r submatrix of B has non-zero determinant.
If the r × r submatrix is from A, only, then it has non-zero determinant since A generates an mds [q − 1, r] code.
Consider the case where the r × r submatrix P is formed by taking the first column of B together with (r − 1) columns of A.
Evaluate the determinant of P by expanding via the first column and get det(P ) = det(A ′′ ) where A ′′ is an (r − 1) × (r − 1) submatrix of F q−1 with rows in sequence. Then det(A ′′ ) = 0 and so det(P ) = 0.
Similarly the case where an r × r submatrix formed by taking the second column of B with (r − 1) columns of A can be shown to have non-zero determinant.
Expand by first column and get that det submatrix of A with rows in sequence from F n . Now expand by first column and get that det(Q) = ± det(A v ) where A v is from F n with rows in sequence and so det(A v ) = 0. Hence det(Q) = 0 as required.
The result depends on the fact that any (square) y × y submatrix of y rows in sequence of the Fourier matrix have non-zero determinant [8,10]; this requires the {v, w} to have the forms given -with one starting with 1 and the other ending with 1 and all other entries equal to zero.
It is clear from Theorem 2.1 that the A may be chosen by taking r rows of F q−1 in arithmetic sequence with difference k satisfying gcd(k, q − 1) = 1.
Encoding and decoding is obtained by adapting the methods in [10] to the present situation.
Moreover the methods in [10] may be adopted to give efficient encoding and decoding algorithms for the code generated by B. The complexity of this is max{O(n log n), t 2 } where t = ⌊ d−1 2 ⌋ with d(= q − r + 2) is the distance of the mds code [q + 1, r]. Consider r = 4 as an illustration; the construction for a general r is similar.
From the general construction above, the following is an [10,4]  Then add the two columns (1, 0, 0) T , (0, 0, 1) T to the front of A to get a 3 × 10 matrix B which is then a generator matrix for a [10,3] mds code over GF (9).
Choosing {2 nd , 5 th , 8 th , 11 th = 3 rd } rows, by wrapping, to construct A and then add the two columns as before to get B which is then a [10,4] mds code over GF (3 2 ).
There are many choices. There are many other ways that the A could be formed as noted previously and the B is obtained by adding on the two extra columns, one beginning with 1 and the other ending with 1 and all other entries zero. This code is the maximum length code that can be formed from GF (257). Note also that the arithmetic for the codes is modular arithmetic performed in GF (257) = Z 257 and with powers of (3 mod 257) only. Note that efficient encoding and decoding algorithms exist of complexity max{O(n log n, t 2 ) where n = 256, t = ⌊ d−1 2 ⌋ where d is the distance which equals 257 − r.

Even q and dimension 3
Consider GF (q) where q is even.

Sample
Consider GF (2 3 ) initially. Let ω be a primitive 7 th root of unity in GF (8). Let A be the first three rows of the Fourier 7 × 7 matrix formed using ω. 1, 0) T , w = (0, 0, 1) T and A is the first three rows of the Fourier 7 × 7 matrix formed using ω as the primitive 7 th root of unity.
Show that B is an mds [10,3] code over GF (8) as follows. Now any 3 × 3 submatrix of B involving columns of A, only, has non-zero determinant as A generates an mds code, Theorem 2.1, [10].
If any of {u, v, w} with two columns of A are used to form a 3 × 3 submatrix then for it to have a zero determinant it must be that A has a 2 × 2 submatrix with zero determinant. It may be verified that no 2 × 2 submatrix of A has zero determinant directly using Lemma 3.1 below; a direct proof of a more general result which includes this is given in Proposition 3.1.
If a 3 × 3 matrix formed with two of {u, v, w} together with a column of A has a zero determinant then this means that A has a zero element which it doesn't.
Thus B is a generator of an mds [10,3] code.
To form an [10,7] mds code we may take B as the check matrix of a [10,3] code.
There are many choices for a 3 × 10 matrix A from the Fourier matrix and then add on the three columns as noted to get a B which is then a [10, 3] mds code.

General case
Consider GF (2 n ). To construct [2 n + 2, 3] mds codes in GF (2 n ) consider the Fourier F (2 n −1)×(2 n −1) matrix formed using the primitive (2 n − 1) th root of unity ω in GF (2 n ). Take the first three rows, or any three rows in arithmetic sequence with difference k satisfying gcd(k, 2 n − 1) = 1, of this Fourier matrix to form a 3 × (2 n − 1) matrix A, which generates an mds code. Now form the matrix B by adding the three columns of I 3 to the front (or anywhere indeed) of A.
The proof that this B is an [2 n + 2, 3] mds code then reduces to showing that this 3 × (2 n − 1) matrix A from the Fourier matrix has no 2 × 2 submatrix with determinant equal to zero.
Using this 3 × (2 n + 2) matrix B as a check matrix gives a [2 n + 2, 2 n − 1] mds code. It remains to show that a 2 × 2 submatrix formed from second and third row cannot have determinant equal to zero.
These are all different and so by Lemma 3.1 there is no 2 × 2 submatrix from second and third rows with determinant equal to zero. Proof: If the 3 × 3 submatrix is taken from A, only, then know it has non-zero determinant as A generates an mds code. If the submatrix involves one of the first three columns of B then by expanding along this column its determinant is zero if and only if A has a 2 × 2 submatrix whose determinant is zero; by Lemma 3.2 this cannot happen. If it involves two of the first three columns of B then by expansion by these columns in order its determinant is non-zero as A has no zero entry. If it involves all three of the first columns of B then obviously the determinant is 1 and is non-zero. The matrix A in the construction may be formed by taking three rows of the Fourier matrix, as in Proposition 3.1, in arithmetic sequence with difference k satisfying gcd(k, 2 n − 1) = 1. Then adding in the columns of the matrix I 3 gives an mds [2 n + 2, 3] code over GF (2 n ).
By taking the matrix B as the check matrix of a code, an [2 n + 2, 2 n − 1] mds code is obtained.
For even q any [n, 3] mds code over GF (q) must satisfy n ≤ q + 2; this is shown in Proposition 3.2. Thus the [q + 2, 3] mds codes and [q + 2, q − 1] produced here are best possible length over the field GF (q) for those dimensions and even q.
That efficient encoding and decoding methods are available by adapting the methods of [10] is a great advantage. The complexity of encoding and decoding is max{n log n, t 2 } where n is the length and t = ⌊ d−1 2 ⌋ with distance d.

