Context Of Patterns Research Articles

Color is Complex: How Colorism Affects Black Leaders’ Opportunities

Being Black is a factor that affects access to leadership positions, as Black leaders are often perceived as less adequate than their white counterparts. However, much of the research has not explored the impact of racial nuances on observers' perceptions about Black leaders. Due to existence bias according to differences in skin tone and phenotype within a racial group, this study aims to understand how colorism affects observers' perceptions of Black leaders' effectiveness. We posit that colorism affects the psychological distance through which people with different racial appearances are evaluated, leading to different impression formation processes and perceptions of fitness for leadership roles. Also, we explore the role of contextual patterns of racial classification and interracial contact, the race of the observer, and membership of the leader in a high-status group. Our proposition can contribute to both theory and practice in leadership, diversity, and inclusion by showing the existence of nuances in the racial categorization of Black leaders and that they affect perceptions of effectiveness. This may lead to more adequate policies of representation and inclusion.

Sex differences in contextual pattern separation, neurogenesis, and functional connectivity within the limbic system

BackgroundFemales are more likely to present with anxiety disorders such as post-traumatic stress disorder (PTSD) compared to males, which are associated with disrupted hippocampal integrity. Sex differences in the structure and function of hippocampus exist. Here, we examined sex differences in contextual pattern separation, functional connectivity, and activation of new neurons during fear memory.MethodsTwo-month-old male and female Sprague-Dawley rats were injected with the DNA synthesis markers, iododeoxyuridine (IdU) and chlorodeoxyuridine (CldU) 3 weeks and 4 weeks before perfusion, respectively. One week after CldU injection, the rats underwent a context discrimination task in which rats were placed in context A (shock) and context A’ (no shock) every day for 12 days. On the test day, rats were placed in the shock context (context A) to measure fear memory and expression of zif268, an immediate early gene across 16 different limbic and reward regions. Repeated-measures or factorial analysis of variance was conducted on our variables of interest. Pearson product-moment calculations and principal component analyses on zif268 expression across regions were also performed.ResultsWe found that females, but not males, showed contextual discrimination during the last days of training. On the test day, both sexes displayed similar levels of freezing, indicating equivalent fear memory for context A. Despite similar fear memory, males showed more positive correlations of zif268 activation between the limbic regions and the striatum, whereas females showed more negative correlations among these regions. Females showed greater activation of the frontal cortex, dorsal CA1, and 3-week-old adult-born dentate granular cells compared to males.ConclusionsThese results highlight the importance of studying sex differences in fear memory and the contribution of adult neurogenesis to the neuronal network and may contribute to differences in susceptibility to fear-related disorders such as post-traumatic stress disorder.HighlightsFemale rats, but not male rats, show faster discrimination during a contextual pattern separation task.Three-week-old adult-born neurons are more active in response to fear memory in females compared to males.Females had greater neural activation compared to males in the frontal cortex and dorsal CA1 region of the hippocampus in response to fear memory.Males and females show distinct patterns in functional connectivity for fear memory across limbic regions.Males have many positive correlations between activated new neurons of different ages between the dorsal and ventral hippocampus, while females show more correlations between activated new neurons and other limbic regions.

CoCoS: Enhancing Semi-supervised Learning on Graphs with Unlabeled Data via Contrastive Context Sharing

Graph Neural Networks (GNNs) have recently become a popular framework for semi-supervised learning on graph-structured data. However, typical GNN models heavily rely on labeled data in the learning process, while ignoring or paying little attention to the data that are unlabeled but available. To make full use of available data, we propose a generic framework, Contrastive Context Sharing (CoCoS), to enhance the learning capacity of GNNs for semi-supervised tasks. By sharing the contextual information among nodes estimated to be in the same class, different nodes can be correlated even if they are unlabeled and remote from each other in the graph. Models can therefore learn different combinations of contextual patterns, which improves the robustness of node representations. Additionally, motivated by recent advances in self-supervised learning, we augment the context sharing strategy by integrating with contrastive learning, which naturally correlates intra-class and inter-class data. Such operations utilize all available data for training and effectively improve a model's learning capacity. CoCoS can be easily extended to a wide range of GNN-based models with little computational overheads. Extensive experiments show that CoCoS considerably enhances typical GNN models, especially when labeled data are sparse in a graph, and achieves state-of-the-art or competitive results in real-world public datasets. The code of CoCoS is available online.

PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature

BackgroundAdverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks.ResultsTwo annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions.ConclusionsThe corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.

The use of cultural contexts for patterning tasks: supporting young diverse students to identify structures and generalise

A key aspect of young children’s development of algebraic reasoning is the process of visualising and identifying structures to both abstract and generalise. There has been a growing body of research focused on how students form generalisations, this article adds to the existing body of research by examining how young culturally diverse students identify mathematical structures in contextual growing patterns and the teaching and learning actions that assist them to generalise. Data were collected from one classroom of 29 Year Two (6 years old) students in a low socio-economic school in New Zealand. Results from the analysis of lessons related to two tasks showed that the contextual tasks led students to notice different mathematical structures. Specific pedagogical actions were used to facilitate students’ engagement with the growing patterns. These included positioning students to engage with different representations (pictorial and numerical, tabular, and natural language) to represent thinking, the use of classroom discussions, noticing and responding to student thinking, and pressing students to find far terms. The findings highlight that both the contextual patterning tasks and teacher actions supported the young students to develop a range of sophisticated generalisations related to the underlying mathematical structure and functional relationships of the growing patterns.

Comparing concordances of language patterns and words by ESL intermediate learners: a preliminary experiment with two mobile concordancers

As a corpus-assisted method for language pedagogy, DDL (data-driven learning) may have the potential to enhance language exposure and promote active learner engagement. Concordancing, or KWIC (Key Words in Context), has been the traditional method used in DDL to retrieve numerous language examples, while the method has limitations with overreliance on individual words to search. This paper aims to propose and promote concordancing alternatively based on grammar patterns, a multi-word concept in corpus linguistics. The conceptualised method is named as PIC (Patterns in Context), an extended form of KWIC. An empirical study was conducted to investigate whether the PIC method has any advantages over the traditional KWIC method, using two custom-built Android apps. The research involved 18 pre-university intermediate learners (and six pilot study participants), who used the apps in a self-directed way for two weeks. Then the assessment of the two apps and methods was conducted based on data from automatic logs and responses from questionnaires and interviews. The results suggest that, compared to KWIC, PIC could be slightly advantageous in efficiently helping learners find the target language use, while this approach seems not strong in user engagement and perceived effectiveness. The implications for DDL are discussed, and further investigation is also planned.

Perceptions and Practices of Oral Health Care Professionals in Preventing and Managing Childhood Obesity.

In this study, we aimed to explore the perceptions of oral health care professionals (OHCPs) on childhood overweight and obesity screening and management in oral health settings in the Greater Sydney region in New South Wales, Australia. OHCPs involved in the Healthy Smiles Healthy Kids (HSHK) birth cohort study were purposively selected for this nested qualitative study. A sample of 15 OHCPs completed the face-to-face interviews, and thematic analysis was undertaken to identify and analyse the contextual patterns and themes. Three major themes emerged: (1) obesity prevention and management in dental practice; (2) barriers and enablers to obesity prevention and management in dental settings; and (3) the role of oral health professionals in promoting healthy weight status. This study found that OHCPs are well-positioned and supportive in undertaking obesity screening and management in their routine clinical practice. However, their practices are limited due to barriers such as time constraints, limited knowledge, and limited referral pathways. Strategies including capacity building of OHCPs, development of appropriate training programs and resources, and identification of a clear specialist referral pathway are needed to address the current barriers. This study provides an insight into opportunities for the oral health workforce in promoting healthy weight status among children.

Toward a Knowledge-Based System for African Traditional Herbal Medicine: A Design Science Research Approach.

This article illustrates a design approach for capturing, storing, indexing, and search of African traditional herbal medicine (ATHMed) framed on a hybrid-based knowledge model for efficient preservation and retrieval. By the hybrid approach, the framework was developed to include both the use of machine learning and ontology-based techniques. The search pattern considers ontology design and machine learning techniques for extracting ATHMed data. The framework operates on a semantically annotated corpus and delivers a contextual and multi-word search pattern against its knowledge base. In line with design science research, preliminary data were collected in this study, and a proposed strategy was developed toward processing, storing and retrieving data. While reviewing literature and interview data to reflect on the existing challenges, these findings suggest the need for a system with the capability of retrieving and archiving ATHMed in Ghana. This study contributes to SDG 3 by providing a model and conceptualizing the implementation of ATHMed. We, therefore, envision that the framework will be adopted by relevant stakeholders for the implementation of efficient systems for archival and retrieval of ATHMed.

