A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices.

In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.

Long non-coding RNA lnc-OAD is required for adipocyte differentiation in 3T3-L1 preadipocytes.

Long non-coding RNAs (lncRNAs) have gained extensive attentions due to their significant roles in diverse biological process. However, the potential functions of lncRNAs participation in adipocyte differentiation have not been fully explored. Here we identified a long non-coding RNA called lnc-OAD (lncRNA associated with osteoblast and adipocyte differentiation, transcribed from 1700018A04Rik gene), which modulated 3T3-L1 adipocyte differentiation. Lnc-OAD was up-regulated expression during 3T3-L1 differentiation and stable knockdown of lnc-OAD inhibited adipocyte differentiation in 3T3-L1 cells. Further mechanisms study revealed that silencing of lnc-OAD strongly elevated the protein expression of ß-catenin, and then decreased expression of adipocyte master transcription factors PPAR-γ and C/EBPα. The addition of IWR-1 up-regulated the expression of PPAR-γ and C/EBPα and rescued the impairment of adipocyte differentiation caused by lnc-OAD knockdown. Meanwhile, we also found mitotic clonal expansion (MCE) during the early stage of adipocyte differentiation was inhibited in lnc-OAD-knockdown cells. Taken together, our study reveals a novel function of lnc-OAD in modulating adipogenesis via influencing mitotic clonal expansion and regulating WNT/ß-catenin signaling pathway.

Identification of Pyrvinium, an Anthelmintic Drug, as a Novel Anti-Adipogenic Compound Based on the Gene Expression Microarray and Connectivity Map.

Obesity is a serious health problem, while the current anti-obesity drugs are not very effective. The Connectivity Map (C-Map), an in-silico drug screening approach based on gene expression profiles, has recently been indicated as a promising strategy for drug repositioning. In this study, we performed mRNA expression profile analysis using microarray technology and identified 435 differentially expressed genes (DEG) during adipogenesis in both C3H10T1/2 and 3T3-L1 cells. Then, DEG signature was uploaded into C-Map, and using pattern-matching methods we discovered that pyrvinium, a classical anthelminthic, is a novel anti-adipogenic differentiation agent. Pyrvinium suppressed adipogenic differentiation in a dose-dependent manner, as evidenced by Oil Red O staining and the mRNA levels of adipogenic markers. Furthermore, we identified that the inhibitory effect of pyrvinium was resulted primarily from the early stage of adipogenesis. Molecular studies showed that pyrvinium downregulated the expression of key transcription factors C/EBPa and PPARγ. The mRNA levels of notch target genes Hes1 and Hey1 were obviously reduced after pyrvinium treatment. Taken together, this study identified many differentially expressed genes involved in adipogenesis and demonstrated for the first time that pyrvinium is a novel anti-adipogenic compound for obesity therapy. Meanwhile, we provided a new strategy to explore potential anti-obesity drugs.

A Digitally Dynamic Power Supply Technique for 16-Channel 12 V-Tolerant Stimulator Realized in a 0.18- µm 1.8-V/3.3-V Low-Voltage CMOS Process.

A new digitally dynamic power supply technique for 16-channel 12-V-tolerant stimulator is proposed and realized in a 0.18-µm 1.8-V/3.3-V CMOS process. The proposed stimulator uses four stacked transistors as the pull-down switch and pull-up switch to withstand 4 times the nominal supply voltage (4 × V DD). With the dc input voltage of 3.3 V, the regulated three-stage charge pump, which is capable of providing 11.3-V voltage at 3-mA loading current, achieves dc conversion efficiency of up to 69% with 400-pF integrated capacitance. Power consumption is reduced by implementing the regulated charge pump to provide a dynamic dc output voltage with a 0.5-V step. The proposed digitally dynamic power supply technique, which is implemented by using a p-type metal oxide semiconductor (PMOS) inverter with pull-down current source and digital controller, greatly improves the power efficiency of a system. The silicon area of the stimulator is approximately 3.5 mm2 for a 16-channel implementation. The functionalities of the proposed stimulator have been successfully verified through animal test.

A High-Voltage-Tolerant and Precise Charge-Balanced Neuro-Stimulator in Low Voltage CMOS Process.

This paper presents a 4 × VDD neuro-stimulator in a 0.18- µm 1.8 V/3.3 V CMOS process. The self-adaption bias technique and stacked MOS configuration are used to prevent transistors from the electrical overstress and gate-oxide reliability issue. A high-voltage-tolerant level shifter with power-on protection is used to drive the neuro-stimulator The reliability measurement of up to 100 million periodic cycles with 3000- µA biphasic stimulations in 12-V power supply has verified that the proposed neuro-stimulator is robust. Precise charge balance is achieved by using a novel current memory cell with the dual calibration loops and leakage current compensation. The charge mismatch is down to 0.25% over all the stimulus current ranges (200-300 µA) The residual average dc current is less than 6.6 nA after shorting operation.