Draft genome sequence of the mulberry tree Morus notabilis

Human utilization of the mulberry–silkworm interaction started at least 5,000 years ago and greatly influenced world history through the Silk Road. Complementing the silkworm genome sequence, here we describe the genome of a mulberry species Morus notabilis. In the 330-Mb genome assembly, we identify 128 Mb of repetitive sequences and 29,338 genes, 60.8% of which are supported by transcriptome sequencing. Mulberry gene sequences appear to evolve ~3 times faster than other Rosales, perhaps facilitating the species’ spread worldwide. The mulberry tree is among a few eudicots but several Rosales that have not preserved genome duplications in more than 100 million years; however, a neopolyploid series found in the mulberry tree and several others suggest that new duplications may confer benefits. Five predicted mulberry miRNAs are found in the haemolymph and silk glands of the silkworm, suggesting interactions at molecular levels in the plant–herbivore relationship. The identification and analyses of mulberry genes involved in diversifying selection, resistance and protease inhibitor expressed in the laticifers will accelerate the improvement of mulberry plants

Investigating event-based temporal patterns of design rainfall in a tropical region

This study investigated the temporal rainfall pattern in order to facilitate rainfall design, which normally requires a good understanding of the temporal patterns of rainstorm events. The analysis employed a storm-event-based approach using the concept of inter-event time definition (IETD) and rainfall depth/duration/intensity thresholds. The 5-min rainfall data at three raingauge stations were analysed to determine representative quartiles of a design storm in a tropical city. The temporal characteristics of the design storm could be determined from the rainfall depth ratios of consecutive peak rainfalls for each interval of storm duration, and time to the first peak rainfall depending on each quartile’s rainstorm events. The determination of the quantile distribution of tropical rainfall could help improve the representativeness of design rainfall and facilitate rainfall–runoff modelling for urban flood control in a tropical region.Ministry of Education (MOE)This work was partly supported by Academic Research Fund Tier 1 from the Ministry of Education (MOE), Singapore (2019-T1-001-160), and partly by the Chung-Ang University Research Grants in 2019

Comprehensive Evaluation of Water–Energy–Food System Security in the China–Pakistan Economic Corridor

The safety of the water–energy–food (WEF) system in the China–Pakistan Economic Corridor (CPEC) is critical to the sustainable development of resources, the economy, and society in the region. This paper uses the projection pursuit model of a real-code accelerated genetic algorithm (RAGA-PP) to comprehensively evaluate the WEF system security of the CPEC for the period 2000–2016. The results show that from 2000 to 2016, the projection value of the WEF system was reduced from 2.61 to 0.53, and the overall system security showed a downward trend. Moreover, the CPEC increased by 6.13 × 107 people, resulting in a rapid decrease in per capita water resources and decreased security of the water resources subsystem. With the rising social and economic development in recent years, the per capita energy consumption has likewise risen, leading to a decline in the energy subsystem. At the same time, the per capita grain output in the study area has increased from 185 to 205 kg, and the safety of the food subsystem has been enhanced. However, the significant increase in irrigated areas (from 1.82 × 1010 to 1.93 × 1010 hectares) has further highlighted the contradiction between the supply and demand of surface water resources, and the number of tube wells increased by 7.23 × 105, resulting in the consumption of a large amount of electricity and diesel resources. The water–energy (WE) subsystem also became less safe. With the implementation of water resources management policies over the past few decades, the proportion of agricultural water consumption dropped from 95.06% in 2000 to 93.97% in 2016, and the safety of the water–food (WF) subsystem increased. Unfortunately, agricultural irrigation consumes a large amount of power resources, leading to a reduction in the security of the energy–food (EF) subsystem. The research results from the present study could provide a scientific basis for the coordinated development of WEF systems across the CPEC region

AnatomySketch : An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development

The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human–computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software.peerReviewe

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images

Purpose Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. Methods We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading. Results For validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set. Conclusion Taking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.peerReviewe

Efficient Coproduction of Mannanase and Cellulase by the Transformation of a Codon-Optimized Endomannanase Gene from Aspergillus niger into Trichoderma reesei

Cellulase and mannanase are both important enzyme additives in animal feeds. Expressing the two enzymes simultaneously within one microbial host could potentially lead to cost reductions in the feeding of animals. For this purpose, we codon-optimized the Aspergillus niger <i>Man5A</i> gene to the codon-usage bias of Trichoderma reesei<i>.</i> By comparing the free energies and the local structures of the nucleotide sequences, one optimized sequence was finally selected and transformed into the T. reesei pyridine-auxotrophic strain TU-6. The codon-optimized gene was expressed to a higher level than the original one. Further expressing the codon-optimized gene in a mutated T. reesei strain through fed-batch cultivation resulted in coproduction of cellulase and mannanase up to 1376 U·mL^–1 and 1204 U·mL^–1, respectively