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Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a novel coronavirus that emerged from Wuhan, China in late 2019 causing coronavirus disease-19 (COVID-19). SARS-CoV-2 infection begins by attaching to angiotensin-converting enzyme 2 receptor (ACE2) via the spike glycoprotein, followed by cleavage by TMPRSS2, revealing the viral fusion domain. Other presumptive receptors for SARS-CoV-2 attachment include CD147, neuropilin-1 (NRP1), and Myeloid C-lectin like receptor (CLR), each of which might play a role in the systemic viral spread. The pathology of SARS-CoV-2 infection ranges from asymptomatic to severe acute respiratory distress syndrome, often displaying a cytokine storm syndrome, which can be life-threatening. Despite progress made, the detailed mechanisms underlying SARS-CoV-2 interaction with the host immune system remain unclear and are an area of very active research. The process’s key players include viral non-structural proteins and open reading frame products, which have been implicated in immune antagonism. The dysregulation of the innate immune system results in reduced adaptive immune responses characterized by rapidly diminishing antibody titers. Several treatment options for COVID-19 are emerging, with immunotherapies, peptide therapies, and nucleic acid vaccines showing promise. This review discusses the advances in the immunopathology of SARS-CoV-2, vaccines and therapies under investigation to counter the effects of this virus, as well as viral variants.

Soil moisture is a key factor in the reclamation of wetland habitats. Understanding the distribution and relative amount of water can be critical in reintroducing trees and grasses to disturbed soils. Soil moisture is also one of the main factors affecting microwave radar backscatter from the ground; while there are other factors determining backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, high resolution, multitemporal synthetic aperture radar (SAR). In this work, relative soil moisture indicators are derived from a time series of Sentinel-1 SAR data over previously mined oil sands in Alberta, Canada. The algorithm provides a relative assessment of soil moisture and requires calibration over wet and dry periods. An evaluation of the soil moisture product is validated using in situ measurements at multiple sites with observations showing agreement from May to August. Comparisons with precipitation records show that SAR derived surface soil moisture is influenced by discreet precipitation events; that is, rainfall that is coincident with the satellite observation reduces the effectiveness of the measurement. The resulting algorithm controls for rain events by including local weather records to adjust estimates based on the known precipitation.

The axis of Programmed cell death-1 receptor (PD-1) with its ligand (PD-L1) plays a critical role in colorectal cancer (CRC) in escaping immune surveillance, and blocking this axis has been found to be effective in a subset of patients. Although blocking PD-L1 has been shown to be effective in 5–10% of patients, the majority of the cohorts show resistance to this checkpoint blockade (CB) therapy. Multiple factors assist in the growth of resistance to CB, among which T cell exhaustion and immunosuppressive effects of immune cells in the tumor microenvironment (TME) play a critical role along with other tumor intrinsic factors. We have previously shown the polyketide antibiotic, Mithramycin-A (Mit-A), an effective agent in killing cancer stem cells (CSCs) in vitro and in vivo in a subcutaneous murine model. Since TME plays a pivotal role in CB therapy, we tested the immunomodulatory efficacy of Mit-A with anti-PD-L1 mAb (αPD-L1) combination therapy in an immunocompetent MC38 syngeneic orthotopic CRC mouse model. Tumors and spleens were analyzed by flow cytometry for the distinct immune cell populations affected by the treatment, in addition to RT-PCR for tumor samples. We demonstrated the combination treatment decreases tumor growth, thus increasing the effectiveness of the CB. Mit-A in the presence of αPD-L1 significantly increased CD8+ T cell infiltration and decreased immunosuppressive granulocytic myeloid-derived suppressor cells and anti-inflammatory macrophages in the TME. Our results revealed Mit-A in combination with αPD-L1 has the potential for augmented CB therapy by turning an immunologically “cold” into “hot” TME in CRC.

