The rapid expansion of artificial intelligence (AI) tools, including generative AI, is reshaping task completion and interaction, leading educational researchers to examine learning opportunities as well as emerging concerns in the present technological landscape. Despite ongoing discussions, it is less clear how students may envision such tools being integrated into computing education. We applied the Technology Acceptance Model and conducted a survey across three tertiary academic institutions in the United States, gathering quantitative and qualitative insights on n = 340 computing students' perspectives. Closed-ended items were examined with descriptive statistics, and an open-ended prompt around AI tool use in computing education was explored through reflexive thematic analysis. We found that 64.1% of students somewhat or strongly agreed that using AI tools (e.g., ChatGPT or GitHub Copilot) made them feel as though they were not truly doing the work themselves. Additionally, 48.8% of students somewhat or strongly agreed that they felt conflicted about using AI tools because competent computing/tech students should be able to figure things out without help. The qualitative themes spoke to instances where students may find adoption beneficial, like ''Expediting and enhancing learning, task completion, and organization,'' or problematic, such as ''Moral concerns and regard for social and environmental impact.'' Based on our results, we discuss strategies for teaching alongside AI tools and future considerations for computing educators and developers to support students' agency, ethical reasoning, and decision-making.

Our goal is to educate individuals on how to create technology that is fair, responsible, and respectful of all people and communities. Too often, software is developed without considering important ethical dimensions such as bias, privacy, or transparency, which can result in harmful or exclusionary outcomes, particularly in fields like healthcare, education, and finance.

Accessibility remains underrepresented in undergraduate computing curricula, despite its critical role in modern software development. This poster presents an upper-level elective in Accessible Software Engineering that addresses this gap by (1) integrating accessibility principles through web-based, experiential labs and (2)leveraging AI agents that simulate users with diverse disabilities to support empathetic, role-based learning. The hands-on labs provide practical experience recognizing accessibility barriers and applying inclusive design strategies, while the AI agent interactions enable students to gather requirements directly from the perspectives of users with visual, cognitive, learning, and motor impairments. This approach helps students transform abstract accessibility standards into meaningful, actionable design decisions. Student feedback and project outcomes demonstrated substantial gains in accessibility awareness, technical competence, and human-centered design skills. The course model offers a scalable pathway for strengthening accessibility education within the computing curriculum.

Ensuring accessibility is essential for developing software that is inclusive for all users. Unfortunately, research shows that a significant portion of modern software fails to meet accessibility standards. Many students not only lack the knowledge of how to create accessible software but also do not fully grasp why it matters. To bridge this educational gap, we created a series of labs called the Accessible Learning Labs (ALL). These labs are designed to teach the techniques for developing accessible software, while also emphasizing the importance of accessibility in software design. Through hands-on activities, students can experience the consequences of inaccessible software and learn to fix these issues to make the software more accessible. This collection of labs is beneficial to learners at various stages, from beginners to professionals looking to improve their skills in creating accessible software. All materials are available on the project website: https://all.rit.edu

Accessibility is a crucial component in developing inclusive software. However, studies reveal that much of today's software is not designed with accessibility in mind. This issue is compounded by the fact that students often lack an understanding of both how to create accessible software and why it is important. To address this educational gap, we developed a set of labs known as Accessible Learning Labs (ALL). These experiential labs aim to teach participants the proper methods for creating accessible software while highlighting the significance of inclusivity. Additionally, these labs allow students to experience the effects of inaccessible software firsthand and apply corrective measures to improve accessibility. All project materials are freely accessible on the project website: https://all.rit.edu.

As AI tools like ChatGPT become more prevalent in educational settings, their potential to assist undergraduate teaching assistants (uTAs) in large Computer Science 2 (CS2) courses presents both opportunities and challenges. This work focuses on how ChatGPT can be strategically utilized by uTAs during office hours to enhance student support, particularly in complex topics such as data structures, algorithm development, and object-oriented programming. We explored effective strategies for uTAs to use ChatGPT in ways that promote deeper student understanding without compromising the development of independent problem-solving skills. Key strategies include leveraging ChatGPT for real-time code debugging assistance, offering alternative approaches to solving coding problems, comparing and critiquing self and AI generated documentation, and code reviewing. This work also identifies potential challenges, such as the risk of students or uTAs becoming overly dependent on AI-generated solutions and the possibility of inaccurate or incomplete responses from the AI. Hence, our findings highlight the dual role of ChatGPT as both an asset and a potential hindrance, depending on how it is utilized. To mitigate these risks, we propose a set of best practices that ensure ChatGPT enhances, rather than replaces, the uTA's role as a facilitator of learning. The findings from this research provide valuable insights into how uTAs can integrate AI tools thoughtfully into office hours to offer more effective support, ultimately improving student engagement and learning outcomes in large-scale CS2 courses.