General [n, 3], [n, n − 3]
Suppose G is a generator matrix for an mds [n, r] code. Then the row-reduced echelon form of G is (I r , A). Now all the entries in A must be non-zero for otherwise an r × r submatrix exists with zero determinant.
Theorem 3.1 Let G = (I r , A) be the generator matrix of an [n, r] mds code. Then no j × j submatrix of A has det = 0 for j ≤ r.
In fact: Theorem 3.2 Let G = (I r , A) be the generator matrix of an [n, r] code. The code is an mds code if and only no j × j submatrix of A has det = 0 for j ≤ r.
The proof is not included. 3] is an mds code over GF (q). Then n ≤ (q + 1) when q is odd and n ≤ (q + 2) when q is even.
Proof: Let ω be a primitive element in GF (q) and thus ω is a primitive (q − 1) root of unity. By row operations a generator matrix for the [n, 3] mds code has the form G = ( If any zero appears in A then there exists a 3 × 3 submatrix with determinant equal to zero and then the code would not be an mds code. Hence If there is a repeat in any of the second or third rows then together with a (1, 1) from the first row this gives a 2 × 2 submatrix of A with determinant 0. Thus the second and third rows of the A part of G contain all the elements {1, ω, ω 2 , . . . , ω q−2 } once only.
Thus n ≤ q + 2 in all cases.
No element is repeated in second row and no element is repeated in third row and all powers of ω appear in second and third rows of the A part of G.
Suppose now q is odd and n = q + 2. Then the sum of the powers of ω in rows 2, 3 is congruent to q−1 2 mod q. When subtracting the powers of third row from the powers of the second row above then all the powers must appear or otherwise a 2 × 2 submatrix from second and third rows with determinant 0 is obtained. Thus the subtraction must result in all the powers appearing and hence the sum of these powers is ≡ q−1 2 mod q. But since each of the second and third row sums is ≡ q−1 2 mod q, the subtraction results in powers summing to ≡ 0 mod q. Therefore in all cases there exists a 2 × 2 submatrix of A part with determinant 0. Then G has a 3 × 3 submatrix with determinant 0.
Thus n ≤ q + 1 when q is odd.
In summary then we get the following for dimension 3. Let C be an [n, 3] or an [n, n − 3] mds code over a finite field GF (q). Then


Review on Text Analytics an Approach to Artificial Intelligence

: Text analytics supports organizations in managing unstructured information, identifying connections and relationships in information, and in extracting relevant entities to improve knowledge management activities. For the past decade, the number of text messages sent daily has increased by more than 7%. Younger generations overwhelmingly prefer texting to phone calls. And this is just scratching the surface, as there are many other types of textual data: support tickets, insurance application forms, healthcare records, product descriptions, and many others. Extracting meaning out of this text is an incredibly complicated task since texts may have different contexts and formats. Textual data is usually referred to as unstructured data because it doesn’t have a clear storage format or a predefined data model. Sure, you can put a sentence into an Excel cell. But how would that help you to analyze it? The applications of text analysis are far and wide, from simple automation to advanced interactions between the person inputting the data and the system they interact with. A rudimentary example of that is a chatbot. This paper focuses on how text analytics is a new approach to Artificial Intelligence. The results demonstrate that visual text analysis can be used to identify the risks in a small-scale test set but differences in language use by different cohorts of people interferes with straightforward risk identification in larger sets. It also demonstrates how new possibilities open up to develop interactive visualizations tools that allow data analysts to use text analysis techniques for risk analysis.

INTRODUCTION
Text is one of the traditional ways of communication between people. With the growing availability of text data in electronic form, handling and analysis of text by means of computers gained popularity. Handling text data with machine learning methods brought interesting challenges to the area that got further extended by incorporation of some natural language specifics. As the methods were capable of addressing more complex problems related to text data, the expectations become bigger calling for more sophisticated methods, in particular a combination of methods from different research areas including information retrieval, machine learning, statistical data analysis, data mining, natural language processing, semantic technologies. Automatic text analysis become an integral part of many systems, pushing boundaries of research capabilities towards what one can refer to as an artificial intelligence dreams never ending learning from text aiming at mimicking ways of human learning. Dunja Mladenic, Marko Grobelnik [1] Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining of patterns and trends through means such as statistical pattern learning. Arturas Kaklauskas, Mark Seniut [2] Text analytics is the process of transforming unstructured text documents into usable, structured data. Text analysis works by breaking apart sentences and phrases into their components, and then evaluating each part's role and meaning using complex software rules and machine learning algorithms. Text analytics forms the foundation of numerous natural language processing (NLP) features, including named entity recognition, categorization, and sentiment analysis. In broad terms, these NLP features aim to answer four questions: A. Who is talking? B. What are they talking about? C. What are they saying about those subjects? D. How do they feel?
Machine learning for natural language processing and text analytics involves using machine learning algorithms and "narrow" artificial intelligence (AI) to understand the meaning of text documents. These documents can be just about anything that contains text: social media comments, online reviews survey responses, even financial, medical, legal and regulatory documents. The role of machine learning and AI in natural language processing (NLP) and text analytics is to improve, accelerate and automate the underlying text analytics functions and NLP features that turn unstructured text into useable data and insights. Natural Language Processing (NLP) is "ability of machines to understand and interpret human language the way it is written or spoken". The objective of NLP is to make computer/machines as intelligent as human beings in understanding language. The ultimate goal of NLP is to the fill the gap how the humans communicate(natural language) and what the computer understands(machine language).

