Crystallization-Induced Flower-like Superstructures via Peptoid Helix Assembly.

We report a unique method to construct hierarchical superstructures based on molecular programming of peptidomimetics. Chiral steric hindrance in the polymer backbone stabilizes peptoid helices that crystallize into nanosheets during solvent evaporation. The stacking of nanosheets results in flower-like superstructures. The helical peptoid, nucleated from chiral monomers, is characterized as locally stiffer and more extended than the unstructured peptoid. Molecular dynamics (MD) simulations further suggest a constraint on the dihedral angles and a preference toward the trans configuration, resulting in an extended chain structure. The nanosheet assemblies at various length scales indicate an extent of intermolecular ordering amplified by chiral steric hindrance. Such molecular programming and processing protocols will benefit the future design and controlled assembly of hierarchical peptidomimetics.

Improved super-resolution ribosome profiling reveals prevalent translation of upstream ORFs and small ORFs in Arabidopsis.

A crucial step in functional genomics is identifying actively translated ORFs and linking them to biological functions. The challenge lies in identifying short ORFs, as their identification is greatly influenced by data quality and depth. Here, we improved the coverage of super-resolution Ribo-seq in Arabidopsis (Arabidopsis thaliana), revealing uncharacterized translation events for nuclear, chloroplastic, and mitochondrial genes. Assisted by a transcriptome assembly, we identified 7,751 unconventional translation events, comprising 6,996 upstream ORFs (uORFs) and 209 downstream ORFs on annotated protein-coding genes, as well as 546 ORFs in presumed noncoding RNAs. Proteomic data confirmed the production of stable proteins from some of these unannotated translation events. We present evidence of active translation from primary transcripts of trans-acting small interfering RNAs (TAS1-4) and microRNAs (pri-MIR163 and pri-MIR169) and periodic ribosome stalling supporting cotranslational decay. Additionally, we developed a method for identifying extremely short uORFs, including 370 minimum uORFs (AUG-stop), and 2,921 tiny uORFs (2 to 10 amino acids) and 681 uORFs that overlap with each other. Remarkably, these short uORFs exhibit strong translational repression as do longer uORFs. We also systematically discovered 594 uORFs regulated by alternative splicing, suggesting widespread isoform-specific translational control. Finally, these prevalent uORFs are associated with numerous important pathways. In summary, our improved Arabidopsis translational landscape provides valuable resources to study gene expression regulation.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Transforming medical education to strengthen the health professional training in Viet Nam: A case study.

The competency-based undergraduate curriculum reform at the University of Medicine and Pharmacy at Ho Chi Minh City, Faculty of Medicine (UMP-FM) is detailed and reviewed in reference to the instructional and institutional reforms, and enabling actions recommended by the Lancet 2010 Commission for Health Professional Education. Key objectives are to: revise the overall 6-year curriculum to be more integrated and competency-based; reinforce students' knowledge application, problem-solving, clinical competence, self-directed learning and soft skills; develop a comprehensive and performance-based student assessment programme; and establish a comprehensive quality monitoring programme to facilitate changes and improvements. New features include early introduction to the practice of medicine, family- and community-based medicine, professionalism, interprofessional education, electives experiences, and a scholarly project. Institutional reform introduces a faculty development programme, joint planning mechanism, a "culture of critical inquiry", and a transparent faculty reward system. Lessons learnt from the curriculum reform at UMP-FM could be helpful to medical schools from low- and middle-income countries considering transitioning from a traditional to a competency-based curriculum. Funding: This work receives no external funding.

A Community-Based Model of Care During the Fourth Wave of the COVID-19 Outbreak in Ho Chi Minh City, Vietnam.

In response to a call for help during a surge in coronavirus disease-19 (COVID-19) cases in Ho Chi Minh City in July 2021, the University of Medicine and Pharmacy at Ho Chi Minh City developed and implemented a community care model for the management of patients with COVID-19. This was based on three main principles: home care; providing monitoring and care at a distance; and providing timely emergency care if needed. One team supported patients at home with frequent contacts and remote monitoring, while a second team transferred and cared for patients requiring treatment at field emergency care facilities. COVID-19-related mortality rates at the two districts where this approach was implemented (0.43% and 0.57%) were substantially lower than the overall rate in Ho Chi Minh City over the same period (4.95%). Thus, utilization of a community care model can increase the number of patients with COVID-19 who can be effectively managed from home, and use of field emergency care facilities limited the number of patients that had to be referred for tertiary care. Importantly, the community care model also markedly reduced the mortality rate compared with traditional methods of COVID-19 patient management.

Propagating Visual Designs to Numerous Plots and Dashboards.