Preparing future computing researchers requires more than traditional classroom education; it calls for immersive, hands-on experiences that build research identity and critical skills. This work presents a year-long undergraduate research engagement program in socially relevant computing designed to address this need. This work underscores the importance of immersive, socially relevant research experiences in cultivating the next generation of computing researchers and supporting their professional advancement.

Abstract Background Long non-coding RNAs (lncRNAs) are crucial regulators in cancer, yet their epigenetic control in Lung Adenocarcinoma (LUAD) remains underexplored. Aim This study aimed to integrate multi-omics data to identify novel prognostic biomarkers and elucidate their potential mechanism. Methods We analyzed RNA-Seq and Illumina 450k methylation data from 473 LUAD and 32 normal The Cancer Genome Atlas (TCGA) samples. We performed differential expression and methylation analysis to identify candidate lncRNAs whose expression was negatively correlated with promoter methylation. The prognostic value was evaluated using Kaplan-Meier curves and multivariate Cox regression. A lncRNA-miRNA-mRNA regulatory network was constructed to investigate potential mechanisms. For validation, FAM83A-AS2 expression was quantified in a preliminary set of clinical blood samples using RT-qPCR. Results Our multi-omics analysis identified two lncRNAs, FAM83A-AS2 and AC012213.1, whose high expression was driven by significant promoter hypomethylation and correlated with significantly lower patient survival. Differential analysis and correlation revealed promoter hypomethylation of specific CpG sites (cg19924352 for FAM83A-AS2; cg16648062 and cg20129213 for AC012213.1) drives their upregulation. Multivariate Cox regression confirmed their status as independent prognostic markers after adjusting for clinical covariates (HR=1.55, p<0.01 for FAM83A-AS2; HR=1.30, p<0.05 for AC012213.1), with strong diagnostic potential (AUC=0.72). A regulatory network analysis implicated these lncRNAs in modulating key LUAD-associated genes like RALGPS2, HOXA13 via miRNA MIR126 and MIR34C. Gene set enrichment analysis further linked these lncRNAs to fundamental molecular processes like chromatin modification and DNA methylation. Importantly, the pilot wet-lab validation on an initial set of clinical blood samples supported these findings, demonstrating a marked upregulation of FAM83A-AS2 in patients compared to healthy controls. Conclusion This study presents FAM83A-AS2 and AC012213.1 as promising biomarkers for risk stratification and potential therapeutic targets in LUAD.

Abstract Background Long non-coding RNAs (lncRNAs) are crucial regulators in cancer, yet their epigenetic control in Lung Adenocarcinoma (LUAD) remains underexplored. Aim This study aimed to integrate multi-omics data to identify novel prognostic biomarkers and elucidate their potential mechanism. Methods We analyzed RNA-Seq and Illumina 450 k methylation data from 473 LUAD and 32 normal The Cancer Genome Atlas (TCGA) samples. We performed differential expression and methylation analysis to identify candidate lncRNAs whose expression was negatively correlated with promoter methylation. The prognostic value was evaluated using Kaplan–Meier curves and multivariate Cox regression. A lncRNA-miRNA-mRNA regulatory network was constructed to investigate potential mechanisms. For validation, FAM83A-AS2 expression was quantified in a preliminary set of clinical blood samples using RT-qPCR. Results Our multi-omics analysis identified two lncRNAs, FAM83A-AS2 and AC012213.1, whose high expression was driven by significant promoter hypomethylation and correlated with significantly lower patient survival. Differential analysis and correlation revealed promoter hypomethylation of specific CpG sites (cg19924352 for FAM83A-AS2; cg16648062 and cg20129213 for AC012213.1) drives their upregulation. Multivariate Cox regression confirmed their status as independent prognostic markers after adjusting for clinical covariates (HR = 1.55, p < 0.01 for FAM83A-AS2; HR = 1.30, p < 0.05 for AC012213.1), with strong diagnostic potential (AUC = 0.72). A regulatory network analysis implicated these lncRNAs in modulating key LUAD-associated genes like RALGPS2, HOXA13 via miRNA MIR126 and MIR34C. Gene set enrichment analysis further linked these lncRNAs to fundamental molecular processes like chromatin modification and DNA methylation. Importantly, the pilot wet-lab validation on an initial set of clinical blood samples supported these findings, demonstrating a marked upregulation of FAM83A-AS2 in patients compared to healthy controls. Conclusion This study presents FAM83A-AS2 and AC012213.1 as promising biomarkers for risk stratification and potential therapeutic targets in LUAD.

