Arthrospira sp.) in bread formulated with four wheat flours with different alveograph strengths. The flours used were Manitoba Flour (00/251), Ground-force wholemeal (Whole/126), Standard Bakery Flour (0/W105), and Organic Bakery Flour (2/W66). Powdered Spirulina biomass was used as a new ingredient with a high nutritional profile and bioactive compounds; incorporation was made at two levels: 1.5% and 2.5% of the flour amount. The same bread recipe was used for all formulations, but for the 1.5 and 2.5% variations, 6 g and 10 g of Spirulina were added, respectively. Antioxidant capacity increased with increasing microalgal biomass. The visual and taste attributes of the breads with microalgae underwent noticeable changes compared to their counterparts without microalgae. Biomass addition significantly (p < 0.05) affected bread weight and volume, and different trends were found based on the type of wheat flour. Spirulina-containing breads showed a greener coloration while the microalgae concentration was augmented. The moisture and texture were slightly affected by the addition of the biomass at both levels. The 2.5% concentration samples were well accepted in most cases by consumers, emphasizing the salty flavor as a pleasant feature. No significant sensory differences were observed between samples, and the acceptability index was always higher than 72%. The results show that Spirulina could be an environmentally friendly ingredient for the reformulation of nutritionally enhanced bread with a good texture that is well-accepted by consumers. Consumers within the EU are increasingly asking for natural and healthier food products, which are additive-free and environmentally friendly. The aim of this study was to assess the effects of Spirulina ( Arthrospira sp.) is a promising source of nutrients and biologically active compounds as it contains around 50–70% protein, 20% carbohydrates (polysaccharides), 5% lipids, 7% minerals , a* , and b* [blueness (−60) /yellowness (+60)]. Calibration was carried out using a standard white tile  provided by the manufacturer and the D65 illuminant, which approximates daylight. Three measurements were taken for each loaf in different points (top and sides), while just one for each slice. This represents a total sum of 24 crust color measurements and 8 measurements of crumb color for each formulation.The Texture Profile Analysis (TPA) was performed as described by Lafarga et al. . A TA.XTDigital pictures of the bread loaves and slices were taken with a digital camera, Digi Eye , to record the visual aspect  of the bread formulations.Thirty semi-trained panelists, 21 women and 9 men ranging from 18 to 65 years participated in the sensory sessions. These semi-trained panelists were familiar with the evaluated product and had evaluated previous similar products. The sensory evaluation was conducted following the methodology described by Millar et al.  with somTastings were performed on different days at the same time, with a control sample for each day. A small cup of water was provided to the panelists, so they could drink between sample tastings. The panelists were asked to evaluate the following parameters: texture, taste, and overall acceptability. A 9-point hedonic scale was used, 1 being the worst mark (extremely dislike) and 9 the best one (extremely like). Moreover, the purchase intention (PI) was assessed with a hedonic scale of 5 points where number 1 represented absolutely no intention to purchase and number 5 a clear purchase intention. The acceptability index (AI) was calculated according to Lucas et al. . The SenThe determination of in vitro protein digestibility of bread formulations and Spirulina biomass was carried out using the method described by Severini et al.  with somv/v) in 2 mL Eppendorf. The dilution was 1:2 (v/v) for control and 1:10 (v/v) for the samples containing Spirulina. Absorbance was read at 760 nm using a GENESYS 10S UV-Vis spectrophotometer . The TPC was determined in triplicate, and results were expressed as mg of gallic acid equivalents (GAE) per 100 g of dry weight (dw).The total phenolic content (TPC) of the bread samples was determined by the Folin–Ciocalteu method, following the protocol described by Lafarga et al.  with somTo determine the antioxidant activity of breads, the DPPH· scavenging activity assay was performed as previously described by Lafarga et al.  on the sp < 0.05). Physico-chemical, rheological, and nutritional results are expressed by mean ± standard deviation (SD) of 3 repetitions. The statistical analysis of the experimental data was completed with the JMP 13 software  using a two-way analysis of variance (ANOVA). The comparison of means was made according to the Tukey test with a significance level of 95% , while dough extensibility (L) also increased. It was proven that the refining and water content of the analyzed flours have important impacts on the technological properties. With this in mind, according to the needs of every flour and the desired finished product, it is very important to find the optimum level of water, and, of course, the following would be assessing an alveographic analysis of the flours with added microalgae.i et al.  reportedFlour farinographic mixing properties are shown in −4 J) presented the highest protein content (11.7%), followed by the wholemeal flour (W = 126 × 10−4 J), while the bakery flour (W = 105 × 10−4 J) presented the lowest protein content (7.9%). However, the organic bakery flour (W = 66 × 10−4 J), which was the weakest, presented a percentage of protein higher than that of the bakery flour (BF), probably due to the presence of other proteins different from gliadins and glutenins. As expected, the protein content increased proportionally with the addition of Spirulina powder (protein content of 65 ± 1%). The different flours showed moisture content, ranging from 6.9 to 11.6%. The addition of Spirulina powder (moisture 4.72 ± 0.1%) showed no effect on the moisture content of the flour due to the small amount used. The highest moisture values were found in the MF and BF samples, while OF had the lowest moisture content. The protein content significantly changed according to the type of flour, suggesting that a different protein network can be formed. Generally, the flour strength value is positively correlated with the gluten content and protein quality . For instance, strong flours, such as Manitoba, usually contain more gluten-forming proteins, which provide elasticity to the dough and a balanced elasticity is related to a higher volume of loaves . This reThe mixing properties of the flours allowed the adequate incorporation of water into the dough and the optimal mixing time to be determined. As water absorption was assessed on flour as is, it was expected that flour with the lowest moisture content would have the highest water absorption value. However, the flour water absorption is also related to protein content and quality; in fact, BF, which had the lowest protein content (7.9%), showed a water absorption significantly lower than MF, even if they had similar moisture. Dietary fiber has also been shown to have an important impact on water absorption in baked goods. According to the nutritional label information of these commercial flours, the one with the lowest fiber content is BF (2.9%), while WF and OF were the ones with higher fiber content . Accordingly, the WF and OF mixtures had the highest water absorption. Water absorption increased with the introduction of Spirulina, probably due to the exopolysaccharides (EPS) present in Spirulina’s external membrane. Similar results were observed by Montevecchi et al. , who repp < 0.05) reduced the development and stability time by about 25% and 50%, respectively. BF, in contrast, showed a short development time since it only took 2 min to arrive at 500 BU and the stability time was also very short, indicating a weak flour. In this case, the addition of Spirulina significantly increased (p < 0.05) the dough stability, probably due to the contribution of protein and fiber. However, in the present work, the leavening phase lasted 60 min, so the use of this flour may not represent a problem. The stability time of WF and OF samples containing Spirulina was significantly (p < 0.05) shorter compared to their respective controls, suggesting that Spirulina fiber somehow interfered with the protein network, like in the Manitoba flour [In the studied conditions, MF presented the longest development and stability time. This means it took a long time to reach the given consistency, but it also better withstood the mixing stress; this is typical of strong flours such as Manitoba. The enrichment with Spirulina significantly , accounting for a higher resistance to the pressure exerted by gases generated during leavening and for a higher elasticity [−4 J), its bran content penalized the gluten network of the dough, thus giving an intermediate-to-low volume expansion with respect to the other flours. Product development  is a key quality parameter in bread products. The MF-0 sample had the highest height, volume, and specific volume compared to the other control formulations. This is due to the higher alveographic strength of the Manitoba flour (W = 251 × 10asticity . The intThe bakery flour (BF), having low strength and a low protein content, resulted in breads with a low specific volume. Surprisingly, Spirulina addition at 2.5% significantly increased the specific volumes of WF and BF, probably due to an increase in protein. The organic bakery flour (OF) gave intermediately developed bread  characterized by the lowest specific volume and not affected by Spirulina addition at both levels. Achour et al.  enrichedAspects and colorimetric indices of the different bread samples are reported in p < 0.05) harder bread than the corresponding control (WF-0). Thus, in line with Garcia-Segovia et al. [Results of the texture profile analysis  of bread crumbs are shown in a et al.  and Lafaa et al. , the adda et al.  enrichedplatensis were considered satisfactory even if the marine flavor was perceived. No significant differences were observed based on Spirulina incorporation levels or flour strength in the acceptability index  lower digestibility. These results can be explained considering that there are multiple factors that affect protein digestibility in bread, including the characteristics of the flour , the tridimensional structure of the dough, kind of gluten, and baking process [In this study, the protein digestibility of Spirulina biomass was 78 ± 3%; the high value was due to the lack of cellulose in its cell wall. De Marco et al. , after iour used . For ins process . ConsideC. vulgaris resulted in an increase in bread TPC. However, the bread prepared with the commercial C. vulgaris powder presented a similar TPC than the control. Rózylo et al. [The total phenolic content and antioxidant capacity of bread samples are shown in o et al.  did not Lafarga et al.  evaluateIncorporation of Spirulina in bread greatly increased the antioxidant activity measured by the DPPH· assay, possibly due to the presence of microalga polyphenols. However, a previous gastro-intestinal in vitro digestion as the one performed by Lafarga et al.  would haThe antioxidant capacity of Spirulina was 200 ± 5 µm AAE/100 g dw. As previously reported by Goiris et al. , it is dIt is denoted that the addition of Spirulina biomass to products such as bread could be a reliable, safe, and rich source of both protein and compounds with specific functions, such as phenolic compounds, inducing antioxidative activity very beneficial for those who consume these products.P/L ratio, being the most resistant flours; furthermore, due to the increase in water absorption facilitated by the microalgae addition, these flours behave similarly to the most refined flours that had lower P/L ratios. However, the number of products launched on the market and achieving commercial success is very limited. The most promising food matrices for use as microalgae delivery vehicles, in terms of consumer acceptance, are baked goods, pasta, or condiments. The composition of microalgae and their easy mode of production make them a new and innovative food ingredient for human food staples in the future. On the other hand, Spirulina, due to its high protein content, its desirable essential amino acid profile, and other compounds such as polyphenols, vitamins, and minerals, can be considered as a nutraceutical ingredient. Thus, when added to other food matrices, it provides beneficial health effects such as the prevention of cardiovascular diseases thanks to its high content of antioxidant compounds. In addition, baked goods are particularly relevant, as they are regularly consumed almost everywhere in different presentations, are recognized as staple foods, and are classified as healthy by consumers. Microalgae are rich in sustainable and health-promoting compounds, and their use in food will undoubtedly increase in the coming years. In conclusion, Spirulina shows potential to improve not only traditional breads but also those with different proportions of protein, and it could thus be a nutritional alternative to enrich gluten-free baked goods. As evidenced in this study and in previous studies related to the fortification of foods with microalgae, whether bread or other cereal derivatives, microalgae can be an important source of protein and other bioactive compounds with specific functions in the bodies of consumers. As a source of protein, microalgae are more sustainable than conventional protein sources, such as animal and other vegetable sources, due to their low requirement of soil, water, and other resources compared to conventional farming and agriculture. In addition, by capturing CO2 during their respiration process, they contribute to the reduction in CO2 in the environment. However, more studies are needed to evaluate the effect of microalgal biomass on the physical, chemical, organoleptic, and bioactive properties of these foods.Overall, both the Manitoba flour and traditional baker’s flour had the most favorable results in terms of acceptability and sensory values. In protein digestibility, it was observed that traditional baker’s flour and wholemeal flour had the best performance. In terms of polyphenol content and antioxidant activity, there were similar results between the Manitoba and the wholemeal flour, with the traditional flour being even better. According to the literature reviewed, the results agree with previous studies. This makes it increasingly clear that microalgal biomass is of growing interest as a food ingredient. When it comes to the flour strength, it was shown that the alveograph provides information very valuable to comprehend how a dough could possibly behave. According to the literature reviewed, both wholemeal and organic flours, thanks to their meal content, had a higher Although the present study found interesting results, a limitation should be highlighted. The sample could be considered non-representative of the Spanish population. Therefore, we suggest that future research on the topic should be conducted with larger samples. Sensorially speaking, the visual and taste aspects have the greatest impact on consumers. Among the Western population, the consumption of microalgae is neither widespread nor customary, marking a limitation. The salty taste of microalgae being more or less pleasant depending on who consumes it also defines the percentage of use, decreasing the possibilities for its application in various products and/or recipes.


Multiple subject-invariant classifiers on different subdomains are then co-learned in a unified framework by minimizing local regression loss with low-rank regularization for considering the shared knowledge among local domains. In the inference stage, the learned local classifiers are discriminatively selected according to their importance of adaptation. Extensive experiments are conducted on two benchmark databases (DEAP and SEED) under two cross-validation evaluation protocols, i.e., cross-subject within-dataset and cross-dataset within-session. The experimental results under the 5-fold cross-validation demonstrate the superiority of the proposed method compared with several state-of-the-art methods.As an important branch in the field of affective computing, emotion recognition based on electroencephalography (EEG) faces a long-standing challenge due to individual diversities. To conquer this challenge, domain adaptation (DA) or domain generalization  techniques have been introduced into EEG-based emotion recognition to eliminate the distribution discrepancy between different subjects. The preceding DA or domain generalization (DG) methods mainly focus on aligning the global distribution shift between source and target domains, yet without considering the correlations between the subdomains within the source domain and the target domain of interest. Since the ignorance of the fine-grained distribution information in the source may still bind the DG expectation on EEG datasets with multimodal structures, multiple patches (or subdomains) should be reconstructed from the source domain, on which multi-classifiers could be learned collaboratively. It is expected that accurately aligning relevant subdomains by excavating multiple distribution patterns within the source domain could further boost the learning performance of DG/DA. Therefore, we propose in this work a novel DG method for EEG-based emotion recognition, i.e., Local Domain Generalization with low-rank constraint (LDG). Specifically, the source domain is firstly partitioned into multiple local domains, each of which contains only one positive sample and its positive neighbors and  In the field of affective computing research , automatUnfortunately, in some practical AER applications, the whole target data of interest may be unavailable in the stage of training a subject-specific classifier . In thisIn DA/DG, one major problem is how to reduce or eliminate the distribution discrepancy between different domains . First oConcerning the challenge of global domain shift, several works pay attention to semantic alignment or matching conditional distribution , 2017. TMotivated by the idea of subdomain adaptation, we propose in this work a Local Domain Generalization (LDG) scheme to implicitly align the relevant local domain distributions from a single source with that of the target domain. A key improvement of LDG over previous DG/DA methods is the capability of the fine-grained alignment of a domain shift by capturing the local discriminative structures in the source domain by excavating multiple subdomains as per each positive sample with its two k-NN subsets , we consider the local distribution structures of the source domain and their relevance with the target domain to further enhance the effectiveness and generalizability of the learned adaptation model. Our algorithm can adapt as much knowledge as possible from a certain source domain, even if the EEG features between domains are partially distinct but overlapping. To the best of our knowledge, there is no prior work imposing DG with multiple local domains on solving AER problems. The main contributions of this paper are summarized as follows.We propose a local domain generalization framework (LDG) for EEG-based emotion recognition by leveraging multiple structure-similar local domains from the source domain with multi-model distribution patterns. Using this framework, the capacity of MMD-based DA methods can be extended by excavating the local discriminative structures for each domain by aligning KNN-based local domain distributions.k1 positive and k2 negative neighbors. Multiple local classifiers can be, respectively, trained on each local domain. We then formulate a new objective function by imposing a nuclear-norm-based regularizer on the model matrix in the objective function to further enhance the discriminative capability of the learned local classifiers by exploiting the intrinsic discriminative structure in the source domain.We present a subdomain division strategy, i.e., splitting the source domain into multiple local domains, each of which is composed of each positive (exemplar) sample  and its An iterative optimization algorithm is presented for solving the objective of LDG that can be applied to EEG-based AER problems. The convergence of the optimization procedure can be guaranteed in terms of the proof of the proposed convergence theorem.Extensive experiments are conducted on two benchmark databases (DEAP and SEED) under two cross-validation evaluation protocols (cross-subject within-dataset and cross-dataset within-session). The remarkable experimental results show that our method outperforms other state-of-the-art methods on emotion recognition tasks.The rest of the paper is organized as follows. Section 2 reviews several related works in emotion recognition, DG, and subdomain adaptation. Section 3 introduces our LDG framework including the overall objective function, and then the optimization algorithm and its convergence analysis are successively provided in Section 4. Section 5 provides a series of experiments to evaluate the effectiveness of LDG for AER. Finally, we summarize the entire paper in Section 6.In recent decades, increasing attention has been given to emotion recognition with brain-computer interfaces BCI;  in the aWhile preceding methods have obtained remarkable achievements on EEG-based AER , the perIn some real DA-based AER applications, the whole target data of interest may be unavailable in the stage of training . In thisAs is known, a major task in vanilla UDA/DG methodology is to mitigate the domain discrepancy either by aligning the statistical moments ,b,c or bIt is worth noting that the discriminative structures could still be mixed up in extant subdomain adaptation schemes when the source (or target) domain presents a multimodal distribution structure : For a certain domain k1 positive nearest neighbor set k2 negative neighbor set p positive samples sn – p negative samples, one can reconstruct p local domains k2 negative neighbor set for each positive sample According to Definition 1, for any source domain Definition 2 : Let m local domains and tX as the training examples by alleviating the distribution difference between source and target domains.Definition 3 : In this scenario, the target domain is inaccessible in the training stage. Given m local domains a-th subdomain, the task of LDG is to co-learn multiple adaptive functions As is known, a major task in vanilla UDA/DG methodology is to diminish the domain discrepancy either by aligning