The burgeoning field of nanotechnology aims to create and deploy nanoscale structures, devices, and systems with novel, size-dependent properties and functions. The nanotechnology revolution has sparked radically new technologies and strategies across all scientific disciplines, with nanotechnology now applied to virtually every area of research and development in the US and globally. NanoFlorida was founded to create a forum for scientific exchange, promote networking among nanoscientists, encourage collaborative research efforts across institutions, forge strong industry-academia partnerships in nanoscience, and showcase the contributions of students and trainees in nanotechnology fields. The 2019 NanoFlorida International Conference expanded this vision to emphasize national and international participation, with a focus on advances made in translating nanotechnology. This review highlights notable research in the areas of engineering especially in optics, photonics and plasmonics and electronics; biomedical devices, nano-biotechnology, nanotherapeutics including both experimental nanotherapies and nanovaccines; nano-diagnostics and -theranostics; nano-enabled drug discovery platforms; tissue engineering, bioprinting, and environmental nanotechnology, as well as challenges and directions for future research.

Detection and quantification of cell viability and growth in two-dimensional (2D) and three-dimensional (3D) cell cultures commonly involve harvesting of cells and therefore requires a parallel set-up of several replicates for time-lapse or dose–response studies. Thus, developing a non-invasive and touch-free detection of cell growth in longitudinal studies of 3D tumor spheroid cultures or of stem cell regeneration remains a major unmet need. Since surface acoustic waves (SAWs) permit mass loading-based biosensing and have been touted due to their many advantages including low cost, small size and ease of assembly, we examined the potential of SAW-biosensing to detect and quantify cell growth. Herein, we demonstrate that a shear horizontal-surface acoustic waves (SH-SAW) device comprising two pairs of resonators consisting of interdigital transducers and reflecting fingers can be used to quantify mass loading by the cells in suspension as well as within a 3D cell culture platform. A 3D COMSOL model was built to simulate the mass loading response of increasing concentrations of cells in suspension in the polydimethylsiloxane (PDMS) well in order to predict the characteristics and optimize the design of the SH-SAW biosensor. The simulated relative frequency shift from the two oscillatory circuit systems (one of which functions as control) were found to be concordant to experimental data generated with RAW264.7 macrophage and A549 cancer cells. In addition, results showed that SAW measurements per se did not affect viability of cells. Further, SH-SAW biosensing was applied to A549 cells cultured on a 3D electrospun nanofiber scaffold that generate tumor spheroids (tumoroids) and the results showed the device's ability to detect changes in tumor spheroid growth over the course of eight days. Taken together, these results demonstrate the use of SH-SAW device for detection and quantification of cell growth changes over time in 2D suspension cultures and in 3D cell culture models, which may have potential applications in both longitudinal 3D cell cultures in cancer biology and in regenerative medicine.

The toxic side effects of doxorubicin (Dox) limit its long-term use as a lung cancer chemotherapeutic. Additionally, drug delivery to the deep lung is challenging. To address these challenges, isolated rat Sertoli cells (SCs) were preloaded with Dox conjugated to lipid micelle nanoparticles (SC-DLMNs) and delivered to mouse lungs. These immunocompetent cells, when injected intravenously, travel to the lung, deliver the payload, and get cleared by the system quickly without causing any adverse reaction. We observed that SC-DLMNs effectively treated Lewis lung carcinoma 1-induced lung tumors in mice and the drug efficacy was comparable to SC-Dox treatment. Mice treated with SC-DLMNs also showed significantly less toxicity compared to those treated with SC-Dox. The encapsulation of Dox in lipid micelle nanoparticles reduced the toxicity of Dox and the SC-based delivery method ensured drug delivery to the deep lung without evoking any immune response. Taken together, these results provide a novel SC-based nanoparticle drug delivery method for improved therapeutic outcome of cardiotoxic antilung cancer drugs.

Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. This paper presents a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The diagnostic process involves signal processing and statistical techniques for feature extraction, data reduction for implementation in memory-constrained electronic control units, and variety of fault classification methodologies to isolate faults in the regenerative braking system. The results demonstrate that highly accurate fault diagnosis is possible with the classification methodologies. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems.