Mapping Digital Discourses of the Capital Region of Finland

This article discusses the three Finnish city names Helsinki, Espoo, and Vantaa, and the urban discourses that surround them. The study reveals patterns of socio-spatial differentiation by examining what meanings people attach to these capital region cities and investigating how these meanings are expressed in online discourses. Using the methodological approach of corpus-assisted onomastics (CAO), this study incorporates onomastics, geographical information systems (GIS), and corpus linguistics. This interdisciplinary research also examines how corpus-assisted discourse studies (CADS) and GIS can be combined to reveal and visualize the contextual information and discursive patterns of toponyms. For this investigation, a data set of 2.7 billion words was collected from the Suomi24 Corpus, one of the biggest discussion fora in Finland. The use of social media corpus as a data source increases the authenticity of this research, as the data was not collected specifically for this study. The analysis reveals that the most frequent digital discourses about the cities refer to places and directions, housing, and mobility. The occurrences differ quantitatively and qualitatively between each city. This paper paves the way for future onomastic studies to research actual name usage using this new methodology. Knowledge gained from such research may not only enrich the field of onomastics, but also facilitate more socially sustainable urban planning.

The fasciola cinereum subregion of the hippocampus is important for the acquisition of visual contextual memory.

The fasciola cinereum (FC) is a subregion of the hippocampus that has received relatively little attention compared with other hippocampal subregions with respect to anatomical characteristics and functional significance. Here, we show that the FC exhibits clear anatomical borders with the distalmost region of the CA1. Principal neurons in the FC resemble the granule cells in the dentate gyrus (DG). However, adult neurogenesis was not found unlike in the DG. The FC receives inputs mostly from the lateral entorhinal cortex and perirhinal cortex while projecting exclusively to the crest of the DG within the hippocampus. Neurotoxic lesions in the FC using colchicine impaired the acquisition, but not retrieval, of visual contextual memory in rats. FC lesions also impaired place recognition and object-in-place memory. As the rat performed the contextual memory task on the T-maze, place cells in the FC exhibited robust place fields and were indiscriminable from those in CA1 with respect to the basic firing properties. However, place cells in the FC fired only transiently in their place fields on the maze compared with those in CA1. Our findings suggest that the episodic firing patterns of the place cells in the FC may play critical roles in learning a novel contextual environment by facilitating temoporally structured contextual pattern separation in the DG of the hippocampus.

Contextualization Of Short Story Patterns In Indonesian Texbooks For Class IX SMP

Literary works of prose fiction has two main elements, i.e. the elements of the intrinsic and extrinsic elements. The elements of the intrinsic is the most important element to support the passage of the story. Extrinsic elements are outside elements that help build and support a story that is the background of the author of the story and the social conditions of a culture. The purpose of this research is to know is in the pattern of the elements contained in a short story in the book text Bahasa Indonesia kelas IX SMP. This study uses a qualitative method for solving the problem are investigated to describe or write down the circumstances in each story. The source of the data used for this study using the book text Book of Indonesian Language and Literature SMP/MTS class IX Revised Edition 2013 curriculum written by Endah Tri Priyanti-M. Thamrin - Hadi Widodo. The Publisher Of The Earth Script. Book Bahasa Indonesia (Electronic School Book) for SMP/MTS written by Atikah Anindryani-Yuwono-Suhartono, published in 2018. Book Bahasa Indonesia SMP/MTS class IX the Ministry of Education And Culture Republic of Indonesia Revised 2018. Book Language Independent Indonesia SMP/MTS Erlangga Publisher, written by Engkos Kosasish-Restudi, Curriculum revision 2013. The results of the analysis of contextual pattern of short stories in the book text Bahasa Indonesia kelas IX SMP includes elements, characters/characterizations, plot,setting, point of view, the mandate and the style of the language. The value of a character in a short story Bahasa Indonesia kelas IX SMP namely, curiosity, Responsibility, Friendly, Creative, Peace-loving, Religious, patriotism, Discipline, Social Care, Democratic Values, tolerance,hard Work,Social Care

