Camellia oil,which contains a high content of triterpene alcohol,is known for a series of bioactivities including anti-inflammation.Amyrins are recognized as high bioactivity of anti-inflammation.However,no comparativ...Camellia oil,which contains a high content of triterpene alcohol,is known for a series of bioactivities including anti-inflammation.Amyrins are recognized as high bioactivity of anti-inflammation.However,no comparative study on triterpene alcohols from camellia oil.In this study,four high content triterpene alcohols from camellia oil,namelyβ-amyrin,ψ-taraxasterol,parkeol,and butyrospermol were evaluated through lipopolysaccharide induced RAW264.7 cell inflammation.The results showed that butyrospermol exhibited the highest anti-inflammatory activity,surpassing that ofβ-amyrin.Characterization of signaling pathways showed that butyrospermol inhibited Toll-like receptor 4(TLR4),nuclear factor KB(NF-kB)and mitogenactivated protein kinase(MAPK)pathways,suppressing the transcription of Tlr4,expression of p65,NF-kB inhibitorα(IkBa),extracellular signal-regulated kinase(ERK),c-Jun N-terminal kinase(JNK),and p38,and the phosphorylation of p65,IkBa,ERK,and p38.The anti-inflammatory effect of butyrospermol was further validated by phorbol 12-myristate 13-acetate induced mouse ear edema.The results in mouse showed that butyrospermol could inhibit the increase of tumor necrosis factorα(TNF-α),interleukin 1β(IL-1β),p-JNK,P-p38,p-IkBa,and their corresponding mRNA levels.Our study provides new perspective on the antiinflammatory role of different triterpene alcohols and explaining the bioactivity of camellia oil.展开更多
Single-cell RNA sequencing(scRNA-seq)technology enables a deep understanding of cellular differentiation during plant development and reveals heterogeneity among the cells of a given tissue.However,the computational c...Single-cell RNA sequencing(scRNA-seq)technology enables a deep understanding of cellular differentiation during plant development and reveals heterogeneity among the cells of a given tissue.However,the computational characterization of such cellular heterogeneity is complicated by the high dimensionality,sparsity,and biological noise inherent to the raw data.Here,we introduce PhytoCluster,an unsupervised deep learning algorithm,to cluster scRNA-seq data by extracting latent features.We benchmarked PhytoCluster against four simulated datasets and five real scRNA-seq datasets with varying protocols and data quality levels.A comprehensive evaluation indicated that PhytoCluster outperforms other methods in clustering accuracy,noise removal,and signal retention.Additionally,we evaluated the performance of the latent features extracted by PhytoCluster across four machine learning models.The computational results highlight the ability of PhytoCluster to extract meaningful information from plant scRNA-seq data,with machine learning models achieving accuracy comparable to that of raw features.We believe that PhytoCluster will be a valuable tool for disentangling complex cellular heterogeneity based on scRNA-seq data.展开更多
Machine learning and deep learning are extensively employed in genomic selection(GS)to expedite the identification of superior genotypes and accelerate breeding cycles.However,a significant challenge with current data...Machine learning and deep learning are extensively employed in genomic selection(GS)to expedite the identification of superior genotypes and accelerate breeding cycles.However,a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability.To address these challenges,we developed Cropformer,a deep learning framework for predicting crop phenotypes and exploring downstream tasks.This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy.The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops:maize,rice,wheat,foxtail millet,and tomato.Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness,achieving up to a 7.5%improvement in prediction accuracy compared to the runner-up model.Additionally,Cropformer enhances the analysis and mining of genes associated with traits.We identified numerous single nucleotide polymorphisms(SNPs)with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences.Cropformer represents a significant advancement in predictive performance and gene identification,providing a powerful general tool for improving genomic design in crop breeding.Cropformer is freely accessible at https://cgris.net/cropformer.展开更多
We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phasechange ...We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phasechange material (PCM) photonic switches. In this neuromorphic system, the passive silicon-based ADRMR,equipped with a power-tunable auxiliary light, effectively demonstrates nonlinearity-induced dual neural dynamics encompassing spiking response and synaptic plasticity that can generate single-wavelength optical neural spikes with synaptic weight. By cascading these ADRMRs with different resonant wavelengths, weighted multiple-wavelength spikes can be feasibly output from the ADRMR-based hardware arrays when external wavelengthaddressable optical pulses are injected;subsequently, the cumulative power of these weighted output spikes is utilized to ascertain the activation status of the reconfigurable PCM photonic switches. Moreover, the reconfigurable mechanism driving the