Over the last 100 years,significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs.Technological progress has enabled a shift from labour intensive,on-farm co...Over the last 100 years,significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs.Technological progress has enabled a shift from labour intensive,on-farm collection and processing of samples that assess yield and fat levels in milk,to large-scale processing of samples through centralised laboratories,with the scope extended to include quantification of other traits.Fourier-transform midinfrared(FT-MIR)spectroscopy has had a significant role in the transformation of milk composition phenotyping,with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally.Increasingly,there is interest in analysing the individual FT-MIR wavenumbers,and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs.This includes traits related to the nutritional value of milk,the processability of milk into products such as cheese,and traits relevant to animal health and the environment.The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits,and the genetic correlations between the FT-MIR predicted and actual trait values.Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis.The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes.Additionally,there are other molecular phenotypes such as those related to the metabolome,chromatin accessibility,and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest.Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets,and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.展开更多
AIM: To determine if Fourier-transform infrared (FT-IR)spectroscopy of endoscopic biopsies could accurately diagnose gastritis and malignancy.METHODS: A total of 123 gastroscopic samples, including 11 cases of cancero...AIM: To determine if Fourier-transform infrared (FT-IR)spectroscopy of endoscopic biopsies could accurately diagnose gastritis and malignancy.METHODS: A total of 123 gastroscopic samples, including 11 cases of cancerous tissues, 63 cases of chronic atrophic gastritis tissues, 47 cases of chronic superficial gastritis tissues and 2 cases of normal tissues, were obtained from the First Hospital of Xi'an Jiaotong University, China. A modified attenuated total reflectance (ATR) accessory was linked to a WQD-500 FT-IR spectrometer for spectral measurement followed by submission of the samples for pathologic analysis. The spectral characteristics for different types of gastroscopic tissues were summarized and correlated with the corresponding pathologic results.RESULTS: Distinct differences were observed in the FTIR spectra of normal, atrophic gastritis, superficial gastritis and malignant gastric tissues. The sensitivity of FT-IR for detection of gastric cancer, chronic atrophic gastritis and superficial gastritis was 90.9%, 82.5%, 91.5%, and specificity was 97.3%, 91.7%, 89.5% respectively.CONCLUSION: FT-IR spectroscopy can distinguish gastric inflammation from malignancy.展开更多
Cyanobacteria are gram-negative photosynthetic bacteria capable of producing toxins responsible for morbidity and mortality in humans and domestic animals. They are capable of forming concentrated blooms, referred to ...Cyanobacteria are gram-negative photosynthetic bacteria capable of producing toxins responsible for morbidity and mortality in humans and domestic animals. They are capable of forming concentrated blooms, referred to as harmful algal blooms (HABs). Characterization of HABs is necessary to reduce risks from human and animal exposures to toxins. Current methods used to classify cyanobacteria and cyanotoxins have limitations related to time, analyst skills, and cost. Fourier-Transform Infrared Spectroscopy (FTIR) is a potential tool for rapid, robust cyanobacterial classification that is not limited by these factors. To examine the practicality of this method, library screening with default software algorithms was performed on HAB samples, followed by principle component cluster analyses and dendrogram analysis of samples meeting minimum quality requirements. Two tested spectrometers and software packages were successful at distinguishing cyanobacteria from green algae. Principle component cluster analysis and dendrogram analysis also resulted in clear differentiation between cyanobacteria and green algae. While these methods cannot be used independently to fully characterize HABs, they show the potential and practicality of FTIR as a screening tool.展开更多
