Challenging the Estrangela / Serto Divide: Why the Standard Model of Syriac Scripts Just Doesn\u27t Work

As part of a larger digital paleography project, our team has assembled a database of tens of thousands of individual Syriac letters and letter data from 96% of extant early Syriac manuscripts that have a secure composition date. Long term, such data can help scholars develop more accurate ways to classify Syriac scripts. In the present article we use this data to illustrate just how frequently the most common way of categorizing Syriac scripts as either Estrangela or Serto does not accurately convey the ways early scribes actually wrote. In addition to challenging this “Standard Model” of Syriac scripts, the project illustrates how large data sets, digital analysis, and visual analytics can help researchers address key philological and historical problems

Mycotoxigenic potentials of Fusarium species in various culture matrices revealed by mycotoxin profiling

In this study, twenty of the most common Fusarium species were molecularly characterized and inoculated on potato dextrose agar (PDA), rice and maize medium, where thirty three targeted mycotoxins, which might be the secondary metabolites of the identified fungal species, were detected by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Statistical analysis was performed with principal component analysis (PCA) to characterize the mycotoxin profiles for the twenty fungi, suggesting that these fungi species could be discriminated and divided into three groups as follows. Group I, the fusaric acid producers, were defined into two subgroups, namely subgroup I as producers of fusaric acid and fumonisins, comprising of F. proliferatum, F. verticillioides, F. fujikuroi and F. solani, and subgroup II considered to only produce fusaric acid, including F. temperatum, F. subglutinans, F. musae, F. tricinctum, F. oxysporum, F. equiseti, F. sacchari, F. concentricum, F. andiyazi. Group II, as type A trichothecenes producers, included F. langsethiae, F. sporotrichioides, F. polyphialidicum, while Group III were found to mainly produce type B trichothecenes, comprising of F. culmorum, F. poae, F. meridionale and F. graminearum. A comprehensive picture, which presents the mycotoxin-producing patterns by the selected fungal species in various matrices, is obtained for the first time, and thus from an application point of view, provides key information to explore mycotoxigenic potentials of Fusarium species and forecast the Fusarium infestation/mycotoxins contamination

The chromosome-scale reference genome of black pepper provides insight into piperine biosynthesis

Black pepper (Piper nigrum), dubbed the ‘King of Spices’ and ‘Black Gold’, is one of the most widely used spices. Here, we present its reference genome assembly by integrating PacBio, 10x Chromium, BioNano DLS optical mapping, and Hi-C mapping technologies. The 761.2 Mb sequences (45 scaffolds with an N50 of 29.8 Mb) are assembled into 26 pseudochromosomes. A phylogenomic analysis of representative plant genomes places magnoliids as sister to the monocots-eudicots clade and indicates that black pepper has diverged from the shared Laurales-Magnoliales lineage approximately 180 million years ago. Comparative genomic analyses reveal specific gene expansions in the glycosyltransferase, cytochrome P450, shikimate hydroxycinnamoyl transferase, lysine decarboxylase, and acyltransferase gene families. Comparative transcriptomic analyses disclose berry-specific upregulated expression in representative genes in each of these gene families. These data provide an evolutionary perspective and shed light on the metabolic processes relevant to the molecular basis of species-specific piperine biosynthesis

The complex hexaploid oil‐Camellia genome traces back its phylogenomic history and multi‐omics analysis of Camellia oil biosynthesis

Summary: Oil‐Camellia (Camellia oleifera), belonging to the Theaceae family Camellia, is an important woody edible oil tree species. The Camellia oil in its mature seed kernels, mainly consists of more than 90% unsaturated fatty acids, tea polyphenols, flavonoids, squalene and other active substances, which is one of the best quality edible vegetable oils in the world. However, genetic research and molecular breeding on oil‐Camellia are challenging due to its complex genetic background. Here, we successfully report a chromosome‐scale genome assembly for a hexaploid oil‐Camellia cultivar Changlin40. This assembly contains 8.80 Gb genomic sequences with scaffold N50 of 180.0 Mb and 45 pseudochromosomes comprising 15 homologous groups with three members each, which contain 135 868 genes with an average length of 3936 bp. Referring to the diploid genome, intragenomic and intergenomic comparisons of synteny indicate homologous chromosomal similarity and changes. Moreover, comparative and evolutionary analyses reveal three rounds of whole‐genome duplication (WGD) events, as well as the possible diversification of hexaploid Changlin40 with diploid occurred approximately 9.06 million years ago (MYA). Furthermore, through the combination of genomics, transcriptomics and metabolomics approaches, a complex regulatory network was constructed and allows to identify potential key structural genes (SAD, FAD2 and FAD3) and transcription factors (AP2 and C2H2) that regulate the metabolism of Camellia oil, especially for unsaturated fatty acids biosynthesis. Overall, the genomic resource generated from this study has great potential to accelerate the research for the molecular biology and genetic improvement of hexaploid oil‐Camellia, as well as to understand polyploid genome evolution

Characterization of Non-heading Mutation in Heading Chinese Cabbage (Brassica rapa L. ssp. pekinensis)

Heading is a key agronomic trait of Chinese cabbage. A non-heading mutant with flat growth of heading leaves (fg-1) was isolated from an EMS-induced mutant population of the heading Chinese cabbage inbred line A03. In fg-1 mutant plants, the heading leaves are flat similar to rosette leaves. The epidermal cells on the adaxial surface of these leaves are significantly smaller, while those on the abaxial surface are much larger than in A03 plants. The segregation of the heading phenotype in the F2 and BC1 population suggests that the mutant trait is controlled by a pair of recessive alleles. Phytohormone analysis at the early heading stage showed significant decreases in IAA, ABA, JA and SA, with increases in methyl IAA and trans-Zeatin levels, suggesting they may coordinate leaf adaxial-abaxial polarity, development and morphology in fg-1. RNA-sequencing analysis at the early heading stage showed a decrease in expression levels of several auxin transport (BrAUX1, BrLAXs, and BrPINs) and responsive genes. Transcript levels of important ABA responsive genes, including BrABF3, were up-regulated in mid-leaf sections suggesting that both auxin and ABA signaling pathways play important roles in regulating leaf heading. In addition, a significant reduction in BrIAMT1 transcripts in fg-1 might contribute to leaf epinastic growth. The expression profiles of 19 genes with known roles in leaf polarity were significantly different in fg-1 leaves compared to wild type, suggesting that these genes might also regulate leaf heading in Chinese cabbage. In conclusion, leaf heading in Chinese cabbage is controlled through a complex network of hormone signaling and abaxial-adaxial patterning pathways. These findings increase our understanding of the molecular basis of head formation in Chinese cabbage

Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric Machines

The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar

Advances in Thermal Management Technologies of Electrical Machines

Given the fact that the operation of electrical machines generates various loss components that finally become heat, developing advanced thermal management technologies is essential to control temperature increases and to guarantee safe operations. Meanwhile, the armature winding can stand larger currents when the machines are equipped with advanced cooling systems, which directly improves torque/power densities. This paper aims to provide a systematic review of the latest developments of advanced thermal management technologies of electrical machines. According to different heat dissipation mechanisms, the cooling systems studied in this paper are categorized into five major types: enclosed housing cooling, enhanced conductive cooling, embedded heat pipe cooling, direct oil cooling, and enhanced rotor cooling. The advantages and disadvantages of these cooling systems are researched and compared comprehensively. This study contributes to the revelation of insights on the thermal management of electrical machines and offers good guidance for the thermal management of electrical machines

Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric Machines

The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar