Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images: They either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. Then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about the gland by explicitly grouping the semantics of its sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated, complete predictions. We conduct experiments on GlaS dataset and CRAG dataset. Our method exceeds the second-best counterpart over 10.56% at mIOU.Comment: MICCAI 2023 Accepte

Dissecting the genome-wide evolution and function of R2R3-MYB transcription factor family in Rosa chinensis

Rosa chinensis, an important ancestor species of Rosa hybrida, the most popular ornamental plant species worldwide, produces flowers with diverse colors and fragrances. The R2R3-MYB transcription factor family controls a wide variety of plant-specific metabolic processes, especially phenylpropanoid metabolism. Despite their importance for the ornamental value of flowers, the evolution of R2R3-MYB genes in plants has not been comprehensively characterized. In this study, 121 predicted R2R3-MYB gene sequences were identified in the rose genome. Additionally, a phylogenomic synteny network (synnet) was applied for the R2R3-MYB gene families in 35 complete plant genomes. We also analyzed the R2R3-MYB genes regarding their genomic locations, Ka/Ks ratio, encoded conserved motifs, and spatiotemporal expression. Our results indicated that R2R3-MYBs have multiple synteny clusters. The RcMYB114a gene was included in the Rosaceae-specific Cluster 54, with independent evolutionary patterns. On the basis of these results and an analysis of RcMYB114a-overexpressing tobacco leaf samples, we predicted that RcMYB114a functions in the phenylpropanoid pathway. We clarified the relationship between R2R3-MYB gene evolution and function from a new perspective. Our study data may be relevant for elucidating the regulation of floral metabolism in roses at the transcript level

Generic photonic integrated linear operator processor

Photonic integration platforms have been explored extensively for optical computing with the aim of breaking the speed and power efficiency limitations of traditional digital electronic computers. Current technologies typically focus on implementing a single computation iteration optically while leaving the intermediate processing in the electronic domain, which are still limited by the electronic bottlenecks. Few explorations have been made of all-optical recursive architectures for computations on integrated photonic platforms. Here we propose a generic photonic integrated linear operator processor based on an all-optical recursive system that supports linear operations ranging from matrix computations to solving equations. We demonstrate the first all-optical on-chip matrix inversion system and use this to solve integral and differential equations. The absence of electronic processing during multiple iterations indicates the potential for an orders-of-magnitudes speed enhancement of this all-optical computing approach compared to electronic computers. We realize matrix inversions, Fredholm integral equations of the second kind, 2^{nd} order ordinary differential equations, and Poisson equations using the generic photonic integrated linear operator processor

High Performance 400 Gigabit Ethernet Links using Hybrid Multiband CAP/QAM Scheme

We propose the first combined 4×100Gb/s hybrid multiband CAP-16 transmitter and QAM-16 receiver system and simulations show that it has 0.7 dBo (2.2 dBo) more power margin than 8×50Gb/s (4×100Gb/s) PAM-4 over DML (EML) SMF link.This work was partly supported by the European Union under a Marie Curie Intra-European Fellowship for Career Development (CEEOALAN project) and by the UK EPSRC via the INTERNET project.This is the accepted manuscript. The final version is available at https://www.osapublishing.org/abstract.cfm?uri=OFC-2015-Th2A.65

Redesigning spectroscopic sensors with programmable photonic circuits

Optical spectroscopic sensors are a powerful tool to reveal light-matter interactions in many fields, such as physics, biology, chemistry, and astronomy. Miniaturizing the currently bulky spectrometers has become imperative for the wide range of applications that demand in situ or even in vitro characterization systems, a field that is growing rapidly. Benchtop spectrometers are capable of offering superior resolution and spectral range, but at the expense of requiring a large size. In this paper, we propose a novel method that redesigns spectroscopic sensors via the use of programmable photonic circuits. Drawing from compressive sensing theory, we start by investigating the most ideal sampling matrix for a reconstructive spectrometer and reveal that a sufficiently large number of sampling channels is a prerequisite for both fine resolution and low reconstruction error. This number is, however, still considerably smaller than that of the reconstructed spectral pixels, benefitting from the nature of reconstruction algorithms. We then show that the cascading of a few engineered MZI elements can be readily programmed to create an exponentially scalable number of such sampling spectral responses over an ultra-broad bandwidth, allowing for ultra-high resolution down to single-digit picometers without incurring additional hardware costs. Experimentally, we implement an on-chip spectrometer with a fully-programmable 6-stage cascaded MZI structure and demonstrate a 200 nm bandwidth using only 729 sampling channels. This achieves a bandwidth-to-resolution ratio of over 20,000, which is, to our best knowledge, about one order of magnitude greater than any reported miniaturized spectrometers to date. We further illustrate that by employing dispersion-engineered waveguide components, the device bandwidth can be extended to over 400 nm