MOTIVATION: Wikipedia is a vital open educational resource in computational biology; however, a significant knowledge gap exists between English and non-English Wikipedias. Reducing this knowledge gap via intensive editing events, or "editathons," would be beneficial in reducing language barriers that disadvantage learners whose native language is not English. Results: We present a framework to guide educators in organizing editathons for learners to improve and create relevant Wikipedia articles. As a case study, we present the results of an editathon held at the 2024 ISCB Latin America conference, in which ten new articles were created for the Spanish-language edition of Wikipedia. We also present a web tool, "compbio-on-wiki," which identifies relevant English Wikipedia articles missing in other languages. We demonstrate the value of editathons to expand the accessibility and visibility of computational biology content in multiple languages. AVAILABILITY AND IMPLEMENTATION: Source code for the compbio-on-wiki Toolforge site is available at: https://github.com/lubianat/compbio-on-wiki.

The COVID-19 pandemic led to devastating physical, psychological, and financial impacts on millions of people across the world. Amidst a rapidly evolving research landscape, the global scientific community was forced to swiftly adapt to novel working methods, including remote collaboration tools, virtual conferences, and online research platforms. Surveys of life sciences researchers have indicated that computational biologists experienced less disruption and a smoother transition to remote working than experimental biologists, due to their reduced reliance on laboratory equipment. Despite this adaptability, the sudden shift to remote work, compounded by stress and social isolation, has posed significant mental health challenges for these workers. However, remote work has also facilitated opportunities for more flexible work arrangements and increased collaboration across geographical boundaries. To investigate these impacts, we conducted surveys of computational biologists during the Intelligent Systems for Molecular Biology (ISMB) conferences in 2020 and 2021, which were held virtually due to the COVID-19 lockdowns. This study implements a thorough statistical analysis of the survey results to offer insights into the repercussions of the lockdowns on researchers and their work. Key areas of investigation include the effects of institutional support (or lack thereof), the difference in productivity compared to pre-lockdown levels, and the significance of gender in determining these impacts. Notably, a lack of institutional support with regard to mental health and finances was shown to have a significant negative effect on early-career researchers. Although limited by a small sample size, our study sets the stage for a more robust exploration of these trends in future research. Importantly, by illuminating the challenges and opportunities arising from the COVID-19 pandemic and lockdowns, our study offers hope for potential solutions supporting the well-being of early-career researchers in unprecedented circumstances.

This study presents an end-to-end deep learning framework for protein-to-metal-ion binding prediction, a critical task in understanding protein function, structural stability, and metal transport mechanisms. A binding site is a residue location in a protein sequence where a metal binds to a protein. Manual curation of metal binding sites is a tedious process involving mining through research articles, making it expensive, laborious, and time-consuming. Therefore, developing a computational pipeline is essential to predict metal ion binding of unannotated proteins. A significant shortcoming of existing computational methods is the failure to capture the long-term dependency of the residues, the absence of positional information, and a pre-determined set of residues and metal ions. In this paper, we propose a metal-ion binding prediction pipeline using a large language model, emphasizing 1) the comparative performance of five state-of-the-art protein language models (pLMs), 2) the impact of positional encoding of binding sites, and 3) the comparison with classical machine learning techniques. A 10-fold cross-validation evaluation yielded a Matthews Correlation Coefficient (MCC) of 0.89, along with precision, recall, and F1 scores exceeding 95% for the six most extensively studied metal ions reported in the literature.

MOTIVATION: Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large 'knowledge gap' between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology. RESULTS: Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages. AVAILABILITY AND IMPLEMENTATION: Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish.

