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Unhealthy diets and excess energy intake are the greatest contributors to disease and disability in Aotearoa. Unhealthy diets are heavily influenced by obesogenic food environments. Governments and the private sector have critical roles to play in creating healthier food environments, yet New Zealand consistently falls behind international best practice, suggesting a lack of accountability. The accountability cycle has five phases: setting the account, taking account, sharing the account, holding to account, and responding to the account. The ongoing monitoring of the healthiness of food environments is essential to identify key problems, assess the impact of policies, hold governments and food companies to their commitments, measure progress, and support future implementation. The Food Environments Dashboard Aotearoa was created to collate and translate more than a decade’s worth of food environment monitoring studies and their findings for policy-makers and public health advocacy groups to encourage policy change. Based on Australia’s Food Environments Dashboard, the key indicators from these studies have been identified and assessed against defined criteria to give a green (promotes health), amber (needs improvement) or red (unhealthy) rating. Data from studies implemented between 2014 and 2022 were reviewed and 65 key indicators were selected for ten domains: government, food composition, settings (schools, hospitals), food labelling, food affordability, food promotion, food retail, private sector, trade and investment, and equity. Most domains were assessed as red and none as green. The Dashboard contributes to sharing and holding to account by providing key indicators in an accessible format that will be regularly updated. We encourage the public health nutrition community of practice to contribute to, and utilise, the Dashboard to improve food environments for Aotearoa.

The quality of sugarcane broth can be affected by soil management. In compacted soils the productivity is reduced, and the raw material is poor. This research aimed to evaluate productivity and quality of sugarcane for four soil management types: (CT) heavy harrow + light harrow; (CTI) Subsoiler + light harrow; (MT) Subsoiler and (NT) no soil movement. The variables investigated were resistance to soil penetration (SPR), the chemical raw material quality (broth) and crop yield. In crop rows, SPR did not reach severe levels up to 0.3 m for sugarcane root development. However, below this layer, MT, NT, and CTI reached SPR limiting values of 2.50, 2.35 and 1.95 MPa, respectively. In inter-crop rows, compaction was concentrated in soil surface layers (0–0.3 m). In addition, all adopted managements presented SPR above the critical value (2 MPa). The soil preparation forms qualitatively affected the sugarcane broth, showing higher fiber and protein contents in NT, MT, and CT. The PS, Brix, TRS, and Pol were not affected by soil management. Still, higher absolute values were found in the NT, indicating an increase in broth quality when applying conservation management. The highest yields were obtained by reduced tillage (MT), surpassing the lowest yield management (NT) with an increase of 10.5 Mg ha−1.

Animal models are used in preclinical trials to test vaccines, antivirals, monoclonal antibodies, and immunomodulatory drug therapies against SARS-CoV-2. However, these drugs often do not produce equivalent results in human clinical trials. Here, we show how different animal models infected with some of the most clinically relevant SARS-CoV-2 variants, WA1/2020, B.1.617.2/Delta, B.1.1.529/Omicron, and BA5.2/Omicron, have independent outcomes. We show that in K18-hACE2 mice, B.1.617.2 is more pathogenic, followed by WA1, while B.1.1.529 showed an absence of clinical signs. Only B.1.1.529 was able to infect C57BL/6J mice, which lack the human ACE2 receptor. B.1.1.529-infected C57BL/6J mice had different T cell profiles compared to infected K18-hACE2 mice, while viral shedding profiles and viral titers in lungs were similar between the K18-hACE2 and the C57BL/6J mice. These data suggest B.1.1.529 virus adaptation to a new host and shows that asymptomatic carriers can accumulate and shed virus. Next, we show how B.1.617.2, WA1 and BA5.2/Omicron have similar viral replication kinetics, pathogenicity, and viral shedding profiles in hamsters, demonstrating that the increased pathogenicity of B.1.617.2 observed in mice is host-dependent. Overall, these findings suggest that small animal models are useful to parallel human clinical data, but the experimental design places an important role in interpreting the data. Importance: There is a need to investigate SARS-CoV-2 variant phenotypes in different animal models due to the lack of reproducible outcomes when translating experiments to the human population. Our findings highlight the correlation of clinically relevant SARS-CoV-2 variants in animal models with human infections. Experimental design and understanding of correct animal models are essential to interpreting data to develop antivirals, vaccines, and other therapeutic compounds against COVID-19.