the statistical moments , 2020 orAs far as we know, limited effort, however, has been witnessed in improving DA/DG performance by leveraging local knowledge among multiple subdomains from a single source. The ignorance of the fine-grained local discriminative structures may result in unsatisfying generalization capacity in DA/DG. Exploiting the relationships among multiple local domains to match their distribution divergences could not only align the global statistical distributions but also the local discriminative patterns. In many real applications, the local structure is more important than the global structure , and thek1 positive neighbors and k2 negative neighbors. Each local domain is expected to be relatively more similar and possess a simpler distribution structure. Then multiple subject-invariant local classifiers are co-learned on these local domains by minimizing a unified local regression loss. Instead of evaluating the importance of each classification model individually, LDG selects models in a collaborated mode for considering the shared knowledge among local domains by additionally introducing a nuclear-norm-based regularizer into the objective function. In the inference stage, the learned local classifiers are discriminatively selected and reweighted according to the distribution distance between each local domain and the target domain of interest.Motivated by this idea, we propose in this paper a novel domain generalization framework for EEG-based emotion recognition, i.e., Local Domain Generalization (LDG) with low-rank constraints. Under this framework, LDA is a relaxed extension of LDG, where the target domain of interest is provided during the training process. Specifically, the source domain of the auxiliary is firstly partitioned into multiple local domains, each of which contains only one positive sample  and its In the following sections, we will present the objective formulation of our framework followed by its effective optimization algorithm.In LDA/LDG learning, however, there still exists two challenges worthy to be effectively addressed: (1) how to divide one source into multiple local domains and (2) how to compute the weight of each sample in its local domain. Until now, little research has been reported to address these challenges for EEG-based emotion recognition through local regression learning by decomposing the source domain into multiple local domains. To address these challenges, in this section, we propose the general formulation of our framework underpinned by the robust local regression principle and the regularization theory. Concretely, our proposed method will possess several complementary characters, which can be combined into one unified optimization formulation so that a more effective target learning model and distribution alignment between local domains and the target domain can be jointly achieved.m local domains sX, we define the v-th  from (22), we have:The above formula is equivalent to:Therefore, we have proved the theorem. Because of the updating rule in Algorithm 1, the objective function shown in (5) monotonically decreases, and it is easy to see that the algorithm converges.m local classifiers. In the following sections, we will separately propose ways to effectively use these learned classifiers in two cases.After training the LDG, we get m local classifiers to achieve the prediction of the test sample by assigning different weights. Given a target sample x, the predictive label y can be obtained by the following formula.1. LDG: The first is a domain generalization scenario where the target domain samples are not available during training. The other is the domain adaptation scenario with a specific target domain in which we have unlabeled data in it during the training process. In the domain generalization scenario, under the premise that we have no prior information about the target domain, we can only fuse the LDA: When there is unlabeled data in the target domain, we can assign different weights to each local classifier by measuring the similarity between the target domain and each locality in the source domain to achieve a better prediction effect. In other words, when a certain local domain is closer to the target domain, we should assign a higher weight to the classifier trained on this subdomain, and vice versa.K is the number of samples in the target domain. By measuring the distance between the training sample and the target domain by the Maximum Mean Discrepancy (MMD), we get the following formula:Given a set of target domain samples vX, X are the v-th local source domain and target domain datasets respectively, and vX and X, and KH denotes a regenerative kernel Hilbert space. where jx by the following formula:Then we can predict the test sample In this section, we will conduct comprehensive experiments to validate the effectiveness of our method compared with several state-of-the-art ones.Two widely used benchmark databases, i.e., SEED  and DEAPAs a DG method, we compare our method with several representative domain generalization/adaptation methods, which can be summarized into the following two groups (here we only report the better models):Shallow learning methods: Undo-Bias , UML Fa, DICA M, LRE-SVMDeep learning methods: Deep subdomain adaptation network DSAN; , Deep doσ computed by the median heuristic as the kernel function for the kernel-based methods. For a fair comparison, all deep learning baselines use the same architecture  as the base classifier for DICA and SCA. The tunable hyper-parameters are selected according to labels from the source domain. We adopt the Gaussian kernel with a kernel bandwidth μ, α, and β that need to be determined beforehand in our objective (5) since they are employed to balance the importance of structure characterization and regularizers. Considering that parameter determination is a yet unaddressed open issue in the field of machine learning, we determine them by grid search in a heuristic way  in the grid σ is set to 1/d (d is the feature dimension).There are several vital parameters such as stic way . ConcretNote that different subjects even from the same dataset still have different EEG feature distributions due to their characteristics. We therefore conduct the so-called leave-one-out cross-validation strategy conducted also in Each subject from DEAP includes 180 samples belonging to three categories, i.e., 60 samples per class. Each subject from SEED contributes 2,775 samples, i.e., 925 samples per class and per session. Following the same strategy adopted by As is known, when the size of training data increases to infinity, the theoretical performance (about 33.33%) of the random prediction can be approximately approached by the real chance level . When tht-test, value of p > 0.05), the absolute accuracy is still rather low, which suggests that there still exists adverse impact incurred by substantial distribution discrepancies between different subjects.As observed from An interesting result that can be observed from Following the same settings in In this part, we will particularly evaluate our method LDG with deeply extracted features by comparing it with several recently proposed deep adaptation models. We additionally denote our method with deep features as LDG + ResNet101. As for other deep benchmarks, their opened source codes are directly borrowed to fine-tune the pre-trained models adopted in their works, respectively. Different from these deep adaptation models, which typically pursue gaining certain domain-invariant representations, our proposed method explores optimizing a domain-invariant recognition model with strong generalization ability from the single source domain to the unseen target. We expect our method leveraging the deeply extracted features can further upgrade the recognition performance with the proposed subdomain generalization strategy.As shown in i-th kernel matrix from the As an open problem, how to choose an effective kernel is a challenge for learning a kernel machine. Fortunately, the previously proposed multiple kernel learning (MKL) trick can be adapted to overcome this confusion. In the sequence, we further evaluate the performance improvement in our method via introducing MKL (denoted by LDG-mkl for short) for each subdomain, in which a new feature space spanned by multiple kernel projections will be first constructed. Specifically, given an empirical kernel function set In this subsection, we further evaluate the broad and consistent generalization capacity of our LDG method on cross-dataset emotion recognition. Intuitively speaking, cross-data generalization must be more challenging than cross-subject generalization due to the significant difference between datasets.xy” means domain generalization from the dataset x into the dataset y, and SI, SII, and SIII are, respectively, denoted as the Session I, Session II, and Session III from SEED. When DEAP is regarded as the source dataset, 2,520 data are sampled from DEAP and 2,775 data taken as the target datasets are, respectively, sampled from three different sessions  of SEED. When each session of SEED is taken as the source dataset, we resample 41,625 data from it as a training set and 180 samples from DEAP regarded as the target dataset. We report the mean generalization results on six cross-dataset in Following the same settings in It can be seen from the experimental results in Regarding the previously reported results in As shown in Since our LDG is an iterative algorithm, it is crucial to evaluate its convergence. We evaluate the convergence of the LDG algorithm by conducting several experiments for emotion recognition in three settings such as cross-subject within DEAP, DEAP→SI, and {SI, SII, SIII} → DEAP. We plotted the mean experimental results in To deal with cross-subject/dataset EEG-based emotion recognition tasks, we proposed a local domain generalization (LDG) framework. In multiple subdomain spaces, LDG aims at transferring local knowledge into target learning mainly by leveraging correlation knowledge among subdomain models via low-rank constraint on the local models, which discriminatively screens unimportant prior evidence in subdomains. The comprehensive experiments performed on two public datasets verify the effectiveness of LDG in dealing with cross-subject/dataset emotion recognition. In most scenarios, our LDG and LDG + Resnet101 obtain the best results or comparable performance concerning several representative baselines.Since the implementation of the LDG algorithm needs an iterative optimization procedure, how to improve the efficiency of LDG and seek a more efficient algorithm would be an issue worthy of further study in our future research. The unreliable and misleading pseudo-label strategy may be another potential problem in our LDG. Consequently, our successive work would be to explore seamlessly incorporating target labels into the framework of LDG.http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/eegdata.html and http://bcmi.sjtu.edu.cn/~seed.Publicly available datasets were analyzed in this study. This data can be found here: in our research, the datasets DEAP and SEED can be, respectively, accessed from DZ extensively conducted all experiments in the paper. All authors contributed to the article and approved the submitted version.