II. LITERATURE REVIEW
When we talk about applying artificial intelligence methods on text data, what we have in mind is a whole range of methods and problems that in some way involve analysis of text data. Many of these problems have been addressed in the area of Text Mining. To be more concrete, we will briefly look into some example tasks that have been addressed in our group during the last twenty years by applying artificial intelligence methods on text data. These include: A. Visualization of text available in news articles, visualization of named entities over time, visualization of document corpus, visualization of Web pages. B. Triplet extraction from text, document representation using semantic graph, document summarization. C. Text enrichment, contextual question answering. D. Semi-automatic ontology construction from document corpus, ontology extension. E. Knowledge extraction from text, text mining combined with social network analysis. Arturas Kaklauskas, Mark Seniut [2] Analysis of Academic Libraries' Facebook Posts: Text and Data Analytics analyzed a dataset of academic libraries posts on Facebook. It applied a text and data analytics approach to a dataset collected from the Facebook posts of academic libraries at the top 100 English-speaking universities, as listed by the 2014 Shanghai World University Rankings. The dataset is from a two-year posting history of 18,333 unique posts, 113,621 likes, and 3401 comments. Less than a quarter of the libraries had more than 2000 post-related likes, and only seven received more than 100 comments on their postings. Content analysis identified the most prevalent single word (unigrams), bigrams (two-word sequences), and trigrams (three-word sequences) in high and low engagement content. Sultan M. Al-Daihani, Alan Abrahams [3] Broadcast interviews have been the subject of study over some considerable time examining both news interviews as well as celebrity and talk show interviews. These studies have ranged from the collection and analysis of large amounts of interviews and subject using content analysis approaches, through to examining the media interview (both news and non-news) as a locally produced, on-going, and participant negotiated formalized interactional event Broadcast interview hosts are increasingly adopting hybrid forms of interview through the utilization of interview techniques from different genres within the one interview. Methods that can visually represent interviews in their entirety have the potential to assist in tracking and tracing genre shifts within a single interview. In this study we examine traditional genres and hybrid forms of broadcast interviewing using visual text analytic software Discursis. Discursis provides visual representations of whole interviews at-a-glance as well as the ability to focus into particular sections for closer analysis. Drawing on a corpus of 101 interviews from a single television program, this study examines if Discursis can meaningfully visually represent forms of interviewing genres and highlight where shifting techniques are used within a single interview. Daniel Angus, RichardFitzgerald [4] Enterprise adoption of information technology (IT) innovations has been a topic of tremendous interest to both practitioners and researchers. The study of technological, managerial, strategic, and economic factors as well as adoption processes and contexts has led the field to become a rich tapestry of many theoretical and conceptual foundations. This paper provides a comprehensive multidisciplinary classification and analysis of the scholarly development of the enterprise-level IT innovation adoption literature by examining articles over the past three decades . We identify 472 articles and classify them by functional discipline, publication, research methodology, and IT type. The paper applies text analytic methods to this document repository to (1) identify salient adoption determinants and their relationships, (2) discover research trends and patterns across disciplines, and (3) suggest potential areas for future research in IT innovation adoption at the enterprise level. Rahul C. Basole, C. David Seuss [5] In the GB Railways, two systems are in operation today to exploit these benefits: in the Close Call System and the Confidential Incident Reporting and Analysis System. The GB railways collect about 150,000 text-based records each year on potentially dangerous events and the numbers are on the increase in the Close Call System. The huge volume of text requires considerable human effort to its interpretation. This work focuses on visual text analysis techniques of Close Call records to extract safety lessons more quickly and efficiently. This paper treats basic steps for visual text analysis based on an evaluation test using a pre-constructed test set of 150 Close Call records for ''Trespass", ''Slip/Trip hazards on site" and ''Level crossing". The results demonstrate that visual text analysis can be used to identify the risks in a small-scale test set but differences in language use by different cohorts of people interferes with straightforward risk identification in larger sets. It also demonstrates how new possibilities open up to develop interactive visualizations tools that allow data analysts to use text analysis techniques for risk analysis. Esteban, Peter Hughes [6] III.
PROPOSED WORK Text analytics is used to answer business questions and to optimize day-to-day operational efficiencies as well as improve long-term strategic decisions in automotive, healthcare, and finance sector. Techniques like categorization, entity extraction, and sentiment analysis are used to identify insights, patterns, and trends in large volumes of unstructured data. Text analytics is an increasingly important task in marketing, as it can provide insights around a company's text data. This study focus on a developing Intelligence Platform that processes, analyzes and provides insights around a company's text data -i.e., surveys, call logs, social media posts, message boards, comments, etc. There are three key needs for using text analytics for market research 1 Built for surveys-A text analytics solution should be able to handle survey specifics, such people's responses that fall into the 'Nothing' category (no clear answer to the question), or 'Other' (different answer to most). 2 Value-based pricing model. A text analytics solution needs a pricing model based on the value it delivers rather than the number of transactions. 3 Easy to use A text analytics solution must be able to be operated by an in-house data analyst (with little skills in NLP) who should be able to do occasional tweaks. Infotools [7] Proposed Intelligence Platform helps businesses work better with text. This could be as part of a decision support system, or an analytics offering, or predictions. Text is language. To understand text is to understand meaning, in a way, it is to understand the nature of being human, by being able to untangle how we communicate. To understand the meaning of a piece of text, you need to understand three separate things simultaneously. One: The syntax (what words are related to what other words, and how they act on each other), the semantics (what the words mean, and any modifications that are made), and the context (what this syntax and semantics mean coming from this person, in this place, at this time, talking about this topic).

IV.
APPLICATION AND FUTURE SCOPE Text Analytics is used to answer business questions and to optimize day-to-day operational efficiencies as well as improve longterm strategic decisions in automotive, healthcare, and finance sector. here are few real-life application of text mining. 1) Risk Management: Inadequate risk analysis accounts for the biggest reasons for failure in any industry. However, text analytics helps us resolve the issue of robust risk analysis. 2) Knowledge Management: Managing large amount of data volumes often makes finding specific information, on short notice, a difficult task. Here, knowledge management software based on text analytics offers a clear and reliable solution for the "infoglut" problem.

3) Prevention of Cybercrime:
The random availability of data on the internet and the consequential exchanges often bear the brunt of cybercrimes. The unidentified criminal soon becomes untraceable. Enterprise and law enforcement or intelligence agencies make use of text mining techniques to analyze and observe the source and nature of data extraction. 4) Customer Care Service: Text analytics are widely used for customer care applications. Acceptance of text analytics software ensures improve customer experience using different sources of valuable information such as surveys, trouble tickets, and customer call notes for optimized quality, effectiveness, and speed in resolving problems. 5) Contextual Advertising: Text mining has given a new start to digital advertising. Companies are using text mining as the core engine for contextual retargeting for better results. Also, as compared to the traditional cookie-based approach, contextual advertising provides better accuracy, and completely safe, as it preserves the user's privacy. 6) Business Intelligence: Companies are using Text Mining techniques to maintain and support decision making. Text mining helps in faster and better analysis. 7) Spam Filtering: Spam emails are a pain area for most internet service providers, accounting for the higher cost of service management and hardware\software updating. spam is an entry point for viruses and impacts productivity. Text mining techniques are implemented to improve the effectiveness of statistical-based filtering methods. 8) Social Media Data Analysis: The social media which is a potential source of unstructured data is considered as a valuable source of information for market and customer intelligence. Many companies are using text analytics to analyze or predict customer needs and evaluate the perception of their brand. Text analytics can address both the issues analyzing large amount of unstructured data, extracting opinions, emotions and sentiment and their relations with brands and products.
V. CONCLUSION Text analytics is an increasingly important task in marketing, as it can provide insights around a company's text data. In this study focus on a developing Intelligence Platform that processes, analyzes and provides insights around a company's text data. AI will provide powerful frameworks for decision support systems, and will eventually go so far as to prescribe a course of action.


Need Analysis of Web Model Based On Project Based Learning to Enhancing History Knowledge among Form One Students

History subject consists of many facts, concepts and difficult to understand. This has become a cause for many students to fail in History subject. Project-based learning (PBL) is an appropriate method to improve the knowledge of History among students. Project-based learning (PBL) is an approach based on constructivist learning theory, learning through experience and constructionism theory. Although the PBL has several advantages, there are still many deficiencies in implementation. Lack of PBL can be overcome by the use of technology. The use of effective technology as an integrated part of the pedagogical process is found to help students who are less skilled and build strong knowledge in project-based learning environments. Modern digital technology is the ultimate catalyst for students who are comfortable engaging in the design and development of their projects as they can document the whole process and easily share their creations in digital format. There are no clear guidance on the use of web model based on project based learning to enhancing History knowledge among students. Thus, in this research, the issues in web model based on project based learning development and strategies are being investigated. he research method focuses on following stages: requirement analysis, design and development including alpha testing by validation from expert and beta testing which is user acceptances test to support efficiency and effectiveness of the courseware for further improvement. The result from the alpha and beta testing indicates a positive feedback on this research.

I. INTRODUCTION
The standard curriculum secondary school (KSSM) was introduced in 2017 to provide an understanding of the community, Malaysia and the world. It is an effort to nurture and strengthen our identity and create a spirit of loyalty among students (Shaareny 2016). According to the Ministry Of Education (2016) students must be exposed to the knowledge of the outside world as they are always competing globally. However some studies show that History knowledge is less among secondary school students. In the aspect of education, the process of knowledge building is very important to see the level of mastery and involvement of each student. But Historical subjects are less likely to be at the heart of the students as they feel bored and are said to be overly crowded with the facts (Zunaida Zakaria et.al, 2015).
According to Voet (2018) project based learning (PBP) is an ideal method for mastering history subjects easily and students appreciate every fact of the concept and events contained in the KSSM history. Sharipah Aini (2015) mentioned that history teachers can make history subjects as the subject of interest to all students by using model web based on projects based learning. According to Ahmat Adam (2016) teaching and learning should include investigative, reflective thinking and discovery. Teachers can plan historical processes based on their knowledge and attitude so that they can live real life based on project based learning processes (Shaareny, 2016). A student may have the opportunity to understand and present situations, things, events, concepts, principles with their own research in History if they are used in History learning. C.L. Chiang (2016) study shows that PBP is very effective in helping students with learning disabilities. PBP can be adjusted according to subject and student levels to improve knowledge.
The use of PBP is less than the History teacher because it is less skillful in implementing the elements contained in PBP. In addition, the six elements contained in the PBP should also be modified to have a great impact on the achievement and involvement of students in the History subject. Outside of the country, the PBP process has 8 elements and has a great impact to increase the knowledge and the spirit of inquiry among students (Voet, 2016). Additionally, the process of building History knowledge through a web-based learning approach is a worthwhile aspect of education (Rohani, 2017). The PBP approach based on web model aims to see the achievement and involvement of students in learning History as well as improving knowledge. Web model based on procect-based learning is believed to help form 1 student to improve history knowledge.not underline.

II. LITERATURE REVIEW
According to Sharipah Aini et al., (2015) the subjects of History contain many abstract facts, concepts, abstract ideas. This causes many students to fail in History (Arbiyah.et al., 2016). In this case, Mansor (2015) notes that the History of Malaysia subject focuses more on aspects of the course and lessens into any aspect of teaching and learning. This situation causes students to lose interest and to make passive students in the classroom. This problem also affects students to understand the facts contained in the Secondary Standard Curriculum textbook. According to Anuar Ahmad (2017), the students' knowledge is lacking in History subjects due to the learning method.
Students often memorize and recall a fact, concept or event contained in the History curriculum. Azalina (2013) mentioned that students are arguing that learning the History curriculum that contains Western History, European History and World History does not affect their own development. The students achievement in history subject is very low (Affan Budi Sentosa, 2014). The data collected in the District of Johor Bahru shows that the percentage of achievement in Form History 1 decreased for 2017 to 2018. In 2017 and 2018 the number of percentages failed to increase and this has increased the average grade of History subjects. Within 2 years of passage in History subjects showed a declineSo students do not focus when teachers teach in the classroom. According to Anuar Ahmad (2017) the same situation happened abroad, namely Australia, Canada, Sweden and Spain. These countries also prioritize the content of European History, World History in the curriculum filling of Historical subjects. So Australian and Canadian students have less knowledge in National history and lack of enthusiasm for studying History subjects.
The use of the appropriate method for teaching History will shape the learning environment that is very conducive, interesting and beneficial to the students (Ramakrisnan et al., 2013). While there are many methods in History, inquiry is a very appropriate teaching method and creates a sense of learning among students (Voet.M, 2017). However, the process of using the existing inquiry model of the existing findings for the History subject is not specified in detail and the guide does not provide a clear overview of its use in History (Voet, 2017). Caroline (2017) says that discovery inquiry is a teaching practice where students explore content by disguising, investigating, and responding to questions. Sharipah's study (2015) shows that the inquiry is not fully implemented in teaching and learning. This is because there are some drawbacks in the model of inquiry method for the History subject (Lazonder, 2016). According to Kelly (2018) educators do not follow the model's sequence during Teaching and Learning history. This statement is supported by Cherif (2013) which states that teachers and students are not exposed to the use of an inquiry model on a regular basis. Additionally, Historical teachers argue that the use of inquiry in History subjects takes quite a while and this will slow down the teachers to complete the syllabus (Achinstein, 2015). Anuar Ahmad's study (2017) shows that Historical teachers argue that the process of solving problems using the inquiry process takes a long time and students can not find a solution in the teaching and learning time. Mansor (2015) notes that due to time constraints, historical teachers do not design learning activities that integrate the use of inquiries as the process is prolonged. Historical teachers are also looking for an easy solution and making learning activities easy to handle such as finding information based on the use of textbooks (Voet, 2017).
Studies show that project-based learning strategies will encourage in-depth learning (KPM, 2016). According to project-based learning is a teaching approach that gives students the opportunity to investigate complex questions and problems faced by teachers (Siti Aminah, 2016). Therefore, there are many problems in project-based learning (Ong Eng Tek, 2016). According to Holmes (2016) six elites contained in the PBP have some drawbacks to use in History teaching. The Shaareny study (2016) concludes that the History teachers form non-provocative questions. Questions asked in History teaching are also not open and do not create enthusiasm for students to answer. This statement is supported by Voet (2018) stating that the question is also not relevant to the material that the student should study. Sometimes projects are also determined by teachers in the classroom (Atadero, 2015). So students feel depressed and uncomfortable while making the project. During the implementation of PBL in the classroom the students were less exposed to 21st century skills as teachers thought that the activities of the 21st century were meaningless to the students (Ong Eng Tek, 2016). Yahya Othman (2014) notes that in the world of undergraduates students are given the opportunity to provide feedback during the classroom-based learning. But students are less guided to make a presentation on their results (Holmes, 2016). PBP privileges and inquiries are the final product construction (Chee Hoe Ng, 2018) representing students' understanding, knowledge, and attitude on issues under investigation. Students will also dare to present their project results.
Habok (2015) of the last PBL results comes in the form of 3D, models, materials, and folio. The results of the last project built by the students as such are difficult to share with other classmates or other schools. This is because they need to be kept in a safe place and should also be kept. Students are less likely to use these materials to share their ideas because they are difficult to move from place to place (Sabilan, S., Ishak, M.F., & Din & Nasirudin, 2014). So to solve this problem, web models have been introduced and are often used overseas. Students' motivational attitude has also increased the effect of using web models in PBL (Boubouka, M. and Papanikolaou, K. A. 2013). Based on the Spiritual (2017) modern digital technology is the ultimate catalyst for students who are comfortable engaging in designing and developing their projects as they can document the whole process and easily share their creation in digital format.

III. PURPOSE OF STUDY
History subjects must be learned and graduated by all high school students. The Ministry of Education Malaysia has introduced the Secondary School Standard Curriculum (KSSM) which contains 1 theme ie 10 chapters on Form 1. According to Azalina Abdul Wahap (2013), History subjects provide understanding about society, country and world to students. But student achievement in history is very low (Affan Budi Sentosa, 2014). According to Anuar Ahmad (2017), the students' knowledge is lacking in History subjects due to the learning method. Students often memorize and recall a fact, concept or event contained in the History curriculum. Therefore, the main focus of this study is to identify the incomprehensible chapters of form one pupils, design and develop web-based project-based learning models by integrating John Dewey's model (2016), Kath Murdoch's (2007) inquiry model, Addie's capital 1987) and Alavi's knowledge management process model (1997). Lastly, alpha and beta testing are conducted to support the suitability and acceptance of a web-based learning based project model. This paper discusses the initial stage of the project such as analysis, design and development and testing phase. The evaluation areas that focus on performance will be discussed in the future.

