Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e. without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01× and up to 23.10×) and reduces peak memory usage (on average by 5.74× and up to by 22.00×) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that around one-third of Rawsamble's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are (i) as accurate as those built from minimap2's overlaps and (ii) up to half a chromosome in length (e.g. 2.3 million bases for E. coli). Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.
Software-Defined Wide Area Networks (SD-WAN) have emerged as a rapidly evolving technology designed to meet the growing demand for flexible, secure, and scalable network infrastructures. This paper provides a review of SD-WAN techniques, focusing on their principles of operation, mechanisms, and evolution, with particular attention to applications in resource-constrained environments such as mobile, satellite, and radio networks. The analysis highlights key architectural elements, including security mechanisms, monitoring methods and metrics, and management protocols. A classification of both commercial (e.g., Cisco SD-WAN, Fortinet Secure SD-WAN, VMware SD-WAN, Palo Alto Prisma SD-WAN, HPE Aruba EdgeConnect) and research-based solutions is presented. The overview covers overlay protocols such as Overlay Management Protocol (OMP), Dynamic Multipath Optimization (DMPO), App-ID, OpenFlow, and NETCONF, as well as tunneling mechanisms such as IPsec and WireGuard. The discussion further covers control plane architectures (centralized, distributed, and hybrid) and network monitoring methods, including latency, jitter, and packet loss measurement. The growing importance of Artificial Intelligence (AI) in optimizing path selection and improving threat detection in SD-WAN environments, especially in resource-constrained networks, is emphasized. Analysis of solutions indicates that SD-WAN improves performance, reduces latency, and lowers operating costs compared to traditional WAN architectures. The paper concludes with guidelines and recommendations for using SD-WAN in resource-constrained environments.
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Progress toward gender equality is thwarted by the underrepresentation of women in political leadership, even as most Americans report they would vote for women candidates. Here, we hypothesize that women candidates are often disadvantaged by pragmatic bias, a tendency to withhold support for members of groups for whom success is perceived to be difficult or impossible to achieve. Across six studies (N = 7,895), we test whether pragmatic bias impedes women's access to a highly significant political leadership position-the US presidency. In two surveys, 2020 Democratic primary voters perceived women candidates to be less electable, and these beliefs were correlated with lower intentions to vote for women candidates (Studies 1 and 2). Voters identified many reasons women would be less electable than men, including others' unwillingness to vote for women, biased media coverage, and higher requirements to prove themselves. We next tested interventions to reduce pragmatic bias. Merely correcting misperceptions of Americans' reported readiness for a woman president did not increase intentions to vote for a woman (Study 3). However, across three experiments (including one preregistered on a nationally representative sample), presenting evidence that women earn as much support as men in US general elections increased Democratic primary voters' intentions to vote for women presidential candidates, an effect driven by heightened perceptions of these candidates' electability (Studies 4 to 6). These findings highlight that social change efforts can be thwarted by people's sense of what is possible, but this may be overcome by credibly signaling others' willingness to act collectively.
As the workforce shifts to being predominantly hybrid and remote, how can companies help employees-particularly early-career women in science, technology, engineering, and mathematics (STEM) fields-develop greater confidence in their soft skills, shown to improve organizational retention? We evaluate the effects of an online longitudinal intervention to develop soft skills among early-career women employees at a North American biotechnology company during the height of the COVID-19 pandemic. Controlling for baseline levels collected immediately prior to nationwide lockdowns, we find that a 6-month online intervention increased early-career women's assessments of their soft skills at work by an average of 9% (P < 0.001), compared with a decrease of about 3.5% for a matched control group (P < 0.05), resulting in an average treatment effect of nearly 13% on the treated group. Furthermore, we find evidence that the intervention led to an increase in manager-assessed performance for early-career women relative to employees not in the intervention, and that overall, increased self-assessments of soft skill competencies were associated with greater odds of retention. Results show how employee soft skill development was affected by the pandemic and provide insights for a feasible and cost-effective method to train and engage a hybrid or fully remote workforce.
Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. Available at https://doi.org/10.5281/zenodo.5121618.
In the past few years, researchers have proposed numerous indexing schemes for searching large datasets of raw sequencing experiments. Most of these proposed indexes are approximate (i.e. with one-sided errors) in order to save space. Recently, researchers have published exact indexes-Mantis, VariMerge and Bifrost-that can serve as colored de Bruijn graph representations in addition to serving as k-mer indexes. This new type of index is promising because it has the potential to support more complex analyses than simple searches. However, in order to be useful as indexes for large and growing repositories of raw sequencing data, they must scale to thousands of experiments and support efficient insertion of new data. In this paper, we show how to build a scalable and updatable exact raw sequence-search index. Specifically, we extend Mantis using the Bentley-Saxe transformation to support efficient updates, called Dynamic Mantis. We demonstrate Dynamic Mantis's scalability by constructing an index of ≈40K samples from SRA by adding samples one at a time to an initial index of 10K samples. Compared to VariMerge and Bifrost, Dynamic Mantis is more efficient in terms of index-construction time and memory, query time and memory and index size. In our benchmarks, VariMerge and Bifrost scaled to only 5K and 80 samples, respectively, while Dynamic Mantis scaled to more than 39K samples. Queries were over 24× faster in Mantis than in Bifrost (VariMerge does not immediately support general search queries we require). Dynamic Mantis indexes were about 2.5× smaller than Bifrost's indexes and about half as big as VariMerge's indexes. Dynamic Mantis implementation is available at https://github.com/splatlab/mantis/tree/mergeMSTs. Supplementary data are available at Bioinformatics online.
The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.
JABAWS is a web services framework that simplifies the deployment of web services for bioinformatics. JABAWS:MSA provides services for five multiple sequence alignment (MSA) methods (Probcons, T-coffee, Muscle, Mafft and ClustalW), and is the system employed by the Jalview multiple sequence analysis workbench since version 2.6. A fully functional, easy to set up server is provided as a Virtual Appliance (VA), which can be run on most operating systems that support a virtualization environment such as VMware or Oracle VirtualBox. JABAWS is also distributed as a Web Application aRchive (WAR) and can be configured to run on a single computer and/or a cluster managed by Grid Engine, LSF or other queuing systems that support DRMAA. JABAWS:MSA provides clients full access to each application's parameters, allows administrators to specify named parameter preset combinations and execution limits for each application through simple configuration files. The JABAWS command-line client allows integration of JABAWS services into conventional scripts. JABAWS is made freely available under the Apache 2 license and can be obtained from: http://www.compbio.dundee.ac.uk/jabaws.
High-throughput sequencing (HTS) data for small RNAs (noncoding RNA molecules that are 20-250 nucleotides in length) can now be routinely generated by minimally equipped wet laboratories; however, the bottleneck in HTS-based research has shifted now to the analysis of such huge amount of data. One of the reasons is that many analysis types require a Linux environment but computers, system administrators, and bioinformaticians suppose additional costs that often cannot be afforded by small to mid-sized groups or laboratories. Web servers are an alternative that can be used if the data is not subjected to privacy issues (what very often is an important issue with medical data). However, in any case they are less flexible than stand-alone programs limiting the number of workflows and analysis types that can be carried out.We show in this protocol how virtual machines can be used to overcome those problems and limitations. sRNAtoolboxVM is a virtual machine that can be executed on all common operating systems through virtualization programs like VirtualBox or VMware, providing the user with a high number of preinstalled programs like sRNAbench for small RNA analysis without the need to maintain additional servers and/or operating systems.
Extended multi-locus sequence typing (eMLST) methods have become popular in the field of genomic epidemiology. Before eMLST methods can be applied in epidemiological investigations, the selection of a suitable scheme is critical. The core genome scheme (cgMLST) has become the most popular eMLST approach for strain typing in the epidemiological domain. In addition to strain typing, many public health researchers and clinical microbiologists wish to investigate which genes cause genetic differences between compared strains. Therefore, a tool that can be used to extract canonical genes with an eMLST scheme would be particularly useful. In this study, we present cano-eMLST, a well-designed program that applies a feature-selection methodology to create a canonical locus combination with discriminatory power by traversing a genetic relatedness tree based on a user-selected scheme. The cano-eMLST program is provided mainly to help infectious disease laboratory researchers identify potential factors related to bacterial pathogenesis. The core program (tree-traversing approach) of cano-eMLST is implemented in Perl and Python. All the necessary dependencies and environmental settings are provided in the encapsulated version (VirtualBox or VMware) and self-installation version (all use source code and libraries).
Virtual Machine Monitors (VMM) have become popular in different application areas. Some applications may require to generate the timer events with high resolution and precision. This however may be challenging due to the complexity of VMMs. In this paper we focus on the timer functionality provided by five different VMMs-Xen, KVM, Qemu, VirtualBox and VMWare. Firstly, we evaluate resolutions and precisions of their timer events. Apparently, provided resolutions and precisions are far too low for some applications (e.g. networking applications with the quality of service). Then, using Xen virtualization we demonstrate the improved timer design that greatly enhances both the resolution and precision of achieved timer events.
In this study, a mobile cloud healthcare system was implemented to assist middle- and old-aged people with diabetes preventive healthcare. First of all, a prototype system was developed. It was a system relying on data mining computing technology and big data analytics. Besides, it was constructed under the environment architecture of VMware cloud computing. This mobile cloud healthcare system was developed via mobile devices. Its purpose was to set up a diabetes preventive healthcare service for users, and to further assess the usability of this mobile cloud care system.
Molecular imaging is the visualization and identification of specific molecules in anatomy for insight into metabolic pathways, tissue consistency, and tracing of solute transport mechanisms. This paper presents the Molecular Imaging Data Grid (MIDG) which utilizes emerging grid technologies in preclinical molecular imaging to facilitate data sharing and discovery between preclinical molecular imaging facilities and their collaborating investigator institutions to expedite translational sciences research. Grid-enabled archiving, management, and distribution of animal-model imaging datasets help preclinical investigators to monitor, access and share their imaging data remotely, and promote preclinical imaging facilities to share published imaging datasets as resources for new investigators. The system architecture of the Molecular Imaging Data Grid is described in a four layer diagram. A data model for preclinical molecular imaging datasets is also presented based on imaging modalities currently used in a molecular imaging center. The MIDG system components and connectivity are presented. And finally, the workflow steps for grid-based archiving, management, and retrieval of preclincial molecular imaging data are described. Initial performance tests of the Molecular Imaging Data Grid system have been conducted at the USC IPILab using dedicated VMware servers. System connectivity, evaluated datasets, and preliminary results are presented. The results show the system's feasibility, limitations, direction of future research. Translational and interdisciplinary research in medicine is increasingly interested in cellular and molecular biology activity at the preclinical levels, utilizing molecular imaging methods on animal models. The task of integrated archiving, management, and distribution of these preclinical molecular imaging datasets at preclinical molecular imaging facilities is challenging due to disparate imaging systems and multiple off-site investigators. A Molecular Imaging Data Grid design, implementation, and initial evaluation is presented to demonstrate the secure and novel data grid solution for sharing preclinical molecular imaging data across the wide-area-network (WAN).
The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.
The use of remote desktop services on virtualized machines is a general trend to reduce the cost of desktop seats. Instead of assigning a physical machine with its operating system and software to each user, it is considerably easier to manage a light client machine that connects to a server where the instance of the user's desktop machine actually executes. Citrix and VMware have been major suppliers of these systems in private clouds. Desktop-as-a-Service solutions such as Amazon WorkSpaces offer a similar functionality, yet in a public cloud environment. In this paper, we review the main offerings of remote desktop protocols for a cloud deployment. We evaluate the necessary network resources using a traffic model based on self-similar processes. We also evaluate the quality of experience perceived by the user, in terms of image quality and interactivity, providing values of Mean Opinion Score (MOS). The results confirm that the type of application running on the remote servers and the mix of users must be considered to determine the bandwidth requirements. Applications such as web browsing result in unexpectedly high traffic rates and long bursts, more than the case of desktop video playing, because the on-page animations are rendered on the server.