Direct-comb spectroscopy techniques uses optical frequency combs(OFCs)as spectroscopic light source.They deliver high sensitivity,high frequency resolution and precision in a broad spectral range.Due to these features...Direct-comb spectroscopy techniques uses optical frequency combs(OFCs)as spectroscopic light source.They deliver high sensitivity,high frequency resolution and precision in a broad spectral range.Due to these features,the field has burgeoned in recent years.In this work we constructed an OFC-based cavity-enhanced Fourier-transform spectrometer in the nearinfrared region and used it for a line-shape study of rovibrational transitions of CO perturbed by Ar.The highly sensitive measurements spanned the wavenumber range from 6270 cm^-1 to 6410 cm^-1,which covered both P and R branch of the second overtone band of CO.The spectrometer delivers high-resolution surpassing the Fourier-transform resolution limit determined by interferogram length,successfully removing ringing and broadening effects caused by instrumental line shape function.The instrumental-line-shape-free method and high signal-to-noise ratio in the measurement allowed us to observe collisional effects beyond those described by the Voigt profile.We retrieved collisional line-shape parameters by fitting the speed-dependent Voigt profile and found good agreement with the values given by precise cavity ring-down spectroscopy measurements that used a continuous-wave laser referenced to a stabilized OFC.The results demonstrate that OFC-based cavity-enhanced Fouriertransform spectroscopy is a strong tool for accurate line-shape studies that will be crucial for future spectral databases.展开更多
针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题,以傅里叶-贝塞尔级数展开(Fourier-Bessel series expansion,FBSE)为基础,提出了一种将FBSE和基于能量的尺度空间经验小波变换(energy scale space empirica...针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题,以傅里叶-贝塞尔级数展开(Fourier-Bessel series expansion,FBSE)为基础,提出了一种将FBSE和基于能量的尺度空间经验小波变换(energy scale space empirical wavelet transform,ESEWT)相结合的齿轮振动信号降噪方法,即FBSE-ESEWT。首先,将采集到的齿轮振动信号利用FBSE技术获得其频谱,以替代传统的傅里叶谱,接着凭借能量尺度空间划分法对获取的FBSE频谱进行自适应分割和筛选,以精确定位有效频带的边界点。随后通过构建小波滤波器组得到信号分量并进行重构,以减小噪声和冗余信息干扰;然后,为捕捉到更全面的特征信息将处理后的信号进行广义S变换得到时频图,输入2D卷积神经网络进行故障诊断验证算法可行性。通过对Simulink仿真信号和实际采集信号进行实验,结果表明,相对于原始经验小波变换(EWT)、经验模态分解(EMD)等方法,FBSE-ESEWT具有更好的降噪效果,信噪比提高了13.96 dB,诊断准确率高达98.03%。展开更多
基金funded by Livestock Improvement Corporation(LIC)the New Zealand Ministry for Primary Industries,through the Sustainable Food&Fibre Futures programme.
文摘Over the last 100 years,significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs.Technological progress has enabled a shift from labour intensive,on-farm collection and processing of samples that assess yield and fat levels in milk,to large-scale processing of samples through centralised laboratories,with the scope extended to include quantification of other traits.Fourier-transform midinfrared(FT-MIR)spectroscopy has had a significant role in the transformation of milk composition phenotyping,with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally.Increasingly,there is interest in analysing the individual FT-MIR wavenumbers,and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs.This includes traits related to the nutritional value of milk,the processability of milk into products such as cheese,and traits relevant to animal health and the environment.The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits,and the genetic correlations between the FT-MIR predicted and actual trait values.Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis.The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes.Additionally,there are other molecular phenotypes such as those related to the metabolome,chromatin accessibility,and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest.Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets,and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.
基金Supported by the National Natural Science Foundation of China, No. 30371604 and State Key Project of China, No. 2002CCA01900
文摘AIM: To determine if Fourier-transform infrared (FT-IR)spectroscopy of endoscopic biopsies could accurately diagnose gastritis and malignancy.METHODS: A total of 123 gastroscopic samples, including 11 cases of cancerous tissues, 63 cases of chronic atrophic gastritis tissues, 47 cases of chronic superficial gastritis tissues and 2 cases of normal tissues, were obtained from the First Hospital of Xi'an Jiaotong University, China. A modified attenuated total reflectance (ATR) accessory was linked to a WQD-500 FT-IR spectrometer for spectral measurement followed by submission of the samples for pathologic analysis. The spectral characteristics for different types of gastroscopic tissues were summarized and correlated with the corresponding pathologic results.RESULTS: Distinct differences were observed in the FTIR spectra of normal, atrophic gastritis, superficial gastritis and malignant gastric tissues. The sensitivity of FT-IR for detection of gastric cancer, chronic atrophic gastritis and superficial gastritis was 90.9%, 82.5%, 91.5%, and specificity was 97.3%, 91.7%, 89.5% respectively.CONCLUSION: FT-IR spectroscopy can distinguish gastric inflammation from malignancy.
