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Organic soil amendments are used to improve soil quality and mitigate climate change. However, their effects on soil structure, nutrient and water retention as well as greenhouse gas (GHG) emissions are still poorly understood. The purpose of this study was to determine the residual effects of a single field application of four ligneous soil amendments on soil structure and GHG emissions. We conducted a laboratory incubation experiment using soil samples collected from an ongoing soil-amendment field experiment at Qvidja Farm in south-west Finland, two years after a single application of four ligneous biomasses. Specifically, two biochars (willow and spruce) produced via slow pyrolysis, and two mixed pulp sludges from paper industry side-streams were applied at a rate of 9-22 Mg ha-1 mixed in the top 0.1 m soil layer. An unamended fertilized soil was used as a control. The laboratory incubation lasted for 33 days, during which the samples were kept at room temperature (21°C) and at 20%, 40%, 70% or 100% water holding capacity. Carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) fluxes were measured periodically after 1, 5, 12, 20 and 33 days of incubation. The application of ligneous soil amendments increased the pH of the sampled soils by 0.4-0.8 units, whereas the effects on soil organic carbon content and soil structure varied between treatments. The GHG exchange was dominated by CO2 emissions, which were mainly unaffected by the soil amendment treatments. The contribution of soil CH4 exchange was negligible (nearly no emissions) compared to soil CO2 and N2O emissions. The soil N2O emissions exhibited a positive exponential relationship with soil moisture. Overall, the soil amendments reduced N2O emissions on average by 13%, 64%, 28%, and 37%, at the four soil moisture levels, respectively. Furthermore, the variation in N2O emissions between the amendments correlated positively with their liming effect. More specifically, the potential for the pulp sludge treatments to modulate N2O emissions was evident only in response to high water contents. This tendency to modulate N2O emissions was attributed to their capacity to increase soil pH and influence soil processes by persisting in the soil long after their application., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Peltokangas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R 2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Petri Pellikka reports financial support was provided by Academy of Finland., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Cone beam computed tomography (CBCT) images are being increasingly used to acquire three-dimensional (3D) models of the skull for additive manufacturing purposes. However, the accuracy of such models remains a challenge, especially in the orbital area. The aim of this study is to assess the impact of four different CBCT imaging positions on the accuracy of the resulting 3D models in the orbital area., Methods: An anthropomorphic head phantom was manufactured by submerging a dry human skull in silicon to mimic the soft tissue attenuation and scattering properties of the human head. The phantom was scanned on a ProMax 3D MAX CBCT scanner using 90 and 120 kV for four different field of view positions: standard; elevated; backwards tilted; and forward tilted. All CBCT images were subsequently converted into 3D models and geometrically compared with a "gold-standard" optical scan of the dry skull., Results: Mean absolute deviations of the 3D models ranged between 0.15 ± 0.11 mm and 0.56 ± 0.28 mm. The elevated imaging position in combination with 120 kV tube voltage resulted in an improved representation of the orbital walls in the resulting 3D model without compromising the accuracy., Conclusions: Head positioning during CBCT imaging can influence the accuracy of the resulting 3D model. The accuracy of such models may be improved by positioning the region of interest ( e.g. the orbital area) in the focal plane (Figure 2a) of the CBCT X-ray beam.