The demand for data-driven models to inform sustainable transportation planning has become more important as countries address the complexities of urban mobility. However, data collection and curation are time-consuming and can be challenging due to data inaccessibility and inaccuracy. The Transport Starter Data Kit therefore aims to address these challenges, offering a one-stop-shop for transport modelling-related data. The Kit contains historical annual data (1990–2021) on passenger and freight activity, energy intensities, load factors, and vehicle stock, segregated by mode and fuel where available. Additionally, population and GDP data, which influence transport activity, are included. The value of the dataset lies not only in the range of variables it offers but also in the compilation from multiple authoritative sources, providing researchers, consultants, and policymakers interested in data-based transport modelling with a foundational base for their model development. By adopting, adapting, and applying the data, clear policies may be developed which can underpin the necessary finances for sustainable transport development.

Viet Nam is at a critical juncture in planning for its future energy mix due to its fast-growing economy and recent climate commitments. This paper reviews the opportunities and challenges towards a clean energy transition in Viet Nam. Focusing on the power system, this study incorporates the Open Source Energy Modelling System (OSeMOSYS) to explore six different energy scenarios based on existing plans and proposed targets: (i) Power Development Plan VII (revised); (ii) Power Development Plan VIII (draft); (iii) Renewable Energy Development Strategy; (iv) Renewable-Led Pathway; (v) Net Zero; and (vi) Clean Efficient Transition. These scenarios assess trade-offs and synergies of policy decisions, offering insights into potential energy mix alternatives through 2050. Key findings highlight that a fossil fuel phase-out by 2050, supported by approximately 1100 PJ of solar technologies and electricity imports by 2050, aligns with climate commitments and ensures energy security. Energy-efficient technologies could also reduce electricity consumption by 20%, cutting costs and emissions by USD 18 billion and 37 Mton CO2e, respectively. Based on modelling, this study emphasizes renewable energy prioritization, energy efficiency, fossil fuel phase-out, and reinforced emission limits as recommendations to allow Viet Nam to achieve its climate goals. Overall, this paper provides a comprehensive assessment that enhances transparency and reproducibility, supporting the optimal pathway for the development of Viet Nam’s energy sector.

Power sector and energy systems models are widely used to explore the impacts of demographic, socio-economic or policy changes on the cost and emissions of electricity generation. Technology cost and performance data are essential inputs to such models. Despite the ubiquity and importance of these parameters, there is no standardised database which collates the variety of values from across the literature, so modellers must collect them independently each time they populate or update model inputs, leading to duplicated efforts and inconsistencies which can profoundly influence model results. Technology cost and performance varies between countries, regions and over time, meaning that data must be country- or region-specific and frequently updated. Values also vary widely between sources, so obtaining a broad consensus view is critical. Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92 % of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5518 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.

Energy systems modelling plays a pivotal role in understanding and optimizing complex energy systems. By integrating various factors such as energy demand, supply, infrastructure, and environmental considerations, energy systems modelling provides valuable insights for policymakers, industry stakeholders, and researchers. This can be key to informing stakeholder and policy decisions and facilitate the mobilisation of capital and market development to support the development of the energy sector. This article presents the data, assumptions, and related calculations used for the development of a national scale power system model for Sierra Leone. The focus of the model was a techno-economic analysis of specific hydropower expansion plans. Where possible, this data has been collected from publicly available sources such as scientific literature, feasibility studies, international databases, data from existing modelling efforts in Sierra Leone, and local stakeholders. The collection of the data, development of the model, and creation of scenarios was done in collaboration with the Ministry of Energy in Sierra Leone and verified by a broad range of stakeholders.The following paper outlines the key data presented in the full database which can be accessed through the link found in the Specifications Table. The link also contains the Reference Energy System (RES) used in this study.

Managing complex behaviors in disability support settings requires competent de-escalation skills. However, the current training methods often lack sufficient opportunities for realistic practice. This study details a three-stage development and evaluation of a virtual reality (VR) application for disability support staff to safely build de-escalation skills through simulated interactions. The first phase involved creating VR prototype scenarios depicting escalations with adolescent clients. Next, 12 disability support experts conducted content validation by refining the scenarios to confirm appropriateness and realism. Finally, a pilot study tested the tool’s usability and examined the initial construct validity in 20 participants. The prototype achieved high usability ratings (average 81.0 (SD 10.7) on the System Usability Scale). Additionally, a positive correlation between VR performance and empathy levels, as measured using the Toronto Empathy Questionnaire, was found (Pearson’s r = 0.487, p = 0.035). The promising results highlight the VR application’s potential as a transformative training tool. Future research should expand scenario diversity and compare VR with traditional methods to establish its efficacy in diverse settings and offer a path to enhance staff and student capabilities in challenging environments.

The electrical power and land transportation systems of the Dominican Republic are facing significant challenges due to growing demand in both sectors. These two systems are responsible for around 62% of greenhouse gas (GHG) emissions. With the deployment of electric mobility, the power and transport sectors can synergise and contribute significantly to long-term sustainable planning. This article analyses the impact of the penetration of electric mobility programmed by the Dominican Republic's National Institute of Transit and Land Transport (INTRANT), the country's goal of reducing GHG emissions by 27% by 2030 and the decarbonisation process. Using the open-source modelling system OSeMOSYS, four scenarios are built to explore strategies that contribute to sustainable development in the long term. Meeting climate change commitments necessitates that all coal-fired plants in the country will be closed or transformed by 2040. Despite the positive role of electric mobility in reducing GHG, decarbonising the electricity sector while facing the energy crisis requires investments totalling around 16 billion USD. Failing to make these investments will incur expenses of approximately 6 billion USD on the user's side in the period from 2024 to 2030 to cover energy needs; this figure represents more than 270% of the amount that should be invested in the electrical power system to supply these users' needs.

Energy modelling plays a crucial role in assisting governmental and policymaking bodies to strategise long-term investments within the context of energy transition. Among the well-established open-source optimisation models, OSeMOSYS—the Open-Source Energy Modelling System—stands out. This paper introduces clicSAND, a novel user interface designed for OSeMOSYS, aimed at reducing the learning curve and supporting novice energy modelers in efficiently conducting long-term investment analyses. clicSAND, freely available and open-source, features a user-friendly Excel interface for data input, integrated solvers, and a visualisation dashboard for result interpretation. The outcomes, projected up to 2070, hold the potential to inform policy decisions and mobilise financial resources for sustainable development endeavors, such as ensuring affordable and secure energy supply and mitigating climate change impacts. This advancement not only democratises access to energy modelling tools but also empowers policymakers and stakeholders to conduct thorough long-term investment analyses with ease. This paper elaborates on clicSAND’s key advantages, architecture, and functionalities. Additionally, it discusses the evolutionary journey from clicSAND 1.0 to 3.0, emphasising a commitment to continuous improvement and user-centric adaptation, thereby enhancing its utility and relevance. The inclusion of a South African case study, conducted during the EMP-A (Energy Modelling Platform for Africa) 2021 international capacity-building event, showcases clicSAND’s efficacy in facilitating knowledge transfer and skill development among inexperienced users, while providing a tangible example of its application in addressing specific regional energy challenges and policy contexts. Finally, current applications and future extensions of the software are also presented.

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.