Magnetization promoted activity of magnetic catalysts towards the oxygen evolution reaction (OER) has attracted great attention, but remains a puzzle where the increment comes from. Magnetization of a ferromagnetic material only changes its magnetic domain structure. It does not directly change the spin orientation of unpaired electrons in the material. The confusion is that each magnetic domain is a small magnet and theoretically the spin-polarization promoted OER already occurs on these magnetic domains, and thus the enhancement should have been achieved without magnetization. Here, we demonstrate that the enhancement comes from the disappeared domain wall upon magnetization. Magnetization leads to the evolution of the magnetic domain structure, from a multi-domain one to a single domain one, in which the domain wall disappears. The surface occupied by the domain wall is reformatted into one by a single domain, on which the OER follows the spin-facilitated pathways and thus the overall increment on the electrode occurs. This study fills the missing gap for understanding the spin-polarized OER and it further explains the type of ferromagnetic catalysts which can give increment by magnetization. Magnetic field has been observed to promote oxygen evolution at some circumstance, however the reason for the enhancement remains unclear. Here, the authors show that enhancement is due to the disappearance of magnetic domain walls. Recently, it has been reported that OER on some magnetic catalysts can be promoted by applying an external magnetic field6. Such a phenomenon has attracted great interest crossing the fields of energy, materials, chemistry, and physics, and it is also under debate. Most recently, it has been confirmed by excluding other possible contributing factors, including Lorenz force-promoted mass transportation5, lowered resistance by magnetization6, and magnetic flux-induced bias7. On the other hand, the earlier theoretical study by Jose et al. has predicted that the spin-polarized electron transfer is a prerequisite for the fast generation of triplet oxygen from singlet reactants (H2O and OH−)9 and it can be facilitated on the surface of ferromagnetic catalysts where the open-shell spin interactions reduce the barrier of electron transfer11. The most recent progress by us has revealed the critical role of M-O radical species in magnetization-promoted OER12, which is concluded from the fact that the increment is pH-dependent. However, it should be noted that the magnetization of catalysts has no influence on the spin ordering of unpaired electrons in atoms in ferromagnetic materials, including the metal active sites13. Magnetization only leads to the evolution of magnetic structure in a ferromagnetic material from a multi-domain one towards a single-domain one14. Each magnetic domain is a small magnet, and theoretically, the spin-polarization promoted OER already occurs on these magnetic domains. Therefore, the enhancement is already achieved, and magnetization under an external magnetic field is not necessary for principle. A puzzle has been thus created, i.e., why magnetization under an external magnetic field can promote the OER, and what is the origin of the observed increment?Toward a carbon-zero energy infrastructure, it is critical to implement the techniques such as water electrolysis powered by sustainable energy resources for green hydrogen generation. However, the electrolysis efficiency is greatly limited by the sluggish anode kinetics in water electrolyzers, i.e., the oxygen evolution reaction (OER)15. With such strip-type domains of NiFe films, the domain walls can be clearly identified, and the area on the film surface occupied by the domain wall can be quantified. By reducing the thickness of the NiFe film, the magnetic domains shrink in width, and accordingly, the area occupied by the domain wall increases. Under a sufficiently strong magnetic field, e.g., a saturated field, the domain wall disappears, and the NiFe thin film electrodes evolve into a single domain state. A strong correlation has been found between the degree of OER enhancement under magnetization and the area of domain walls originally occupied on the thin film surface.Here, we demonstrate that the origin of the OER enhancement under the magnetic field (magnetization) is from the disappeared magnetic domain walls. Using a series of NiFe thin film electrodes with controlled domain wall size, we found a strong correlation between the area of domain walls occupied on the thin films and their OER increment under magnetization. NiFe thin films were prepared by the magnetron sputtering method. The film thickness is varied from 200 to 800 nm, in which the magnetic domains are in a stripe shape, and the neighboring domains adopt antiparallel magnetization. The spin configuration within each single strip domain is identical, and the OER on every single domain follows the spin-facilitated pathways as reported earlierThe NiFe thin films were prepared by radio frequency (RF) magnetron sputtering on Si substrates. The cross-section of NiFe thin films was observed under the scanning electron microscope (SEM). As shown in Fig. Mz|) to all magnetizations in NiFe films, which is plotted as a function of the film thickness  to zero fields can be found in all loops17. Such slope persists because the anisotropy field is introduced during the relaxation of strip domains18. In addition, we extract the remanence ratio Mr/Ms and saturation field Hs from hysteresis loops, which are plotted versus the film thickness in Supplementary Fig. Mr/Ms decreases while Hs increases gradually as the film thickness increases, which strongly indicates enhanced strip domain structures19. The results confirm the strip domains throughout the NiFe films and are consistent with what has been observed by MFM.The NiFe thin films were investigated under magnetic force microscopy (MFM). The magnetic domains in strip shape were observed, and the neighboring domains are in the opposite directions of magnetization (blue and red) with a domain wall (white) in between Fig. . Besidesess Fig. . The domlms Fig. , a typic2-saturated 1.0 M KOH. The field strength is held at 2000 Oe in the plane, which is strong enough for a complete in-plane magnetization of the NiFe films. All NiFe films were pretreated with 5 CV cycles to reach steady CV profiles before applying the magnetic field  of NiFe films with different thicknesses, in which a clear correlation is found. The magnetic domains are the basic unit in ferromagnetic materials that respond to the external magnetic field (a strong enough field). In NiFe films, without an applied magnetic field, the neighboring strip domains show magnetization in opposite directions, which gives a zero net magnetization. As illustrated in Fig. 23. The domain wall region becomes less in the overall thin film as the film becomes thicker, and the magnetic domain becomes bigger  magnetron sputtering on naturally oxidized Si (1 1 1) substrates at a common base pressure of <5 × 102) was exposed to 1.0 M KOH during electrochemical measurements. The electrochemical measurements were carried out by a Biologic SP-150 potentiostat. The KOH electrolyte with a concentration of 1.0 M was prepared using deionized water (>18 MΩ cm) and KOH pellets . Oxygen was bubbled to ensure O2/OH− equilibrium at 1.23 V vs. RHE. All potentials were converted to RHE scale with iR correction according to the equation: E (vs. RHE) = E (vs. Hg/HgO) + 0.059 × pH + 0.098-iR, where i is the current and R is the electrolyte resistance, determined by high-frequency AC impedance (~15 Ω for 1.0 M KOH). All electrochemical data were normalized using film surface area (Supplementary Table The OER tests were operated in a three-electrode cell with a NiFe film as the working electrode (WE), a platinum foil counter electrode, and a Hg/HgO 1.0 M KOH) reference electrode. The schematic diagram of working electrode preparation shows in Supplementary Fig. .0 M KOH −1, under Cu-Kα radiation (λ = 1.5418 Å). Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were performed using a HITACHI S5000 with an energy-dispersive analysis system, Bruker XFlash 6|60. DC magnetization measurements were performed on a Superconducting Quantum Design (SQUID) magnetometer (MPMS-XL). The SQUID measurements of the magnetization of samples as a function of the magnetic field were carried out at 300 K in fields between −2000 Oe and +2000 Oe. Film surface morphologies were examined by atomic force microscopy (AFM) under tapping mode (Asylum Research). The Cypher AFM/MFM manufactured by Asylum Research was performed for MFM experiments. The MFM tips used were PPP-MFMR from NANOSENSORS with 300 Oe coercivity, 300 emμ cm−3 magnetic moment, and magnetic resolution better than 50 nm, optimized for non-perturbative magnetic imaging with high spatial resolution.The X-ray diffraction (XRD) patterns of these films were recorded by a Bruker D8 diffractometer at a scanning rate of 2° min28. The micromagnetic simulations are based on the Landau-Lifshitz-Gilbert (LLG) equation with the magnetic Hamiltonian described asThe domain structures formed in thin films were investigated by micromagnetic simulation of a thin film model with different thicknesses, in which perpendicular magnetic anisotropy with slight tilt was applied. Micromagnetic simulations were performed using the mumax3 software packageIt should be noted that Mumax3 does not provide two different kinds of uniaxial anisotropy straightforwardly. Fortunately, the parameter 80Fe20 film29—the saturation magnetization 3, the exchange constant 3. The formation of stripe domains in NiFe films, in general, is caused by competing exchange interaction energy, perpendicular magnetic anisotropy energy, and demagnetization energy. When the thickness