Genomic tiling arrays have been described in the scientific literature since 2003, yet there is a shortage of user-friendly applications available for their analysis. Tiling Array Analyzer (TiArA) is a software program that provides a user-friendly graphical interface for the background subtraction, normalization, and summarization of data acquired through the Affymetrix tiling array platform. The background signal is empirically measured using a group of nonspecific probes with varying levels of GC content and normalization is performed to enforce a common dynamic range. TiArA is implemented as a standalone program for Linux systems and is available as a cross-platform virtual machine that will run under most modern operating systems using virtualization software such as Sun VirtualBox or VMware. The software is available as a Debian package or a virtual appliance at http://purl.org/NET/tiara.
Germinal studies have described the prevalence of sex-based harassment in high schools and its associations with adverse outcomes in adolescents. Studies have focused on students, with little attention given to the actions of high schools themselves. Though journalists responded to the #MeToo movement by reporting on schools' betrayal of students who report misconduct, this topic remains understudied by researchers. Gender harassment is characterized by sexist remarks, sexually crude or offensive behavior, gender policing, work-family policing, and infantilization. Institutional betrayal is characterized by the failure of an institution, such as a school, to protect individuals dependent on the institution. We investigated high school gender harassment and institutional betrayal reported retrospectively by 535 current undergraduates. Our primary aim was to investigate whether institutional betrayal moderates the relationship between high school gender harassment and current trauma symptoms. In our pre-registered hypotheses (https://osf.io/3ds8k), we predicted that (1) high school gender harassment would be associated with more current trauma symptoms and (2) institutional betrayal would moderate this relationship such that high levels of institutional betrayal would be associated with a stronger association between high school gender harassment and current trauma symptoms. Consistent with our first hypothesis, high school gender harassment significantly predicted college trauma-related symptoms. An equation that included participant gender, race, age, high school gender harassment, institutional betrayal, and the interaction of gender harassment and institutional betrayal also significantly predicted trauma-related symptoms. Contrary to our second hypothesis, the interaction term was non-significant. However, institutional betrayal predicted unique variance in current trauma symptoms above and beyond the other variables. These findings indicate that both high school gender harassment and high school institutional betrayal are independently associated with trauma symptoms, suggesting that intervention should target both phenomena.
The colored de Bruijn graph (cdbg) and its variants have become an important combinatorial structure used in numerous areas in genomics, such as population-level variation detection in metagenomic samples, large-scale sequence search, and cdbg-based reference sequence indices. As samples or genomes are added to the cdbg, the color information comes to dominate the space required to represent this data structure. In this article, we show how to represent the color information efficiently by adopting a hierarchical encoding that exploits correlations among color classes-patterns of color occurrence-present in the de Bruijn graph (dbg). A major challenge in deriving an efficient encoding of the color information that takes advantage of such correlations is determining which color classes are close to each other in the high-dimensional space of possible color patterns. We demonstrate that the dbg itself can be used as an efficient mechanism to search for approximate nearest neighbors in this space. While our approach reduces the encoding size of the color information even for relatively small cdbgs (hundreds of experiments), the gains are particularly consequential as the number of potential colors (i.e., samples or references) grows into thousands. We apply this encoding in the context of two different applications; the implicit cdbg used for a large-scale sequence search index, Mantis, as well as the encoding of color information used in population-level variation detection by tools such as Vari and Rainbowfish. Our results show significant improvements in the overall size and scalability of representation of the color information. In our experiment on 10,000 samples, we achieved >11 × better compression compared to Ramen, Ramen, Rao (RRR).