Let denote the maximum number of disjoint bases in a matroid . For a connected graph , let , where is the cycle matroid of . The well-known spanning tree packing theorem of Nash-Williams and Tutte characterizes graphs...Let denote the maximum number of disjoint bases in a matroid . For a connected graph , let , where is the cycle matroid of . The well-known spanning tree packing theorem of Nash-Williams and Tutte characterizes graphs with . Edmonds generalizes this theorem to matroids. In [1] and [2], for a matroid with , elements with the property that have been characterized in terms of matroid invariants such as strength and -partitions. In this paper, we consider matroids with , and determine the minimum of , where is a matroid that contains as a restriction with both and . This minimum is expressed as a function of certain invariants of , as well as a min-max formula. These are applied to imply former results of Haas [3] and of Liu et al. [4].展开更多
Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,a...Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development.展开更多
Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative a...Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging(MRI)features could differentiate anorectal malignant melanoma from low rectal cancer.Methods:Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent preoperative rectal MRI from three hospitals were retrospectively enrolled.All patients were divided into the primary cohort(N=84)and validation cohort(N=51).Quantitative image analysiswas performed on T1-weighted(T1WI),T2-weighted(T2WI),and contrast-enhanced T1-weighted imaging(CE-T1WI).The subjective qualitative MRI findings were evaluated by two radiologists in consensus.Multivariable analysis was performed using stepwise logistic regression.The discrimination performance was assessed by the area under the receiver operating characteristic curve(AUC)with a 95%confidence interval(CI).Results:The skewness derived from T2WI(T2WI-skewness)showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer(primary cohort:AUC=0.852,95%CI 0.788–0.916;validation cohort:0.730,0.645–0.815).Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors,and incorporating both factors achieved good discrimination performance in two cohorts(primary cohort:AUC=0.913,95%CI 0.868–0.958;validation cohort:0.902,0.844–0.960).Conclusions:Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer.The quantitative image analysis helps improve diagnostic accuracy.展开更多
Radiomics,with its transformative potential for predicting colorectal cancer(CRC)prognosis,encounters challenges in clinical translation due to the unclear biological basis of risk stratification[1].Bridging this gap ...Radiomics,with its transformative potential for predicting colorectal cancer(CRC)prognosis,encounters challenges in clinical translation due to the unclear biological basis of risk stratification[1].Bridging this gap is pivotal,and the emerging field of radiogenomics,situated at the intersection of radiomics and genomics,presents an opportunity to unravel the intricate relationship between imaging representations and molecular pathway dysregulation[2,3].However,one-way association analyses have provided insufficient evidence,prompting the need for a multi-phase radiogenomics strategy.This strategy encompasses(1)the employment of tightly integrated forward and reverse radiogenomics engineering approaches[4],(2)the verification of micro–macro level biological associations[5],and(3)the validation of the radiomics signature across different datasets[6].展开更多
Nitrogen(N)and phosphorus(Pi)are essential macronutrients that affect plant growth and development by influencing the molecular,metabolic,biochemical,and physiological responses at the local and whole levels in plants...Nitrogen(N)and phosphorus(Pi)are essential macronutrients that affect plant growth and development by influencing the molecular,metabolic,biochemical,and physiological responses at the local and whole levels in plants.N and Pi stresses suppress the physiological activities of plants,resulting in agricultural productivity losses and severely threatening food security.Accordingly,plants have elaborated diverse strategies to cope with N and Pi stresses through maintaining N and Pi homeostasis.MicroRNAs(miRNAs)as potent regulators fine-tune N and Pi signaling transduction that are distinct and indivisible from each other.Specific signals,such as noncoding RNAs(ncRNAs),interact with miRNAs and add to the complexity of regulation.Elucidation of the mechanisms by which miRNAs regulate N and Pi signaling transduction aids in the breeding of plants with strong tolerance to N and Pi stresses and high N and Pi use efficiency by fine-tuning MIR genes or miRNAs.However,to date,there has been no detailed and systematic introduction and comparison of the functions of miRNAs in N and Pi signaling transduction from the perspective of miRNAs and their applications.Here,we summarized and discussed current advances in the involvement of miRNAs in N and Pi signaling transduction and highlighted that fine-tuning the MIR genes or miRNAs involved in maintaining N and Pi homeostasis might provide valuable sights for sustainable agriculture.展开更多
文摘Let denote the maximum number of disjoint bases in a matroid . For a connected graph , let , where is the cycle matroid of . The well-known spanning tree packing theorem of Nash-Williams and Tutte characterizes graphs with . Edmonds generalizes this theorem to matroids. In [1] and [2], for a matroid with , elements with the property that have been characterized in terms of matroid invariants such as strength and -partitions. In this paper, we consider matroids with , and determine the minimum of , where is a matroid that contains as a restriction with both and . This minimum is expressed as a function of certain invariants of , as well as a min-max formula. These are applied to imply former results of Haas [3] and of Liu et al. [4].