varies, the perpendicular magnetic anisotropy energy and demagnetization energy may both vary dependently. The demagnetization energy can be calculated automatically during the simulation. The perpendicular magnetic anisotropy energy should be set manually. Here we make an estimation to take the thickness-dependent perpendicular anisotropy into consideration: the perpendicular anisotropy of NiFe films increases linearly from 2.5 5 erg/cm3 to 4 5 erg/cm3 in the range of 200–800 nm19. The in-plane anisotropy was oriented along the x-axis, while the perpendicular anisotropy lay along the z-axis . The size of each discretization cell was 4 × 4 × 4 nm. We set a 2048 × 50 × N mesh with in-plane periodic boundary conditions to avoid the boundary effect. N = 50, 75, 100, 125, 150, 175, 200 correspond with the film thickness 200–800 nm. Note that, in our simulations, the length of the films along the x-axis must be much longer than the width of domains to reduce the impact of integer periods on magnetic domains. The approach can, consequently, accurately get the volume ratios of domain walls to the whole system. Starting with a random initial magnetic state, the system is applied with a small external magnetic field along the y-axis to speed up the evolution. Then the field is repealed to obtain the ground states. All the data were extracted from the ground states. Technical details on the image attached can be extracted from the mumax3 input file used to generate the data:We consider the model of a thin film with magnetic parameters corresponding to that of the typical permalloy NiMs: = 1000 // emu/cm3Msat = Ms*1000 // A/mA: = 1e−6 // erg/cmAex = A*1e−5 //J/mHin: = 6.5 // OeKin: = Hin * Ms/2 * 1e−1// J/m3Hp: = 517 // OeKp: = Hp * Ms/2 * 1e−1 // J/m3u1 = sqrt(Kin * Kin + Kp * Kp)KKin, 0, Kp)anisU = vectorSetGridSize SetCellSize m = randommag B_ext = vector Relax B_ext = vector Run (2e−8)Relax Save (m)Supplementary InformationPeer review file


Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant’s naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients’ comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities. In recent years, due to advancements in sensor technologies, there has been a rapid growth in the use of wearable devices to obtain high-resolution and objective measures of physical function and behavior in participants’ free-living environments, for both public health research and clinical trials . WearablAmong wearable devices, accelerometers, typically a key component of most wearable activity trackers and smartphones, are commonly used to track 24-h motor activity such as walking, climbing, cycling, and sleep ,3,4. WalThe location of the accelerometer on the body can impact the prediction of activity types . Lumbar-In drug development, it is of interest to select reliable and sensitive endpoints collected from the most appropriate body location. For example, to evaluate the treatment effect for Duchenne muscular dystrophy, stride velocity 95th percentile is considered a regulatory acceptable clinical endpoint , which cTo evaluate whether it is possible to avoid having to deploy both lumbar- and wrist-worn devices in a clinical study, we need to explore the association between gait and PA features measured at the two locations. Studies have demonstrated that specific gait features such as gait speed are associated with PA ,31,37. TThe purpose of this paper is to fill the gap by evaluating if gait features measured by lumbar-worn devices can be represented by PA features measured by wrist-worn devices. In the remainder of the paper, we first review multiple statistical models to evaluate the univariate and multivariate association between measurements from the gait domain and the PA domain. These methods range from simple feature-to-feature correlations, including linear regression analysis and principal component regression, to more complex multivariate dimension reduction methods, such as partial least square (PLS)  and joinA large number of algorithms have been proposed in the literature to evaluate the association or the joint information of data from different domains. In this section, we review and summarize commonly used methods in three parts: (1) feature-on-domain models, (2) domain-on-domain models, and (3) multilevel models. P dimension vector Q dimensional vector P features in the PA domain and Q features in the gait domain, respectively, for subject pth feature vector in PA domain and qth feature vector in PA domain. These vectors are stacked to form the PA feature domain We use the l models , the meaFirst, correlation and regression analysis are implemented to explore each feature individually from both gait and PA domains and its association with the other domain. The Pearson correlation coefficient is a measure of linear correlation between two sets of data, with the form of the covariance of the two variables divided by the product of their standard deviations. For each pair of features In addition to pairwise correlation evaluation, linear multiple regression models are included to further explore the linear associations between two domains, via regressing each univariate variable from the gait domain on the PA domain. Multicollinearity within the PA feature domain can be addressed with enforced sparsity via Lasso regressions, and the model selection is based on a five-fold cross-validation procedure incorporating ’one-standard-error’ rule, that is, choosing the model with the fewest coefficients that is less than one standard error away from the sub-model with the lowest error. Therefore, the penalized linear regression is utilized to explore the linear association between a single feature from the one measurement domain with multiple features from the other measurement domain, extending the one-on-one correlation analysis.Another common approach to address multicollinearity when analyzing multiple regression data is principal component regression (PCR) . SpecifiWe expand our modeling strategy to quantify the associations between the gait and PA domains using multivariate models. These models, referred to as domain-on-domain models, provide a more comprehensive analysis of the relationship between gait and PA features.pls. The resulting relation between gait and PA domains can be explained with the correlation and loading plots. Compared with the PCR model introduced in The partial least squares (PLS) regression  is a tecr and r.jive.JIVE has been proposed to deal with scenarios where different sources or views of the data are simultaneously available for the same set of samples . JIVE deThe above mentioned feature-on-domain and domain-on-domain models are all considered one level because they are implemented on the mean feature values recorded at subject level. They can be further extended to multilevel models which include repeated measures for each subject, since both lumbar-worn and wrist-worn devices are normally worn continuously for multiple days.The multilevel PLS regression  borrows To incorporate repeated measurements from multidomain data, Di et al. (2019) proposed multilevel JIVE  that comMultilevel JIVE utilizes a two-step procedure. First, a two-way ANOVA like decomposition is performed via MPCA to separate the joint variation into random between- and within-subject effects  study was conducted at the Pfizer Innovation Research (PfIRe) Lab in Cambridge, Massachusetts. The study recruited 65 healthy participants in total, with 33 of them in the younger (18–40) cohort ) Python library [Raw acceleration signals from lumbar-worn GENEActiv devices were used to derive gait features. Gait features were generated using the implementation in the open-source SciKit Digital Health (SKDH)  variation . In the We then compared the results from the one-level and multilevel models. The estimated lower dimension components from the one- and the multilevel PLS and JIVE are shown in In this paper, we systematically investigated the association between gait features measured with lumbar-worn accelerometers and PA features measured with wrist-worn accelerometers, which, to the best of our knowledge, is the first study of its kind. We applied multiple univariate and multivariate statistical models to data collected from the STRYDE study to evaluate the association based on the average feature values across days and repeated measurements across days. Our results showed that gait and PA are interrelated and share joint information. Specifically, gait quantity measures, such as total steps and total bout length, and gait speed show a higher correlation with PA features when compared to other gait features. As a result, they are more likely to be represented by a combination of PA features, including those summarizing peak activity during the day, such as maximum activity within the most active 6-, 15-, and 60-min windows, vigorous activity, and the 95th percentile of SVMg. This result has been corroborated by multiple univariate and multivariate methods, such as multilevel JIVE and multilevel PLS.We implemented multiple statistical models, including both univariate and multivariate methods which can be categorized into feature-on-domain analyses and domain-on-domain analyses. Specifically, the feature-on-domain analyses aim to reveal the association between each individual gait feature with all PA features. The domain-on-domain analyses aim to reveal the overall covariance and shared variation between the two domains of gait and PA. We specifically highlighted the use of JIVE, which has been previously used to explore the joint association among different physiological domains of PA, sleep, and circadian rhythms . JIVE caPrevious research has indeed established connections between specific gait characteristics and PA features. For instance, Wanigatunga et