The disparity of women in computing and technology field is quite evident. There have been many national initiatives over the past decade that tried to enhance diversity in these fields. However, one group of population, returning women, have received little to no attention. Covid-19 pandemic has made this situation even worse by sharpening the inequality in America’s economy, where nearly eight times the number of women left the workforce compared to men last year. Yet, returning women, remain to be one of the largest untapped talent pools in the nation. The field of computing and Emerging Technology (EmTech) can use the untapped potential of returning women to fill the gap in workforce, which is growing at a rapid rate. Hence, to broaden participation and to understand the barriers and challenges faced by returning women to (re-)enter EmTech, a national virtual conference, RESET, was organized in 2021 amid the COVID-19 pandemic. In this paper, we present results from a mixed method study to investigate if the attendance at the RESET conference is a predictor of returning women’s elevated knowledge in (re-)entering EmTech education and/or workforce, and if the conference was successful in identifying resources that would facilitate transition of returning women in computing and tech discipline. In our quest to answer the questions we also report on – 1) challenges and barriers returning women face when pursuing computing degree, after a break, 2) challenges and barriers returning women face to transition to computing jobs, after a career break, 3) strategies and technical preparations that can be adopted to ease this transition, 4) level of satisfaction with the resources offered by the conference; 5) knowledge benefit from various technical sessions; 6) compliance with effectiveness of the resources offered; and 7) preparedness in pursuing EmTech education and workforce.

Recruitment, retention, and graduation of women in science, technology, engineering, and mathematics (STEM) training are critical needs in our nation [1, 2]. Within STEM, the computing and tech industry, specifically some niches, are expected to grow job opportunities more quickly than others. Emerging Technology (EmTech) concentrations like cybersecurity, data science, mobile development, machine learning, and cloud computing will have thousands of jobs in the next decade which will require a large pool of technical professionals. EmTech can use the largest untapped talent pool of women and returning women to fill the gap in the workforce. Hence, to understand the barriers and challenges faced by returning women to (re-) enter computing and tech fields, a three-day virtual conference, NSF RESET, was organized in March 2021. In this poster, we present the preliminary results on conference attendees' satisfaction level and effectiveness in facilitating resources to (re-)enter EmTech educational and professional pipeline.

MOTIVATION: Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield. RESULTS: We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.

Many connected and autonomous vehicle (CAV) applications benefit from navigation technologies that reliably achieve lane-level accuracy. Global organizations have recently begun to provide real-time common-mode error corrections for Global Navigation Satellite Systems (GNSS) that enable this level of positioning accuracy using Precise Point Positioning (PPP). Incorporating an Inertial Measurement Unit (IMU) with a GNSS receiver can achieve this positioning performance reliably on moving platforms while providing a full state estimate, with high bandwidth at a high sampling rate. For commercial automotive applications cost is critical; therefore, this article focuses on commercial grade IMU’s and single-frequency GNSS code and Doppler measurements. This article presents and demonstrates a tightly-coupled PPP GNSS aided Inertial Navigation System (INS) using only publicly available real-time data sources. The article discusses the role of the INS in automotive applications and how the quality of the IMU affects the ability to achieve that role. The experimental results demonstrate positioning accuracy that surpasses the Society of Automotive Engineering (SAE) J2945 specification (horizontal error ≤1.5 m and vertical error ≤ 3 m 68%).

Studies suggest women dropout of college and leave the workforce due to their family, finances, and military duty. However, women interested in (re-)entering the tech fields can be the largest untapped talent pool that may fulfill the needs of the future computing workforce. In this panel, five passionate women will share their experiences with identifying the challenges for women to re-enter emerging technology professions and the role of industry-academic relationship in facilitating such initiatives in order to develop future relevant initiatives.

introduction Re-Entering Computing through Emerging Technology: Current State and Special Issue Introduction Share on Authors: Farzana Rahman Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USAView Profile , Elodie Billionniere School of Engineering & Technology, Miami Dade College, Miami, FL, USA School of Engineering & Technology, Miami Dade College, Miami, FL, USAView Profile Authors Info & Claims ACM Transactions on Computing EducationVolume 21Issue 2June 2021 Article No.: 9pp 1–5https://doi.org/10.1145/3446840Online:23 April 2021Publication History 1citation76DownloadsMetricsTotal Citations1Total Downloads76Last 12 Months76Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

One of the critical needs of the 21st-century workforce development is the recruitment, retention, and graduation of women in STEM fields. Research suggests that women drop out of academic programs and leave the workforce to deal with financial setbacks, tend to personal obligations and offer service in military programs. It is important these women, i.e. returning women, have pathways for reentry to college and opportunities to advance their careers. Some areas within STEM fields, such as Emerging Technology (EmTech) in computer science are expected to experience increases in job opportunities more quickly than traditional areas. The demands of these jobs can only be fulfilled by creating pathways for untapped STEM talent pools, including returning women. Therefore, we propose a panel to discuss the barriers and opportunities women face (re-)entering the STEM education and career paths, especially in EmTech fields. The panel of experts will provide different perspectives to spark conversation and reflection. The objective of the panel is to share experiences, advice, and ideas to advance the current state of knowledge about the complex challenges that women encounter and support structures for their reentry to the education and professional pipeline.

In the 21st century, Computer science (CS) academic programs are becoming as diverse and innovative as computer science itself. Due to the increasing number of jobs and national demands for more computing professionals, we see a surge of non-traditional students (aged over 25), entering the CS1 course, who already have a degree in a different discipline and work either full-time or part-time. These students have very low retention rate beyond CS1 course. We researched at a large metropolitan public research university, Florida International University (FIU), where we have designed an online CS1 course, integrating peer-instruction, to addresses the divergent background of our student population so they can thrive in computing major/minor, beyond CS1 course. In this poster, we report our findings on how the learning experience of non-traditional students is impacted by the peer-instruction integrated in the online CS1 course.

The ability of smart devices to recognize their owners or valid users gains attention with the advent of widespread highly sensitive usage of these devices such as storing secret and personal information. Unlike the existing techniques, in this paper, we propose a very lightweight single-time user identification technique that can ensure a unique authentication by presenting a system near-to-impossible to breach for intruders. Here, we have conducted a thorough study over single-time usage data collected from 33 users. The study reveals several new findings, which in turn, leads us to a novel solution exploiting a new machine learning technique. Our evaluation confirms that the proposed solution operates with only 5% False Acceptance Rate (FAR) and only 6% False Rejection Rate (FRR) over the data collected from 33 users. We further evaluate the performance through comparing its performance with some existing machine learning techniques. Finally, we perform a real implementation of our proposed solution as a mobile application to conduct a rigorous user evaluation over 27 participants using three different devices in order to show how the solution works in practical situations. Outcomes of the user evaluation demonstrate as low as 0% FAR after letting intruders to mimic the actual user, which ensures extremely low probability of being breached. Moreover, we let 2 users to continuously use our application over 25 days in different states during their operation. Outcomes of this evaluation demonstrate as low as 1% FRR confirming the usability of our technique in long-term usage.

The use of Process-Oriented Guided Inquiry Learning model (POGIL) in introductory computer science courses has shown to be very useful in active learning delivery of fundamentals of computer science. Moreover, the aspect of organized teamwork in POGIL helps students develop professional skills to be ready to participate in team-based upper division CS courses such as software engineering or capstone courses. POGIL introduces a structured, yet flexible model for group activities. It also resolves the issue of member participations in activities since everyone needs to take a role and switch them in different activities. Time-management is also enhanced with POGIL. Though POGIL is a promising pedagogy, it also presents some unique challenge such as how to adopt the current activities to specific model of the classrooms. The implementation of courses can follow various approaches depending on factors like: class size, institutional culture, background of the students, the nature of facilities, and instructor preferences. In particular, faculty might need to invest significant amount of time to develop and/or work on modifying existing materials for specific courses. Therefore, instructors who choose to be POGIL facilitators has various decision choices. Detailed discussion about all these challenges, choices, options, and approaches are provided which can benefit faculty who are using and/or interested in adopting POGIL in CS courses. The discussions could be helpful to those who are only interested in group-based active learning teaching of CS courses.

