Crossing Lilium Orientals of different ploidy creates Fusarium-resistant hybrid

Oriental hybrid lily is of great commercial value, but it is susceptible to Fusarium disease that causes a significant loss to the production. A diploid Oriental hybrid resistant to Fusarium, Cai-74, was diploidized from triploid obtained from the offspring of tetraploid (from ‘Star Fighter’) and diploid (‘Con Amore’, ‘Acapulco’) by screening the hybrids of different cross combinations following inoculating Fusarium oxysporum to the tissue cultured plantlets in a greenhouse. By analyzing saponins content in bulbs of a number of lily genotypes with a known Fusarium resistance, it was found that the mutant Cai-74 had a much higher content of saponin than its parents. Highly resistant wild _L. dauricum_ had the highest level (4.59mg/g), followed by the resistant Cai-74 with 4.01mg/g. The resistant OT cultivars ‘Conca d’or’ and ‘Robina’ had a higher saponins content (3.70 mg/g) and 2.83 mg/g, than the susceptible Oriental lily cultivars ‘Sorbonne’, ‘Siberia’ and ‘Tiber’. The hybrid Cai-74 had a different karyotype compared with the normal Lilium Oriental hybrid cultivars. The results suggested that Cai-74 carries a chromosomal variation correlated to Fusarium resistance. Cai-74 might be used as a genetic resource for breeding of Fusarium resistant cultivars of Oriental hybrid lilies

Distributed Equivalent Substitution Training for Large-Scale Recommender Systems

We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features. DES introduces fully synchronous training to large-scale recommendation system for the first time by reducing communication, thus making the training of commercial recommender systems converge faster and reach better CTR. DES requires much less communication by substituting the weights-rich operators with the computationally equivalent sub-operators and aggregating partial results instead of transmitting the huge sparse weights directly through the network. Due to the use of synchronous training on large-scale Deep Learning Recommendation Models (DLRMs), DES achieves higher AUC(Area Under ROC). We successfully apply DES training on multiple popular DLRMs of industrial scenarios. Experiments show that our implementation outperforms the state-of-the-art PS-based training framework, achieving up to 68.7% communication savings and higher throughput compared to other PS-based recommender systems.Comment: Accepted by SIGIR '2020. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 202

Dynamic non-line-of-sight imaging system based on the optimization of point spread functions

Non-line-of-sight (NLOS) imaging reveals hidden objects reflected from diffusing surfaces or behind scattering media. NLOS reconstruction is usually achieved by computational deconvolution of time-resolved transient data from a scanning single-photon avalanche diode (SPAD) detection system. However, using such a system requires a lengthy acquisition, impossible for capturing dynamic NLOS scenes. We propose to use a novel SPAD array and an optimization-based computational method to achieve NLOS reconstruction of 20 frames per second (fps). The imaging system's high efficiency drastically reduces the acquisition time for each frame. The forward projection optimization method robustly reconstructs NLOS scenes from low SNR data collected by the SPAD array. Experiments were conducted over a wide range of dynamic scenes in comparison with confocal and phase-field methods. Under the same exposure time, the proposed algorithm shows superior performances among state-of-the-art methods. To better analyze and validate our system, we also used simulated scenes to validate the advantages through quantitative benchmarks such as PSNR, SSIM and total variation analysis. Our system is anticipated to have the potential to achieve video-rate NLOS imaging

Non-line-of-sight imaging over 1.43 km

Non-line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial-temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial-temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.https://www.pnas.org/content/pnas/118/10/e2024468118.full.pdfPublished versio

Associations of Educational Attainment, Occupation, Social Class and Major Depressive Disorder among Han Chinese Women

Background The prevalence of major depressive disorder (MDD) is higher in those with low levels of educational attainment, the unemployed and those with low social status. However the extent to which these factors cause MDD is unclear. Most of the available data comes from studies in developed countries, and these findings may not extrapolate to developing countries. Examining the relationship between MDD and socio economic status in China is likely to add to the debate because of the radical economic and social changes occurring in China over the last 30 years. Principal findings We report results from 3,639 Chinese women with recurrent MDD and 3,800 controls. Highly significant odds ratios (ORs) were observed between MDD and full time employment (OR = 0.36, 95% CI = 0.25–0.46, logP = 78), social status (OR = 0.83, 95% CI = 0.77–0.87, logP = 13.3) and education attainment (OR = 0.90, 95% CI = 0.86–0.90, logP = 6.8). We found a monotonic relationship between increasing age and increasing levels of educational attainment. Those with only primary school education have significantly more episodes of MDD (mean 6.5, P-value = 0.009) and have a clinically more severe disorder, while those with higher educational attainment are likely to manifest more comorbid anxiety disorders. Conclusions In China lower socioeconomic position is associated with increased rates of MDD, as it is elsewhere in the world. Significantly more episodes of MDD occur among those with lower educational attainment (rather than longer episodes of disease), consistent with the hypothesis that the lower socioeconomic position increases the likelihood of developing MDD. The phenomenology of MDD varies according to the degree of educational attainment: higher educational attainment not only appears to protect against MDD but alters its presentation, to a more anxious phenotype