Editorial: orchid genomics and developmental biology

Orchidaceae is the second largest family of flowering plants with more than 27,000 species inhabiting nearly every habitat worldwide. Orchids exhibit an exceptional morphological and ecological diversity, and are highly valued on the global horticultural market. Orchids possess unique morphological and physiological characteristics, such as highly reduced seeds with an immature embryo, complex flower structures such as the gynandrium and labellum, and have evolved crassulacean acid metabolism and mycoheterotrophy multiple times independently. This range of traits renders orchids prime non-model plants for studying different aspects of evolution through mechanistic studies considering gene function, physiology, and phylogenetic relationships. As summarized below, this Research Topic presents recent advances in orchid biology and consists of 12 publications in the fields of reproductive development, evolution, biotechnology, and photosynthesis

Editorial: Orchid genomics and developmental biology, volume II

[Extract] Orchidaceae constitute the second-largest flowering plant family worldwide with over 27,000 species found on all continents except Antarctica. Orchids are frequently celebrated for their exceptional morphological and ecological diversity and are highly valued in the horticultural trade. Orchids exhibit distinct floral and physiological features, such as fused male and female flower parts forming the gynostemium, a floral lip often adorned with calli, glands, spurs, and distinctive color patterns, and the crassulacean acid metabolism (CAM), a water-saving physiological pathway which has evolved multiple times independently within the family. Orchids possess highly specialized ecological relationships, such as often species-specific plant-pollinator interactions including food- and sexual deception and dependence on mycorrhizal fungi for germination of their minute seed

In Vitro Organogenesis of a Slipper Orchid, Paphiopedilum ‘Alma Gavaert’

The aim of the present study was to improve the regeneration efficiency of callus lines in a slipper orchid, Paphiopedilum ‘Alma Gavaert’. Three kinds of vegetative tissues, root, stem and leaf segments, were used as explants to induce callogenesis; out of these, only root explants formed callus and was subcultured in the presence of 5 mg/L dicamba and 5 mg/L 2,4-D combined with 1 or 2 mg/L TDZ. The resulting four callus lines, assigned as 5Di1T, 5Di2T, 5D1T and 5D2T, respectively, were used to test the effect of NAA to BA ratios on re-differentiation, wherein the highest number of shoots (approximately 2 shoots/0.1 g callus clump) were obtained in callus line 5D2T at ratios of 0.001 and 0.002. A largely improvement of shoot regeneration efficiency was obtained by continuous selection of callus lines which derived from different explant positions. Eventually, six callus lines, including 5D2T-T6-G5 to 5D2T-T6-G10, were able to produce approximately 10 times of shoots per callus clump when compared with the parental callus line 5D2T

A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation

Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment. To make the labeling process less laborious and dependent on expertise, weakly-supervised semantic segmentation (WSSS) methods using class activation mapping (CAM) have been proposed. However, current CAM-based WSSS methods generate the object localization map using internal neural network information, such as gradient or trainable parameters, which can lead to suboptimal solutions. To address these issues, we propose the confidence-induced CAM (Cfd-CAM), which calculates the weight of each feature map by using the confidence of the target class. Our experiments on two brain tumor datasets show that Cfd-CAM outperforms existing state-of-the-art methods under the same level of supervision. Overall, our proposed Cfd-CAM approach improves the accuracy of brain tumor segmentation and may provide valuable insights for developing better WSSS methods for other medical imaging tasks

Ultrasonication-Assisted Spray Ionization Mass Spectrometry for the Analysis of Biomolecules in Solution

In this paper, we describe a novel technique—ultrasonication-assisted spray ionization (UASI)—for the generation of singly charged and multiply charged gas-phase ions of biomolecules (e.g., amino acids, peptides, and proteins) from solution; this method employs a low-frequency ultrasonicator (ca. 40 kHz) in place of the high electric field required for electrospray ionization. When a capillary inlet is immersed into a sample solution within a vial subjected to ultrasonication, the solution is continually directed to the capillary outlet as a result of ultrasonication-assisted capillary action; an ultrasonic spray of the sample solution is emitted at the outlet of the tapered capillary, leading to the ready generation of gas-phase ions. Using an ion trap mass spectrometer, we found that singly charged amino acid and multiply charged peptides/proteins ions were generated through this single-step operation, which is both straightforward and extremely simple to perform. The setup is uncomplicated: only a low-frequency ultrasonicator and a tapered capillary are required to perform UASI. The mass spectra of the multiply charged peptides and proteins obtained from sample solutions subjected to UASI resemble those observed in ESI mass spectra

Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications

A Novel In Vitro

An alternative in vitro protocol for embryo induction directly from intact living seedlings of Phalaenopsis aphrodite subspecies formosana was established in this study. Without the supplementation of plant growth regulators (PGRs), no embryos were obtained from all the seedlings when cultured on the solid medium. In contrast, embryos formed from the seedlings on the 2-layer medium and the 2-step culture system without the use of PGRs. It was found that the age of the seedlings affected embryo induction. The 2-month-old seedlings typically had higher embryogenic responses when compared with the 4-month-old seedlings in the 2-layer medium or 2-step system. For the 2-month-old seedlings, 1 mg/L TDZ resulted in the highest number of embryos at the distal site of the shoot. However, on the leaves’ surface, 0.5 mg/L TDZ induced the highest number of embryos. When the 2-month-old seedlings were cultured using the 2-step method at 1 mg/L of TDZ, the highest embryogenic response was obtained, with an average of 44 embryos formed on each seedling. These adventitious embryos were able to convert into plantlets in a PGR-free 1/2 MS medium, and the plantlets had normal morphology and growth

Evaluation of the estimate bias magnitude of the Rao’s quadratic diversity index

Rao’s quadratic diversity index is one of the most widely applied diversity indices in functional and phylogenetic ecology. The standard way of computing Rao’s quadratic diversity index for an ecological assemblage with a group of species with varying abundances is to sum the functional or phylogenetic distances between a pair of species in the assemblage, weighted by their relative abundances. Here, using both theoretically derived and observed empirical datasets, we show that this standard calculation routine in practical applications will statistically underestimate the true value, and the bias magnitude is derived accordingly. The underestimation will become worse when the studied ecological community contains more species or the pairwise species distance is large. For species abundance data measured using the number of individuals, we suggest calculating the unbiased Rao’s quadratic diversity index