IV. METHOD
The purpose of this study is to increase the knowledge of History among the students of form one. The researchers conducted screening tests to 60 students categorized as Form 1 students in two secondary schools from Johor, Malaysia to identify the level of historical knowledge, achievement and involvement. Screening tests were conducted in two secondary schools. Teachers can identify the subjects that students are weak and lacking in knowledge. Inspection tests have been verified by content experts before implementation. This examination test was conducted to generally examine the level of historical knowledge among Form 1 students. Thus, screening tests were conducted to diagnose historical knowledge among students.
The examination examinations contain 3 parts as determined by the Malaysian Examination Board. This test is tailored to the Screening Test. 8 chapters were tested by constructing 20 objective questions, 4 structural questions and two essay questions. Students are responsible for all divisions. Students must get 60 marks. If less than 60 scores, students will be categorized as less knowledgeable students in mastering History content. According to Ramakishnan (2013), diagnostic tests need to be conducted to identify the causes of students' failure to master the historical facts contained in the Curriculum and Assessment Standard Documents (DSKP and specific groups according to the capabilities and difficulties they face.) The diagnostic test results also help to design specific content in the web model for individuals or groups more precise and systematic according to the student's level. KPM (2016), students need to master all the chapters contained in DSKP form 1. In that regard, researchers provide diagnostic tests that examine three sections.Subsequently, researchers collect a variety of information related to student difficulties to enhance History knowledge before designing and developing a project-driven web based project. Web models are the primary catalyst for students who are comfortable engaging in designing and developing their projects as they can document the whole process and easily share their creation in digital format (Patton, 2012). Researchers have developed a guide to using web models on a regular basis. During the design and development phase, the researcher used 8 elemental learning based on Jown Dewey (2016) and Kath Murdoch's inquiry model (2007). Finally, project-based web-based learning models are assessed by adapting alpha tests, beta testing, and user accept tests to support efficiency and effectiveness for further improvements.

A. Requirement Analysis
A total of 60 form 1 students answered the screening test. The initial test results found that the number of students facing difficulties in history subject was different. Table 1 shows the number of students and their difficulties in history subject by topic. The results of this screening test show that many students are still failing to master historical facts and this has caused their level of historical knowledge to be relatively low. According to Melisa (2018), the first theme of the theme is one of the most important and fundamental to understanding the chronology of the History subject. There are 60% of students who are unable to master the sixth topic of increasing Greek and Roman civilization. So these students will face problems in the history subject when in the form of two. Thus researchers conclude that the achievement in the sixth topic in the form of one is very meaningful to enhance the knowledge of History. This examination test allows researchers to identify difficult topics in Form 1 history to continuously review specific disabilities in their respective learning topics. Researchers have undergone another measure known as a diagnostic test to detect the weaknesses in detail faced by students in the sixth topic. Researchers selected 36 out of 60 students who did not master the topic of Form 1 history in screening tests for diagnostic tests. Table 2 shows the number of pupils and difficult sub-topics in the topic six.

TABLE 2: Number of Students and difficulties based on sub topic in topic six
Researchers found that 66% of the students still did not dominate the sub topic of greek civilization and 78% of the students did not master the sub topic of government administration and administration of civilization. In addition, 72% of students do not master the third sub topic of rom civilization. While 78% of students do not master the sub topic of architectural improvement in rom civilization. A total of 74% of students do not master the facts contained in chapter six. Researchers can summarize this problem as the primary cause of student's historical knowledge to be at a low level. So researchers want to build a project-based learning web based model to enhance the history of form 1.

VI. CONCLUSION
The paper discuses on requirement analysis and development of web model based on project based learning (PBL) to enhancing history knowledge among form one students. The web model based on project based learning will increase the number of student participation in the learning of History subjects. Therefore, it will lead to a better understanding of the learning topic. Web model based on project based learning create more enjoyable learning and enable students to understand the content of knowledge by keeping their attention on learning. Overall theorical framework is suitable framework for designing a web model based on project based learning as a historical learning material for Form 1 students because it projects better results as discussed above and can meet the needs of Form 1 students in historical learning points.

AUTHORS PROFILE
The first author's name is Siva Sankar A / L R. Mahalingam. The author has made a bachelor's degree in malaya university in 2008-2011. In 2015, the writer of my studies to the master's degree in education at the national university of malaysia. The author has written as many as 6 articles and has been published from 2015 until now. The author also won several awards such as a 3-minute presentation of thesis, poster presentation and national level innovation competition. Now the writer is doing PHD in malaysia technology university.irst author's name is Siva Sankar A / L R. Mahalingam. The author has made a bachelor's degree in arts at Malaya University in 2008-2011. In 2015, the author continue further studies to the master's degree in education at the National University of Malaysia. The author has written as many as 6 articles and has been published since 2015 until now. The author also won several awards such as a 3-minute presentation of thesis, poster presentation and national level innovation competition. Now the writer is doing PHD in University Technology of Malaysia.
The second author's name is Professor Madya Dr Ahmad Johari Bin Sihes. The author working at Department of Educational Foundation and Social Science at Technology University Malaysia. In 1986 the writer received a bachelor's degree in chemistry at the University of Minnesota at Duluth USA. In 1988 the writer received masters degree in Curriculum Construction at the same university. In 2009 the authors graduated PHD at Universiti Teknologi Malaysia in the field of Curriculum and Instruction. The author has written as many articles and has been published .The author received award and several honors such as Science education award, excellent government service sward (MARA), excellence service award, best paper in national student development conference 2013, excellent residential college principal award and excellence service award 2016.
The third author's name is Sivasangary A/P Raveendran. The author has made a bachelor's degree in Temenggong Ibrahim Teacher Training College in 2009-2013. In 2016, the writer of my studies to the master's degree in education at the Technology University of Malaysia. Now the author working as a Mathematics teacher at SJK (T) Kangkar Pulai. The author also won several awards in international level innovation competition.


extfieldsofstudy:
Computer Science
Biology
Medicine

text:
LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins

Abstract The LOMETS2 server (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is an online meta-threading server system for template-based protein structure prediction. Although the server has been widely used by the community over the last decade, the previous LOMETS server no longer represents the state-of-the-art due to aging of the algorithms and unsatisfactory performance on distant-homology template identification. An extension of the server built on cutting-edge methods, especially techniques developed since the recent CASP experiments, is urgently needed. In this work, we report the recent advancements of the LOMETS2 server, which comprise a number of major new developments, including (i) new state-of-the-art threading programs, including contact-map-based threading approaches, (ii) deep sequence search-based sequence profile construction and (iii) a new web interface design that incorporates structure-based function annotations. Large-scale benchmark tests demonstrated that the integration of the deep profiles and new threading approaches into LOMETS2 significantly improve its structure modeling quality and template detection, where LOMETS2 detected 176% more templates with TM-scores >0.5 than the previous LOMETS server for Hard targets that lacked homologous templates. Meanwhile, the newly incorporated structure-based function prediction helps extend the usefulness of the online server to the broader biological community.