al. identified associations between faster gait speed and several PA metrics, including increased activity counts, daily active minutes, and reduced activity fragmentation . SimilarThe association between gait features and PA features identified in this work provides preliminary evidence that a single wrist-worn device can be deployed in a clinical trial. Specifically, as our results suggest, total gait quantity and gait speed exhibit a strong association with PA features, which indicates that they can be analytically represented by PA features. Furthermore, in scenarios where the primary interest lies in assessing total gait quantity or gait speed, PA features hold potential as surrogate endpoints. From an operational point of view, deploying a single wrist-worn, watch-like device will increase compliance and reduce data loss due to difficulty in wearing the devices. This will be a good step further toward decentralized clinical trials and more patient-centric clinical trials in general ,54. HoweThere has been recent research attempting to directly quantify walking gaits from wrist-worn accelerometers. However, it is worth noting that challenges persist when utilizing a single wrist-worn accelerometer for comprehensive and reliable gait characterization. For instance, Trost et al.  employedA significant contribution of this work is to provide a comprehensive analytic framework to study the inter-correlation and association between multiple data modalities with repeated measurements. Using this framework, we comprehensively evaluate the relationship between PA and gait, taking into account the repeated measurement design and a wide spectrum of PA and gait characteristics. This approach distinguishes our research from previous studies with similar objectives. We have implemented our methods within a hierarchical fashion, making it more accessible for researchers with less statistical background in this field. As demonstrated in this work, while one could consider a subset of the introduced methods to assess the association between features from different domains, our comprehensive implementation has provided new insights and interpretations from various perspectives. Additionally, cross-checking between methods has been performed to validate the results obtained from them. In this paper, even though the two modalities are gait and PA features from lumbar- and wrist-worn devices, respectively, such an analytic framework can be generalized to other comparisons involving repeated measurements to explore the associations, interactions, and shared information across different data modalities, domains, and/or time points.We acknowledge that the biggest limitation of this work is the relatively small sample size. One important future work is to validate our findings in a separate study with a larger sample size and with both lumbar- and wrist-worn devices deployed, which may be challenging. Another direction to pursue validation is to pool multiple studies with small sample sizes which utilize both device locations. If the studies use different brands of devices, a device-agnostic data processing pipeline, such as Scikit Digital Health (SKDH) , could b


Heterobasidion spore infections in summer cuttings and protects the residual stand and the next tree generation from damage caused by Heterobasidion root rot. Thus far, stump treatment experiments have been carried out in mineral soils, and no information is available on the efficacy of stump treatment agents in boreal peatland conditions. In the present study, biological and chemical control agents  were tested in Scots pine and Norway spruce stands subjected to thinning, cap cutting, and clearcutting on drained peatland in Central Finland. The control efficacy of urea was high in both spruce and pine stumps , while the efficacy of P. gigantea was highly variable on both tree species and ranged from full protection down to negative control effect, i.e., there were more Heterobasidion infections on the treated than untreated half of the stumps. The moisture content of the stump wood or the thickness of the peat layer did not affect the control efficacy of either control agent. These results emphasize a need for further studies to determine the reasons for the unsteadiness of the biological control in peatland conditions.Treatment of conifer stumps with a control agent effectively prevents  Heterobasidion species is economically one of the most damaging diseases of conifer forests in the Northern Hemisphere [Heterobasidion genotype may infect dozens of conifer trees during the rotation time and even infect trees of the next stand generation [Root and butt rot caused by misphere ,2. Infecmisphere ,4. As a neration ,6. Once Heterobasidion sp. spore infection. The loggings can be restricted to the cold season when the temperature is below zero degrees and the fungus is not sporulating [Currently, there are only two methods available to protect freshly cut conifer stumps from rulating , or sporrulating .Heterobasidion spore infections. The biological control agent contains oidia  and mycelium fragments of Phlebiopsis gigantea (Fr.) Jülich. This common saprophytic basidiomycete is widely distributed throughout the conifer forests of the Northern Hemisphere and is a strong competitor of Heterobasidion sp. on a fresh stump surface [P. gigantea, Rotstop®, was formulated in Finland by Verdera Inc. in 1991 [In Finland, both biological and chemical control agents are used to protect conifer stumps from  surface . The com in 1991 .Heterobasidion spp. are suppressed [In chemical treatment, the active ingredient is urea. The control effect of urea is based on the increased pH value on the stump surface achieved by the hydrolysis of urea by urease enzyme in the living sapwood tissues, resulting in the formation of ammonia and a rise in pH up to a level where germination of spores and viability of mycelium of ppressed . SeveralP. gigantea has been high, preventing the vast majority of Heterobasidion spore infections and reducing the area of fungal colonies [Heterobasidion spp. [The efficacy of both biological and chemical control agents has been tested in several experiments conducted in a variety of conditions in Finland and elsewhere, e.g., ,16,17,18colonies ,20,21,22colonies ,23,24,25colonies  or the icolonies ,26,27, oion spp. . Environion spp. ,30.H. annosum in spruce stumps in wetter climates [Most field studies on the efficacy of stump treatment agents have been carried out in conifer forests growing on mineral soils. However, in Scotland and northern England, the efficacy of urea was tested on both mineral and peat soils and was found to fail in providing a consistent control on peat soil subjected to high annual rainfall . The faiclimates ,31.Heterobasidion species, H. parviporum Niemelä & Korhonen and H. annosum sensu stricto (Fr.) Bref, cause damage to forests. In this area, ca. 2.33 million hectares of forests grow on drained peatland [Heterobasidion fruiting bodies do not sporulate, which may have protected these stands from infections. Improved planning tools for logging operations and the development of harvesting and extraction machinery suitable for sensitive peatland sites to minimize soil disturbance have increased summer loggings on drained peatlands [Heterobasidion spore infections and mycelial spreading have been observed to be increasing in Norway spruce stands on peat soils in Latvia [In southern and central Finland, two peatland . Until reatlands ,35. Thern Latvia ,37. Moren Latvia . HoweverP. gigantea (Rotstop® SC) and 32.5% urea solution  provided sufficient protection against Heterobasidion spore infection in harvested spruce and pine forests on drained peatland in Central Finland.The aim of the present study was to test whether stump treatment with Sphagnum moss-dominated mire vegetation towards transformed peatland forest vegetation. In the Finnish site type classification system [Vaccinium myrtillus type (Mtkg), Vaccinium vitis-idaea type (Ptkg), Dwarf shrub type (Vatkg) and Cladonia type (Jätkg). Further, all these site types are divided into two subtypes (subtype I and II) depending on the hydrology of the original mire in its natural state before the drainage. Subtype II origins from either open treeless mires or partly open pine or spruce mires with a combination of open wet parts and dryer hummocks with tree stands . Subtypes I, in turn, have originally been drier and entirely forest-covered mires with hummock-like vegetation and significantly lower water table levels.The stump treatment experiments were conducted in five Norway spruce and five Scots pine stands in Central Finland. All experimental areas have a decades-long drainage history, with a post-drainage vegetation succession developed from original n system , these tVaccinium myrtillus types (Vaccinium vitis-idaea (Ptkg II) and partly Vaccinium myrtillus (Mtkg II). The risk of nutrient leaching was concretely expressed in part of the Keuruu 1 experiment, where mild potassium deficiency symptoms were observed.In our experiments, the spruce stands were either herb-rich or us types . They weThe experimental stands were subjected to different logging methods: clearcutting, cap cutting, and thinning. The area of the clearcuttings varied from two to five hectares, and the area of cap cuttings was 0.1 to 0.3 hectares. The location and main characteristics of the forest stands are given in Depending on the size of the study area, 10 to 20 soil core samples evenly distributed over the area were taken to determine the soil pH value. Before taking the soil core (ca. 15 cm in depth), the green part of mosses and other living surface vegetation was removed. The pH value was measured by dissolving 5 g of dried soil sample into 25 mL of distilled water and calculated as the mean of