Establishment of an Efficient and Rapid Regeneration System for a Rare Shrubby Desert Legume Eremosparton songoricum.

Eremosparton songoricum (Litv.) Vass. is a rare and extremely drought-tolerant legume shrub that is distributed in Central Asia. E. songoricum naturally grows on bare sand and can tolerate multiple extreme environmental conditions. It is a valuable and important plant resource for desertification prevention and environmental protection, as well as a good material for the exploration of stress tolerance mechanisms and excellent tolerant gene mining. However, the regeneration system for E. songoricum has not yet been established, which markedly limits the conservation and utilization of this endangered and valuable desert legume. Assimilated branches derived from seedlings were cultured on several MS mediums supplemented with various concentrations of TDZ or 6-BA in different combinations with NAA. The results showed that the most efficient multiplication medium was MS medium supplemented with 0.4 mg/L 6-BA and 0.1 mg/L NAA. The most efficient rooting medium was WPM + 25 g/L sucrose. The highest survival rate (77.8%) of transplantation was achieved when the ratio of sand to vermiculite was 1:1. In addition, the optimal callus induction medium was MS + 30 g/L sucrose + 2 mg/L TDZ + 0.5 mg/L NAA in darkness. The E. songoricum callus treated with 100 mM NaCl and 300 mM mannitol on MS medium could be used in proper salt and drought stress treatments in subsequent gene function tests. A rapid and efficient regeneration system for E. songoricum that allowed regeneration within 3 months was developed. The protocol will contribute to the conservation and utilization of this rare and endangered desert stress-tolerant species and also provide a fundamental basis for gene functional analysis in E. songoricum.

Efficient Single-Photon Sources Based on Chlorine-Doped ZnSe Nanopillars with Growth Controlled Emission Energy.

Isolated impurity states in epitaxially grown semiconductor systems possess important radiative features such as distinct wavelength emission with a very short radiative lifetime and low inhomogeneous broadening, which make them promising for the generation of indistinguishable single photons. In this study, we investigate chlorine-doped ZnSe/ZnMgSe quantum well (QW) nanopillar (NP) structures as a highly efficient solid-state single-photon source operating at cryogenic temperatures. We show that single photons are generated due to the radiative recombination of excitons bound to neutral Cl atoms in ZnSe QW and the energy of the emitted photon can be tuned from about 2.85 down to 2.82 eV with ZnSe well width increase from 2.7 to 4.7 nm. Following the developed advanced technology, we fabricate NPs with a diameter of about 250 nm using a combination of dry and wet-chemical etching of epitaxially grown ZnSe/ZnMgSe QW structures. The remaining resist mask serves as a spherical- or cylindrical-shaped solid immersion lens on top of NPs and leads to the emission intensity enhancement by up to an order of magnitude in comparison to the pillars without any lenses. NPs with spherical-shaped lenses show the highest emission intensity values. The clear photon-antibunching effect is confirmed by the measured value of the second-order correlation function at a zero time delay of 0.14. The developed single-photon sources are suitable for integration into scalable photonic circuits.

Genome-wide investigation of AP2/ERF gene family in the desert legume Eremosparton songoricum: Identification, classification, evolution, and expression profiling under drought stress.

Eremosparton songoricum (Litv.) Vass. is a rare leafless legume shrub endemic to central Asia which grows on bare sand. It shows extreme drought tolerance and is being developed as a model organism for investigating morphological, physiological, and molecular adaptations to harsh desert environments. APETALA2/Ethylene Responsive Factor (AP2/ERF) is a large plant transcription factor family that plays important roles in plant responses to various biotic and abiotic stresses and has been extensively studied in several plants. However, our knowledge on the AP2/ERF family in legume species is limited, and no respective study was conducted so far on the desert shrubby legume E. songoricum. Here, 153 AP2/ERF genes were identified based on the E. songoricum genome data. EsAP2/ERFs covered AP2 (24 genes), DREB (59 genes), ERF (68 genes), and Soloist (2 genes) subfamilies, and lacked canonical RAV subfamily genes based on the widely used classification method. The DREB and ERF subfamilies were further divided into A1-A6 and B1-B6 groups, respectively. Protein motifs and exon-intron structures of EsAP2/ERFs were also examined, which matched the subfamily/group classification. Cis-acting element analysis suggested that EsAP2/ERF genes shared many stress- and hormone-related cis-regulatory elements. Moreover, the gene numbers and the ratio of each subfamily and the intron-exon structures were systematically compared with other model plants ranging from algae to angiosperms, including ten legumes. Our results supported the view that AP2 and ERF evolved early and already existed in algae, whereas RAV and DREB began to appear in moss species. Almost all plant AP2 and Soloist genes contained introns, whereas most DREB and ERF genes did not. The majority of EsAP2/ERFs were induced by drought stress based on RNA-seq data, EsDREBs were highly induced and had the largest number of differentially expressed genes in response to drought. Eight out of twelve representative EsAP2/ERFs were significantly up-regulated as assessed by RT-qPCR. This study provides detailed insights into the classification, gene structure, motifs, chromosome distribution, and gene expression of AP2/ERF genes in E. songoricum and lays a foundation for better understanding of drought stress tolerance mechanisms in legume plants. Moreover, candidate genes for drought-resistant plant breeding are proposed.

Highly Sensitive Humidity Sensor Based on Oblique Carbon Nanoplumes.

In this work, we fabricated three carbon nanoplume structured samples under different temperatures using a simple hot filament physical vapor deposition (HFPVD) process, and investigated the role of surface morphology, defects, and graphitic content on relative humidity (RH) sensing performances. The Van der Drift growth model and oblique angle deposition (OAD) technique of growing a large area of uniformly aligned and inclined oblique arrays of carbon nanoplumes (CNPs) on a catalyst-free silicon substrate was demonstrated. The optimal growing temperature of 800 °C was suitable for the formation of nanoplumes with larger surface area, more defect sites, and less graphitic content, compared to the other samples that were prepared. As expected, a low detection limit, high response, capability of reversible behavior, and rapid response/recovery speed with respect to RH variation, was achieved without additional surface modification or chemical functionalization. The holes' depletion has been described as a RH sensing mechanism that leads to the increase of the conduction of the CNPs with increasing RH levels.

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection.

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy.