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Aspergillus flavus is an opportunistic pathogen of crops, including peanuts and maize, and is the second leading cause of aspergillosis in immunocompromised patients. A. flavus is also a major producer of the mycotoxin, aflatoxin, a potent carcinogen, which results in significant crop losses annually. The A. flavus isolate NRRL 3357 was originally isolated from peanut and has been used as a model organism for understanding the regulation and production of secondary metabolites, such as aflatoxin. A draft genome of NRRL 3357 was previously constructed, enabling the development of molecular tools and for understanding population biology of this particular species. Here, we describe an updated, near complete, telomere-to-telomere assembly and re-annotation of the eight chromosomes of A. flavus NRRL 3357 genome, accomplished via long-read PacBio and Oxford Nanopore technologies combined with Illumina short-read sequencing. A total of 13,715 protein-coding genes were predicted. Using RNA-seq data, a significant improvement was achieved in predicted 5' and 3' untranslated regions, which were incorporated into the new gene models.

With the development of convolutional neural networks, many improved algorithms have been successively proposed to promote the accuracy of dense crowd counting. However, these algorithms are deployed with expensive computing resources, which is unbearable for small devices such as embedded systems with limited computing resources. To realize the real-time counting on the small devices, it is of great significance how to trade off the computation cost and processing accuracy of the dense crowd-counting algorithm. Thus, we propose a lightweight dense crowd-counting algorithm (LCNNet) to improve this issue. Specifically, the proposed LCNNet consists of two subnetworks, a feature extraction subnetwork, and a regression subnetwork, with a bottleneck depth-separable convolution with a residuals module as the basic module. The LCNNet effectively improves computational efficiency and reduces the computational cost, which can be performed on small devices. Extensive evaluations on four benchmark datasets well demonstrate the effectiveness of the proposed LCNNet for dense crowd-counting models. Meanwhile, the proposed LCNNet can maintain a comparable level of computational accuracy and computational cost on Vehicle counting datasets. [ABSTRACT FROM AUTHOR]

Monoterpene Perillaldehyde (PAE) is a major component of the essential oil extracted from perilla plants (Perilla frutescens), which has been used as a leafy vegetable and a medicinal agent. PAE has gained a lot of attention in recent years because of its antifungal and other microbial activities and, human health benefits. PAE has also been used as food additives, perfume ingredients, and traditional medicine concoctions. Biological analyses of PAE have revealed that it has good antioxidant activities and can serve as organic fruit and food preservative. Animal studies indicated potent anticancer, anti-depressant, and anti-inflammatory effects of PAE. Also, PAE is certified "generally recognized as safe" (GRAS) and not mutagenic. However, moderation during usage is advisable, as minor adverse effects are associated with a very high dosage. Despite the newly reported findings, its properties have not been thoroughly summarized and reviewed. Also, clinical trials and official large-scale field applications of PAE in the agricultural sectors are yet to be reported. In this review, updated PAE research progress was provided, focusing on its antifungal and other antimicrobial properties and the mechanisms behind it, phytochemical profile, pharmacological effects, and safety concerns. Isolation and recovery techniques of PAE from perilla plants have been developed and improved in recent years. PAE is a potential anti-oxidant and antifungal agent that can be widely used in the food industry. PAE can be developed into drug ingredients for pharmaceutical industries due to its anti-inflammatory, anti-cancer and anti-depressant activities. PAE can be safely used in human when low and moderate dosage is used. [ABSTRACT FROM AUTHOR]

Coupling heterojunction is an effective and feasible approach to suppressing the electron–hole recombination of a photocatalyst. However, the heterojunction performance is limited by poor interface contact between two semiconductors. Therefore, an in situ partial conversion procedure was proposed to fabricate the flower-spherical BiOI/Bi5O7I (BBOI) heterojunction using bacitracin as a template. The introduction of bacitracin can regulate the assembly of nanosheets into a flower-spherical structure, which caused the BBOI photocatalysts to have larger specific surface areas than BiOI or Bi5O7I. In particular, the dosage of bacitracin can control the thermal conversion of BiOI to Bi5O7I. The in situ partial conversion of BiOI caused the formation of an intimate S-scheme heterojunction interface between BiOI and Bi5O7I, which efficiently inhibited the combination of photogenerated carriers. Upon visible-light irradiation, BBOI-3 exhibited the highest catalytic ability for the simultaneous reduction of Cr-(VI) (100%) and oxidative degradation of tetracycline hydrochloride (TH) (80.0%). This work provides an opinion to construct the high-performance heterojunction photocatalysts to environmental remediation. [ABSTRACT FROM AUTHOR]