Towards efficient and reconfigurable next-generation optical fronthaul networks for massive MIMO

This paper summaries our recent research on digital radio over fibre (DRoF) based optical fronthaul links and experimentally demonstrates a novel last-mile wireless coverage system incorporating data compression, time-division multiplexing (TDM) based packetization, and wavelength division multiplexing (WDM) based optical transmission. Compression reduces the fronthaul data rate required per service by a factor of 3 when compared with the common public radio interface (CPRI) standard, enabling efficient radio resource distribution over optical fibre infrastructure. The new packetization mechanism and WDM architecture enable fully reconfigurable resource allocation in a fronthaul network for 20MHz-bandwidth RF inputs with 64x64 MIMO carried over an aggregated compressed optical data rate of 32Gbps using 4 wavelengths. The experimental results show over 40dB RF dynamic range with < 8% error value magnitude (EVM) for the 64 quadrature amplitude modulation (64-QAM) input signals across all the WDM channels while the lowest EVM is less than 2%. Meanwhile, this field-programmable gate array (FPGA) based DRoF system allows flexible, software definable and easy-scalable dynamic antenna resource allocatio

Identification and QTL Analysis of Flavonoids and Carotenoids in Tetraploid Roses Based on an Ultra-High-Density Genetic Map

Roses are highly valuable within the flower industry. The metabolites of anthocyanins, flavonols, and carotenoids in rose petals are not only responsible for the various visible petal colors but also important bioactive compounds that are important for human health. In this study, we performed a QTL analysis on pigment contents to locate major loci that determine the flower color traits. An F1 population of tetraploid roses segregating for flower color was used to construct an ultra-high-density genetic linkage map using whole-genome resequencing technology to detect genome-wide SNPs. Previously developed SSR and SNP markers were also utilized to increase the marker density. Thus, a total of 9,259 markers were mapped onto seven linkage groups (LGs). The final length of the integrated map was 1285.11 cM, with an average distance of 0.14 cM between adjacent markers. The contents of anthocyanins, flavonols and carotenoids of the population were assayed to enable QTL analysis. Across the 33 components, 46 QTLs were detected, explaining 11.85–47.72% of the phenotypic variation. The mapped QTLs were physically clustered and primarily distributed on four linkage groups, namely LG2, LG4, LG6, and LG7. These results improve the basis for flower color marker-assisted breeding of tetraploid roses and guide the development of rose products

Genome-wide DNA polymorphisms in two cultivars of mei (Prunus mume sieb. et zucc.)

BACKGROUND: Mei (Prunus mume Sieb. et Zucc.) is a famous ornamental plant and fruit crop grown in East Asian countries. Limited genetic resources, especially molecular markers, have hindered the progress of mei breeding projects. Here, we performed low-depth whole-genome sequencing of Prunus mume ‘Fenban’ and Prunus mume ‘Kouzi Yudie’ to identify high-quality polymorphic markers between the two cultivars on a large scale. RESULTS: A total of 1464.1 Mb and 1422.1 Mb of ‘Fenban’ and ‘Kouzi Yudie’ sequencing data were uniquely mapped to the mei reference genome with about 6-fold coverage, respectively. We detected a large number of putative polymorphic markers from the 196.9 Mb of sequencing data shared by the two cultivars, which together contained 200,627 SNPs, 4,900 InDels, and 7,063 SSRs. Among these markers, 38,773 SNPs, 174 InDels, and 418 SSRs were distributed in the 22.4 Mb CDS region, and 63.0% of these marker-containing CDS sequences were assigned to GO terms. Subsequently, 670 selected SNPs were validated using an Agilent’s SureSelect solution phase hybridization assay. A subset of 599 SNPs was used to assess the genetic similarity of a panel of mei germplasm samples and a plum (P. salicina) cultivar, producing a set of informative diversity data. We also analyzed the frequency and distribution of detected InDels and SSRs in mei genome and validated their usefulness as DNA markers. These markers were successfully amplified in the cultivars and in their segregating progeny. CONCLUSIONS: A large set of high-quality polymorphic SNPs, InDels, and SSRs were identified in parallel between ‘Fenban’ and ‘Kouzi Yudie’ using low-depth whole-genome sequencing. The study presents extensive data on these polymorphic markers, which can be useful for constructing high-resolution genetic maps, performing genome-wide association studies, and designing genomic selection strategies in mei

Silicon photonic 2.5D integrated multi-chip module receiver

We demonstrate the first 2.5D integrated, wavelength division multiplexing, silicon photonic receiver. The multi-chip module utilizes a silicon interposer to integrate the four-channel photonic cascaded microdisk receiver with four electronic transimpedance amplifiers