Although Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) plantations are widely grown for timber production in southern China, they have low biodiversity and provide limited ecosystem services. To address this problem, C. lanceolata are increasingly mixed with broadleaf Schima superba Gardn. & Champ. (Theaceae). The success of these mixed plantations relies on introducing each species in the appropriate sequence, which requires understanding how tree species respond to light variations. We therefore compared S. superba and C. lanceolata seedling light tolerance in shaded houses under five light gradients (5%, 15%, 40%, 60%, and 100% sunlight). Our findings showed that S. superba seedlings exhibited greater net height increment (ΔHt), net diameter growth (ΔDia), leaf area, root mass, stem mass, leaf mass, and total mass under low light conditions (15% sunlight). However, as sunlight increased, these growth variables became higher in C. lanceolata seedlings. With more sunlight, both species experienced a drop in height to diameter ratio (HDR), and specific leaf area (SLA), but an elevated root to shoot ratio. Additionally, under the same light levels, S. superba seedlings exhibited greater leaf area and root to shoot ratio than C. lanceolata seedlings. Our results suggested that S. superba might be more suitable for underplanting beneath a heavy canopy due to its shade-tolerant traits. In contrast, C. lanceolata was less shade-tolerant, having an optimum seedling growth under full sunlight. These findings suggest that underplanting S. superba seedlings in C. lanceolata monoculture plantation (i.e., underplanting regeneration approach) could be a better silvicultural alternative than simultaneously planting both seedlings.

Abstract:Stemflow of xerophytic shrubs was monitored on event basis within a revegetated sand dune. Quantity of stemflow showed a clear species-specific dependence in combination with the rainfall characteristics. Results obtained revealed that for ovate-leaved C. korshinskii with an inverted cone-shaped canopy and smooth bark, the quantity of stemflow in depth accounted for 7.2% of the individual gross rainfall, while it was 2.0% for needle-leaved A. ordosica with a cone-shaped canopy and coarse bark. There were significant positive linear relationships between stemflow and individual gross rainfall and rainfall intensity for the two shrubs. An individual gross rainfall of 1.4 and 1.8 mm was necessary for stemflow generation for C. korshinskii and A. ordosica, respectively. Multiple regression analysis showed that the abiotic and biotic variables including the individual gross rainfall, mean windspeed (WS), canopy height, branch length, and canopy volume have significant influence on stemflow for C. korshinskii, whereas for A. ordosica, the notable influencing variables were individual gross rainfall, stem diameter, and leaf area index. Generally, WS has less effect on stemflow than that of rainfall for A. ordosica. The correlation relationship between individual gross rainfall and funneling ratio showed that the funneling ratio attains its peak when the gross rainfall is 13 and 16 mm for C. korshinskii and A. ordosica, respectively, implying that the canopy morphology emerged as determining factors on funneling ratio decrease when the individual gross rainfall exceeds these values. In comparison, higher WS increased the funneling ratio remarkably for C. korshinskii than A. ordosica due partly to the greater branch length and canopy projection area in C. korshinskii. Funneling ratio can be used as an integrated variable for the effects of canopy morphology and rainfall characteristics on stemflow. The implication of stemflow on water balance and its contribution to sustain the shrubs and the revegetation efforts was discussed.

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Seed germination strongly affects plant population growth and persistence, and it can be dramatically influenced by phylogeny, seed traits, and ecological factors. In this study, we examined the relationships among seed mass, seed shape, and germination percentage (GP), and assessed the extent to which phylogeny, seed traits (seed mass, shape, and color) and ecological factors (ecotype, life form, adult longevity, dispersal type, and onset of flowering) influence GP at the community level. All analyses were conducted on the log-transformed values of seed mass and arcsine square root-transformed values of GP. We found that seed mass and GP were significantly negatively correlated, whereas seed shape and GP were significantly positively correlated. The three major factors contributing to differences in GP were phylogeny, dispersal type, and seed shape (explained 5.8, 4.9, and 3.1% of the interspecific variations independently, respectively), but GP also influenced by seed mass and onset of flowering. Thus, GP was constrained not only by phylogeny but also by seed traits and ecological factors. These results indicated that GP is shaped by short-term selective pressures, and long-term phylogenetic constrains. We suggest that correlates of phylogeny, seed traits, and ecology should be taken into account in comparative studies on seed germination strategies.

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Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders, which brings enormous burdens to the families of patients and society. However, due to the lack of representation of variance for diseases and the absence of biomarkers for diagnosis, the early detection and intervention of ASD are remarkably challenging. In this study, we proposed a self-attention deep learning framework based on the transformer model on structural MR images from the ABIDE consortium to classify ASD patients from normal controls and simultaneously identify the structural biomarkers. In our work, the individual structural covariance networks are used to perform ASD/NC classification via a self-attention deep learning framework, instead of the original structural MR data, to take full advantage of the coordination patterns of morphological features between brain regions. The self-attention deep learning framework based on the transformer model can extract both local and global information from the input data, making it more suitable for the brain network data than the CNN- structural model. Meanwhile, the potential diagnosis structural biomarkers are identified by the self-attention coefficients map. The experimental results showed that our proposed method outperforms most of the current methods for classifying ASD patients with the ABIDE data and achieves a classification accuracy of 72.5% across different sites. Furthermore, the potential diagnosis biomarkers were found mainly located in the prefrontal cortex, temporal cortex, and cerebellum, which may be treated as the early biomarkers for the ASD diagnosis. Our study demonstrated that the self-attention deep learning framework is an effective way to diagnose ASD and establish the potential biomarkers for ASD.

Allenone has been identified as a highly effective peptide coupling reagent for the first time. The peptide bond was formed with an α-carbonyl vinyl ester as the key intermediate, the formation and subsequent aminolysis of which proceed spontaneously in a racemization-/epimerization-free manner. The allenone coupling reagent not only is effective for the synthesis of simple amides and dipeptides but is also amenable to peptide fragment condensation and solid-phase peptide synthesis (SPPS). The robustness of the allenone-mediated peptide bond formation was showcased incisively by the synthesis of carfilzomib, which involved a rare racemization-/epimerization-free N to C peptide elongation strategy. Furthermore, the successful synthesis of the model difficult peptide ACP (65–74) on a solid support suggested that this method was compatible with SPPS. This method combines the advantages of conventional active esters and coupling reagents, while overcoming the disadvantages of both strategies. Thus, this allenone-mediated peptide bond formation strategy represents a disruptive innovation in peptide synthesis.

This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner. Therefore, in an attempt to address the aforementioned problems associated with traditional integer-order computation-based contrast-enhancement algorithms, we have studied here, as an interesting theoretical problem, whether it will be possible to hybridize the capabilities of preserving the edges and the textural details of fractional calculus with texture image multi-scale nonlocal contrast enhancement. Motivated by this need, in this paper, we introduce a novel conceptual formulation of the fractional-order variational framework for retinex. First, we implement the FPDE by means of the fractional-order steepest descent method. Second, we discuss the implementation of the restrictive fractional-order optimization algorithm and the fractional-order Courant-Friedrichs-Lewy condition. Third, we perform experiments to analyze the capability of the FPDE to preserve edges and textural details, while enhancing the contrast. The capability of the FPDE to preserve edges and textural details is a fundamental important advantage, which makes our proposed algorithm superior to the traditional integer-order computation-based contrast enhancement algorithms, especially for images rich in textural details.