all the replicates of each site.The stands were harvested normally by a forest harvester, but the experimental stumps were left ca. 0.8 m in height. The stumps were randomly selected, considering, however, that they were evenly distributed across the logging area, and the thickness of the peat layer in the vicinity of the stumps exceeded 30 cm.P. gigantea or urea). This was made to eliminate the variation in wood properties (such as wood moisture content) between the treated and non-treated controls. A total of 134 Scots pine stumps were treated with P. gigantea and 125 stumps with urea. The corresponding figures for treated Norway spruce stumps were 148 and 143, respectively.On the same day or the day after harvesting, the stumps were sawn with a chainsaw to a height of ca. 50 cm to expose a fresh stump surface. Wood samples for the measurement of moisture content (c. 2 × 4 cm in size) were taken both from the heartwood and sapwood immediately after lowering the stump and stored in separate grip-seal plastic bags. The stump surface was then sawn evenly and divided into two halves using a marker line. One half of the stump surface was covered with a weatherproof sheet of paper, and the other half was sprayed with the control agent . An approximately 1 mm thick layer of the control agent was sprayed on the uncovered half of a stump surface. About ½–1 h later, both halves were sprayed with a conidial suspension of Heterobasidion. The Heterobasidion inoculum was prepared on the same day by mixing conidia of four local Heterobasidion strains collected from stumps and standing spruce and pine trees on peat soil. To inoculate pine and spruce stumps, strains of H. annosum and H. parviporum, respectively, were used. Spores were harvested from 3- to 4-week-old fungal cultures growing on malt agar plates at room temperature. The surface was washed to obtain conidia concentration, which was adjusted to ca. 10 to 50 spores cm−2. The number of living spores was determined after the treatments by spraying an approximately 1 mm layer of suspension onto five malt agar plates. After four days of incubation at ca. 20 °C, the average number of germinated spores/1 cm−2 was calculated  stumps were left as untreated controls in sites where the number of stumps was sufficiently high for this extra control to obtain information on the amount of natural The wood samples for moisture content (MC) determinations were kept in the fridge overnight, and the fresh weights were determined the next day. The wood samples were then stored in a freezer for one to two weeks. The dry weight was determined after drying the samples at 70 °C until no further weight loss was observed. MC was determined twice for each stump, i.e., before stump treatment and before harvesting, and calculated by applying the following equation: MC% = 100 × (weight of water in wood/oven-dry weight of wood).Stereum sanquinolentum or Armillaria sp. The rest of the stumps were sampled 9–12 weeks after treatment by cutting two 3–4 cm-thick discs from the top of each stump. The upper disc was discarded, and the lower sample disc was used in the analysis. The wood samples for the measurement of moisture content were sawn under the lower sample disc and processed as described above. The sample discs were barked, washed, and incubated at room temperature for 5–7 days in polythene bags. After incubation, the area occupied by Heterobasidion sp. (based on conidiophore formation) on the upper surface of the disc was determined under a dissection microscope. The outlines of Heterobasidion colonies were marked on the surface of the disc and then traced onto a sheet of transparent paper. The area of the fungal colonies, as well as the area of both halves of the discs, was measured using the ImageJ program (public domain). Before measuring, a 1 cm wide strip on both sides of the borderline between the treatment and control was reduced from both halves of the surface area. Because Heterobasidion infections had only exceptionally reached the depth of 8–10 cm from the treated surface, the underside of the discs was not analyzed. The percentage area occupied by P. gigantea on the disc surface, based on the characteristic orange-brown color on the disc surface , was not measured but was roughly estimated visually.Seven pine and spruce stumps had to be discarded because they had been overdriven during timber forwarding, not found, or had old decay caused by Heterobasidion colonies on the disc surface were sawn from ground level and the base of the main roots and analyzed to ensure that decay in the stumps did not originate from infections already established in the root system before the trees were felled. Since there were no Heterobasidion infections in the lower part of these stumps, it was concluded that they were infected through the stump surface.Additional discs from the stumps with exceptionally large Heterobasidion infections in the control half of the stump surface. These stumps were excluded from the statistical analyses. In addition, the clear-cut pine stand in Kuru, where Heterobasidion inoculation failed almost completely, was excluded from further analyses. Thus, the final number of analyzed pine and spruce stumps were 151 and 265, respectively.Sample disc analyses revealed that—despite the artificial infection—38.7% of all inoculated pine stumps and 6.7% of spruce stumps were not infected, i.e., there were no P. gigantea and urea was calculated by comparing the area occupied by Heterobasidion sp. on both (treated and untreated) halves of the disc, using the following formula:Heterobasidion sp. in treated and untreated halves. Only stumps with successful infection by Heterobasidion spores on the untreated half of the disc were included in the analyses. For each site, the control efficacy (E%) was calculated as means by tree species and treatments. For tree species by treatments, the control efficiency was calculated from the mean values of each site.The control efficacy (E%) of Heterobasidion sp. in different harvesting methods were investigated by using statistical models. Models were estimated separately for spruce and pine stumps. Each treatment area, i.e., the half of the stump treated with P. gigantea or urea, and the untreated control half, was considered as a separate observational unit in the data, although the control and the treatments (P. gigantea or urea) were applied on the same stumps. Thus, spruce models included 535 and pine data 351 observational units.The effects of different treatments on the cover of Heterobasidion sp. (%) was a response variable in the models. Logit transformation was applied to cover values when models were estimated [P. gigantea or urea) and harvesting methods  on the percentage of area occupied by Heterobasidion sp. on stump surfaces were investigated using linear mixed models with function lme in library nlme in the statistical program R [P. gigantea or urea), (2) harvesting method , (3) diameter of a stump , (4) moisture content of sapwood before the treatments (%), (5) moisture content of heartwood before the treatments (%), (6) peat thickness (cm), and (7) an interaction term between treatment and harvesting method. However, clearcutting was not applied for spruce stands, i.e., it was not included as a harvesting method for spruce models. Furthermore, the thickness of the peat layer could not be included in the pine model since it correlated too strongly with harvesting methods. The treatment year, and identification code for each experimental stand and stump, were included in the models as nested random factors to take into account the fact that different treatments were performed within the same years  and sites and treatment-control combinations within same stumps. Treatment year as a random factor was excluded from the pine model since only one stand was treated in 2021.The percentage of the area (including sapwood and heartwood) occupied by stimated . Howeverrogram R ,42. ExplAICcmodavg [Predicted values in different treatments and harvesting methods were calculated based on estimated models using the library Ccmodavg . Other vP. gigantea and urea treatments by tree species. An independent-samples t-test was used to compare the means of single variables  between spruce and pine stumps. The analyses were performed using SPSS Statistic Software 15.01 for Windows.Kruskal-Wallis H-test with post hoc tests was used to test differences in the control efficacy (E%) between the Heterobasidion sp. on the control half of the stump surface was significantly larger on spruce stumps than on pine stumps. i.e., mean 16.7 and 5.4%, respectively .The variation in the success of artificial infection was high, especially among the pine stands: The proportion of infected stumps varied from 5.9 to 96.6% in pine stands and from 76.3 to 100% in spruce stands. In addition, the proportion of the area occupied by Heterobasidion spores was completely absent in all other stands except spruce thinning in Suonenjoki, where all four untreated control stumps were infected naturally by Heterobasidion spores, and spruce cap cutting in Hämeenlinna, where 11 out of 12 control stumps were naturally infected.Natural infection by P. gigantea and 1.4% of pine stumps treated with urea were infected by Heterobasidion spores. Correspondingly, in spruce stands, 71.2% of P. gigantea-treated and 59.5% of urea-treated stumps were infected by Heterobasidion sp.A total of 47.5% of pine stumps treated with Heterobasidion sp. on treated and untreated halves of the disc surface, varied from negative results obtained in two spruce and one pine stand subjected to P. gigantea treatment to a complete control effect achieved in three urea- and one P. gigantea-treated pine stands , while the efficacy of P. gigantea treatment did not differ significantly between