基金This work was supported by the National Key Research and Development Program of China(grant No.2017YFC1309102)the National Science Fund for Distinguished Young Scholars(grant No.81925023)+1 种基金the National Natural Science Foundation of China(grant Nos.81771912,81701782,81702322,82001986,and 82071892)the High-level Hospital Construction Project(grant Nos.DFJH201805 and DFJH201914).
文摘Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development.
基金This work was supported by the National Key Research and Development Program of China(Grant No.2017YFC1309100)the National Science Fund for Distinguished Young Scholars(Grant No.81925023)+1 种基金the National Natural Science Foundation of China(Grants No.81771912,82071892,and 82072090)the High-level Hospital Construction Project(Grant No.DFJH201805).
文摘Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging(MRI)features could differentiate anorectal malignant melanoma from low rectal cancer.Methods:Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent preoperative rectal MRI from three hospitals were retrospectively enrolled.All patients were divided into the primary cohort(N=84)and validation cohort(N=51).Quantitative image analysiswas performed on T1-weighted(T1WI),T2-weighted(T2WI),and contrast-enhanced T1-weighted imaging(CE-T1WI).The subjective qualitative MRI findings were evaluated by two radiologists in consensus.Multivariable analysis was performed using stepwise logistic regression.The discrimination performance was assessed by the area under the receiver operating characteristic curve(AUC)with a 95%confidence interval(CI).Results:The skewness derived from T2WI(T2WI-skewness)showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer(primary cohort:AUC=0.852,95%CI 0.788–0.916;validation cohort:0.730,0.645–0.815).Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors,and incorporating both factors achieved good discrimination performance in two cohorts(primary cohort:AUC=0.913,95%CI 0.868–0.958;validation cohort:0.902,0.844–0.960).Conclusions:Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer.The quantitative image analysis helps improve diagnostic accuracy.
基金supported by the National Science Fund for Distinguished Young Scholars(81925023)the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(U22A20345)+5 种基金the National Key Research and Development Program of China(2021YFF1201003)the National Natural Scientific Foundation of China(82371954,82072090)Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application(2022B1212010011)Science and Technology Projects in Guangzhou(202201020001 and 202201010513)the Key-Area Research and Development Program of Guangdong Province(2021B0101420006)the Natural Science Foundation of Guangdong Province(2023A1515030251)。
文摘Radiomics,with its transformative potential for predicting colorectal cancer(CRC)prognosis,encounters challenges in clinical translation due to the unclear biological basis of risk stratification[1].Bridging this gap is pivotal,and the emerging field of radiogenomics,situated at the intersection of radiomics and genomics,presents an opportunity to unravel the intricate relationship between imaging representations and molecular pathway dysregulation[2,3].However,one-way association analyses have provided insufficient evidence,prompting the need for a multi-phase radiogenomics strategy.This strategy encompasses(1)the employment of tightly integrated forward and reverse radiogenomics engineering approaches[4],(2)the verification of micro–macro level biological associations[5],and(3)the validation of the radiomics signature across different datasets[6].
基金supported by the National Natural Science Foundation of China(32371577 and 32071504)Beijing Natural Science Foundation(6232030).
文摘Nitrogen(N)and phosphorus(Pi)are essential macronutrients that affect plant growth and development by influencing the molecular,metabolic,biochemical,and physiological responses at the local and whole levels in plants.N and Pi stresses suppress the physiological activities of plants,resulting in agricultural productivity losses and severely threatening food security.Accordingly,plants have elaborated diverse strategies to cope with N and Pi stresses through maintaining N and Pi homeostasis.MicroRNAs(miRNAs)as potent regulators fine-tune N and Pi signaling transduction that are distinct and indivisible from each other.Specific signals,such as noncoding RNAs(ncRNAs),interact with miRNAs and add to the complexity of regulation.Elucidation of the mechanisms by which miRNAs regulate N and Pi signaling transduction aids in the breeding of plants with strong tolerance to N and Pi stresses and high N and Pi use efficiency by fine-tuning MIR genes or miRNAs.However,to date,there has been no detailed and systematic introduction and comparison of the functions of miRNAs in N and Pi signaling transduction from the perspective of miRNAs and their applications.Here,we summarized and discussed current advances in the involvement of miRNAs in N and Pi signaling transduction and highlighted that fine-tuning the MIR genes or miRNAs involved in maintaining N and Pi homeostasis might provide valuable sights for sustainable agriculture.