Mountain birch forests (Betula pubescens Ehrh. ssp. czerepanovii) at the subarctic treeline not only benefit from global warming, but are also increasingly affected by caterpillar outbreaks from foliage-feeding geometrid moths. Both of these factors have unknown consequences on soil organic carbon (SOC) stocks and biogeochemical cycles. We measured SOC stocks down to the bedrock under living trees and under two stages of dead trees (12 and 55 years since moth outbreak) and treeless tundra in northern Finland. We also measured in-situ soil respiration, potential SOC decomposability, biological (enzyme activities and microbial biomass), and chemical (N, mineral N, and pH) soil properties. SOC stocks were significantly higher under living trees (4.1 ± 2.1 kg m²) than in the treeless tundra (2.4 ± 0.6 kg m²), and remained at an elevated level even 12 (3.7 ± 1.7 kg m²) and 55 years (4.9 ± 3.0 kg m²) after tree death. Effects of tree status on SOC stocks decreased with increasing distance from the tree and with increasing depth, that is, a significant effect of tree status was found in the organic layer, but not in mineral soil. Soil under living trees was characterized by higher mineral N contents, microbial biomass, microbial activity, and soil respiration compared with the treeless tundra; soils under dead trees were intermediate between these two. The results suggest accelerated organic matter turnover under living trees but a positive net effect on SOC stocks. Slowed organic matter turnover and continuous supply of deadwood may explain why SOC stocks remained elevated under dead trees, despite the heavy decrease in aboveground C stocks. We conclude that the increased occurrence of moth damage with climate change would have minor effects on SOC stocks, but ultimately decrease ecosystem C stocks (49% within 55 years in this area), if the mountain birch forests will not be able to recover from the outbreaks., (© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Climate warming is anticipated to make high latitude ecosystems stronger C sinks through increasing plant production. This effect might, however, be dampened by insect herbivores whose damage to plants at their background, non-outbreak densities may more than double under climate warming. Here, using an open-air warming experiment among Subarctic birch forest field layer vegetation, supplemented with birch plantlets, we show that a 2.3 °C air and 1.2 °C soil temperature increase can advance the growing season by 1-4 days, enhance soil N availability, leaf chlorophyll concentrations and plant growth up to 400%, 160% and 50% respectively, and lead up to 122% greater ecosystem CO 2 uptake potential. However, comparable positive effects are also found when insect herbivory is reduced, and the effect of warming on C sink potential is intensified under reduced herbivory. Our results confirm the expected warming-induced increase in high latitude plant growth and CO 2 uptake, but also reveal that herbivorous insects may significantly dampen the strengthening of the CO 2 sink under climate warming.

Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal. Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes, with mandibular canals being coarsely annotated by radiologists, and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation. We show that our deep learning model, trained on the coarsely annotated volumes, localises mandibular canals of the voxel-level annotated set, highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm, respectively. These unparalleled accurate results highlight that deep learning integrated into dental implantology workflow could significantly reduce manual labour in mandibular canal annotations.

Climate warming and organic matter decomposition are connected in a recursive manner; this recursion can be described by temperature sensitivity. We conducted a multifactorial laboratory experiment to quantify the temperature sensitivity of organic carbon (C) and nitrogen (N) decomposition processes of common boreal organic soils. We incubated 36 mor and 36 slightly decomposed Carex-Sphagnum peat samples in a constant moisture and ambient temperature for 6 months. The experiment included three temperature and two moisture levels and two food web manipulations (samples with and without fungivore enchytraeid worms). We determined the release of carbon dioxide (CO2) and dissolved organic carbon (DOC) in seven molecular size classes together with ammonium N and dissolved organic N in low molecular weight and high molecular weight fractions. The temperature sensitivity function Q10 was fit to the data. The C and N release rate was almost an order of magnitude higher in mor than in peat. Soil fauna increased the temperature sensitivity of C release. Soil fauna played a key role in N release; when fauna was absent in peat, the N release was ceased. The wide range of the studied C and N compounds and treatments (68 Q10 datasets) allowed us to recognize five different temperature sensitivity patterns. The most common pattern (37 out of 68) was a positive upwards temperature response, which was observed for CO2 and DOC release. A negative downward pattern was observed for extractable organic nitrogen and microbial C. Sixteen temperature sensitivity patterns represented a mixed type, where the Q10function was not applicable, as this does not allow changing the sign storage change rate with increasing or decreasing temperature. The mixed pattern was typically connected to intermediate decomposition products, where input and output fluxes with different temperature sensitivities may simultaneously change the storage. Mixed type was typical for N processes. Our results provide useful parameterization for ecosystem models that describe the feedback loop between climate warming, organic matter decomposition, and productivity of N-limited vegetation., Competing Interests: The authors have declared that no competing interests exist.

Fire is a major factor controlling global carbon (C) and nitrogen (N) cycling. While direct C and N losses caused by combustion have been comparably well established, important knowledge gaps remain on postfire N losses. Here, we quantified both direct C and N combustion losses as well as postfire gaseous losses (N 2 O, NO and N 2 ) and N leaching after a high-intensity experimental fire in an old shrubland in central Spain. Combustion losses of C and N were 9.4 Mg C/ha and 129 kg N/ha, respectively, representing 66% and 58% of initial aboveground vegetation and litter stocks. Moreover, fire strongly increased soil mineral N concentrations by several magnitudes to a maximum of 44 kg N/ha 2 months after the fire, with N largely originating from dead soil microbes. Postfire soil emissions increased from 5.4 to 10.1 kg N ha -1  year -1 for N 2 , from 1.1 to 1.9 kg N ha -1  year -1 for NO and from 0.05 to 0.2 kg N ha -1  year -1 for N 2 O. Maximal leaching losses occurred 2 months after peak soil mineral N concentrations, but remained with 0.1 kg N ha -1  year -1 of minor importance for the postfire N mass balance. 15 N stable isotope labelling revealed that 33% of the mineral N produced by fire was incorporated in stable soil N pools, while the remainder was lost. Overall, our work reveals significant postfire N losses dominated by emissions of N 2 that need to be considered when assessing fire effects on ecosystem N cycling and mass balance. We propose indirect N gas emissions factors for the first postfire year, equalling to 7.7% (N 2 -N), 2.7% (NO-N) and 5.0% (N 2 O-N) of the direct fire combustion losses of the respective N gas species., (© 2018 John Wiley & Sons Ltd.)

Purpose: Medical additive manufacturing requires standard tessellation language (STL) models. Such models are commonly derived from computed tomography (CT) images using thresholding. Threshold selection can be performed manually or automatically. The aim of this study was to assess the impact of manual and default threshold selection on the reliability and accuracy of skull STL models using different CT technologies., Method: One female and one male human cadaver head were imaged using multi-detector row CT, dual-energy CT, and two cone-beam CT scanners. Four medical engineers manually thresholded the bony structures on all CT images. The lowest and highest selected mean threshold values and the default threshold value were used to generate skull STL models. Geometric variations between all manually thresholded STL models were calculated. Furthermore, in order to calculate the accuracy of the manually and default thresholded STL models, all STL models were superimposed on an optical scan of the dry female and male skulls ("gold standard")., Results: The intra- and inter-observer variability of the manual threshold selection was good (intra-class correlation coefficients >0.9). All engineers selected grey values closer to soft tissue to compensate for bone voids. Geometric variations between the manually thresholded STL models were 0.13 mm (multi-detector row CT), 0.59 mm (dual-energy CT), and 0.55 mm (cone-beam CT). All STL models demonstrated inaccuracies ranging from -0.8 to +1.1 mm (multi-detector row CT), -0.7 to +2.0 mm (dual-energy CT), and -2.3 to +4.8 mm (cone-beam CT)., Conclusions: This study demonstrates that manual threshold selection results in better STL models than default thresholding. The use of dual-energy CT and cone-beam CT technology in its present form does not deliver reliable or accurate STL models for medical additive manufacturing. New approaches are required that are based on pattern recognition and machine learning algorithms.

Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists , that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top- k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple [Formula: see text] model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.

Motif recognition is a challenging problem in bioinformatics due to the diversity of protein motifs. Many existing algorithms identify motifs of a given length, thus being either not applicable or not efficient when searching simultaneously for motifs of various lengths. Searching for gapped motifs, although very important, is a highly time-consuming task due to the combinatorial explosion of possible combinations implied by the consideration of long gaps. We introduce a new graph theoretical approach to identify motifs of various lengths, both with and without gaps. We compare our approach with two widely used methods: MEME and GLAM2 analyzing both the quality of the results and the required computational time. Our method provides results of a slightly higher level of quality than MEME but at a much faster rate, i.e., one eighth of MEME's query time. By using similarity indexing, we drop the query times down to an average of approximately one sixth of the ones required by GLAM2, while achieving a slightly higher level of quality of the results. More precisely, for sequence collections smaller than 50000 bytes GLAM2 is 13 times slower, while being at least as fast as our method on larger ones. The source code of our C++ implementation is freely available in GitHub: https://github.com/hirvolt1/debruijn-motif.

Rising global temperatures may increase the rates of soil organic matter decomposition by heterotrophic microorganisms, potentially accelerating climate change further by releasing additional carbon dioxide (CO2) to the atmosphere. However, the possibility that microbial community responses to prolonged warming may modify the temperature sensitivity of soil respiration creates large uncertainty in the strength of this positive feedback. Both compensatory responses (decreasing temperature sensitivity of soil respiration in the long-term) and enhancing responses (increasing temperature sensitivity) have been reported, but the mechanisms underlying these responses are poorly understood. In this study, microbial biomass, community structure and the activities of dehydrogenase and β-glucosidase enzymes were determined for 18 soils that had previously demonstrated either no response or varying magnitude of enhancing or compensatory responses of temperature sensitivity of heterotrophic microbial respiration to prolonged cooling. The soil cooling approach, in contrast to warming experiments, discriminates between microbial community responses and the consequences of substrate depletion, by minimising changes in substrate availability. The initial microbial community composition, determined by molecular analysis of soils showing contrasting respiration responses to cooling, provided evidence that the magnitude of enhancing responses was partly related to microbial community composition. There was also evidence that higher relative abundance of saprophytic Basidiomycota may explain the compensatory response observed in one soil, but neither microbial biomass nor enzymatic capacity were significantly affected by cooling. Our findings emphasise the key importance of soil microbial community responses for feedbacks to global change, but also highlight important areas where our understanding remains limited., Competing Interests: The authors have declared that no competing interests exist.

Soils store about four times as much carbon as plant biomass, and soil microbial respiration releases about 60 petagrams of carbon per year to the atmosphere as carbon dioxide. Short-term experiments have shown that soil microbial respiration increases exponentially with temperature. This information has been incorporated into soil carbon and Earth-system models, which suggest that warming-induced increases in carbon dioxide release from soils represent an important positive feedback loop that could influence twenty-first-century climate change. The magnitude of this feedback remains uncertain, however, not least because the response of soil microbial communities to changing temperatures has the potential to either decrease or increase warming-induced carbon losses substantially. Here we collect soils from different ecosystems along a climate gradient from the Arctic to the Amazon and investigate how microbial community-level responses control the temperature sensitivity of soil respiration. We find that the microbial community-level response more often enhances than reduces the mid- to long-term (90 days) temperature sensitivity of respiration. Furthermore, the strongest enhancing responses were observed in soils with high carbon-to-nitrogen ratios and in soils from cold climatic regions. After 90 days, microbial community responses increased the temperature sensitivity of respiration in high-latitude soils by a factor of 1.4 compared to the instantaneous temperature response. This suggests that the substantial carbon stores in Arctic and boreal soils could be more vulnerable to climate warming than currently predicted.

Feedback to climate warming from the carbon balance of terrestrial ecosystems depends critically on the temperature sensitivity of soil organic carbon (SOC) decomposition. Still, the temperature sensitivity is not known for the majority of the SOC, which is tens or hundreds of years old. This old fraction is paradoxically concluded to be more, less, or equally sensitive compared to the younger fraction. Here, we present results that explain these inconsistencies. We show that the temperature sensitivity of decomposition increases remarkably from the youngest annually cycling fraction (Q10 < 2) to a decadally cycling one (Q10 = 4.2-6.9) but decreases again to a centennially cycling fraction (Q10 = 2.4-2.8) in boreal forest soil. Compared to the method used for current global estimates (temperature sensitivity of all SOC equal to that of the total heterotrophic soil respiration), the soils studied will lose 30-45% more carbon in response to climate warming during the next few decades, if there is no change in carbon input. Carbon input, derivative of plant productivity, would have to increase by 100-120%, as compared to the earlier estimated 70-80%, in order to compensate for the accelerated decomposition.

Many retroviral vectors for hematopoietic cell and other clinical gene therapy are derived from murine packaging cell lines. The exposure of these retroviruses and packaging cell lines to adult human serum (AS) inactivates them by complement and anti-alpha-galactosyl natural antibody-mediated mechanisms. We show that virus stability and infection efficiency of CRIP/BAG, a murine packaging cell line derived amphotropic retrovirus vector is reduced > 95% following a 30-min incubation in AS. This inactivation is prevented by replacing AS with umbilical cord serum (CS), wherein full retroviral transduction efficiency is maintained after 30 min of incubation. The loss of retrovirus transduction efficiency in AS was smaller upon blockage of anti-alpha-galactosyl antibodies with galactose alpha 1-3-galactose. Serum levels of CH 100, as well as C1q complement which inactivates retroviruses by an antibody-independent mechanism were similar in AS and CS. The high stability of CRIP/BAG retrovirus vector in CS is likely due to its lower levels of maternally derived anti-alpha-galactosyl antibodies. These results have implications for in vivo gene transfer in adults and also in newborns since neonates do not produce natural antibodies during the initial months of life.

Pollen of mugwort (Artemisia vulgaris L.) is the most important allergenic pollen in urban areas of south and central Finland in late summer. The purpose of this study was to investigate, experimentally, whether the cutting of mugwort stands affects its airborne pollen concentrations. Experimental plots were either cut (4 plots) or uncut (4 plots) in 2 previous seasons: 4 of them were small (less than 0.5 hectare) and 4 large (greater than 5 hectares). Finally, the plots were divided randomly into 2 groups according to a third variable, cutting in the study season, 1989. Samples were taken on 2 rainless mornings at the peak mugwort flowering time. Two rotorod type samplers were used at heights of 1 and 2 m from ground level, simulating the inhalation heights of children and adults, respectively. The results indicate that cutting mugwort stands significantly reduces airborne pollen concentrations, but the treated areas have to be large, since in the town area there are plenty of mugwort pollen sources. The pollen concentrations at the 2 heights tested did not differ significantly.