INTRODUCTION
Template-based modeling (TBM) represents the most accurate and reliable approach to protein structure prediction. The core procedure of TBM is fold-recognition, also known as threading, which aims to identify correct templates from the PDB with similar folds to the target protein and correctly align the target and template sequences. There is evidence that indicates the structural space of the current PDB library is complete and that TBM can be used to solve the protein structure prediction problem, at least for single-domain proteins (1). In reality, however, identification of ideal templates with correct alignments remains a major challenge, especially for protein targets that have no closely homologous templates in the PDB (2,3). To address this challenge, a large variety of threading methods have been developed over the last two decades based on various scoring functions and alignment algorithms (4)(5)(6)(7)(8)(9)(10)(11)(12). Because each threading algorithm has its own inherent advantages and disadvantages, there is no method that outperforms all others for every target protein (13,14). Thus, to improve the quality and reliability of TBM, several metaserver approaches were developed to combine threading results from multiple complementary algorithms (15)(16)(17). Early attempts at developing meta-threading-webservers, such as 3D-jury (15), submitted prediction jobs to different online servers and then combined the output results from each server after-the-fact. Such dependency on external online servers made these meta-servers difficult to maintain, especially when the external servers updated their APIs or stopped operating.
To improve the reliability of the meta-threading approach, we proposed LOMETS (16) which combined 9 locally-installed threading programs for protein structure prediction. Due to the robustness of the server's operation and the advantages associated with combining the results of multiple complementary algorithms over individual thread-W430 Nucleic Acids Research, 2019, Vol. 47, Web Server issue ing programs for fold-recognition, LOMETS has been one of the most widely used meta-threading servers for protein structure prediction. Since its initial release in 2007, LOMETS has generated fold-recognition and full-length structure modeling for more than 30,000 protein sequences submitted by 15,201 users. With rapid developments in the field, however, the previous LOMETS server no longer represents the state-of-the-art in protein threading. In particular, the recent CASP experiments (3,18) have witnessed significant new progress in TBM, where contact-maps predicted through deep neural-network training, in conjunction with metagenomic sequencing, have been found to be vitally useful for modeling distant-and non-homologous protein sequences. Thus, integration of the new threading methods and sequence and structural data resources is urgently needed to further improve the quality of the metathreading approach. This is the major motivation for the development of the second generation meta-threading server, LOMETS2, in this work.
Compared to the previous LOMETS server, LOMETS2 introduces three major new extensions: (i) the server now integrates a new and more comprehensive set of state-of-theart threading programs, including contact-guided threading approaches; (ii) it utilizes deep profiles which are generated using a novel deep Multiple Sequence Alignment (MSA) construction method to improve the profile-based alignment accuracy; (iii) it adopts a re-designed, user-friendly interface and adds structure-based function annotations for a more comprehensive results report. The benchmark tests demonstrated that the LOMETS2 structure model quality is significantly better than any of its component threading programs as well as the previous meta-threading approach, LOMETS (16).

Overview of the LOMETS2 pipeline
The LOMETS2 server pipeline consists of four consecutive steps: generation of deep sequence profiles, fold-recognition through its component threading programs, template alignment selection, and full-length model construction ( Figure  1). Starting from a target protein sequence provided by the server user, a deep MSA is generated by iterative sequence homology searches against multiple sequence databases. The resulting deep MSA is used to calculate deep profiles in the form of sequence profiles (or position-specific scoring matrices) and profile Hidden Markov Models (HMMs). The deep profiles are then used by the 11 LOMETS2 threading programs, which are all locally installed on our computer cluster, to identify template structures from the template library, where deep profiles are pre-built for each template. Next, 220 templates (the top 20 templates per program) are combined to give the final LOMETS2 threading results. These 220 templates are ranked by a scoring function that integrates Z-score, confidence score for each method, and sequence identity between the identified templates and query sequence (see the detailed description in Text S1 in the Supporting Information, SI). Meanwhile, the function annotations associated with each template are collected from the BioLiP structure-function database (19). Finally, full-length models are constructed by MODELLER (20) utilizing the multiple distance restraints collected from the top-ranked templates identified by LOMETS2.

New developments in LOMETS2
DeepMSA method. The profiles (sequence profiles or profile HMMs), which are derived from MSAs for the query/template sequences, are one of the most important features for template recognition and threading-based sequence alignment. General MSA generation methods, such as PSI-BLAST (21) and HHblits (22), sometimes cannot collect a sufficient number of homologs in an MSA, especially for Hard targets, which can result in poor alignment quality when using profile-based threading algorithms. LOMETS2 employs a new composite approach, called DeepMSA, to create sensitive MSAs with higher numbers of diverse sequences than general MSA generation methods.
Starting from a query sequence, a deep MSA is generated by a three stage procedure (Supplementary Figure S1). In Stage 1, HHblits from the HH-Suite package is used to search the query against UniClust30 (23) to generate the first-level MSA. If the number of effective sequences (Neff, defined in Text S2) generated by Stage 1 is <128, Stage 2 will be performed. In Stage 2, Jackhmmer from the HMMER package (24) is used to search the query sequence against UniRef90 (25) to extract full-length sequences (hits). These hits are then converted into a custom HHblits-formatted database. Jump-starting it from the first-level MSA, HHblits is again applied to search this custom database to generate the second-level MSA. If the Neff of the secondlevel MSA is still <128, Stage 3 will be performed, where the second-level MSA is converted into a profile HMM by HMMbuild from the HMMER package. This HMM is then searched against the Metaclust (26) metagenome sequence database by HMMsearch from HMMER to extract full-length hits. Similar to Stage 2, hits from HMMsearch are built into a custom HHblits database. The second-level MSA is used to jump-start an HHblits search against this new custom HHblits database to get the third-level MSA. Using these three Stages, we generate deep MSAs, which in turn are utilized to build the final sequence profiles used by each of the LOMETS2 threading methods.
New threading methods. The LOMETS2 server integrates predictions from 11 different state-of-the-art threading algorithms, which can be classified into three types: the contact-based method CEthreader; sequence profile-based methods, including FFAS3D (10), MUSTER (9), Neff-MUSTER, PPAS (16), PROSPECT2 (6), SP3 (27) and SparksX (8); and profile HMM-based methods, consisting of HHsearch (11), HHpred (28) and PRC (29). Among them, CEthreader, FFAS3D, HHpred, MUSTER, Neff-MUSTER and SparksX are newly added to the LOMETS2 server. HHpred is a profile HMM-based threading method extended from HHsearch. SparksX and FFAS3D are both based on profile-profile comparison and use similar features, but FFAS3D employs a template re-ranking strategy. MUSTER, Neff-MUSTER and CEthreader are our inhouse threading programs. MUSTER and Neff-MUSTER are profile-profile alignment algorithms that use dynamic programming together with a myriad of features, including sequence profiles, secondary structures, structure fragment profiles, solvent accessibility, dihedral torsional angles, and hydrophobic scoring matrices for fold-recognition. The major difference between MUSTER and Neff-MUSTER is that MUSTER uses a set of fixed weights for the sequence profile term while Neff-MUSTER employs dynamic weights derived from the Neff of the MSA used to build the sequence profile. Lastly, CEthreader is a contact-driven threading method, which first predicts the contact-map for a query sequence using our in-house, deep learning-based contact-map prediction method, ResPRE (30) (see Text S3). From the predicted contact-map, CEthreader generates single-body contact eigenvectors through the eigendecomposition technique, and finally searches and aligns the query to templates using dynamic programming based on the contact eigenvectors as well as secondary structure and sequence profile comparison (see Text S4). A more detailed description for each of the 11 LOMETS2 threading methods is provided in Text S5.
A unified deep profile-based template library. In the previous version of the LOMETS server, the template libraries were collected from the original developers of each individual threading program, which negatively impacted its performance since some libraries were not comprehensive or were not updated regularly. In LOMETS2, a unified library of non-redundant protein structure templates is regularly collected for each of the threading methods. This library currently consists of 71 684 structures with a pair-wise sequence identity <70%. In addition to the structures and structure-based feature files, the DeepMSA program is used to create MSAs and then pre-build the deep sequence profiles and deep profile HMMs for each template. The library is updated weekly in accordance with the update cycle of the PDB database and has been made publicly available at https://zhanglab.ccmb.med.umich.edu/library/.
Re-designed server output. The output page of the LOMETS2 server was re-designed to make the server more convenient to use and to incorporate more comprehensive function annotations. The 3Dmol (31)

