Pathological bone loss is caused by an imbalance between bone formation and resorption.The bone microenvironments are hypoxic,and hypoxia-inducible factor (HIF) is known to play notable roles in bone remodeling.Howeve...Pathological bone loss is caused by an imbalance between bone formation and resorption.The bone microenvironments are hypoxic,and hypoxia-inducible factor (HIF) is known to play notable roles in bone remodeling.However,the relevant functions of HIF-2α are not well understood.Here,we have shown that HIF-2α deficiency in mice enhances bone mass through its effects on the differentiation of osteoblasts and osteoclasts.In vitro analyses revealed that HIF-2α inhibits osteoblast differentiation by targeting Twist2 and stimulates RANKL-induced osteoclastogenesis via regulation of Traf6.In addition,HIF-2α appears to contribute to the crosstalk between osteoblasts and osteoclasts by directly targeting RANKL in osteoprogenitor cells.Experiments performed with osteoblast- and osteoclast-specific conditional knockout mice supported a role of HIF-2α in this crosstalk.HIF-2α deficiency alleviated ovariectomy-induced bone loss in mice,and specific inhibition of HIF-2α with ZINC04179524 significantly blocked RANKLmediated osteoclastogenesis.Collectively,our results suggest that HIF-2α functions as a catabolic regulator in bone remodeling,which is critical for the maintenance of bone homeostasis.展开更多
This study evaluates the efficacy of the hyperspectral imaging(HSI)technique for the nondestructive detection of adulteration ratios of Phytophthora blight-infected red pepper powder(PBRPP)to red pepper powder(RPP).PB...This study evaluates the efficacy of the hyperspectral imaging(HSI)technique for the nondestructive detection of adulteration ratios of Phytophthora blight-infected red pepper powder(PBRPP)to red pepper powder(RPP).PBRPP contains elevated concentrations of capsaicinoids,phenolic acids,and carotenoids,which may result in increased pungency and bitterness,potentially diminishing consumer acceptance.Partial least squares discriminant analysis(PLS-DA)models were employed to differentiate between different adulteration levels of PBRPP(0%,5%,10%,15%,and 20%).The accuracy of the developed model was 87.5%when coupled with multiplicative scatter correction(MSC)preprocessing.Key wavelengths identified at 950 nm,1399 nm,1453 nm,and 1501 nm were instrumental in detecting these adulterants because of their association with water content,capsaicin levels,and other critical bioactive compounds.Visualized distribution maps generated from HSI effectively demonstrated the spatial distribution of adulterated powders,with colorimetric shifts corresponding to increasing PBRPP levels.These findings suggest that HSI,when combined with effective pre-processing and visualization techniques,can significantly enhance the quality control of RPP products.展开更多
This study investigated the utilization of hyperspectral imaging(HSI)in conjunction with pixel-based segmentation to predict the thiobarbituric acid-reactive substances(TBARS)and volatile basic nitrogen(VBN)content in...This study investigated the utilization of hyperspectral imaging(HSI)in conjunction with pixel-based segmentation to predict the thiobarbituric acid-reactive substances(TBARS)and volatile basic nitrogen(VBN)content in beef.Hyperspectral images were acquired in the visible near-infrared(VIS-NIR)and shortwave infrared(SWIR)ranges to examine temporal alterations in the fat and protein regions.A partial least squares discriminant analysis(PLS-DA)model was employed to segment fat and protein pixels,followed by a partial least squares regression(PLSR)model to predict the TBARS and VBN content from the segmented spectra.The SWIR range yielded the most accurate predictions,with an R_(p)^(2) of 0.899 for the early freshness indicators.Utilizing hyperspectral information from individual fat and protein pixels,rather than modeling the entire beef image,resulted in enhanced prediction accuracy for R_(p)^(2) of TBARS(0.814-0.899)and VBN(0.394-0.532)in the early stages of storage.These findings elucidate the potential of HSI with pixel-based segmentation as a nondestructive and realtime methodology for precise monitoring of beef freshness.展开更多
The quality of laver is significantly affected by adulteration with sea lettuce(Ulva lactuca),a green seaweed that adheres to Pyropia nets during cultivation and adversely impacts productivity and quality.Acid treatme...The quality of laver is significantly affected by adulteration with sea lettuce(Ulva lactuca),a green seaweed that adheres to Pyropia nets during cultivation and adversely impacts productivity and quality.Acid treatment agents are commonly utilized;however,residual sea lettuce may persist in the final product if treatment is insufficient.Traditional detection methods,such as sensory evaluation,are susceptible to human error,time-consuming,and inefficient,while DNA sequencing is ineffective for processed laver due to DNA degradation.Given these limitations,non-destructive technologies are garnering interest in seafood quality assessment.This study evaluates the potential of hyperspectral imaging for detecting sea lettuce in laver.Hypercubes collected in two spectral ranges(visible/near-infrared(VIS/NIR)and short-wave infrared(SWIR))were utilized to establish a partial least squares regression(PLSR)model for quantification.Characteristic wavelengths were selected using competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and their hybrid methods(CARS-UVE,UVE-CARS).Model efficiency and robustness improved with spectral feature selection.For raw laver,UVE-CARS achieved the highest Rp2(0.86)with 14.3%of full wavelengths in VIS/NIR,while for dried laver,SWIR with CARS-UVE yielded Rp2(0.90)using 18.3%of full wavelengths.This study addresses a critical gap in seafood quality control by demonstrating that hyperspectral imaging enables non-destructive,efficient quantification of sea lettuce contamination in laver,contributing to improved industry standards.展开更多
