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Globally, rising population and rapid urbanisation have resulted in the dual issues of increased electricity demand and waste generation. These exacerbate diverse global problems, ranging from irregular electricity supply and inadequate waste management systems to water/air/soil pollution, climate change, etc. Waste-to-Energy (WtE) approaches have been proposed and developed to address simultaneously these two issues through energy recovery from waste. However, the variety of available waste materials and different WtE technologies make the choice of an appropriate technology challenging for decision-makers. The evaluation of the different WtE technologies in terms of their sustainability could provide a solid comparative base for strategic decision making in the power and waste management domains. This paper presents research conducted using a multidimensional Life Cycle Sustainability Assessment (LCSA) approach to estimate and compare the environmental, economic, and social impacts associated with the generation of electricity from Municipal Solid Waste (MSW) in two major cities, Lagos and Abuja, in Nigeria. These cities provide case studies in a developing world context to explore how their similarities and differences may influence the LCSA impacts for four WtE systems (Anaerobic Digestion, Incineration, Gasification, and Landfill Gas to Energy), and this is the first research of its kind. An LCSA ranking and scoring system and a muti-attribute value theory (MAVT) multi-criteria decision analysis (MCDA) were employed to evaluate the overall sustainability of the prospective use of WtE over a 20-year timeframe. The results from both approaches indicated that the adoption of WtE offered sustainability benefits for both cities, marginally more so for Lagos than Abuja. It was concluded that, for optimal benefits to be achieved, it is vital for decision-makers to think about the various trade-offs revealed by this type of analysis and the varying priorities of relevant stakeholders.

Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales.

This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the distribution of the data. This poses challenges for higher-level algorithms which use the hyperspectral data (e.g., those that map the environment). Feature-learning mitigates this by projecting the data into a lower-dimensional space where the important information is either preserved or enhanced. In many applications, the amount of labelled hyperspectral data that can be acquired is limited. Hence, there is a need for feature-learning algorithms to be unsupervised. This work proposes unsupervised techniques that incorporate spectral measures from the remote-sensing literature into the objective functions of autoencoder feature learners. The proposed techniques are evaluated on the separability of their feature spaces as well as on their application as features for a clustering task, where they are compared against other unsupervised feature-learning approaches on several different datasets. The results show that autoencoders using spectral measures outperform those using the standard squared-error objective function for unsupervised hyperspectral feature-learning.

Cellular senescence is an irreversible side effect of some pharmaceuticals which can contribute to tissue degeneration. Objective To determine whether pharmaceutical glucocorticoids induce senescence in tenocytes. Methods Features of senescence (β-galactosidase activity at pH 6 (SA-β-gal) and active mammalian/mechanistic target of rapamycin (mTOR) in cell cycle arrest) as well as the activity of the two main pathways leading to cell senescence were examined in glucocorticoid-treated primary human tenocytes. Evidence of senescence-inducing pathway induction in vivo was obtained using immunohistochemistry on tendon biopsy specimens taken before and 7 weeks after subacromial Depo-Medrone injection. Results Dexamethasone treatment of tenocytes resulted in an increased percentage of SA-βgal-positive cells. Levels of phosphorylated p70S6K did not decrease with glucocorticoid treatment indicating mTOR remained active. Increased levels of acetylated p53 as well as increased RNA levels of its pro-senescence effector p21 were evident in dexamethasone-treated tenocytes. Levels of the p53 deacetylase sirtuin 1 were lower in dexamethasone-treated cells compared with controls. Knockdown of p53 or inhibition of p53 activity prevented dexamethasone-induced senescence. Activation of sirtuin 1 either by exogenous overexpression or by treatment with resveratrol or low glucose prevented dexamethasone-induced senescence. Immunohistochemical analysis of tendon biopsies taken before and after glucocorticoid injection revealed a significant increase in the percentage of p53-positive cells (p=0.03). The percentage of p21-positive cells also tended to be higher post-injection (p=0.06) suggesting glucocorticoids activate the p53/p21 senescence-inducing pathway in vivo as well as in vitro. Conclusion As cell senescence is irreversible in vivo, glucocorticoid-induced senescence may result in long-term degenerative changes in tendon tissue.