interconversion of the PCMs between the resonant-bonded crystalline states and the covalent-bonded amorphous states is achieved through precise thermal modulation. Drawing from the thermal properties, an innovative thermodynamic leaky integrate-and-firing (TLIF) neuron system is proposed. With the TLIF neuron system as the fundamental unit, a fully connected SNN is constructed to complete a classic deep learning task:the recognition of handwritten digit patterns. The simulation results reveal that the exemplary SNN can effectively recognize 10 numbers directly in the optical domain by employing the surrogate gradient algorithm. The theoretical verification of our architecture paves a whole new path for integrated photonic SNNs, with the potential to advance the field of neuromorphic photonic systems and enable more efficient spiking information processing.展开更多
Objective To summarize the diagnosis and treatment experience of children with intermittent hydronephrosis caused by ureteral fibroepithelial polyp(UFP).Methods From 2017 to 2020,cases of hydronephrosis caused by uret...Objective To summarize the diagnosis and treatment experience of children with intermittent hydronephrosis caused by ureteral fibroepithelial polyp(UFP).Methods From 2017 to 2020,cases of hydronephrosis caused by ureteral polyp in Anhui Provincial Children’s Hospital were retrospectively enrolled for investigation.Demographic data,clinical manifestations,operation details,pathology and outcomes were collected from patients’medical data for analysis.Results All seven cases of UFP were boys,including six cases on the left side of the ureter and one case on the right side,at the median age of 7.1 years(3-14 years),with abdominal intermittent pain as the first symptom.All cases underwent laparoscopic pyeloureteroplasty.All the operations were completed successfully;postoperative pathology indicated the presence of primary UFP.Postoperative follow-ups of 1-30 months showed satisfactory recovery and relief from hydronephrosis.Conclusions Laparoscopic pyeloureteroplasty or ureteroureterostomy is one of the optimal treatments for ureteral polyp at present.The surgical method should be determined according to the number of polyps,the length and the diameter of the affected ureter,and also the status of renal function of the patients.展开更多
We present the performance analysis of ll2Gb/s-4 wavelength division multiplexing （WDM） 100GHz channel spacing polarization division multiplexed-differential quadrature phase shift keying （PDM-DQPSK） optical label...We present the performance analysis of ll2Gb/s-4 wavelength division multiplexing （WDM） 100GHz channel spacing polarization division multiplexed-differential quadrature phase shift keying （PDM-DQPSK） optical label switching system with frequency swept coherent detected spectral amplitude code labels. Direct detection is chosen to demodulate the payload by applying a polarization tracker, while 4-bits of 156Mb/s spectral amplitude code label is coherently detected with a scheme of frequently-swept coherent detection. We optimize the payload laser linewidth as well as the frequency spacing between the payload and label. The label and payload signal performances are assessed by the eye-diagram opening factor （EOF） and bit-error rate （BER） at 10 9 as a function of the received optical power （ROP） and the optical signal to noise ratio （OSNR）. The payload could well be demodulated after 900 km at a bit error rate of 10-3 using forward error correction （FEC）.展开更多
基金the Key R&D Plan of Shaanxi Province(2022GD-TSLD-58-5)Shaanxi Science and Technology Innovation Team Project(2024RS-CXTD-70)for the financial support.
文摘Camellia oil,which contains a high content of triterpene alcohol,is known for a series of bioactivities including anti-inflammation.Amyrins are recognized as high bioactivity of anti-inflammation.However,no comparative study on triterpene alcohols from camellia oil.In this study,four high content triterpene alcohols from camellia oil,namelyβ-amyrin,ψ-taraxasterol,parkeol,and butyrospermol were evaluated through lipopolysaccharide induced RAW264.7 cell inflammation.The results showed that butyrospermol exhibited the highest anti-inflammatory activity,surpassing that ofβ-amyrin.Characterization of signaling pathways showed that butyrospermol inhibited Toll-like receptor 4(TLR4),nuclear factor KB(NF-kB)and mitogenactivated protein kinase(MAPK)pathways,suppressing the transcription of Tlr4,expression of p65,NF-kB inhibitorα(IkBa),extracellular signal-regulated kinase(ERK),c-Jun N-terminal kinase(JNK),and p38,and the phosphorylation of p65,IkBa,ERK,and p38.The anti-inflammatory effect of butyrospermol was further validated by phorbol 12-myristate 13-acetate induced mouse ear edema.The results in mouse showed that butyrospermol could inhibit the increase of tumor necrosis factorα(TNF-α),interleukin 1β(IL-1β),p-JNK,P-p38,p-IkBa,and their corresponding mRNA levels.Our study provides new perspective on the antiinflammatory role of different triterpene alcohols and explaining the bioactivity of camellia oil.
基金supported by the National Natural Science Foundation of China(32371996 and 62372158)the National Key R&D Program of China(2022YFF0711802)+1 种基金the STI 2030-Major Projects(2022ZD04017)the National Key Research and Development Program of China(2019YFA0802202 and 2020YFA0803401).