文摘Cyanobacteria are gram-negative photosynthetic bacteria capable of producing toxins responsible for morbidity and mortality in humans and domestic animals. They are capable of forming concentrated blooms, referred to as harmful algal blooms (HABs). Characterization of HABs is necessary to reduce risks from human and animal exposures to toxins. Current methods used to classify cyanobacteria and cyanotoxins have limitations related to time, analyst skills, and cost. Fourier-Transform Infrared Spectroscopy (FTIR) is a potential tool for rapid, robust cyanobacterial classification that is not limited by these factors. To examine the practicality of this method, library screening with default software algorithms was performed on HAB samples, followed by principle component cluster analyses and dendrogram analysis of samples meeting minimum quality requirements. Two tested spectrometers and software packages were successful at distinguishing cyanobacteria from green algae. Principle component cluster analysis and dendrogram analysis also resulted in clear differentiation between cyanobacteria and green algae. While these methods cannot be used independently to fully characterize HABs, they show the potential and practicality of FTIR as a screening tool.
文摘Direct-comb spectroscopy techniques uses optical frequency combs(OFCs)as spectroscopic light source.They deliver high sensitivity,high frequency resolution and precision in a broad spectral range.Due to these features,the field has burgeoned in recent years.In this work we constructed an OFC-based cavity-enhanced Fourier-transform spectrometer in the nearinfrared region and used it for a line-shape study of rovibrational transitions of CO perturbed by Ar.The highly sensitive measurements spanned the wavenumber range from 6270 cm^-1 to 6410 cm^-1,which covered both P and R branch of the second overtone band of CO.The spectrometer delivers high-resolution surpassing the Fourier-transform resolution limit determined by interferogram length,successfully removing ringing and broadening effects caused by instrumental line shape function.The instrumental-line-shape-free method and high signal-to-noise ratio in the measurement allowed us to observe collisional effects beyond those described by the Voigt profile.We retrieved collisional line-shape parameters by fitting the speed-dependent Voigt profile and found good agreement with the values given by precise cavity ring-down spectroscopy measurements that used a continuous-wave laser referenced to a stabilized OFC.The results demonstrate that OFC-based cavity-enhanced Fouriertransform spectroscopy is a strong tool for accurate line-shape studies that will be crucial for future spectral databases.
文摘针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题,以傅里叶-贝塞尔级数展开(Fourier-Bessel series expansion,FBSE)为基础,提出了一种将FBSE和基于能量的尺度空间经验小波变换(energy scale space empirical wavelet transform,ESEWT)相结合的齿轮振动信号降噪方法,即FBSE-ESEWT。首先,将采集到的齿轮振动信号利用FBSE技术获得其频谱,以替代传统的傅里叶谱,接着凭借能量尺度空间划分法对获取的FBSE频谱进行自适应分割和筛选,以精确定位有效频带的边界点。随后通过构建小波滤波器组得到信号分量并进行重构,以减小噪声和冗余信息干扰;然后,为捕捉到更全面的特征信息将处理后的信号进行广义S变换得到时频图,输入2D卷积神经网络进行故障诊断验证算法可行性。通过对Simulink仿真信号和实际采集信号进行实验,结果表明,相对于原始经验小波变换(EWT)、经验模态分解(EMD)等方法,FBSE-ESEWT具有更好的降噪效果,信噪比提高了13.96 dB,诊断准确率高达98.03%。