A novel high‐resolution, non‐destructive method for diagenetic screening of fossil corals for geochronologic and paleoclimatic studies using neutron computed tomography (NCT) is proposed. NCT circumvents limitations of traditional techniques, such as destructive sampling and 2‐D imaging by providing detailed 3‐D visualizations of coral structure and carbonate mineral phases. This method differentiates aragonite and calcite phases in fossil coral, crucial for identifying well‐preserved sections suitable for dating and paleoclimatic reconstructions. A key advantage of NCT is its ability to map hydrogen content, providing a reliable indicator for identifying regions of well‐preserved skeletal aragonite, since aragonite typically retains more water organic‐matter than calcite. NCT scans conducted on a Holocene Porites coral (ca. 1.36–1.87 ka BP) from Muschu Island, Papua New Guinea, successfully distinguished between secondary low‐magnesium calcite and aragonite skeletal material. This technique was also applied to an Isopora palifera fossil coral (ca. 39.4 to 44.8 ka BP) from Ashmore Reef, Northwest Shelf, Australia, which presented a more complex diagenetic history. Comparisons were made with results from hyperspectral imaging, X‐Ray CT, scanning electron microscopy, and geochemical and petrological analyses, following calibration using a modern Porites coral from One Tree Reef, Southern Great Barrier Reef, Australia. Additionally, NCT was applied to an altered Acropora humilis coral (ca. 600 ± 280 ka BP) from Ribbon Reef 5, Great Barrier Reef, revealing small, hidden aragonite sections undetected by surficial hyperspectral imaging. This study demonstrates the advantages of combining NCT with traditional screening methods in identifying well‐preserved aragonite for accurate geochronologic and paleoclimatic reconstructions. Recommendations for applying NCT in fossil coral screening are provided.

Climate change-related impacts have hampered the productivity of agricultural lands in recent times, affecting food security globally. Novel technology-based agricultural production systems such as controlled-environment agriculture (CEA) are a way to reduce the impact of climatic variation and pests that harm current global crop production and ensure consistent crop development. These systems often use artificial lighting and soilless mediums to produce crops. This meta-analysis has investigated the key influencing factors on crop production within these systems, using previous studies on lettuce (the most cultivated crop in these systems) to understand what affects yield within CEA. This analysis has found that on average, CEA systems yield twice that of field-based agriculture (3.68 kg m−2 vs. 1.88 kg m−2), with the most influencing factors being the variety of cultivars grown, the season, the nutrient delivery method, and the lighting type. The cultivation time for this study was 40 days, with 94% of papers having trial periods of 70 days or less, much lower than field-based agriculture (60–120 days). Vertical farming (stacked vertical CEA cultivation) studies were found to especially drive up yield per area (6.88 kg m−2). The results of this meta-analysis are useful for starting to understand the key influencing factors on CEA growth and highlight the breadth of research ongoing in the CEA industry.

Background Glucocorticoid injection (GCI) and surgical rotator cuff repair are two widely used treatments for rotator cuff tendinopathy. Little is known about the way in which medical and surgical treatments affect the human rotator cuff tendon in vivo. We assessed the histological and immunohistochemical effects of these common treatments on the rotator cuff tendon. Study design Controlled laboratory study. Methods Supraspinatus tendon biopsies were taken before and after treatment from 12 patients undergoing GCI and 8 patients undergoing surgical rotator cuff repair. All patients were symptomatic and none of the patients undergoing local GCI had full thickness tears of the rotator cuff. The tendon tissue was then analysed using histological techniques and immunohistochemistry. Results There was a significant increase in nuclei count and vascularity after rotator cuff repair and not after GCI (both p=0.008). Hypoxia inducible factor 1α (HIF-1α) and cell proliferation were only increased after rotator cuff repair (both p=0.03) and not GCI. The ionotropic N-methyl-d-aspartate receptor 1 (NMDAR1) glutamate receptor was only increased after GCI and not rotator cuff repair (p=0.016). An increase in glutamate was seen in both groups following treatment (both p=0.04), while an increase in the receptor metabotropic glutamate receptor 7 (mGluR7) was only seen after rotator cuff repair (p=0.016). Conclusions The increases in cell proliferation, vascularity and HIF-1α after surgical rotator cuff repair appear consistent with a proliferative healing response, and these features are not seen after GCI. The increase in the glutamate receptor NMDAR1 after GCI raises concerns about the potential excitotoxic tendon damage that may result from this common treatment.