文摘Single-cell RNA sequencing(scRNA-seq)technology enables a deep understanding of cellular differentiation during plant development and reveals heterogeneity among the cells of a given tissue.However,the computational characterization of such cellular heterogeneity is complicated by the high dimensionality,sparsity,and biological noise inherent to the raw data.Here,we introduce PhytoCluster,an unsupervised deep learning algorithm,to cluster scRNA-seq data by extracting latent features.We benchmarked PhytoCluster against four simulated datasets and five real scRNA-seq datasets with varying protocols and data quality levels.A comprehensive evaluation indicated that PhytoCluster outperforms other methods in clustering accuracy,noise removal,and signal retention.Additionally,we evaluated the performance of the latent features extracted by PhytoCluster across four machine learning models.The computational results highlight the ability of PhytoCluster to extract meaningful information from plant scRNA-seq data,with machine learning models achieving accuracy comparable to that of raw features.We believe that PhytoCluster will be a valuable tool for disentangling complex cellular heterogeneity based on scRNA-seq data.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the National Natural Sci-ence Foundation of China(Grant No.32371996 to S.Y.)+1 种基金the Central Public-interest Scientific Institution Basal Research Fund of China(S2023QH09 to S.Y.)the Agricultural Science and Technology Inno-vation Program(CAAS-ASTIP-2023-ICS01 to the Innovation Team of Crop Germplasm Resources Preservation and Information).
文摘Machine learning and deep learning are extensively employed in genomic selection(GS)to expedite the identification of superior genotypes and accelerate breeding cycles.However,a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability.To address these challenges,we developed Cropformer,a deep learning framework for predicting crop phenotypes and exploring downstream tasks.This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy.The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops:maize,rice,wheat,foxtail millet,and tomato.Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness,achieving up to a 7.5%improvement in prediction accuracy compared to the runner-up model.Additionally,Cropformer enhances the analysis and mining of genes associated with traits.We identified numerous single nucleotide polymorphisms(SNPs)with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences.Cropformer represents a significant advancement in predictive performance and gene identification,providing a powerful general tool for improving genomic design in crop breeding.Cropformer is freely accessible at https://cgris.net/cropformer.
基金National Natural Science Foundation of China(62171087)Sichuan Science and Technology Program(2021JDJQ0023)Fundamental Research Funds for the Central Universities (ZYGX2019J003)。
文摘We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phasechange material (PCM) photonic switches. In this neuromorphic system, the passive silicon-based ADRMR,equipped with a power-tunable auxiliary light, effectively demonstrates nonlinearity-induced dual neural dynamics encompassing spiking response and synaptic plasticity that can generate single-wavelength optical neural spikes with synaptic weight. By cascading these ADRMRs with different resonant wavelengths, weighted multiple-wavelength spikes can be feasibly output from the ADRMR-based hardware arrays when external wavelengthaddressable optical pulses are injected;subsequently, the cumulative power of these weighted output spikes is utilized to ascertain the activation status of the reconfigurable PCM photonic switches. Moreover, the reconfigurable mechanism driving the interconversion of the PCMs between the resonant-bonded crystalline states and the covalent-bonded amorphous states is achieved through precise thermal modulation. Drawing from the thermal properties, an innovative thermodynamic leaky integrate-and-firing (TLIF) neuron system is proposed. With the TLIF neuron system as the fundamental unit, a fully connected SNN is constructed to complete a classic deep learning task:the recognition of handwritten digit patterns. The simulation results reveal that the exemplary SNN can effectively recognize 10 numbers directly in the optical domain by employing the surrogate gradient algorithm. The theoretical verification of our architecture paves a whole new path for integrated photonic SNNs, with the potential to advance the field of neuromorphic photonic systems and enable more efficient spiking information processing.
文摘Objective To summarize the diagnosis and treatment experience of children with intermittent hydronephrosis caused by ureteral fibroepithelial polyp(UFP).Methods From 2017 to 2020,cases of hydronephrosis caused by ureteral polyp in Anhui Provincial Children’s Hospital were retrospectively enrolled for investigation.Demographic data,clinical manifestations,operation details,pathology and outcomes were collected from patients’medical data for analysis.Results All seven cases of UFP were boys,including six cases on the left side of the ureter and one case on the right side,at the median age of 7.1 years(3-14 years),with abdominal intermittent pain as the first symptom.All cases underwent laparoscopic pyeloureteroplasty.All the operations were completed successfully;postoperative pathology indicated the presence of primary UFP.Postoperative follow-ups of 1-30 months showed satisfactory recovery and relief from hydronephrosis.Conclusions Laparoscopic pyeloureteroplasty or ureteroureterostomy is one of the optimal treatments for ureteral polyp at present.The surgical method should be determined according to the number of polyps,the length and the diameter of the affected ureter,and also the status of renal function of the patients.
文摘We present the performance analysis of ll2Gb/s-4 wavelength division multiplexing （WDM） 100GHz channel spacing polarization division multiplexed-differential quadrature phase shift keying （PDM-DQPSK） optical label switching system with frequency swept coherent detected spectral amplitude code labels. Direct detection is chosen to demodulate the payload by applying a polarization tracker, while 4-bits of 156Mb/s spectral amplitude code label is coherently detected with a scheme of frequently-swept coherent detection. We optimize the payload laser linewidth as well as the frequency spacing between the payload and label. The label and payload signal performances are assessed by the eye-diagram opening factor （EOF） and bit-error rate （BER） at 10 9 as a function of the received optical power （ROP） and the optical signal to noise ratio （OSNR）. The payload could well be demodulated after 900 km at a bit error rate of 10-3 using forward error correction （FEC）.