Clear cell renal cell carcinoma (ccRCC) is the main histotype of kidney cancer, which is typically highly resistant to conventional therapies and known for abnormal lipid accumulation. In this context, we focused our attention on miR-21, an oncogenic miRNA overexpressed in ccRCC, and peroxysome proliferator-activated receptor-α (PPAR- α), one master regulator of lipid metabolism targeted by miR-21. First, in a cohort of 52 primary ccRCC samples, using RT-qPCR and immunohistochemistry, we showed that miR-21 overexpression was correlated with PPAR-α downregulation. Then, in ACHN and 786-O cells, using RT-qPCR, the luciferase reporter gene, chromatin immunoprecipitation, and Western blotting, we showed that PPAR-α overexpression (i) decreased miR-21 expression, AP-1 and NF-κB transcriptional activity, and the binding of AP-1 and NF-κB to the miR-21 promoter and (ii) increased PTEN and PDCD4 expressions. In contrast, using pre-miR-21 transfection, miR-21 overexpression decreased PPAR-α expression and transcriptional activity mediated by PPAR-α, whereas the anti-miR-21 (LNA-21) strategy increased PPAR-α expression, but also the expression of its targets involved in fatty acid oxidation. In this study, we showed a double-negative feedback interaction between miR-21 and PPAR-α. In ccRCC, miR-21 silencing could be therapeutically exploited to restore PPAR-α expression and consequently inhibit the oncogenic events mediated by the aberrant lipid metabolism of ccRCC.

Within the Industry 4.0 revolution, manufacturing enterprises are transforming to intelligent enterprises constituted by Smart Manufacturing Systems (SMSs). A key capability of SMSs is the ability to connect and communicate with each other through Industrial Internet of Things technologies, and protocols with standard syntax and semantics. In this context, the GEMMA-GRAFCET Methodology (GG-Methodology) provides a standard approach and vocabulary for the management of the Operational Modes (OMs) of SMSs through the automation software, bringing a common understanding of the exchanged data. Considering the lack of tools to implement the methodology, this work introduces an online tool based on Model-Driven Engineering–GEMMA-GRAFCET Generator (GG-Generator)–to specify and generate PLCopen XML code compliant with the GG-Methodology. The proposed GG-Generator is applied to a case study and validated using Virtual Commissioning and Dynamic Software Testing. Due to the consistency obtained between the GG-Methodology and the generated PLC code, the GG-Generator is expected to support the adoption of the methodology, thus contributing to the interoperability of SMSs through the standardization of the automation software for the management of their OMs.

Adaptation is very important in IOT systems, due to their continuously changing environments. Changes may come from different elements of the architecture underlying an IOT system. Existing literature pays special attention to changes in the Service layer using evolution agents or context aware approaches to manage adaptations to said changes. In this paper, we elaborate on eight challenges that developers face when building adaptive IOT systems. Such challenges take into account changes at the Services layer, but also in the Middleware and Physical layers. These challenges serve us as a research agenda to foster IOT technology. As a starting point, we design an architecture to deal with the posited challenges. Various of the architectural components are inspired on a reference architecture, and complemented by new components to manage dynamic adaptations in response to the identified challenges. Preliminary experiments provide an initial insight about the feasibility and/or impact of our adaptive architecture.

The novel COVID-19, detected in Wuhan, China, has reached almost every city across the globe, and researchers from many countries have used several epidemiologic models to describe the epidemic trends. In this context, it is also important to know the geographic extent of the infected population. Following this approach, a Gumpertz model was adapted with official data from the state of Hidalgo, Mexico, in order to estimate the people infected during this COVID-19 pandemic. We found, based on the adjusted data, the highest value in infected people according to official and theoretical data. Furthermore, using a geographical analysis based on geostatistical measures related to density of demographic and economic data, traffic level and geolocation, raster files were generated to estimate probability of coronavirus cases occurrence using the areas where the contagion may occur. We also distributed the maximum contagion obtained by the epidemic model, using these raster files, and a regression model to weight factors according their importance. Based on this estimated distribution, we found that most of the infected people were located in the southern border, a trend related to the economic strip in the southern part of Hidalgo State, associated with its vicinity to the Megacity of Mexico.

Dendritic cells (DCs) are a type of cells derived from bone marrow that represent 1% or less of the total hematopoietic cells of any lymphoid organ or of the total cell count of the blood or epithelia. Dendritic cells comprise a heterogeneous population of cells localized in different tissues where they act as sentinels continuously capturing antigens to present them to T cells. Dendritic cells are uniquely capable of attracting and activating naïve CD4⁺ and CD8⁺ T cells to initiate and modulate primary immune responses. They have the ability to coordinate tolerance or immunity depending on their activation status, which is why they are also considered as the orchestrating cells of the immune response. The purpose of this review is to provide a general overview of the current knowledge on ontogeny and subsets of human dendritic cells as well as their function and different biological roles.