This study aimed to use hyperspectral imaging in nondestructive monitoring of the changes in radish quality during salting process and to enhance the efficiency and accuracy of the prediction models through feature se...This study aimed to use hyperspectral imaging in nondestructive monitoring of the changes in radish quality during salting process and to enhance the efficiency and accuracy of the prediction models through feature selection techniques.Hyperspectral imaging was utilized to predict the salinity,moisture content,and work of penetration(WOP)of radishes.Salinity increased with the prolonged salting time,whereas the moisture content and WOP decreased.Prediction using a partial least squares regression(PLSR)model based on full-wavelength hyperspectral data,resulted in the Rp2 values for salinity,moisture content,and WOP being 0.909,0.725,and 0.705,respectively.Feature selection methods,including competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and their combinations,were applied to extract informative wavelengths for each quality parameter.With UVE+CARS,high Rp2 values were achieved for salinity(0.934)and moisture content(0.846)using only 32.2%and 25.7%of the full-wavelengths,respectively.Similarly,Rp2 values for WOP(0.717)improved with CARS,utilizing 31.8%of the full-wavelengths.Hyperspectral imaging,coupled with suitable feature selection,not only improved the efficiency and accuracy of quality assessment during the salting process but also enabled the simultaneous prediction of key chemical and physical attributes of salted radishes with informative wavelength selection.This approach provides an efficient strategy for monitoring the quality of salted vegetables,offering valuable insights for optimizing industrial salting processes and advancing real-time quality control applications.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant by the Korea government(MSIT)(2012R1A5A2A39671455,NRF-2015R1D1A1A01057870,and NRF-2018R1A2B2006033)the Korea Healthcare Technology R&D Project of the Korea Health Industry Development Institute(HI16C0287 and H114C3484)
文摘Pathological bone loss is caused by an imbalance between bone formation and resorption.The bone microenvironments are hypoxic,and hypoxia-inducible factor (HIF) is known to play notable roles in bone remodeling.However,the relevant functions of HIF-2α are not well understood.Here,we have shown that HIF-2α deficiency in mice enhances bone mass through its effects on the differentiation of osteoblasts and osteoclasts.In vitro analyses revealed that HIF-2α inhibits osteoblast differentiation by targeting Twist2 and stimulates RANKL-induced osteoclastogenesis via regulation of Traf6.In addition,HIF-2α appears to contribute to the crosstalk between osteoblasts and osteoclasts by directly targeting RANKL in osteoprogenitor cells.Experiments performed with osteoblast- and osteoclast-specific conditional knockout mice supported a role of HIF-2α in this crosstalk.HIF-2α deficiency alleviated ovariectomy-induced bone loss in mice,and specific inhibition of HIF-2α with ZINC04179524 significantly blocked RANKLmediated osteoclastogenesis.Collectively,our results suggest that HIF-2α functions as a catabolic regulator in bone remodeling,which is critical for the maintenance of bone homeostasis.
基金supported by the Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,and Forestry(IPET)through the High Value-Added Food Technology Development Program funded by the Ministry of Agriculture,Food,and Rural Affairs(MAFRA)(321049-5)the Main Research Program(E0211001-04)of the Korea Food Research Institute(KFRI)funded by the Ministry of Science and ICT.Declaration。
文摘This study evaluates the efficacy of the hyperspectral imaging(HSI)technique for the nondestructive detection of adulteration ratios of Phytophthora blight-infected red pepper powder(PBRPP)to red pepper powder(RPP).PBRPP contains elevated concentrations of capsaicinoids,phenolic acids,and carotenoids,which may result in increased pungency and bitterness,potentially diminishing consumer acceptance.Partial least squares discriminant analysis(PLS-DA)models were employed to differentiate between different adulteration levels of PBRPP(0%,5%,10%,15%,and 20%).The accuracy of the developed model was 87.5%when coupled with multiplicative scatter correction(MSC)preprocessing.Key wavelengths identified at 950 nm,1399 nm,1453 nm,and 1501 nm were instrumental in detecting these adulterants because of their association with water content,capsaicin levels,and other critical bioactive compounds.Visualized distribution maps generated from HSI effectively demonstrated the spatial distribution of adulterated powders,with colorimetric shifts corresponding to increasing PBRPP levels.These findings suggest that HSI,when combined with effective pre-processing and visualization techniques,can significantly enhance the quality control of RPP products.
