AIM:To detect of colorectal cancer(CRC) circulating tumour cells(CTCs) surface antigens,we present an assay incorporating cadmium selenide quantum dots(QDs) in these paper.METHODS:The principle of the assay is the imm...AIM:To detect of colorectal cancer(CRC) circulating tumour cells(CTCs) surface antigens,we present an assay incorporating cadmium selenide quantum dots(QDs) in these paper.METHODS:The principle of the assay is the immunomagnetic separation of CTCs from body fluids in conjunction with QDs,using specific antibody biomarkers:epithelial cell adhesion molecule antibody,and monoclonal cytokeratin 19 antibody.The detection signal was acquired from the fluorescence signal of QDs.For the evaluation of the performance,the method under study was used to isolate the human colon adenocarcinoma cell line(DLD-1) and CTCs from CRC patients' peripheral blood.RESULTS:The minimum detection limit of the assay was defined to 10 DLD-1 CRC cells/mL as fluorescence was measured with a spectrofluorometer.Fluorescenceactivated cell sorting analysis and Real Time RT-PCR,they both have also been used to evaluate the performance of the described method.In conclusion,we developed a simple,sensitive,efficient and of lower cost(than the existing ones) method for the detection of CRC CTCs in human samples.We have accomplished these results by using magnetic bead isolation and subsequent QD fluorescence detection.CONCLUSION:The method described here can be easily adjusted for any other protein target of either the CTC or the host.展开更多
In order to quickly explore the quality of cut-off wall in dams, a new method of high-density seismic image was adopted and estimated by model and in-situ wall tests.The vibration exciter was employed and several para...In order to quickly explore the quality of cut-off wall in dams, a new method of high-density seismic image was adopted and estimated by model and in-situ wall tests.The vibration exciter was employed and several parameters such as hypocentral distance, length of signal record and sampling space in signal collection were determined, which are 8 m, 0.25 ms and 128 ms respectively. Through time and frequency field signal analyses, it is concluded that, the smaller arrival times of reflected longitudinal and surface waves, and the higher their main frequencies, the higher the strength of the wall, vice versa. Accordingly the construction quality of the wall can be evaluated quickly by high-density seismic image.展开更多
Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or ...Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.展开更多
Compressed sensing is a new signM acquisition method that acquires signal in a compressed form and then recovers the signal by the use of computational tools and techniques. This means fewer measurements of signal are...Compressed sensing is a new signM acquisition method that acquires signal in a compressed form and then recovers the signal by the use of computational tools and techniques. This means fewer measurements of signal are needed and thus it will save huge amount of time and storage space. We, in this paper, consider the compressed sensing of sparse integer-valued signal （referred as ＂q-states signal＂ throughout the paper）. In order to accelerate the speed of reconstruction, we adopt the sparse rather than dense measurement matrices. Using methods and tools developed in statistical physics, we locate the reconstruction limit for Lo-reconstruction method and propose a belief propagation- based algorithm that can deal with instance with large size and its typical reconstruction performance are also analyzed.展开更多
基金Supported by The John S Latsis Public Benefit FoundationThe Hellenic Society of Medical Oncology
文摘AIM:To detect of colorectal cancer(CRC) circulating tumour cells(CTCs) surface antigens,we present an assay incorporating cadmium selenide quantum dots(QDs) in these paper.METHODS:The principle of the assay is the immunomagnetic separation of CTCs from body fluids in conjunction with QDs,using specific antibody biomarkers:epithelial cell adhesion molecule antibody,and monoclonal cytokeratin 19 antibody.The detection signal was acquired from the fluorescence signal of QDs.For the evaluation of the performance,the method under study was used to isolate the human colon adenocarcinoma cell line(DLD-1) and CTCs from CRC patients' peripheral blood.RESULTS:The minimum detection limit of the assay was defined to 10 DLD-1 CRC cells/mL as fluorescence was measured with a spectrofluorometer.Fluorescenceactivated cell sorting analysis and Real Time RT-PCR,they both have also been used to evaluate the performance of the described method.In conclusion,we developed a simple,sensitive,efficient and of lower cost(than the existing ones) method for the detection of CRC CTCs in human samples.We have accomplished these results by using magnetic bead isolation and subsequent QD fluorescence detection.CONCLUSION:The method described here can be easily adjusted for any other protein target of either the CTC or the host.
文摘In order to quickly explore the quality of cut-off wall in dams, a new method of high-density seismic image was adopted and estimated by model and in-situ wall tests.The vibration exciter was employed and several parameters such as hypocentral distance, length of signal record and sampling space in signal collection were determined, which are 8 m, 0.25 ms and 128 ms respectively. Through time and frequency field signal analyses, it is concluded that, the smaller arrival times of reflected longitudinal and surface waves, and the higher their main frequencies, the higher the strength of the wall, vice versa. Accordingly the construction quality of the wall can be evaluated quickly by high-density seismic image.
基金supported by the Engineering and Physical Sciences Research Council of UK (Grant No. #EP/K00946X/1)
文摘Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.
文摘Compressed sensing is a new signM acquisition method that acquires signal in a compressed form and then recovers the signal by the use of computational tools and techniques. This means fewer measurements of signal are needed and thus it will save huge amount of time and storage space. We, in this paper, consider the compressed sensing of sparse integer-valued signal （referred as ＂q-states signal＂ throughout the paper）. In order to accelerate the speed of reconstruction, we adopt the sparse rather than dense measurement matrices. Using methods and tools developed in statistical physics, we locate the reconstruction limit for Lo-reconstruction method and propose a belief propagation- based algorithm that can deal with instance with large size and its typical reconstruction performance are also analyzed.
