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Automatically identifying abnormal behaviors of caged laying hens in a thermal environment improves manual management efficiency. It also provides reference indicators for breeding heat-tolerant hens. In this study, we propose a deep learning-based method for automatic recognition and evaluation of typical heat stress behaviors in hens. We developed a lightweight object detection algorithm, YOLO-HGP, based on the YOLOv8n as the baseline model. YOLO-HGP achieves Precision (P), Recall (R), and mean average precision (mAP) of 95.952%, 94.127%, and 97.667%, respectively, effectively detecting typical heat stress behaviors in hens. Compared to the original YOLO v8n, YOLO-HGP improves R, and mAP by 6.257%, and 1.963%, respectively. The FLOPs (floating point operations) and parameter count of YOLO-HGP are 4.3G and 1.729M, reducing by 47.56% and 42.58% compared to the original model. Additionally, we introduce the "ORC-ratio" (The ratio of the combined frequency of open-beak breathing and retching behaviors to the frequency of closed-beak behaviors.) as an evaluation indicator for the frequency of typical heat stress behaviors in hens and combine it with the Hybrid-SORT multiobject tracking algorithm to achieve tracking detection of individual hens. The study demonstrates that the proposed model effectively identifies and quantitatively evaluates typical behaviors of hens in a thermal environment, providing an effective approach for the automated recognition of heat stress behaviors in hens., Competing Interests: DISCLOSURES None of the authors have any conflicts of interest to declare., (Copyright © 2024. Published by Elsevier Inc.)

Excessive aromatase can reduce reproductive performance in aged roosters. Aromatase inhibitors (AI) can inhibit the aromatase activity and improve the semen quality of aged roosters. However, relevant molecular mechanism is still unclear. The purpose of this study was to explore the regulatory mechanism of AI letrozole improving semen quality in aged roosters by transcriptomic and proteomic sequencing. In this study, 56-week-old roosters were reared in separate cages on a standard basice diet and oral letrozole 42 days (D) at a daily dose 0.25 mg/kg. Semen quality and serum hormone were measured before (0 D) and after (42 D) letrozole administration. Reactive oxygen species (ROS) and mitochondrial membrane potential (MMP) were detected, respectively. The results indicated that semen volume, sperm motility, sperm density, MMP, testosterone (T) and gonadotropin releasing hormone (GnRH) in letrozole treatment group (LET) were significantly increased than those in control group (CN) (P<0.05); estradiol (E 2 ) and ROS in LET were significantly lower than those in CN (P<0.05). Through transcriptomic and proteomic analysis, we identified 189 differently expressed genes (DEGs) and 64 differentially expressed proteins (DEPs) in the comparison of LET and CN. DEGs and DEPs Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) items are mainly enriched in steroid biosynthetic process, cell differentiation and proliferation, lipid metabolic process, oxidation-reduction process and electron transfer activity. Furthermore, 8 genes including STAR, CYP17A1, NSDHL, SULT1E1, EHF, NRNPA1, PLIN2 and SDHA were identified as key genes for letrozole to regulate semen quality in aged roosters. These results indicate that letrozole can up-regulate the expression of genes related to steroid hormone synthesis, cell differentiation and proliferation, electron transfer activity, and enhance mitochondrial activity, increase testicular weight, and ultimately improve the semen quality of aged roosters., Competing Interests: Declaration of competing interest There are no conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

Forkhead box L2 (FOXL2) is an indispensable key regulator of female follicular development, and it plays important roles in the morphogenesis, proliferation, and differentiation of follicle granulosa cells, such as establishing normal estradiol signaling and regulating steroid hormone synthesis. Nevertheless, the effects of FOXL2 on granulosa cell morphology and the underlying mechanism remain unknown. Using FOXL2 ChIP-seq analysis, we found that FOXL2 target genes were significantly enriched in the actin cytoskeleton-related pathways. We confirmed that FOXL2 inhibited the expression of RhoA, a key gene for actin cytoskeleton rearrangement, by binding to TCATCCATCTCT in RhoA promoter region. In addition, FOXL2 overexpression in granulosa cells induced the depolymerization of F-actin and disordered the actin filaments, resulting in a slowdown in the expansion of granulosa cells, while FOXL2 silencing inhibited F-actin depolymerization and stabilized the actin filaments, thereby accelerating granulosa cell expansion. RhoA/ROCK pathway inhibitor Y-27632 exhibited similar effects to FOXL2 overexpression, even reversed the actin polymerization in FOXL2 silencing granulosa cells. This study revealed for the first time that FOXL2 regulated granulosa cell actin cytoskeleton by RhoA/ROCK pathway, thus affecting granulosa cell expansion. Our findings provide new insights for constructing the regulatory network of FOXL2 and propose a potential mechanism for facilitating rapid follicle expansion, thereby laying a foundation for further understanding follicular development., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The ion gate is a critical element in drift tube ion mobility spectrometry (IMS) as it directly influences the resolving power and sensitivity of the system. However, the conventional Bradbury-Nielsen gate (BNG) often leads to deformation of the ion swarm shape, resulting in reduced resolving power and significant discrimination effects. To address these limitations, we propose a novel method that incorporates a cutting phase following the gate opening. This approach effectively reduces trailing edge deformation, resulting in a maximum resolving power of over 100 and increased signal intensity. Additionally, this method maintains high resolving power even during longer gate opening times. Remarkably, this method not only significantly reduces the mobility discrimination effect but also enables the achievement of reverse discrimination by adjusting the duration of the cutting phase. Consequently, it demonstrates the potential to selectively amplify the peak height of target ions. Our method offers straightforward implementation across all IMS systems utilizing the BNG, thereby significantly improving system performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster&#95;Fruits and Single&#95;Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster&#95;Fruits on a single longan tree and the Single&#95;Fruits on a single Cluster&#95;Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster&#95;Fruits was 2.66%, while the average error rate regarding the number of Single&#95;Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Sun, Jia, Yao, Lin, Chen, Zhou, Zhou, Wu, Shi and Li.)

Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lin, Li, Jia, Chen, Huang, Elkhouchlaa, Yao, Zhou, Zhou, Li and Lu.)