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'.

RAMPVIS: A visualization and visual analytics infrastructure for COVID-19 data.

The COVID-19 pandemic generated large amounts of diverse data, including testing, treatments, vaccine trials, data from modeling, etc. To support epidemiologists and modeling scientists in their efforts to understand and respond to the pandemic, there arose a need for web visualization and visual analytics (VIS) applications to provide insights and support decision-making. In this paper, we present RAMPVIS, an infrastructure designed to support a range of observational, analytical, model-developmental, and dissemination tasks. One of the main features of the system is the ability to "propagate" a visualization designed for one data source to similar ones, this allows a user to quickly visualize large amounts of data. In addition to the COVID pandemic, the RAMPVIS software may be adapted and used with different data to provide rapid visualization support for other emergency responses.

Properties of INDETERMINATE DOMAIN Proteins from Physcomitrium patens: DNA-Binding, Interaction with GRAS Proteins, and Transcriptional Activity.

INDETERMINATE DOMAIN (IDD) proteins are plant-specific transcription factors that interact with GRAS proteins, such as DELLA and SHORT ROOT (SHR), to regulate target genes. The combination of IDD and DELLA proteins regulates genes involved in gibberellic acid (GA) synthesis and GA signaling, whereas the combination of IDD with the complex of SHR and SCARECROW, another GRAS protein, regulates genes involved in root tissue formation. Previous bioinformatic research identified seven IDDs, two DELLA, and two SHR genes in Physcomitrium patens, a model organism for non-vascular plants (bryophytes), which lack a GA signaling pathway and roots. In this study, DNA-binding properties and protein-protein interaction of IDDs from P. patens (PpIDD) were analyzed. Our results showed that the DNA-binding properties of PpIDDs were largely conserved between moss and seed plants. Four PpIDDs showed interaction with Arabidopsis DELLA (AtDELLA) proteins but not with PpDELLAs, and one PpIDD showed interaction with PpSHR but not with AtSHR. Moreover, AtIDD10 (JACKDAW) interacted with PpSHR but not with PpDELLAs. Our results indicate that DELLA proteins have modified their structure to interact with IDD proteins during evolution from moss lineage to seed plants, whereas the interaction of IDD and SHR was already present in moss lineage.

Dashboard Design Patterns.

This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in "dashboard genres" such as narrative, analytical, or embedded dashboard. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online: https://dashboarddesignpatterns.github.io.

Fabrication, Characterization and In Vitro Assessment of Laevistrombus canarium-Derived Hydroxyapatite Particulate-Filled Polymer Composite for Implant Applications.

This paper presents the formulation, characterization, and in vitro studies of polymer composite material impregnated with naturally derived hydroxyapatite (HA) particulates for biomedical implant applications. Laevistrombus canarium (LC) seashells (SS) were collected, washed and cleaned, sun-dried for 24 h, and ground into powder particulates. The SS particulates of different weight percentages (0, 10, 20, 30, 40, 50 wt%)-loaded high-density polyethylene (HDPE) composites were fabricated by compression molding for comparative in vitro assessment. A temperature-controlled compression molding technique was used with the operating pressure of 2 to 3 bars for particulate retention in the HDPE matrix during molding. The HDPE/LC composite was fabricated and characterized using X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray (EDX), differential scanning calorimetry (DSC), and TGA. Mechanical properties such as tensile, compression, flexural, hardness, and also surface roughness were tested as per ASTM standards. Mass degradation and thermal stability of the HDPE/LC composite were evaluated at different temperatures ranging from 10 to 700 °C using thermogravimetric analysis (TGA). The maximum tensile strength was found to be 27 ± 0.5 MPa for 30 wt% HDPE/LC composite. The thermal energy absorbed during endothermic processes was recorded as 71.24 J/g and the peak melting temperature (Tm) was found to be 128.4 °C for the same 30 wt% of HDPE/LC composite specimen. Excellent cell viability was observed during the in vitro biocompatibility study for EtO-sterilized 30 wt% of HDPE/LC composite specimen, except for a report of mild cytotoxicity in the case of higher concentration (50 µL) of the MG-63 cell line. The results demonstrate the potential of the fabricated composite as a suitable biomaterial for medical implant applications.

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.

Face Masks to Combat Coronavirus (COVID-19)-Processing, Roles, Requirements, Efficacy, Risk and Sustainability.

Increasingly prevalent respiratory infectious diseases (e.g., COVID-19) have posed severe threats to public health. Viruses including coronavirus, influenza, and so on can cause respiratory infections. A pandemic may potentially emerge owing to the worldwide spread of the virus through persistent human-to-human transmission. However, transmission pathways may vary; respiratory droplets or airborne virus-carrying particles can have a key role in transmitting infections to humans. In conjunction with social distancing, hand cleanliness, and other preventative measures, the use of face masks is considered to be another scientific approach to combat ubiquitous coronavirus. Different types of face masks are produced using a range of materials (e.g., polypropylene, polyacrylonitrile, polycarbonate, polyurethane, polystyrene, polyester and polyethylene) and manufacturing techniques (woven, knitted, and non-woven) that provide different levels of protection to the users. However, the efficacy and proper disposal/management of the used face masks, particularly the ones made of non-biodegradable polymers, pose great environmental concerns. This review compiles the recent advancements of face masks, covering their requirements, materials and techniques used, efficacy, challenges, risks, and sustainability towards further enhancement of the quality and performance of face masks.

Milk progesterone enzyme-linked immunosorbent assay as a tool to investigate ovarian cyclicity of water buffaloes in relation to body condition score and milk production.

BACKGROUND: Application of assisted reproductive technologies in buffaloes is limited to some extent by farmers' inability to detect oestrus because of its poor expression. The present study aimed at investigating reliability of a milk progesterone enzyme-linked immunosorbent assay (ELISA) to assess the ovarian cyclicity during post partum, oestrus and post-breeding periods in water buffaloes. METHODS: Progesterone concentrations were measured by an ELISA in milk of 23 postpartum buffaloes in relation to oestrus, pregnancy, body condition score (BCS) and milk production. Two milk samples were taken at 10 days intervals, every month starting from day 30 and continued to day 150 post partum. BCS and milk production were recorded during sample collection. Milk samples from bred buffaloes were collected at Day 0 (day of breeding), Days 10-12 and Days 22-24. Defatted milk was preserved at -80°C until analysis. Pregnancy was confirmed by palpation per rectum on Days 70-90. RESULTS: Seventeen buffaloes had 47 ovulatory cycles, one to four in each, 13 were detected in oestrus once (28 % oestrus detection rate). Progesterone concentration ≥1 ng/ml in one of the two 10-day-interval milk samples reflected ovulation and corpus luteum formation. The intervals between calving to first luteal activity and to first detected oestrus varied from 41 to 123 days (n = 17) and 83 to 135 (n = 13) days, respectively. Eight buffaloes were bred in the course of the study and seven were found pregnant. These buffaloes had a progesterone profile of low (<1 ng/ml), high (≥ 1 ng/ml) and high (≥ 1 ng/ml) on Day 0, Days 10-12 and Days 22-24, respectively. Buffaloes cycling later in the postpartum period had fewer missed oestruses (P < 0.05). Buffaloes with a superior BCS had a shorter calving to oestrus interval and produced more milk (P < 0.05). CONCLUSIONS: Milk progesterone ELISA is a reliable tool for monitoring ovarian cyclicity and good BCS may be an indicator of resuming cyclicity in water buffalo.