An efficient segmentation model for abnormal chicken droppings recognition based on improved deep dual-resolution network.

The characteristics of chicken droppings are closely linked to their health status. In prior studies, chicken droppings recognition is treated as an object detection task, leading to challenges in labeling and missed detection due to the diverse shapes, overlapping boundaries, and dense distribution of chicken droppings. Additionally, the use of intelligent monitoring equipment equipped with edge devices in farms can significantly reduce manual labor. However, the limited computational power of edge devices presents challenges in deploying real-time segmentation algorithms for field applications. Therefore, this study redefines the task as a segmentation task, with the main objective being the development of a lightweight segmentation model for the automated monitoring of abnormal chicken droppings. A total of 60 Arbor Acres broilers were housed in 5 specific pathogen-free cages for over 3 wk, and 1650 RGB images of chicken droppings were randomly divided into training and testing sets in an 8:2 ratio to develop and test the model. Firstly, by incorporating the attention mechanism, multi-loss function, and auxiliary segmentation head, the segmentation accuracy of the DDRNet was enhanced. Then, by employing the group convolution and an advanced knowledge-distillation algorithm, a lightweight segmentation model named DDRNet-s-KD was obtained, which achieved a mean Dice coefficient (mDice) of 79.43% and an inference speed of 86.10 frames per second (FPS), showing a 2.91% and 61.2% increase in mDice and FPS compared to the benchmark model. Furthermore, the DDRNet-s-KD model was quantized from 32-bit floating-point values to 8-bit integers and then converted to TensorRT format. Impressively, the weight size of the quantized model was only 13.7 MB, representing an 82.96% reduction compared to the benchmark model. This makes it well-suited for deployment on the edge device, achieving an inference speed of 137.51 FPS on Jetson Xavier NX. In conclusion, the methods proposed in this study show significant potential in monitoring abnormal chicken droppings and can provide an effective reference for the implementation of other agricultural embedded systems.

The characteristics of chicken droppings are closely related to their health. In this study, we developed a lightweight segmentation model for chicken droppings and evaluated its inference speed on the edge device with limited computational power. The results showed that the proposed model exhibits significant potential in the early warning of abnormal chicken droppings, which can help producers implement interventions before disease outbreaks, thereby avoiding great economic losses. Additionally, the model optimization and compression processes proposed in this study can provide an effective reference for the implementation of other embedded systems.

Novel solid acid catalyst for the production of 5-hydroxymethylfurfural with fructose dehydration.

BACKGROUND: 5-Hydroxymethylfurfural (5-HMF) is a high value-added platform compound which can be obtained by dehydration of hexose under acidic conditions. OBJECTIVE: In this paper, a novel impregnation strategy for the molecular sieves (ZSM-5) as carrier and phosphotungstic acid (TPA) as active ingredient is proposed, the influence of the fructose dehydration process were studied and eco-friendliness, low-cost 5-hydroxymethylfurfural (5-HMF) was successfully obtained. METHOD: The structure surface area, pore size, acidity and microstructure of solid acid catalysts were investigated by XRD, BET, NH3-TPD and SEM. The influences of reaction temperature, reaction time, catalyst dosage on the yield of 5-hydroxymethylfurfural (5-HFM) were investigated. RESULTS: The results showed that TPA/ZSM-5 (mass ratio 20:10) has good dispersion and catalytic activity, fructose dosage 5 g, reaction temperature 140 °C, reaction time 2 h, catalyst dosage 0.5 g, and the yield of 5-hydroxymethylfurfural was 80.75% and after five times use the yield of 5-HMF remained above 75%. CONCLUSION: The novel solid acid TPA/ZSM-5 catalyst exhibited good catalytic activity and stability for the fructose dehydration to produce 5-HMF.

Bioinformatics analysis of microarray profiling identifies the mechanism of focal adhesion kinase signalling pathway in proliferation and apoptosis of breast cancer cells modulated by green tea polyphenol epigallocatechin 3-gallate.

OBJECTIVES: This study aimed to investigate potential gene and signal pathway associated with tumour progression. METHODS: Related microarray data set of breast cancer was obtained from Gene Expression Omnibus database, and differential-expressed genes (DEGs) between two control samples and two treated samples were analysed using statistical software R. We collected 50 epigallocatechin-3-gallate(EGCG)-related genes and 119 breast cancer-related genes to create a knowledge base for following pathway analysis. KEY FINDINGS: A total of 502 mRNAs were identified as DEGs based on microarray analysis. Upregulated DEGs mainly enriched in nuclear nucleosome, cell adhesion, DNA packaging complex, Wnt-activated receptor activity, etc., while the downregulated DEGs significantly enriched in ncRNA processing, mitotic nuclear division, DNA helicase activity, etc. DEGs mostly enriched in gap junction, cell cycle, oxidative phosphorylation, focal adhesion, etc. EGCG suppressed FAK signalling pathway. Furthermore, EGCG could inhibit breast cancer cell proliferation and promote apoptosis by modulating CCND1. CONCLUSIONS: Epigallocatechin 3-gallate might exert influence on breast cancer progression through inhibiting focal adhesion kinase (FAK) signalling pathway.

Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm.

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.