An interpenetrating network composite for a regenerative spinal disc application

Severe degeneration of the intervertebral disc has an immensely debilitating effect on quality of life that has become a serious health and economic burden throughout the world. The disc plays an integral role in biomechanical movement and support within the spine. The emergence of tissue engineering endeavours to restore the structural characteristics and functionality of the native tissue. Hydrogels have been widely investigated as a candidate for regeneration of the gelatinous nucleus pulposus due to its architectural resemblance and fluid retention characteristics. However, hydrogels are often limited due to small compressive stiffness and tear resistance, leading to extrusion complications. Reinforcement of the hydrogel network using polymeric scaffolds may address these issues of inadequate mechanical properties and implant instability. This study investigates the potential of a carrageenan gel-infused polycaprolactone scaffold for nucleus pulposus tissue engineering. Mechanical properties were characterised using viscoelastic and poroelastic frameworks via microindentation. The incorporation of polymeric reinforcement within the gels increased material stiffness to that comparable to the native nucleus pulposus, however permeability was significantly greater than native values. A preliminary cell evaluation culturing NIH 3T3s over 21 days suggested the incorporation of polymeric networks also enhanced cellular proliferation compared to gels alone

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Testing a global standard for quantifying species recovery and assessing conservation impact

Recognizing the imperative to evaluate species recovery and conservation impact, in 2012 the International Union for Conservation of Nature (IUCN) called for development of a “Green List of Species” (now the IUCN Green Status of Species). A draft Green Status framework for assessing species’ progress toward recovery, published in 2018, proposed 2 separate but interlinked components: a standardized method (i.e., measurement against benchmarks of species’ viability, functionality, and preimpact distribution) to determine current species recovery status (herein species recovery score) and application of that method to estimate past and potential future impacts of conservation based on 4 metrics (conservation legacy, conservation dependence, conservation gain, and recovery potential). We tested the framework with 181 species representing diverse taxa, life histories, biomes, and IUCN Red List categories (extinction risk). Based on the observed distribution of species’ recovery scores, we propose the following species recovery categories: fully recovered, slightly depleted, moderately depleted, largely depleted, critically depleted, extinct in the wild, and indeterminate. Fifty-nine percent of tested species were considered largely or critically depleted. Although there was a negative relationship between extinction risk and species recovery score, variation was considerable. Some species in lower risk categories were assessed as farther from recovery than those at higher risk. This emphasizes that species recovery is conceptually different from extinction risk and reinforces the utility of the IUCN Green Status of Species to more fully understand species conservation status. Although extinction risk did not predict conservation legacy, conservation dependence, or conservation gain, it was positively correlated with recovery potential. Only 1.7% of tested species were categorized as zero across all 4 of these conservation impact metrics, indicating that conservation has, or will, play a role in improving or maintaining species status for the vast majority of these species. Based on our results, we devised an updated assessment framework that introduces the option of using a dynamic baseline to assess future impacts of conservation over the short term to avoid misleading results which were generated in a small number of cases, and redefines short term as 10 years to better align with conservation planning. These changes are reflected in the IUCN Green Status of Species Standard

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021

Natural disasters and evacuations as a communication and social phenomenon

This article intends to show how system and complexity science can contribute to an understanding and improvement of evacuation processes, especially considering the roles of engaged communities at risk, the concepts of community self help,and clear communication about local threats and remedies. Evacuation ahead of a natural disaster impact is perhaps among the more extreme actions private citizens are encouraged to make by authorities to help them “get safe and stays afe” – the core goal of all disaster risk management and effective risk communication. This article shows researchers in Complexity and Systems Science (CSS) a social science approach to maximize effective and precautionary evacuation, maximize safety, minimize loss, and speed full recovery. The computational and analytical modeling tools of CSS may be considered to apply to a complex interaction of community awareness and inclination to accept the reality of a natural disaster threat, along with achieving background and final preparations to maximize safety and recovery from a natural disaster impact. This article may stimulate CSS researchers to develop detailed models of the complex systems and complexity of melding information from weather bureaus and disaster managers, via contacts and intervening media to communities at risk, with the shared social goal of maximizing safety. This social science task requires cross-disciplinary approaches of respect and response. The old disaster management model lacked the predictive and rapid communication systems now available and developing in disaster predictive models (such as flood maps). An approach to modeling the great complexity of human behavior responding to threat is provided. Such a model must include people’s prior knowledge of a threat type, and consider such fine detail as the overarching language used in a country with threat zones, and the dominant languages of all under threat. Itis hoped this article stimulates CSS models to further engage in this social good of helping people get safe and stay safe through natural disasters by providing predictive tool to authorities to better inform and encourage those at risk to action, including the possible need for precautionary evacuations ahead of a predicted impact. Disaster management in Australia, and increasingly, globally, is focused on mitigation as part of a “threat continuum,” from acceptance that some2 Natural Disasters and Evacuations as a Communication and Social Phenomenon locations are vulnerable to a hazard impact to recovery (COAG 2004). Emergency warnings and a possible need to evacuate are embedded as “spikes” on that continuum. Thus, this article stresses the importance of developing ways, incentives, to mobilize aware at-risk community members to precautionary self-evacuation. For this to happen, people need to know and internalize the reality that they are in a hazard zone. Thus, in the cost-effective philosophy of engendering self-help, the process of understanding the complexity of achieving the shared social goal in maximizing safety and minimizing loss is to engender creation of empowered communities with a high motivation for safety-oriented and precautionary action. This is likely to lead to minimized loss and disruption and maximized recovery. This article details many elements of that process and invites detailed development of the sustainability implementation research (SIR)to achieve that goal through CSS. To model the path to collective safety, the complexity of the dynamics at play need to be clarified: impact preparedness, including possible evacuation, is a communication and social issue. This article demonstrates that mapping hazard zones (where natural hazards can impact) and sharing with those at risk is important for those at risk to internalize the fact they may be at risk as a first psychological step in taking responsibility to do things to minimize that risk. Involving the community in the acceptance of threat and needed action, developing a safety-oriented social norm feeds back to individuals [SoEaES1] and households.[SoEaES1] Each Under “Preparations” Evacuation modeling is needed only for those whose homes may be at real threat of a disaster impact. For those living in a hazard zone (e.g., Figs. 2, 7, 11, and 15), a fully informed community, who have internalized the reality of the threat and have worked for maximum background preparation and have mechanisms to receive alerts and warnings of a looming threat, a community predisposed to precautionary evacuations will result. Evacuation modeling is also needed for those who may manage the travel routes or receive evacuees. Capturing this complexity is the challenge for modelers. Evacuation is about hazard zone residents actively monitoring a looming threat via refined communication channels detailed in this article, within a developed social predisposition to act. Some examples are given. For consideration by scientists and students internationally, this article introduces the Communication Safety Triangle(CST) and the seven steps to community safety on the preparedness continuum, within the new research frame of sustainability implementation research (SIR)