Exposure of banana plants, one of the most important tropical and subtropical plants, to low temperatures causes a severe drop in productivity, as they are sensitive to cold and do not have a strong defense system against chilling. Therefore, this study aimed to improve the growth and resistance to cold stress of banana plants using foliar treatments of chitosan nanoparticles (CH-NPs). CH-NPs produced by nanotechnology have been used to enhance tolerance and plant growth under different abiotic stresses, e.g., salinity and drought; however, there is little information available about their effects on banana plants under cold stress. In this study, banana plants were sprayed with four concentrations of CH-NPs—i.e., 0, 100, 200, and 400 mg L⁻¹ of deionized water—and a group that had not been cold stressed or undergone CH-NP treatment was used as control. Banana plants (Musa acuminata var. Baxi) were grown in a growth chamber and exposed to cold stress (5 °C for 72 h). Foliar application of CH-NPs caused significant increases (p < 0.05) in most of the growth parameters and in the nutrient content of the banana plants. Spraying banana plants with CH-NPs (400 mg L⁻¹) increased the fresh and dry weights by 14 and 41%, respectively, compared to the control. A positive correlation was found between the foliar application of CH-NPs, on the one hand, and photosynthesis pigments and antioxidant enzyme activities on the other. Spraying banana plants with CH-NPs decreased malondialdehyde (MDA) and reactive oxygen species (ROS), i.e., hydrogen peroxide (H₂O₂), hydroxyl radicals (^•OH), and superoxide anions (O₂^•−). CH-NPs (400 mg L⁻¹) decreased MDA, H₂O₂, ^•OH, and O₂^•− by 33, 33, 40, and 48%, respectively, compared to the unsprayed plants. We hypothesize that CH-NPs increase the efficiency of banana plants in the face of cold stress by reducing the accumulation of reactive oxygen species and, in consequence, the degree of oxidative stress. The accumulation of osmoprotectants (soluble carbohydrates, proline, and amino acids) contributed to enhancing the cold stress tolerance in the banana plants. Foliar application of CH-NPs can be used as a sustainable and economically feasible approach to achieving cold stress tolerance.

Although cellulose represents the most abundant biomass resource on Earth, its natural recalcitrance hinders its application for low-cost biofuels and high-value bioproducts on a large scale. In this study, we generated less recalcitrant cellulose nanofibril substrates by site-specific mutations and knockouts of three OsCESA4,7,9 isoforms that are essential as cellulose synthase complexes for the cellulose biosynthesis of secondary cell walls in rice. As a comparison with the wild type form, three genetically-engineered CESAs isoforms independently generated cellulose nanofibrils with the average lengths reduced by 63%, which accounts for the high density of the amorphous cellulose chains as the initial breakpoints for the consistently enhanced biomass enzymatic hydrolysis into fermentable sugars. The genetically modified cellulose nanofibrils efficiently produce ideal nanocrystals with the most reduced dimension to-date in three knockout lines, which are applicable as optimal intermediates for highly valuable bioproducts. The improved cellulose nanofibrils are also effective in inducing the secretion of lignocellulose-degradation enzymes from fungi (T. reesei) with the two cellulase (exoglucanases, β-glucosidases) activities elevated by 100% and 138%, and the total protein level increased by 44%. Our data thus reveal a novel green strategy for achieving high-quality diverse bioproduction by integrating the precise genetic modification of lignocellulose substrates with efficient biomass process technology.

Bananas (Musa spp.) are an important fruit crop worldwide. The fungus Fusarium oxysporum f. sp. cubense (Foc), which causes Fusarium wilt, is widely regarded as one of the most damaging plant diseases. Fusarium wilt has previously devastated global banana production and continues to do so today. In addition, due to the current use of high-density banana plantations, desirable banana varieties with ideal plant architecture (IPA) possess high lodging resistance, optimum photosynthesis, and efficient water absorption. These properties may help to increase banana production. Genetic engineering is useful for the development of banana varieties with Foc resistance and ideal plant architecture due to the sterility of most cultivars. However, the sustained immune response brought about by genetic engineering is always accompanied by yield reductions. To resolve this problem, we should perform functional genetic studies of the Musa genome, in conjunction with genome editing experiments, to unravel the molecular mechanisms underlying the immune response and the formation of plant architecture in the banana. Further explorations of the genes associated with Foc resistance and ideal architecture might lead to the development of banana varieties with both ideal architecture and pathogen super-resistance. Such varieties will help the banana to remain a staple food worldwide.

We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gradient descent and a novel gradient pruning procedure. Under suitable conditions, the algorithm converges linearly and can simultaneously estimate both the low-rank and sparse tensors. The statistical error bounds of final estimates are established in terms of the gradient of loss function. The error bounds are generally sharp under specific statistical models, for example, the sub-Gaussian robust PCA and Bernoulli tensor model. Moreover, our method achieves nontrivial error bounds for heavy-tailed tensor PCA whenever the noise has a finite $2+\epsilon$ moment. We apply our method to analyze the international trade flow dataset and the statistician hypergraph coauthorship network, both yielding new and interesting findings. Supplementary materials for this article are available online.

The genetic modification of plant cell walls has been considered to reduce lignocellulose recalcitrance in bioenergy crops. As a result, it is important to develop a precise and rapid assay for the major wall polymer features that affect biomass saccharification in a large population of transgenic plants. In this study, we collected a total of 246 transgenic rice plants that, respectively, over-expressed and RNAi silenced 12 genes of the OsGH9 and OsGH10 family that are closely associated with cellulose and hemicellulose modification. We examined the wall polymer features and biomass saccharification among 246 transgenic plants and one wild-type plant. The samples presented a normal distribution applicable for statistical analysis and NIRS modeling. Among the 246 transgenic rice plants, we determined largely varied wall polymer features and the biomass enzymatic saccharification after alkali pretreatment in rice straws, particularly for the fermentable hexoses, ranging from 52.8 to 95.9%. Correlation analysis indicated that crystalline cellulose and lignin levels negatively affected the hexose and total sugar yields released from pretreatment and enzymatic hydrolysis in the transgenic rice plants, whereas the arabinose levels and arabinose substitution degree (reverse xylose/arabinose ratio) exhibited positive impacts on the hexose and total sugars yields. Notably, near-infrared spectroscopy (NIRS) was applied to obtain ten equations for predicting biomass enzymatic saccharification and seven equations for distinguishing major wall polymer features. Most of the equations exhibited high R 2/R 2 cv/R 2 ev and RPD values for a perfect prediction capacity. Due to large generated populations of transgenic rice lines, this study has not only examined the key wall polymer features that distinctively affect biomass enzymatic saccharification in rice but has also established optimal NIRS models for a rapid and precise screening of major wall polymer features and lignocellulose saccharification in biomass samples. Importantly, this study has briefly explored the potential roles of a total of 12 OsGH9 and OsGH10 genes in cellulose and hemicellulose modification and cell wall remodeling in transgenic rice lines. Hence, it provides a strategy for genetic modification of plant cell walls by expressing the desired OsGH9 and OsGH10 genes that could greatly improve biomass enzymatic digestibility in rice.