Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Forensic Facial Comparison: Current Status, Limitations, and Future Directions

Global escalation of crime has necessitated the use of digital imagery to aid the identification of perpetrators. Forensic facial comparison (FFC) is increasingly employed, often relying on poor-quality images. In the absence of standardized criteria, especially in terms of video recordings, verification of the methodology is needed. This paper addresses aspects of FFC, discussing relevant terminology, investigating the validity and reliability of the FISWG morphological feature list using a new South African database, and advising on standards for CCTV equipment. Suboptimal conditions, including poor resolution, unfavorable angle of incidence, color, and lighting, affected the accuracy of FFC. Morphological analysis of photographs, standard CCTV, and eye-level CCTV showed improved performance in a strict iteration analysis, but not when using analogue CCTV images. Therefore, both strict and lenient iterations should be conducted, but FFC must be abandoned when a strict iteration performs worse than a lenient one. This threshold ought to be applied to the specific CCTV equipment to determine its utility. Chance-corrected accuracy was the most representative measure of accuracy, as opposed to the commonly used hit rate. While the use of automated systems is increasing, trained human observer-based morphological analysis, using the FISWG feature list and an Analysis, Comparison, Evaluation, and Verification (ACE-V) approach, should be the primary method of facial comparison.

Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types

Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.

Integrating the Webinar as a Tool to Support Adult Training: A Case Study on the Training for Entrepreneurs of SMEs in Thailand

The COVID-19 pandemic has presented significant challenges for the education and training sector. We could have witnessed the rise of technology for webinars which are tools to deliver training and education through video and audio communication in the form of distance learning for instructors and participants. How-ever, commercial webinars such as zoom or webex may lack a management system for organizing the amount of participants for long term courses. The present study aimed to develop a webinar tool for the context of adult training for entrepreneurs of SMEs in Thailand. In order to develop a webinar tool, we proposed a general framework which consisted of three stages. The requirement stage aimed to explore the baseline survey from 411 participants and summarize the feature list of the webinar. The development stage employed the kanban methodology to develop each feature list and proposed the architecture of the system. Finally, the evaluation stage compares two groups of 110 participants between our webinar approach and zoom application with statistics of attendance regarding those who attended the course and also contains a satisfaction survey. The results show that the important feature to engage participants in long term courses were system notifications and the availability of a web-based platform for providing easy access to webinar.

AN EXPERIMENTAL ANALYSIS ON ECOMMERCE REVIEWS,WITH SENTIMENT CLASSIFICATION USING OPINION MINING ON WEB

Sentiment analysis is a big branch in the field of natural language processing. Sentiment analysis mainly text based analysis, but there are some challenges that make it difficult as compared to traditional text based analysis. This paper empathizes on the need of an attempt to improve research process and progress of sentiment analysis on the basis of investigation. Outcome of the analysis are summarized in this paper. This paper analyze the reviews of products manually by collecting data in the form of a excel file. Then it will produce and classify the reviews as positive or negative comments to get the best product. Now it’s more relevant to automate reviews data it is growing exponentially. This method works by web scrapping reviews from e-commerce website. Data cleaning is applied to remove the unwanted data known as stop words. The features are identified. The feature can be camera, battery life etc. Obtain frequency across all the products and for all the reviews per feature. The intended work is to extract the features from the reviews and detecting the polarity for each aspect, thus resulting in feature extraction matrix (FEM). FEM matrix has each row as an observation for a product and each of the columns represent the feature. List of Products based on highest value of FEM for searched features and product recommendations are generated based on the user searched feature.