Dataset
To test the LOMETS2 server, we constructed a nonredundant benchmark set of 614 proteins from the PDB with pairwise sequence identities <30%. The proteins in the benchmark set had lengths ranging from 57 to 500 residues and included 141 α-, 187 βand 286 αβ-proteins. To make it relevant to TBM, each target protein was required to have at least one template with sequence identity <30% and TMscore >0.5 to the target as identified by TM-align structural alignment searches through the template library (32). Based on LOMETS classification, which categorizes proteins into different classes based on the significance scores of multiple threading programs, this dataset contained 403 Easy targets (for which at least one threading program in LOMETS detected a significant template hit) and 211 Hard targets (for which no program in LOMETS identified a significant hit). To remove bias caused by homologous sequences, a uniform sequence identity cutoff of <30% between the target and template sequences was enforced when the threading tests were conducted for each of the programs. Figure 2A presents a head-to-head comparison between the TM-scores of the first templates identified by LOMETS and those identified by LOMETS2. The average TM-scores for the first templates identified by LOMETS2 for All, Easy and Hard targets were 0.62, 0.71 and 0.46, respectively, which were 9%, 4% and 31% higher than those identified by LOMETS. Out of the 614 targets, LOMETS2 successfully detected correct templates with a TM-score >0.5 for 479 targets, which was 17% higher than the number of targets with correct templates identified by LOMETS. For Hard targets in particular, LOMETS2 detected correct templates for 94 targets, while LOMETS only successfully detected correct templates for 34 targets. In Figure 2B, we show a comparison between the average TM-scores for the full-length models constructed by MODELLER based on templates identified by either LOMETS or LOMETS2. The average TM-scores of the first models generated by LOMETS2 for All, Easy and Hard targets were 11%, 5% and 35% higher than those by LOMETS.

Why does LOMETS2 outperform LOMETS?
The data in Figure 2 demonstrates that LOMETS2 significantly outperforms LOMETS in terms of identifying higher quality templates and generating better full-length models, especially for Hard targets that lack homologous templates. This improvement can be mainly attributed to three new developments in LOMETS2. First, the newly developed DeepMSA program detects more homologous sequences compared to traditional methods, thereby improving the accuracy of profile-based alignment algorithms. In Supplementary Figure S2, we list a comparison between the number of effective sequences detected by DeepMSA for the 614 targets versus the numbers detected by PSI-BLAST and HHblits, where the latter (PSI-BLAST and HHblits) were used as the default profile construction methods for the previous threading programs in LOMETS. On average, DeepMSA detected 6,500 sequences per protein (or Neff = 307), which is 8 (or 5) times higher than the number detected by HHblits and 10 (or 9) fold higher than the number identified by PSI-BLAST. In Figure 3, we present a comparison of the performance of each individual threading program using either the deep or default profiles, where the performance is improved by deep profiles for all threading programs. The specific TM-score values for each threading program are listed in Supplementary Table S1 in the SI, which shows that the improvements are statistically significant (with P-values < 10 −5 in Student's t-test) for nearly all of the programs (except for FFAS3D for which the use of deep MSAs increased the TM-score by 1%, but the difference is not statistically significant). Since the only difference between the two sets of programs was the sequence and HHM profiles that were used, the data demonstrates that the deep MSA generation method has a remarkable impact on improving the performance of nearly all individual methods as well as the overall meta-threading approach.
Second, the introduction of the new threading programs improves the overall quality of the LOMETS2 server results. In total, six new programs (CEthreader, HHpred, SparksX, FFAS3D, MUSTER and Neff-MUSTER) were added to LOMETS2 and four programs were retired due to poor performance. As shown in Supplementary Table S1, all of the new programs performed better, in terms of average template TM-score, than the ones inherited from the LOMETS server. In particular, the new CEthreader program identified templates with the highest TM-scores and detected more correct templates than any other threading program. Due to the utilization of de novo contact-map prediction, CEthreader is especially useful for Hard targets that lack homologous templates. This is demonstrated by the fact that the average TM-score of the first templates identified by CEthreader for Hard targets was at least 19% higher than any other LOMETS2 threading program, including the best sequence profile-based method SparksX (average TM-score = 0.378) and the best profile HMM-based method HHpred (average TM-score = 0.374). Meanwhile, the number of targets for which CEthreader identified correct first templates (TM-score > 0.5) was 61% and 74% more than the number of targets for SparksX and HHpred, respectively. The integration of the contact-guided threading method is particularly important for improving the overall ability of LOMETS2 to recognize distant-homology templates.
Finally, the new template-ranking score, which combines Z-score, program-specific confidence scores and sequence identity (Eq. S2 in Text S1), helps to select correct templates. Although CEthreader significantly outperforms other individual programs, on average, the LOMETS2 meta-server strategy still selects templates with higher TM-scores than any of its individual programs, including CEthreader. In Supplementary Table S2, we list the average TM-scores for the first full-length models constructed by MOD-ELLER using templates from different threading programs,  where the average TM-score for LOMETS2 (TM-score = 0.6753) was 5.7% higher than its best threading program, CEtheader (TM-score = 0.6391), with a P-value of 2.9 × 10 −61 . This demonstrates the efficacy of the composite ranking score for template selection. Here, we note that the difference in performance between LOMETS2 and its best component program is more pronounced for fulllength model production (shown in Supplementary Table S2) than for raw template selection/alignment (Supplementary Table S1). This is because LOMETS2 ranks the templates from CEthreader as first in most cases due to the high confidence score given to the CEthreader program, which results in a modest difference between LOMETS2 and CEthreader when only considering the TM-scores of the first template alignments. For the full-length model construction, however, the consensus distance restraints collected from multiple complementary template alignments from different threading programs help MODELLER construct much more reliable full-length models than those pro-duced by the restraints from individual threading programs; the use of such complementarity of different threading algorithms represents one of the major advantages of the metaserver approach (14,17).