There has been a dramatic increase in computer science undergraduate research activity at colleges and universities in recent years. However, developing a research culture that is explicitly designed to empower undergraduates (student-centered research) requires different models and objectives than those traditionally employed at more research-oriented universities. The goal of this BOF is to explore what effective techniques are employed by other primarily undergraduate institutions to build a culture of research that benefits undergraduate students. Some of the key issues covered in this BOF will be: defining student-centered research and its impact (How does student-centered research differ from traditional research? What secondary effects in the classroom and community might undergraduate research have?), redefining success metrics in student-centered research (How can we capture impact beyond publications and grants? How can we define measures that align with student impact?), exploring issues of accessibility and participation (How might student-centered research change models of student selection? How might it change faculty's scope and focus of research?), and structural mechanisms to empower student-centered research (Given constraints on time and/or resources, how can faculty enable undergraduate research?). Through this BOF, we also plan to build a sustainable community of interested academics leaders (using private Google+ or Facebook group) interested to share and collaborate on future undergraduate research efforts.

This article describes the motivation, origin and evolution of the student symposia series organised by the ISCB Student Council. The meeting series started thirteen years ago in Madrid and has spread to four continents. The article concludes with the highlights of the most recent edition of annual Student Council Symposium held in conjunction with the 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology, in Prague, in July 2017.

Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one's field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one's research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.

Connected and automated vehicle applications are facilitated by Enhanced Digital Maps (EDMs) of the roadway environment. Due to the high numbers of roadway miles and signalized intersections, there is significant research interest in the automatic extraction of such maps from georectified LiDAR data. Most existing methods convert the LiDAR point cloud to a set of images for feature extraction and mapping. This rasterization step loses information that could be retained if new methods were developed that work directly on the LiDAR point could for feature extraction and mapping, without rasterization. This article presents one such approach that operates on a road surface point cloud, processing small patches at a time using a locally adaptive version of Otsu's method to discard low intensity reflections while retaining reflections from roadway markings. The main new aspect of the approach is a graph-based clustering algorithm implemented directly on the point cloud. A cluster growing method is used to group similar road markings into the same group to enable detection of the stop bars and lane edges. Finally, a SAE-J2735 map message is created from the extracted roadway features.

In recent years, there has been a dramatic increase in computer science undergraduate research activities at colleges and universities nationwide. Developing and maintaining undergraduate research benefits students, faculty mentors, and the institution. Incorporating a research culture along with a sound academic foundation enables students to develop independent critical thinking skills along with effective oral and written communication skills. However, we are in a time when budgets are being tightened and some institutions do not have the resources to pursue such initiatives. Traditionally research focused universities (like R1) have access to various large funding sources to host Research Experience for Undergraduate (REU) programs. R1 universities have established records of accomplishment for innovative research and the faculties at R1 institutions have lab infrastructure to blend such activities for undergraduate students. However, non-R1 institutions (like community colleges, undergraduate, masters, and to some extent R2 institutions) lack resources, lab infrastructure and above all a track record for innovative research that makes it hard for them to obtain funding to host an REU program. Thus, in this panel, our goal is to present ideas for establishing a track record and building an internally and externally funded Undergraduate Research experience (UR), particularly for non-R1 institutions. This involves obtaining funds to conduct the research and to secure travel funds to disseminate research results.

Undergraduate research is a critical component of high-quality education in any discipline, including Computer Science (CS). Over the past few years, there has been a dramatic increase in CS undergraduate research activities at colleges and universities, and predominantly undergraduate institutions (PUIs) have an important role to play. Not every university has abundant resources to devote to research, and teaching-focused institutions may face the greatest challenges in this respect. Faculty at PUIs, for example, may face funding and infrastructure challenges and may find themselves stretched thin due to especially high teaching and service expectations. A frequently asked question by new faculty at these institutions is: Is it really possible to conduct meaningful research in such a fast-paced discipline as CS, while juggling a very high teaching and service load? Not only is the answer to this question "Yes!" but there are advantages to conducting research at a non-research institution. Faculty here has access to some of the brightest young minds who will potentially be future graduate students in research-intensive universities. They may have the freedom to do research that is too risky for graduate students. They can work on projects they are interested in, rather than those they know must work. With good time management techniques and careful selection of collaborators and student researchers, faculty here really can conduct important CS research. Thus, the focus of this BOF is to share methods that are helpful in conducting significant and meaningful CS research in a primarily undergraduate or teaching institution.

Table of contents A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

It is widely known that computer science students do not reflect the diversity of the population at large. Studies have shown that effective outreach programs can educate the public, increase student interest, help recruit and retain majors in computing disciplines. Traditionally the outreach events are organized by higher education institutions or nonprofit organizations. It will be highly effective if high school teachers are provided with the right tools so they can organize outreach events in their school or local community. Outreach programs can vary widely in terms of target audience, duration and objective. And it might seem daunting to design and deploy outreach events to make it sustainable. Our goal in this BOF is to discuss effective outreach activities to recruit and retain diverse students in CS K-12 pipeline. In particular, each facilitator will address the objective of the activity, the target audience, a description of the activity, and key aspects for successful deployment. This BOF will also provide an effective model for high school teachers to design and implement computing outreach activities in their schools. We will share techniques to increase students and parents interest in computer related discipline and provide them with appropriate tools to pursue their interest more. Facilitators will also share information about various funding agencies that can sponsor CS outreach programs. It will provide an opportunity for high school and college faculty to explore possibilities for collaborations and outreach between high schools and colleges.

The enrollment in introductory computer science courses has been growing steadily for the last couple of years. Interest in the field is exploding across the nation. Colleges and universities of all shapes and sizes have seen enrollment in introductory computer science courses and to some extent in upper-division electives explode as well. Most R1 institutions handle such growth through the use of graduate TAs to teach CS1 courses. Many primarily undergraduate and/or liberal arts institutions don't have these resources, however. Complicating the scenario further, students coming to these non-research institutions expect personal interaction, are from a growingly diverse set of high-school graduates and seem to be less prepared for college than ever. Given the current funding climate for higher education, it is difficult to imagine how most universities and colleges will be able to negotiate this scenario. This BOF will provide a platform to discuss how to handle the growing, diverse, and uninitiated CS1 enrollment from many perspectives - individual, departmental, and institutional. What creative solutions can be brought to improve institutional motivation and how do we ensure that traditionally under-represented groups don't get left behind in the onslaught?

This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) conference in Boston, USA, on July 11th, 2014.

BACKGROUND: Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer's disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. RESULTS: A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast "gain control", a "blob analysis" step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined 'size' and 'aspect' ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating 'Gold-standard' mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson's correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach's alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: -5.53 to 6.10) was detected between the cell counts of the two methods. CONCLUSIONS: The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.

RFID has received considerable attention within the healthcare for almost a decade now. The technology's promise to efficiently track hospital supplies, medical equipment, medications and patients is an attractive proposition to the healthcare industry. However, the prospect of wide spread use of RFID tags in healthcare has also triggered discussions regarding privacy, particularly because RFID data in transit may easily be intercepted. In a nutshell, this technology has not really seen its true potential in healthcare since privacy concerns raised by the tag bearers are not properly addressed by existing protocols and frameworks. The two major types of privacy preservation techniques that are required in an RFID based healthcare are: 1) a privacy preserving authentication protocol is required while sensing RFID tags for different identification and monitoring purposes 2) a privacy preserving access control mechanism is required to restrict unauthorized access of private information while providing healthcare services using the tag ID. In this paper, we propose a component based framework (PriSens-HSAC) that makes an effort to address the above mentioned two privacy issues. To the best of our knowledge, this is the first framework to provide better privacy in RFID based healthcare systems, using authentication and access control technique.

Privacy protection is a very important issue during authentications in RFID systems. In order to achieve high-speed authentication in large-scale RFID systems, researchers propose tree-based approaches, in which any pair of tags share a number of key components. Another technique can be to perform group based private authentication that improves the tradeoff between scalability and privacy by dividing the tags into a number of groups. This is a novel authentication scheme that ensures privacy of the provers. However, one limitation of this technique is that the level of privacy provided by the scheme decreases as more and more tags are compromised Therefore, in this paper, we propose a group based anonymous private authentication protocol (AnonPri) that provides higher level of privacy than the above mention group based scheme and achieves better efficiency than the approaches that prompt the reader to perform an exhaustive search. Our protocol provides unlinkability and thereby preserves privacy. The adversary cannot link the responses with the tags, even if she can learn the identifier that the tags are using to produce the response. To evaluate AnonPri, we have compared both the protocols, AnonPri and the group based authentication. The experiment results establish that the level of privacy provided by AnonPri is higher than that of the group based authentication.