In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization.

Tuning of the elastic modulus of a soft polythiophene through molecular doping.

Molecular doping of a polythiophene with oligoethylene glycol side chains is found to strongly modulate not only the electrical but also the mechanical properties of the polymer. An oxidation level of up to 18% results in an electrical conductivity of more than 52 S cm-1 and at the same time significantly enhances the elastic modulus from 8 to more than 200 MPa and toughness from 0.5 to 5.1 MJ m-3. These changes arise because molecular doping strongly influences the glass transition temperature Tg and the degree of π-stacking of the polymer, as indicated by both X-ray diffraction and molecular dynamics simulations. Surprisingly, a comparison of doped materials containing mono- or dianions reveals that - for a comparable oxidation level - the presence of multivalent counterions has little effect on the stiffness. Evidently, molecular doping is a powerful tool that can be used for the design of mechanically robust conducting materials, which may find use within the field of flexible and stretchable electronics.

A pair of DUF538 domain-containing proteins modulates plant growth and trichome development through the transcriptional regulation of GLABRA1 in Arabidopsis thaliana.

SMALLER TRICHOMES WITH VARIABLE BRANCHES (SVB) is an emerging plant growth regulator in trichome development, endoplasmic reticulum stress response, and phosphoinositide signaling, and belongs to the land plant-specific DUF538 domain-containing protein family. Despite its multifaceted roles, the functions of this protein family are poorly understood in plant growth and development. Here, we report that SVB-like (SVBL), the closest homolog of SVB, modulates plant growth and trichome development with SVB in Arabidopsis thaliana. Although none of the single mutants showed an obvious growth defect, the double mutants of svb svbl exhibited dwarfed plant growth. In trichome development, the defects in svb mutant were greatly enhanced by the additional mutation in SVBL, despite the single knockout of SVBL showing the mild defects. The double mutation reduced the transcript level of one of the central hub genes for trichome development, GLABRA1 (GL1), which in turn affects the other downstream genes, GLABRA2 (GL2), TRANSPARENT TESTA GLABRA2 (TTG2), TRIPTYCHON (TRY), CAPRICE (CPC), and ENHANCER OF TRY AND CPC1 (ETC1). In situ translational reporter assays showed that SVB and SVBL share highly similar localization patterns both at tissue and subcellular levels. The present study suggests that SVB and SVBL play a pivotal role in plant growth and trichome development by affecting a specific subset of known trichome developmental regulators, highlighting the importance of the DUF538 protein family in higher plants.

TimeSets: Temporal Sensemaking in Intelligence Analysis.

TimeSets is a temporal data visualization technique designed to reveal insights into event sets, such as all the events linked to one person or organization. In this article, we describe two TimeSets-based visual analytics tools for intelligence analysis. In the first case, TimeSets is integrated with other visual analytics tools to support open-source intelligence analysis with Twitter data, particularly the challenge of finding the right questions to ask. The second case uses TimeSets in a participatory design process with analysts that aims to meet their requirements of uncertainty analysis involving fake news. Lessons learned are potentially beneficial to other application domains.

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling.

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques supporting the analysis. Findings and results of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audiences but also the information that needs to be presented. Analysis results may consist of multiple components, which may involve multiple heterogeneous facets. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, within which two main challenges lie: information complexity and display complexity. We address this problem by proposing a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organises story contents. Unlike previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. We focus on selecting, assembling and organizing findings for further presentation rather than on tracking analysis history and enabling dual (i.e., explorative and communicative) use of data displays. In story synthesis, findings are selected, assembled, and arranged in meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed conceptual framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two diverse domains, social media and movement analysis.

VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics.

User behaviour analytics (UBA) systems offer sophisticated models that capture users' behaviour over time with an aim to identify fraudulent activities that do not match their profiles. Motivated by the challenges in the interpretation of UBA models, this paper presents a visual analytics approach to help analysts gain a comprehensive understanding of user behaviour at multiple levels, namely individual and group level. We take a user-centred approach to design a visual analytics framework supporting the analysis of collections of users and the numerous sessions of activities they conduct within digital applications. The framework is centred around the concept of hierarchical user profiles that are built based on features derived from sessions, as well as on user tasks extracted using a topic modelling approach to summarise and stratify user behaviour. We externalise a series of analysis goals and tasks, and evaluate our methods through use cases conducted with experts. We observe that with the aid of interactive visual hierarchical user profiles, analysts are able to conduct exploratory and investigative analysis effectively, and able to understand the characteristics of user behaviour to make informed decisions whilst evaluating suspicious users and activities.