基金supported by the Main Research Program(E0211001-04)of the Korea Food Research Institute(KFRI)funded by the Ministry of Science and ICT in South Korea.
文摘This study investigated the utilization of hyperspectral imaging(HSI)in conjunction with pixel-based segmentation to predict the thiobarbituric acid-reactive substances(TBARS)and volatile basic nitrogen(VBN)content in beef.Hyperspectral images were acquired in the visible near-infrared(VIS-NIR)and shortwave infrared(SWIR)ranges to examine temporal alterations in the fat and protein regions.A partial least squares discriminant analysis(PLS-DA)model was employed to segment fat and protein pixels,followed by a partial least squares regression(PLSR)model to predict the TBARS and VBN content from the segmented spectra.The SWIR range yielded the most accurate predictions,with an R_(p)^(2) of 0.899 for the early freshness indicators.Utilizing hyperspectral information from individual fat and protein pixels,rather than modeling the entire beef image,resulted in enhanced prediction accuracy for R_(p)^(2) of TBARS(0.814-0.899)and VBN(0.394-0.532)in the early stages of storage.These findings elucidate the potential of HSI with pixel-based segmentation as a nondestructive and realtime methodology for precise monitoring of beef freshness.
基金supported by the Korea Institute of Marine Science and Technology Promotion(KIMST),funded by the Ministry of Oceans and Fisheries(No.20210695).
文摘The quality of laver is significantly affected by adulteration with sea lettuce(Ulva lactuca),a green seaweed that adheres to Pyropia nets during cultivation and adversely impacts productivity and quality.Acid treatment agents are commonly utilized;however,residual sea lettuce may persist in the final product if treatment is insufficient.Traditional detection methods,such as sensory evaluation,are susceptible to human error,time-consuming,and inefficient,while DNA sequencing is ineffective for processed laver due to DNA degradation.Given these limitations,non-destructive technologies are garnering interest in seafood quality assessment.This study evaluates the potential of hyperspectral imaging for detecting sea lettuce in laver.Hypercubes collected in two spectral ranges(visible/near-infrared(VIS/NIR)and short-wave infrared(SWIR))were utilized to establish a partial least squares regression(PLSR)model for quantification.Characteristic wavelengths were selected using competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and their hybrid methods(CARS-UVE,UVE-CARS).Model efficiency and robustness improved with spectral feature selection.For raw laver,UVE-CARS achieved the highest Rp2(0.86)with 14.3%of full wavelengths in VIS/NIR,while for dried laver,SWIR with CARS-UVE yielded Rp2(0.90)using 18.3%of full wavelengths.This study addresses a critical gap in seafood quality control by demonstrating that hyperspectral imaging enables non-destructive,efficient quantification of sea lettuce contamination in laver,contributing to improved industry standards.
基金supported by the Main Research Program of the Korea Food Research Institute(KFRI)(E0211001-04)by Institute of Information&communications Technology Planning&Evaluation(IITP)grant(No.RS-2024-00399,252)funded by the Ministry of Science and ICT.
文摘This study aimed to use hyperspectral imaging in nondestructive monitoring of the changes in radish quality during salting process and to enhance the efficiency and accuracy of the prediction models through feature selection techniques.Hyperspectral imaging was utilized to predict the salinity,moisture content,and work of penetration(WOP)of radishes.Salinity increased with the prolonged salting time,whereas the moisture content and WOP decreased.Prediction using a partial least squares regression(PLSR)model based on full-wavelength hyperspectral data,resulted in the Rp2 values for salinity,moisture content,and WOP being 0.909,0.725,and 0.705,respectively.Feature selection methods,including competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and their combinations,were applied to extract informative wavelengths for each quality parameter.With UVE+CARS,high Rp2 values were achieved for salinity(0.934)and moisture content(0.846)using only 32.2%and 25.7%of the full-wavelengths,respectively.Similarly,Rp2 values for WOP(0.717)improved with CARS,utilizing 31.8%of the full-wavelengths.Hyperspectral imaging,coupled with suitable feature selection,not only improved the efficiency and accuracy of quality assessment during the salting process but also enabled the simultaneous prediction of key chemical and physical attributes of salted radishes with informative wavelength selection.This approach provides an efficient strategy for monitoring the quality of salted vegetables,offering valuable insights for optimizing industrial salting processes and advancing real-time quality control applications.