Performance on recent Critical Assessment of Structure Prediction (CASP) experiments
The community-wide CASP experiments provide a unique opportunity to stringently test structure prediction methodologies (33). A preliminary version of LOMETS2 (without deep profiles) was used as the template identification component of the 'Zhang-Server' and 'QUARK' groups, which were ranked as the top two structure prediction algorithms in the automated Server Section in CASP13 in terms of the Z-score of the GDT-TS score (see http://www.predictioncenter.org/casp13/zscores final. cgi?model type=first&gr type=server only).
However, W434 Nucleic Acids Research, 2019, Vol. 47, Web Server issue LOMETS2 did not participate in the CASP experiment as an independent server. To examine its performance, we ran the LOMETS2 program (with deep profiles) for the CASP13 targets, where all templates released after May 1, 2018 were excluded. Supplementary Figure S3 lists the average TMscore of the new LOMETS2 models for the 121 released domains, in comparison to the top server groups in CASP13. Overall, LOMETS2 ranked No. 4, following 'Zhang-Server' and 'QUARK', which used LOMETS2 templates as input templates, and 'RaptorX-DeepModeller'. 'RaptorX-DeepModeller' is an integration of two pipelines, namely, 'RaptorX-TBM', which generates models using a distance-guided threading program DeepThreader (34), and 'RaptorX-Contact', which is a distance-based ab initio folding method (35). In Supplementary Figure S4, we show a head-to-head comparison between models produced by LOMETS2, RaptorX-TBM and CEthreader for the 121 CASP13 domains. While LOMETS2 showed an overall comparable performance with RaptorX-TBM, RaptorX-TBM outperformed CEthreader; this latter difference probably reflects the advantage of distance over contact prediction for the improvement of threading performance. Additionally, RaptorX-TBM uses a combination of MOD-ELLER and Rosetta for full-length model construction, while CEthreader and LOMETS2 only use MODELLER here.
In Supplementary Table S3, we further present a comparison of the models produced by LOMETS2 and its component threading programs for the 121 CASP13 domains. While CEthreader again outperformed other individual programs, LOMETS2 had a significantly higher TMscore than all of its component threading programs, with P-values <10 −6 for all the TM-score differences. This result is consistent with the large-scale benchmark tests shown above ( Figure 3) and demonstrates the robustness and effectiveness of LOMETS2 for template identification and model construction.

Server input
The input to the LOMETS2 server is a single-chain amino acid sequence file in FASTA format. After submitting a job, a URL link with a random job ID is generated which allows the user to view the results while maintaining the user's data privacy. The user can optionally provide an email address and LOMETS2 will automatically send a notification email with a link to the results page upon job completion. This email address can also be used to retrieve the list of all previously submitted jobs by the user through https://zhanglab.ccmb.med.umich.edu/ LOMETS2/check.html. Users will usually receive the prediction results in less than 24 h after submitting a sequence (protein size ≤ 1500 AA). However, the running time depends on the protein size, where a smaller protein takes less time than a larger protein. Additionally, if too many sequences are accumulated in the queue, the procedure may take a longer time (see Supplementary Figure S5 for the practical response time of the LOMETS2 server for 1,433 recently processed jobs). By default, LOMETS2 keeps all identified templates, while an advanced option is provided that enables users to remove templates sharing >30% sequence identity with the input sequence.

Server output
The LOMETS2 results page consists of six sections, including (i) the user input sequence, (ii) predicted secondary structure, (iii) predicted solvent accessibility, (iv) summary of the top 10 template alignments identified by LOMETS2, (v) full-length models built by MODELLER using the top five templates and (vi) function annotations associated with the top templates from the component threading programs.
As an illustration, Figure 4 presents an example from the FokI restriction endonuclease (PDB ID: 2fokA) to explain the major sections (iv-vi) of the results page. When we ran this example, we removed templates with >30% sequence identity to the input sequence.
Section iv lists the top 10 templates identified by LOMETS2 ( Figure 4A). For each template, the left panel displays the template's PDB ID, sequence identity to the query, alignment coverage, normalized Z-score, and the threading method that detected the template. Here, the normalized Z-score is equal to Z/Z 0 , where Z 0 is a program specific cut-off (see Text S1). The normalized Z-score signifies the confidence of the template alignment, where a normalized Z-score ≥1 indicates a good alignment by the corresponding threading program. Users can download the alignment by clicking the threading program name. The right panel of this table shows the predicted secondary structure, predicted solvent accessibility and the querytemplate alignments by LOMETS2. The bottom panel displays 3D structures of the templates, together with the threading methods that detected them.
Section v ( Figure 4B) shows the top five full-length models generated by LOMETS2. Users can drag, rotate or zoom in on the structure to check the model. PDB-formatted structure model files can be downloaded by clicking the link under the figures of the models. The last section ( Figure 4C) displays up to 10 top templates from the component threading programs, where function annotations (Gene Ontology terms and Enzyme Commission numbers) curated from the BioLiP database are listed in the right panel, next to the corresponding template alignments in the left panel. Taking the CEthreader results as an example, the 8th-10th columns show the GO terms for the Molecular Function (MF), Biological Process (BP) and Cellular Component (CC) aspects. The four-digit EC number is shown in the 11th column, which indicates the type of enzyme to which the template belongs. The GO terms and EC number are hyperlinked to the QuickGO (36) Gene Ontology and the ExPASy (37) ENZYME databases.

CONCLUSION
In this work, we report a significantly extended version of the LOMETS server for template-based protein structure prediction and function annotation. The new LOMETS2 server contains 11 state-of-the-art threading programs, which are powered by a new deep MSA pipeline for enhanced profile-and HMM-based template alignment. To provide a solid foundation for template-based modeling, a unified template library has been developed which helps facilitate the maintenance and upkeep of the library for all component threading methods. Meanwhile, the output interface was re-designed to enable interactive visualization of the structure models and function annotations.
The large-scale benchmark tests showed that the new developments significantly improve the accuracy of the metathreading server, especially for Hard targets that lack homologous templates, where LOMETS2 detected 176% more templates with correct folds (TM-score > 0.5) (38) than LOMETS. Detailed data analyses showed these improvements are mainly due to the integration of the new cuttingedge threading algorithms, including those that incorporate deep convolutional network-based contact-map predictions. Meanwhile, the deep MSA-based profiles systematically improve the alignment accuracy for nearly all of the component threading algorithms.
The LOMETS2 server indirectly participated in the CASP experiment as it was used as the template identification component of the top-ranking 'Zhang-Server' and 'QUARK' groups in the Server Section of the most recent CASP13 experiment. Although LOMETS2 represents one of the most accurate servers for structure prediction, it is still outperformed by I-TASSER and QUARK, which implement far more intensive structure assembly simulations. Nevertheless, LOMETS2 has unique value to serve as a robust resource for entry-level template recognition and template-based function annotation with reasonable modeling accuracy; this is particularly important for largescale structure and function modeling for which folding and refinement simulations are prohibitively expensive. Moreover, based on the feedback from our server system users, many biological users are interested in knowing the original templates used to construct the final structural models. This knowledge is often useful in order to better interpret and understand the characteristics and functional implications of the structural models. The detailed template information is typically lost in the composite models produced by I-TASSER and QUARK, but is well-preserved in the LOMETS server models and the output data, which include the rich functional annotations for the templates and multiple template alignments from the different threading programs. Overall, with constant effort on the development and extension of the methodology, we expect LOMETS2 will continue to serve as an important, unique and reliable structure and function modeling resource for the broader biological community.