LDA Ensembles for Interactive Exploration and Categorization of Behaviors.

We define behavior as a set of actions performed by some actor during a period of time. We consider the problem of analyzing a large collection of behaviors by multiple actors, more specifically, identifying typical behaviors and spotting anomalous behaviors. We propose an approach leveraging topic modeling techniques - LDA (Latent Dirichlet Allocation) Ensembles - to represent categories of typical behaviors by topics that are obtained through topic modeling a behavior collection. When such methods are applied to text in natural languages, the quality of the extracted topics are usually judged based on the semantic relatedness of the terms pertinent to the topics. This criterion, however, is not necessarily applicable to topics extracted from non-textual data, such as action sets, since relationships between actions may not be obvious. We have developed a suite of visual and interactive techniques supporting the construction of an appropriate combination of topics based on other criteria, such as distinctiveness and coverage of the behavior set. Two case studies on analyzing operation behaviors in the security management system and visiting behaviors in an amusement park, and the expert evaluation of the first case study demonstrate the effectiveness of our approach.

Understanding User Behaviour through Action Sequences: From the Usual to the Unusual.

Action sequences, where atomic user actions are represented in a labelled, timestamped form, are becoming a fundamental data asset in the inspection and monitoring of user behaviour in digital systems. Although the analysis of such sequences is highly critical to the investigation of activities in cyber security applications, existing solutions fail to provide a comprehensive understanding due to the complex semantic and temporal characteristics of these data. This paper presents a visual analytics approach that aims to facilitate a user-involved, multi-faceted decision making process during the identification and the investigation of "unusual" action sequences. We first report the results of the task analysis and domain characterisation process. Then we describe the components of our multi-level analysis approach that comprises of constraint-based sequential pattern mining and semantic distance based clustering, and multi-scalar visualisations of users and their sequences. Finally, we demonstrate the applicability of our approach through a case study that involves tasks requiring effective decision-making by a group of domain experts. Although our solution here is tightly informed by a user-centred, domain-focused design process, we present findings and techniques that are transferable to other applications where the analysis of such sequences is of interest.

Antimicrobial Coatings for Food Contact Surfaces: Legal Framework, Mechanical Properties, and Potential Applications.

Food contact surfaces (FCS) in food processing facilities may become contaminated with a number of unwanted microorganisms, such as Listeria monocytogenes, Escherichia coli O157:H7, and Staphylococcus aureus. To reduce contamination and the spread of disease, these surfaces may be treated with sanitizers or have active antimicrobial components adhered to them. Although significant efforts have been devoted to the development of coatings that improve the antimicrobial effectiveness of FCS, other important coating considerations, such as hardness, adhesion to a substrate, and migration of the antimicrobial substance into the food matrix, have largely been disregarded to the detriment of their translation into practical application. To address this gap, this review examines the mechanical properties of antimicrobial coatings (AMC) applied to FCS and their interplay with their antimicrobial properties within the framework of relevant regulatory constraints that would apply if these were used in real-world applications. This review also explores the various assessment techniques for examining these properties, the effects of the deposition methods on coating properties, and the potential applications of such coatings for FCS. Overall, this review attempts to provide a holistic perspective. Evaluation of the current literature urges a compromise between antimicrobial effectiveness and mechanical stability in order to adhere to various regulatory frameworks as the next step toward improving the industrial feasibility of AMC for FCS applications.

SensePath: Understanding the Sensemaking Process Through Analytic Provenance.

Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

An extensible framework for provenance in human terrain visual analytics.

We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.

Mandatory helmet legislation and the print media in Viet Nam.

With motorcycle ownership high and rising in Viet Nam, and motorcycle riders vulnerable to both fatal and non-fatal injury, the re-introduction of mandatory helmet legislation in 2007 has been a priority for the Vietnamese government. The paper uses a qualitative analysis of web-based versions of the eight most popular newspapers in Viet Nam to track reporting over four phases of the implementation of the legislation, identifying codes and constructing the dominant themes of the media coverage. The study documents the justification and promotion of the legislation, and the mechanisms for preparing for its implementation at a national and local level, developing solutions and encouraging the replication of successful strategies. It records opposition and obstacles to helmet use, and concerns raised around the quality of helmets purchased. In return, the press notes the response of the market in innovative solutions to these problems. With the successful implementation of the legislation, the functions of the print media in promulgating and promoting the legislation, together with the reporting of ongoing resistance to the process, serve to enable a dialogue between the State and population around expressed concerns. In highlighting quality control of helmets as a key issue, the media have identified a potential ongoing role in monitoring the state's initiative in reducing the road toll from traumatic brain injury in motorcyclists.