The land transport sector, impacting fossil fuel consumption, has been selected as one of the sectors to apply decarbonization strategies. Energy systems modelling is an applied tool to evaluate scenarios and strategies that can be implemented in the transport sector to achieve energy transitions. These energy modelling tools need a dataset that allows the simulation of alternative scenarios of the systems. For this purpose, a collection and processing of technical-economic data is needed to ensure a quality input for simulation tools. This article presents a set of open data to create a model of the energy system of the Dominican Republic to assess alternative scenarios and develop strategies to achieve the energy transition in the land transport sector. This exercise is performed to support the energy planning policies of the country. Although the dataset is presented for the conditions of the Dominican Republic, the insight regarding data gathering and processing can be applied to other island countries. The data obtained are an open-access database of energy regulators, generation agents, and representatives of the generation, transmission, and distribution sector, as well as websites, databases of international organizations, scientific journals, and standards. Therefore, the data presented can be updated as the technical-economic information becomes public.

At the time of writing, 759 million people (2019) still lack access to electricity globally. It is important for energy planning to describe plausible pathways to achieve national goals, using tools such as energy systems models to explore scenarios and provide insight. Until recently, modelling energy access in countries with a low electrification rate was conducted at low spatial (e.g., national) and/or temporal resolutions (e.g., annual time slices or ‘overnight’ electrification). In this paper, we develop methods in an open-source computational workflow with high spatial resolution in an open-source energy systems optimisation model. We use Kenya as our case application where approx. 16 million people still lack access to electricity (2019). One reference scenario and two diagnostic hypothetical scenarios are developed to assess the model. The spatial resolution of approximately 40 by 40 km cells leads to 591 demand cells split between electrified and un-electrified population. The results show that in the reference scenario, the optimal supply option for the unelectrified population is PV panels and batteries. At the same, an oversupply of the planned power plants is observed. The model can capture dynamics between spatially explicit supply options and central power plants in one model.

System models help policymakers to produce net zero decarbonization pathways. In 2019, Costa Rica launched its National Decarbonization Plan (NDP) – constructed using a detailed Open-Source energy Modelling System (OSeMOSYS-CR) model and simplified ones for non-energy sectors. The country then developed an integrated Climate, Land, Energy and Water (CLEW) model to capture synergies across sectors and guide future climate related processes. This resulted in the CLEW-CR model. In 2020, the country updated its Nationally Determined Contributions (NDCs), deploying a highly participatory process supported by the CLEW-CR model to guide the discussions with over 150 stakeholders. This paper presents the framework adopted to develop the CLEW-CR model (from data collection to scenarios creation and cost-benefit analysis) and describes its use to align its NDCs with its NDP. Over 15 different scenarios were produced to inform the country's dialogue during the updating process of its NDCs demonstrating the flexibility of this model to support multiple policy questions. This paper shows that deploying the NDC by 2030 and the NDP by 2050 would result in a net economic benefit of $55 billion – about 85 % of Costa Rica's GDP, highlighting the economic viability of Costa Rica's commitment to a sustainable future.

BackgroundResidents of rural areas have poorer health status, less healthy behaviours and higher mortality than urban dwellers, issues which are commonly addressed in primary care. Strengthening primary care may be an important tool to improve the health status of rural populations.ObjectiveSynthesize and categorize studies that examine interventions to improve rural primary care.Eligibility criteriaExperimental or observational studies published between January 1, 1996 and December 2022 that include an historical or concurrent control comparison.Sources of evidencePubmed, CINAHL, Cochrane Library, Embase.Charting methodsWe extracted and charted data by broad category (quality, access and efficiency), study design, country of origin, publication year, aim, health condition and type of intervention studied. We assigned multiple categories to a study where relevant.Results372 papers met our inclusion criteria, divided among quality (82%), access (20%) and efficiency (13%) categories. A majority of papers were completed in the USA (40%), Australia (15%), China (7%) or Canada (6%). 35 (9%) papers came from countries in Africa. The most common study design was an uncontrolled before-and-after comparison (32%) and only 24% of studies used randomized designs. The number of publications each year has increased markedly over the study period from 1-2/year in 1997-99 to a peak of 49 papers in 2017.ConclusionsDespite substantial inequity in health outcomes associated with rural living, very little attention is paid to rural primary care in the scientific literature. Very few studies of rural primary care use randomized designs.