Alaska has diverse boreal ecosystems across heterogeneous landscapes driven by a wide range of biological and geomorphic processes associated with disturbance and successional patterns under a changing climate. To assess historical patterns and rates of change, we quantified the areal extent of ecotypes and the biophysical factors driving change through photo-interpretation of 2200 points on a time-series (∼1949, ∼1978, ∼2007, ∼2017) of geo-rectified imagery for 22 grids across central Alaska. Overall, 68.6% of the area had changes in ecotypes over ∼68 years. Most of the change resulted from increases in upland and lowland forest types, with an accompanying decrease in upland and lowland scrub types, as post-fire succession led to mid- and late-successional stages. Of 17 drivers of landscape change, fire was by far the largest, affecting 46.5% of the region overall from 1949 to 2017. Fire was notably more extensive in the early 1900s. Thermokarst nearly doubled from 3.9% in 1949 to 6.3% in 2017. Riverine ecotypes covered 7.8% area and showed dynamic changes related to channel migration and succession. Using past rates of ecotype transitions, we developed four state-transition models to project future ecotype extent based on historical rates, increasing temperatures, and driver associations. Ecotype changes from 2017 to 2100, nearly tripled for the driver-adjusted RCP6.0 temperature model (30.6%) compared to the historical rate model (11.5%), and the RCP4.5 (12.4%) and RCP8.0 (14.7%) temperature models. The historical-rate model projected 38 ecotypes will gain area and 24 will lose area by 2100. Overall, disturbance and recovery associated with a wide range of drivers across the patchy mosaic of differing aged ecotypes led to a fairly stable overall composition of most ecotypes over long intervals, although fire caused large temporal fluctuations for many ecotypes. Thermokarst, however, is accelerating and projected to have increasingly transformative effects on future ecotype distributions.

Satellite-observed night-time light (NTL) data provide a measure of the lighting brightness seen from space at different times and spatial and temporal resolutions, thus offering opportunities to explore them in many applications at different spatial locations (global, regional, local). However, most applications to date have been at relatively large spatial scales, despite the need to measure indicators at a local level. This paper sets out an analysis of the potential of NTL data for populating indicators at more local (neighbourhood, street) scales. We first reviewed the overall potential of NTL data for social indicators at different spatial scales by using a systematic search of the literature and applying the Maturity Matrix Framework (MMF). We also explored a case study (Durango State, Mexico) using Visible Infrared Imaging Radiometer Suite (VIIRS) imageries, other geospatial data, and the social gap index (SGI) to identify social gaps at the local scale. The literature review showed that NTL can play a role in supporting 49 out of 192 sustainable development goal (SDG) indicators having a focus on social issues, but most of these have been explored at the global or country scales. In the case study, we found that low radiance is indeed associated with higher SGI levels (i.e., more social deprivation) and vice versa. However, more research is needed from other contexts to support a link between NTL radiance levels and social indicators at local scales.

Climate change has accelerated the degradation of agricultural land, prompting innovation to develop and adapt current global production systems to accommodate more people with increased demand for resources. Novel technologies such as vertical farming offer an opportunity to secure climate‐resilient food production. This study used Life Cycle Assessment to examine how the environmental impact of lettuce production in a commercial vertical farm compares with traditional field farming based on two contrasting UK farms and a Spanish farm. The vertical farm was found to have higher emissions in all impact categories except for water use; however, when using renewable energy sources, vertical farming was found to have higher, yet more comparable greenhouse gas emissions to field farming (0.93 kg CO2eq kg−1 lettuce (VF), 0.58 kg CO2eq kg−1 lettuce (UK 1 + 2)). Energy use (electricity or diesel), the choice of substrate, and soil emissions were the biggest hotspots for lettuce production in this study. Yields per area in vertical farming systems, however, were much higher than the field farming scenarios (97.3 kg m−2 (VF), 3.3 kg m−2 (average of field farms)), and the land sparing potential of vertical farming systems offers an opportunity to use spared land to potentially reap other environmental benefits while securing food production.