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

GUIA PARA PROCESSAMENTO DE DADOS DE CROMATOGRAFIA ACOPLADA A ESPECTROMETRIA DE MASSAS

GUIDE FOR CHROMATOGRAPHY COUPLED TO MASS SPECTROMETRY DATA PROCESSING. In this work, a discussed and step-wise tutorial for LC-MS and GC-MS data processing using the open-access software MZMine2 is presented and discussed. The rationale behind each step was demonstrated to enable the readers to go through their own data and process it accordingly. The main lesson to be learned is that each parameter must be chosen in light of the raw data and no guidelines should suggest a predetermined value. Still, it is worth mentioning that ideal values for each parameter do not exist, and that the user might end up investing too much time futilely optimizing values. Our suggestion is to process your data in light of the raw data (and the study design) following the preview figure result and the resulting feature list generated in each processing step, interpret your data, and go back to process it again to tune the detection of important features.

A Frame-Theoretic Model of Bayesian Category Learning

Bayesian models of category learning typically assume that the most probable categories are those that group input stimuli together around a maximally optimal number of shared features. One potential weakness of such feature list approaches, however, is that it is unclear how to weight observed features to be more or less diagnostic for a given category. In this theoretically oriented paper, we develop a frame-theoretic model of Bayesian category learning that weights the diagnosticity of observed attribute values in terms of their position within the structure of a frame (formalised as distance from the frame’s central node). We argue that there are good grounds to further develop and empirically test frame-based learning models, because they have theoretical advantages over unweighted feature list models, and because frame structures provide a principled means of assigning weights to attribute values without appealing to supervised training data.

Main Feature List as core success criteria of organizing Requirements Elicitation

Innovation process and innovation output is positively affected by adequate reference models and supporting means. For this reason, a New V-Model for Engineering Mechatronic and Smart Systems is being worked out by the Technical Committee VDI GMA 4.10 "Interdisciplinary Product Creation". Thus, the directive VDI 2206 "Development methodology for mechatronic systems" from the year 2004 (VDI 2206 2004) is being revised and adapted to the actual trend towards digital transformation of technical systems, business models and ecosystems. The core of the guideline is the V-Model describing mechatronic engineering (VDI 2206 2004). One core success criterium of organizing Requirements Elicitation is the established main feature list first published by Pahl and Beitz (Pahl et al. 1996). Based on this, a new Main Feature List enhanced for the usage in requirements elicitation of mechatronic and smart products is proposed. This Enhanced Main Feature List comprises additional requirements such as sampling rate, bus system, big data usage and fosters result quality and efficiency of requirements elicitation. This was proven and validated by applying it to Inline spectral measurement systems in the printing industry. The proposed Enhanced Main Feature List establishes new fundamentals in research and theory.

The Platonic-Freudian model of mind: Defining "self" and "other" as psychoinformatic primitives

As “nurtural” (rather than merely natural) kinds of human beings, people are complex and multifaceted. Any complete human science would require a complete theory of persons. Accomplishing the latter is the core objective of the present article.First, a feature list first laid out in [1] is summarized. This list is briefly critiqued. Next, the concept of person engaged with is expanded with the addition of nine novel features. These features follow from “holarchic psychoinformatics” [2], which was first propounded as a step forward from Sood’s analytic treatment of third-force, existential-humanistic psychology. Person is formalized as a function of self and other; they are also granted to be romantic, existential, humanistic, chemical, environmental, hedonic and eudaimonic (happiness-seeking), conservative, and liberal. These are in addition to persons being physical, biological, psychological, social, cultural, and spiritual. Sood’s holarchic view of persons is enlarged.Psychologically, augmented cognition as an established field of research and practice begets the formal studies of augmented affect, augmented behavior, and augmented motivation. All such interdisciplinary fields are required for the human-computer interactionist’s study of augmented mind, more broadly.Additionally, this article builds on the person-situation interaction framework formalized in [1]. It does so by adding a formalization following from the discussion of psychological situations put forward by Rauthmann, Sherman, and Funder in [3]. The formalization of psychological situations sets them as a function of cues, characteristics, and classes. Further psychological equations that follow from this article’s formalisms of person and situation, when considered along with Sood’s formulae for mind and behavior, are then presented.