ПОДАТКОВІ ПІЛЬГИ: БІБЛІОМЕТРИЧНИЙ I ТРЕНДОВИЙ АНАЛІЗ

Анотація. Метою статті є визначення контекстуальних і часових закономірностей розвитку представлення в науковій літературі досліджень у сфері податків і податкових пільг із використанням бібліометричного і трендового аналізу. У результаті проведеного дослідження поглиблено теоретичні засади ідентифікації основних змістовних детермінант оподаткування на основі застосування інструментарію бібліометричного (за допомогою VOSviewer v.1.6.10) і трендового (з використанням GoogleTrends) аналізу. Трендовий аналіз засвідчив зростання кількості публікацій, присвячених податкам і податковим пільгам, у 2008—2020 рр., а інтерес до цієї проблематики серед користувачів мережі «Інтернет» посилювався під час криз у 2008—2009 рр. і 2020—2021 рр. За результатом бібліометричного аналізу, 1 334 наукові публікації, проіндексовані наукометричною базою даних Scopus, і 815 — WebofScience, за періоди 1971—2021 рр., обґрунтовано, що дедалі популярним стає аналіз впливу податків на розвиток окремих секторів економіки, забезпечення екологічної безпеки, подолання нерівності тощо. Зокрема, було виявлено сім кластерів наукових досліджень, присвячених питанням оподаткування (перший сфокусований: на оподаткуванні в системі бухгалтерського обліку та менеджменту, протидії уникненню від сплати податків, удосконаленні державної податкової політики, стимулюванні до сплати податків; другий — на виявленні взаємозв’язку між податками і рівнем корупції у країні, впливу податків на інвестиційний та інноваційний потенціал, реформуванні податкової політики; третій — на оцінці впливу податків на окремі сектори економіки: екологія, сільське господарство, медицина, енергетика, а також споживання та здоров’я людини; четвертий — податки як інструмент розвитку малого бізнесу, впровадження податкових пільг, шляхи подолання нерівності, вдосконалення діяльності податкової поліції, тінізація економіки; п’ятий — декомпозиція видів податків: податок на прибуток, податок на нерухомість, податок на землю, визначення шляхів реформування податкової системи; шостий — податкові системи країн світу в умовах пандемії COVID-19; сьомий — моделювання впливу податків на економічні процеси в країні, державне регулювання податкової системи). Ключові слова: оподаткування, податки, податкова пільга, бібліометричний аналіз, трендовий аналіз, наукометричні бази даних, наукові публікації, наукові дослідження. Формул: 0; рис.: 4; табл.: 4; бібл.: 32.

An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

As a fundamental application, change detection (CD) is widespread in the remote sensing (RS) community. With the increase in the spatial resolution of RS images, high-resolution remote sensing (HRRS) image CD tasks receive growing attention. The change information hidden in multitemporal HRRS images could help discover our planet comprehensively. In the current deep learning era, convolutional neural networks (CNNs) have become one of the most powerful tools for a wide range of RS tasks including HRRS image CD, due to their superb feature learning capacity. However, most of them need a large amount of labeled data to accomplish the CD process, which is challenging or even impractical in many RS applications. Also, given the limited valid receptive field, CNNs can only capture short-range context within HRRS images, which is probably not enough to fully explore change information from the images. To overcome these limitations, in this article, we propose an unsupervised CD method, termed GMCD, based on graph convolutional network (GCN) and metric learning. GMCD consists of a Siamese fully convolution network (FCN), a multiscale dynamic GCN (Mlt-GCN), and a pseudolabel generation mechanism based on metric learning. The Siamese FCN contains a Siamese encoder and a pyramid-shaped decoder, aiming to extract multiscale features and integrate them to generate reliable difference images (DIs). Mlt-GCN focuses on capturing the short- and long-range contextual patterns at feature map level to extract changed and unchanged areas completely. The pseudolabel generation mechanism aims to produce reliable pseudolabels (changed, unchanged, and uncertain) to help accomplish the model training in an unsupervised way. Experiments on four HRRS image CD datasets demonstrate that GMCD outperforms the existing state-of-the-art methods.

The Empathetic Car

An empathetic car that is capable of reading the driver's emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., emotions) based on behavioral and contextual patterns decoded in available sensor streams, e.g., obtained by smartphones. We transfer this rationale to an in-vehicle setting by analyzing the feasibility of inferring driver emotions by passively interpreting the data streams of the control area network (CAN-bus) and the traffic context (inferred from the front-view camera). Therefore, our approach does not rely on particularly privacy-sensitive data streams such as the driver facial video or driver speech, but is built based on existing CAN-bus data and traffic information, which is available in current high-end or future vehicles. To assess our approach, we conducted a four-month field study on public roads covering a variety of uncontrolled daily driving activities. Hence, our results were generated beyond the confines of a laboratory environment. Ultimately, our proposed approach can accurately recognise drivers' emotions and achieve comparable performance as the medical-grade physiological sensor-based state-of-the-art baseline method.