the tree species (p = 0.924).The control efficacy (E%) within the experimental stands, calculated by comparing the relative area occupied by e stands . The aveHeterobasidion sp. was significantly smaller in urea- than in P. gigantea-treated spruce stumps, i.e., 2.1 and 12.9%, respectively . In pine stumps, the difference between the treatments was smaller, although statistically significant, i.e., 0.04 and 1.8% in urea- and P. gigantea-treated stumps, respectively .When all harvesting options were combined, the proportion of the cross-sectional area occupied by Heterobasidion sp. (E%) corresponded to each other, i.e., a high frequency of infected stumps indicated low control efficacy. However, the urea treatment in spruce stands was an exception because the average mean control efficacy was as high as 85.3%, even though 60.9% of treated stumps were infected by Heterobasidion sp. This was a consequence of the fact that the cross-sectional area occupied by Heterobasidion sp. was much smaller in urea-treated than P. gigantea-treated spruce stumps.In general, the proportion of infected stumps and the relative area occupied by Heterobasidion sp.) between the harvesting methods. In spruce stands subjected to cap cutting, the relative area occupied by Heterobasidion sp. on stump surfaces (%) was lower in the stump sections treated with P. gigantea or urea than in the control sections  while the moisture content of heartwood did not differ significantly between spruce and pine, i.e., 46 and 45%, respectively . Detailed information on the stump moisture contents is given in Comparing the pooled data by tree species, the initial moisture content measured before the treatment was significantly higher in pine than in spruce sapwood, i.e., mean 116 and 100%, respectively . The reason for the failed Heterobasidion infection was not determined but may have been due to the intensive natural colonization of pine stumps by P. gigantea [P. gigantea was not exactly quantified, it was conspicuous that the sapwood of the untreated control stumps was almost completely occupied by the fungus. Actually, the untreated halves of most pine stumps were even more intensively colonized by natural P. gigantea than the halves treated with P. gigantea. Favorable moisture conditions in peatlands due to the high water-holding capacity of peat may improve the competitiveness of P. gigantea by increasing its sporulation. It is also known that there is a high variation in competitiveness among different P. gigantea strains [Heterobasidion sp. on the stump surface may have been present in the cutting area in Kuru. Moreover, the competitiveness of P. gigantea may be improved by a low density of Heterobasidion inoculum [−2, e.g., [−2). Failure of Heterobasidion infection has also been reported in inoculation trials carried out on mineral soils [Heterobasidion spore load, which indicates that in boreal peatland forests, both spruce and pine stumps are prone to Heterobasidion spore infection. This observation is consistent with the earlier result obtained in Scotland, where no significant differences in the susceptibility of Picea sitchensis and Pinus contorta stumps to Heterobasidion infection were found between mineral and peat soils [The experimental areas of the present study are mainly located in the central part of Finland, where the prevalence of Heterobasidionroot rot is relatively low, and spore dispersal is more variable and forest-specific than in the most severely damaged areas in southern Finland . Based ogigantea . Althoug strains , and an inoculum ,46. In oinoculum , the ino2, e.g., ,50. Besial soils ,51,52, aP. gigantea treatment effectively prevented Heterobasidion infections (E% 89.7–100) (P. gigantea has provided sufficient protection against Heterobasidion infections with an efficacy (based on the relative area occupied by Heterobasidion sp.) ranging in spruce stumps from 70% to 100% [P. gigantea treatment has been connected to incomplete coverage of the control agent on the stump surface [Heterobasidion spore pressure [Heterobasidion spores in the inoculum was lower than usually used in efficiency trials carried out on mineral soils.There was a high variation in the efficacy of the biological control agent among the experimental stands. It failed in one pine stand and three spruce stands, while in two pine and spruce stands, 9.7–100) . In effi to 100% ,45,53 an surface ,27 and epressure ,54. NeitP. gigantea treatments providing poor control results in our study were established in 2021. Therefore, the harvesting method itself, i.e., cap cutting in a pine stand and thinning in some spruce stands, does not necessarily explain the substandard result. A different production batch of Rotstop® SC was used each year. The quality  of Rotstop® SC is tested by the manufacturer [It should be noted that all the facturer , and theHeterobasidion sp. decreased with increasing stump moisture content in Norway spruce stumps in a Swedish study [Heterobasidion sp. on treated stump sectors.The efficacy of a biological control agent may also be affected by environmental factors. All treatments in 2021 were carried out during a short period between the end of July and the end of August. The early summer (June–July) of 2021 was exceptionally dry and warm, with temperatures 3.0–4.6 degrees above long-term averages, while at the end of summer, there were locally heavy rain showers . Becausesh study , our resP. gigantea both on spruce and pine stumps. Although in spruce stands (pooled harvesting method data), the proportion of infected stumps was high both in urea and P. gigantea treatments, i.e., 60.9 and 70.2%, respectively, the efficacy of urea was nevertheless high (mean 79.5%) due to the small relative area occupied by Heterobasidion sp. on stump surfaces. Only in the thinned spruce stand in Multia, established in 2021, the control result of urea was substandard (E% 42). Overall, there seems not to be significant differences in the control efficacy in spruce stumps between peat and mineral soils, as the efficacy of 30% urea has previously been shown to vary between 60–100% [Urea showed better control efficacy than  60–100% . In our  60–100% ,22,59,60Because drying out of the stump surface reduces the efficacy of urea treatment, a dry period in summer may hinder urea hydrolysis leading to reduced control efficacy . HoweverH. annosum inoculum. Particularly on peat soil, urea treatment promoted the colonization of H. annosum and increased the cross-sectional area colonized by H. annosum [Heterobasidion benefits from additional nitrogen, especially in heartwood which is lower in nitrogen than sapwood. Because heartwood is dryer than sapwood, it is preferred by the fungus on a peatland subjected to high annual rainfall. In stumps analyzed during the present study, Heterobasidion infections were concentrated on the sapwood, and the boundary between sapwood and heartwood, similarly as in stumps on mineral soil [Heterobasidion infection was observed. Only in 4% of the urea-treated spruce stumps, the relative area occupied by Heterobasidion sp. was greater on treated than untreated half of the stumps.On the other hand, urea treatment may fail due to the excessive moisture of stump wood. In efficacy trials carried out in Scotland and northern England, a urea treatment of Sitka spruce stumps did not protect stumps against artificial  annosum . The autral soil , and no The efficiency of urea also depends on the concentration of the treatment solution. A more concentrated urea solution (>30%) provides better protection than a more diluted solution ,14. ThusUnexpectedly, the moisture content of spruce stumps on peatland was, on average lower than those reported in mineral soil. In mineral soil, an average moisture content of 148% has been reported by Bendz-Hellgren and Stenlid  for sapwHeterobasidion spore infection increases with increasing stump diameter [P. gigantea treatment gives better protection against Heterobasidion infection in large than small stumps [Several studies have shown that the susceptibility of both spruce and pine stumps to diameter ,63,64,65diameter . Meanwhil stumps .P. gigantea treatment with increasing stump diameter was evident in the pine cap cutting and spruce thinnings  [Heterobasidion infection or on the control efficacy (proportion of area occupied by Heterobasidion sp.), indicating that stump treatment may be advisable also on peat soils with a peat layer thicker than 30 cm. Although peatland forests seem not to be an optimal habitat for Heterobasidion sp., some recent studies have demonstrated that Heterobasidionroot rot can cause severe damage for both spruce and pine in peatlands [According to our knowledge, no direct information is currently available on the possible connection between the thicknesses of the peat layer and the risk of primary viporum) . In our eatlands ,70. To seatlands .Heterobasidion spore infection on drained peatlands, which encourages the use of stump treatment in summer cuttings. Urea as a 32.5 percent aqueous solution gave good protection against Heterobasidion infections both in pine and spruce stumps, while P. gigantea’s efficacy varied greatly among experimental stands. Further research is needed to determine the reason that led to the failure of the biological control agent, notwithstanding the control agent covered the entire surface of the stumps and the pressure of Heterobasidion spores was extremely low. Since this study provides information on the short-term efficacy of the stump treatments after harvesting, it is important to evaluate the efficacy over a longer period as well. In addition, the possible environmental effects of urea on the peatland ecosystem should be investigated.To conclude, the present study reveals that both spruce and pine stumps are susceptible to