Impacts of acacia longifolia invasion on soil nutrient cycles : from invasion to solution

Anthropogenic alterations of nutrient cycles and the global redistribution of plant species have a profound impact on soils and the ecosystems relying on them. Invasive woody legumes can engineer oligotrophic ecosystems towards increased biomass production and nutrient turnover. This increased biomass is detrimental to native ecosystems, but could also be useful as a compost feedstock for agricultural purposes. The aim of this study was to observe soil changes after Acacia longifolia invasion in Portuguese dune systems, using Corema album foliar δ15N as a tracer for invasion impact. It provides novel insight into initial invasion of nursery shrubs and changes in soil fractions. It also relates community-scale aboveground invasion impact with organic matter pools and fluxes underneath A. longifolia canopy, compared with the native legume S. spectabilis. Furthermore, A. longifolia compost was evaluated for the first time as a potential agricultural soil amendment. Degradation of this compost in soil was observed in controlled conditions and its effects on maize growth and kernel quality studied in an urban garden setting. A. longifolia dune invasion increases the siltclay fraction, root and rhizosphere biomass. Increased soil phosphorus cycling and lower tissue phosphorus concentrations create an N/P imbalance in the oligotrophic system. Co-composted A. longifolia litter/biomass and biomass compost alone are increasing soil microbial activity. Biomass compost has beneficial effects on maize growth and provides, mixed with nutrient-rich compost, maize productivity levels comparable to mineral fertilization. Further results show open-pollinated maize varieties exhibit increased kernel micronutrient concentrations under compost fertilization, compared to hybrid maize. Summarized, N/P co-limitation needs to be considered when observing oligotrophic ecosystem invasion. Also, belowground processes precede aboveground effects, making early removal of young plants imperative for ecosystem conservation. Composted biomass can be safely employed as a soil amendment with beneficial effects on various soil and plant parameters, potentially encouraging future eradication

Ecological complexity effects on thermal signature of different Madeira island ecosystems

From a systemic perspective, evolution and natural succession promote the creation of efficient biological structures and processes that capture and dissipate the solar energy, maximizing the entropy production. This ecological complexification results in better ecosystem thermodynamic performance indicated by lower tem- perature. In a brief period of evolutionary time human-induced disturbance has altered profoundly the structure and functioning of the Earth System, i.e. ecological simplification. The objective is to understand whether remote sensing data can be considered appropriate proxy indicators to test if more mature and complex ecosystems have higher entropy production rates which lead to lower and attenuated ecosystem temperatures. Simple remote sensing measurements of Madeira Island for Thermal Infrared Radiation and Normalized Difference Vegetation Index were used to analyse the surface temperature and biomass cover of Madeira eco- systems spectrum of different states of human-induced disturbance. The findings revealed it was possible to distinguish between ecosystem types using thermodynamic in- dicators, where older ecosystems with more complex structures exhibit more attenuated lower average tem- peratures. It was also found that habitat heterogeneity can represent either artificial (human) or natural disturbance with opposite consequences in the ecosystem thermal signature, i.e. lower temperature when natural disturbance and higher if anthropogenic disturbance. Simple thermal remote sensing data can be used as systemic indicator of ecosystem health by reflecting it levels of eco-exergy, i.e the available work energy in the ecosystem.info:eu-repo/semantics/publishedVersio

Retrospective Evaluation of Implants Placed in Iliac Crest Autografts and Pristine Bone.

OBJECTIVE Iliac crest autografts can compensate for severe mandibular atrophy before implant placement. However, the implant success in the augmented bone is not entirely predictable. Here we performed a retrospective cohort study to determine the success and related parameters of implants placed in augmented bone and pristine bone for up to 11 years. MATERIAL AND METHODS We analyzed 18 patients where 72 implants were placed six months after iliac crest transplantation and 19 patients where 76 implants were placed in pristine bone. The primary endpoint was implant loss. Secondary endpoints were the implant success, peri-implant bone loss, and the clinical parameters related to peri-implantitis. Moreover, we evaluated the oral-health-related quality of life (OHIP). RESULTS Within a mean follow-up of 5.8 ± 2.2 and 7.6 ± 2.8 years, six but no implants were lost when placed in augmented and pristine bone, respectively. Among those implants remaining in situ, 58% and 68% were rated as implant success (p = 0.09). A total of 11% and 16% of the implants placed in the augmented and the pristine bone were identified as peri-implantitis (p = 0.08). Bone loss was similar in both groups, with a mean of 2.95 ± 1.72 mm and 2.44 ± 0.76 mm. The mean OHIP scores were 16.36 ± 13.76 and 8.78 ± 7.21 in the augmentation and the control group, respectively (p = 0.35). CONCLUSIONS Implants placed in iliac crest autografts have a higher risk for implant loss and lower implant success rates compared to those placed in the pristine bone

From a Lose–Lose to a Win–Win Situation: User-Friendly Biomass Models for Acacia longifolia to Aid Research, Management and Valorisation

Woody invasive species pose a big threat to ecosystems worldwide. Among them, Acacia longifolia is especially aggressive, fundamentally changing ecosystem structure through massive biomass input. This biomass is rarely harvested for usage; thus, these plants constitute a nuisance for stakeholders who invest time and money for control without monetary return. Simultaneously, there is an increased effort to valorise its biomass, e.g., for compost, growth substrate or as biofuel. However, to incentivise A. longifolia harvest and usage, stakeholders need to be able to estimate what can be obtained from management actions. Thus, the total biomass and its quality (C/N ratio) need to be predicted to perform cost–benefit analyses for usage and determine the level of invasion that has already occurred. Here, we report allometric biomass models for major biomass pools, as well as give an overview of biomass quality. Subsequently, we derive a simplified volume-based model (BM ~ 6.297 + 0.982 × Vol; BM = total dry biomass and Vol = plant volume), which can be applied to remote sensing data or with in situ manual measurements. This toolkit will help local stakeholders, forest managers or municipalities to predict the impact and valorisation potential of this invasive species and could ultimately encourage its management.info:eu-repo/semantics/publishedVersio

Elucidation of bioinformatic-guided high-prospect drug repositioning candidates for DMD via Swanson linking of target-focused latent knowledge from text-mined categorical metadata

Duchenne Muscular Dystrophy (DMD)’s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations

Elucidation of bioinformatic-guided high-prospect drug repositioning candidates for DMD via Swanson linking of target-focused latent knowledge from text-mined categorical metadata.

Duchenne Muscular Dystrophy (DMD)s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations

Data from: N/P imbalance as a key driver for the invasion of oligothrophic dune systems by a woody legume

Oligotrophic ecosystems, previously considered to be more resilient to invasive plants, are now recognised to be highly vulnerable to invasions. In these systems, woody legumes show belowground ecosystem engineering characteristics that enable invasion, however, the underlying processes are not well understood. Using a Portuguese primary dune ecosystem as an oligotrophic model system, belowground biomass pools, turnover rates and stoichiometry of a native (Stauracanthus spectabilis) and an invasive legume (Acacia longifolia) were compared and related to changes in the foliage of the surrounding native (Corema album) vegetation. We hypothesized that the invasive legume requires less phosphorus per unit of biomass produced and exhibits an enhanced nutrient turnover compared to the native vegetation, which could drive invasion by inducing a systemic N/P imbalance. Compared with the native legumes, A. longifolia plants had larger canopies, higher SOM levels and lower tissue P concentrations. These attributes were strongly related to legume influence as measured by increased foliar N content and less depleted δ15N signatures in the surrounding C. album vegetation. Furthermore, higher root N concentration and increased nutrient turnover in the rhizosphere of the invader were associated with depleted foliar P in C. album. Our results emphasize that while A. longifolia itself maintains an efficient phosphorus use in biomass production, at the same time it exerts a strong impact on the N/P balance of the native system. Moreover, this study highlights the engineering of a belowground structure of roots and rhizosphere as a crucial driver for invasion, due to its central role in nutrient turnover. These findings provide new evidence that, under nutrient-limited conditions, considering co-limitation and nutrient cycling in oligotrophic systems is essential to understand the engineering character of invasive woody legumes

Preoperative buccal bone volume predicts long-term graft retention following augmentation in the esthetic zone: a retrospective case series.

OBJECTIVES Buccal bone augmentation in the esthetic zone is routinely used to achieve optimal clinical outcomes. Nonetheless, long-term data are sparse, and it is unknown how baseline buccal bone volume affects the retention of the augmented volume over time. MATERIAL AND METHODS This is a long-term follow-up retrospective case series. After a preoperative computed tomography scan, implants were placed in the anterior maxilla following guided bone regeneration, autogenous block grafting, or both. At the follow-up, patients received a computed tomography scan and a clinical examination. Buccal bone volume was the primary outcome. Buccal bone thickness, peri-implant, and esthetic parameters were secondary outcomes. RESULTS After a median follow-up of 6.7 years (interquartile range: 4.9-9.4), 28 implants in 19 patients (median age at augmentation: 43.3 years, interquartile range: 34.4-56.7, 53% female) were followed up. Preoperative buccal bone volume at baseline (V0 ) showed a moderate correlation to final buccal bone volume (Vt , rs  = 0.43) but a strong correlation to the absolute volumetric change (ΔV = Vt - V0 , rs  = -0.80). A linear mixed model for Vt had a large intercept of 91.39 (p < 0.001) and a rather small slope of 0.11 for V0 (p = 0.11). Observed differences between treatments were not statistically significant in the mixed model. V0 above 105 mm3 predicted a negative volume change (ΔV < 0) with a specificity of 100% and a sensitivity of 96%. CONCLUSIONS The results suggest higher gains in sites with lower V0 and point to a cutoff V0 above which the augmented volume is not retained long-term

Table2_Elucidation of bioinformatic-guided high-prospect drug repositioning candidates for DMD via Swanson linking of target-focused latent knowledge from text-mined categorical metadata.XLSX

Duchenne Muscular Dystrophy (DMD)’s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations.</p

Table3_Elucidation of bioinformatic-guided high-prospect drug repositioning candidates for DMD via Swanson linking of target-focused latent knowledge from text-mined categorical metadata.XLSX

Duchenne Muscular Dystrophy (DMD)’s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations.</p