Hypergraph based Unsupervised Contextual Pattern Learning and Anomaly Detection for Global Terrorism Data

In a dataset, an event which deviates from the rest of the dataset is a rare event. This rare event can be intrusion or any suspicious activity in the system and is called an anomaly. These anomalies are important to detect because this may be any terrorist attack, outbreak of the disease, malfunctioning or fraud in the system. Anomalies are the deviation from the normal patterns in the dataset. It is important to learn the normal patterns in order to identify the deviation. Labelled data in real life anomaly detection is not available due to rarity of anomalies. It is challenging to identify anomalous combinations and combinatorial patterns of feature instances using conventional machine learning algorithms. We introduce Hypergraph based Unsupervised Contextual Pattern Learning and Anomaly Detection (HUCPLAD) technique for unlabeled datasets and implemented on Global Terrorism Data (GTD). HUCPLAD gets rid of the curse of dimensionality, maintains hierarchy, learns the contextual pattern, detects contextual anomalies and measures the behavior of co-occurring events.

Adoption of Patient-Reported Outcome Measures With Youth With Intellectual and Developmental Disabilities: Contextual Influences and Practice Patterns

Abstract Date Presented Accepted for AOTA INSPIRE 2021 but unable to be presented due to online event limitations. Patient-reported outcome measure (PROM) adoption with youth with intellectual and developmental disabilities (IDD) faces obstacles but is important for client-centered care. We collected data from pediatric practitioners who work with youth with IDD (N = 113) about factors influencing PROM adoption and the frequency of PROM use by age. Results suggest evidence, time, and accessibility are important factors for PROM adoption, and clinicians infrequently use PROMs with this population. Primary Author and Speaker: Kimberly Greenberg Additional Authors and Speakers: Ariel E. Schwartz Contributing Authors: Jessica Kramer

Analysing Generical Moves in Pedagogical Sales Reply Emails

This study aimed to investigate the generic features in the business emails written by the undergraduates of Universiti Malaysia Kelantan (UMK). Besides that, this study intended to propose a standard generic structure in writing a business email. The emails were gathered from the respondents' assignment that comprised of two email-based questions; however, this study analysed the generic structure of sales reply emails. The emails were compared with Bhatia's promotional sales letter moves. The methodology of this study was computer-assisted corpus analysis (CACA) by Manvender (2011). The data were analysed using a genre-based technique by Bhatia (1993). The particular moves and steps in both types of texts were recognised and categorised individually. The rhetorical structure of sales reply emails consists of three major moves: introducing product, product details, offering incentives. Although the respondents showed similarities in their moves, there were some differences such as the number and content of the moves as well as the contextual patterns.

Native and Non-native EFL Teachers' Burnout: No Isolation but Cooperation

The aim of this study is twofold: to investigate the burnout levels of native and non-native ELT teachers, and to account for the reasons behind the most stressful aspects of being an ELT teacher in an EFL context. Employing a mixed method design method, 30 ELT teachers are divided into two groups;15 native and 15 non-native. The data is collected using the Maslach Burnout Inventory Educators Survey online to find out the burnout levels within the three components- emotional exhaustion (EE), depersonalisation (DP) and reduced personal accomplishment (RPA), and the levels of the two groups are compared to find out whether there is any difference. The procedure also includes online interview to find out the reasons for burnout. The findings revealed that there is a difference between the two groups; where there is ‘low’ burnout level of non-native teachers, the scoring indicated a ‘high’ level for native teachers. Content analysis of online interview data indicated hardness of contextual patterns, which are working, interaction and EFL teaching; in contrast, the analysis of non-native teachers’ data indicated positive consideration of EFL teaching and having an increasing interest in post-graduate degrees in education. Implications are discussed, one of which might be the same as what the title of this research study says: no isolation, but co-operation with the two.

Discovering and Analyzing Contextual Behavioral Patterns From Event Logs

Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights.

Full-length ribosome density prediction by a multi-input and multi-output model.

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS.