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With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices., Comment: To appear in IEEE Transactions on Wireless Communications

With the extensive application of highly sensitive genetic techniques in the field of prenatal diagnosis, prenatal chromosomal mosaicisms including true fetal mosaicisms and confined placental mosaicisms are frequently identified in clinical settings, and the diagnostic criteria and principle of genetic counseling and clinical management for such cases may vary significantly among healthcare centers across the country. This not only has brought challenges to laboratory technician, genetic counselor and fetal medicine doctor, but can also cause confusion and anxiety of the pregnant woman and their family members. In this regard, we have formulated a consensus over the prenatal diagnosis and genetic counseling for chromosomal mosaicisms with the aim to promote more accurate and rational evaluation for fetal chromosomal mosaicisms in prenatal clinics.

In medicine, any abnormalities in the number of chromosomes or the structure of chromosomes may cause the newborn baby to suffer from genetic diseases, such as Edward syndrome and so on. Chromosome karyotype analysis is the most important and common method for prenatal diagnosis to determine whether a newborn baby has chromosome defects refers to segment chromosome instances from stained cell images and arrange chromosome instances according to their categories. However, due to the non-rigid nature of chromosomes, chromosome instances may overlap and adhere to each other, which makes the task of segmenting chromosome instances time-consuming and error-prone. This paper proposes a novel chromosome instance segmentation method that includes three stages. First, we segment a given stained cell image into several segments using geometric connectivity. Second, a machine learning method is proposed to distinguish chromosome of individual instances and clusters. Finally, a deep learning-based method is applied to separate chromosome instances from clusters. It shows that the proposed method achieves 97.61% instance segmentation accuracy in a hold-out clinical dataset with 162 cell images consisting of 7,452 chromosome instances, which is a promising result in clinical application. The innovation of this work is to combine geometry and deep learning to handle tasks for different stages of chromosome instance segmentation issue. The benefit of this innovation is that it can obtain a much better performance than existing geometric-based methods with a small number of training samples. Meanwhile, the segmentation performance of the proposed method is superior to existing methods fully based on deep learning.

Introductions: Complex chromosome rearrangement (CCR) is a structural rearrangement involving more than two breakpoints. CCR carriers are at high risk for phenotypic abnormalities or reproductive failure, such as chromosomal abnormalities in fetuses and infertility. In this study, we presented a carriers with chromosome (3,18) balanced translocation, whose fetus had duplications in chromosome 3 and deletions in chromosome 10 demonstrated by chromosomal microarray analysis (CMA).By revealing the cryptical translocation, we aimed to provide CCR carriers with more accurate risk assessment of abnormal pregnancy and better assisted reproduction with CMA and next generation sequencing(NGS).Results: By using the high resolution of GTG-banding technology, a cryptical translocation in chromosome 10 was found and the karyotype of the carrier was revised as 46,XY,t(3;10;18) (p26.3;q26.1;q21.1).In the cycle of preimplantation genetic diagnosis (PGD),21 oocytes were retrieved, and 15 were fertilized. At last 7 embryos were biospied and sent to diagnosis by next generation sequencing(NGS).Unfortunately, none of the NGS results from the 7 biopsy embryos were normal. Combining previous literature and our results, we assessed the odds of a balanced embryo in a CCR carrier to be about 9.3%(28/302).The transferable embryo rate was approximately 71.4%(20/28) and healthy live born delivery rate was 55%(11/20).Conclusions: NGS and CMA featured high automation, relatively low cost, high throughput, and high repeatability, which made them commonly used during prenatal diagnosis and PGD. The multiple technology combination can provide more accurate diagnosis and better fertility services for CCR patients.

Background: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts.Method: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation CILA algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with 77% mAP, 97.5% AP50, and 95.5% AP75 segmentation precision, and 95.38% accuracy, which exceeds results reported in current chromosome instance segmentation methods.

To analyze the indication, karyotyping result, ultrasound finding, pregnancy decision and follow-up of fetuses with sex chromosome aneuploidies (SCA) detected by non-invasive prenatal testing (NIPT) during early and midterm pregnancies.The results of 225 singleton pregnancies with fetal SCA detected by NIPT were reviewed and analyzed.The 225 cases included 45,X (n=37), 47,XXY (n=74), 47,XXX (n=50), 47,XYY (n=56) and mosaicisms (n=8), among which 121 (53.8%) have opted to terminate the pregnancy, including 45,X (n=31), 47,XXY (n=61), 47,XXX (n=14), 47,XYY (n=12) and 3 mosaicisms. The remainder 104 (46.2%) have elected to continue with the pregnancy, among which three have opted to terminate due to abnormalities detected by ultrasonography, and two had spontaneous abortions.NIPT as a first-tier screening method can effectively detect fetal trisomies 21, 13 and 18 as well as SCA. The types of fetal SCA and presence of ultrasound abnormalities are critical factors for the termination of pregnancy.

Background: Complex chromosome rearrangement (CCR) is a structural rearrangement involving more than two breakpoints. CCR carriers are at high risk for phenotypic abnormalities or reproductive failure, such as chromosomal abnormalities in fetuses and infertility.Methods: We presented a carriers with chromosome (3,18) apparent balanced translocation diagnosed in eleswhere, whose fetus had duplications in chromosome 3 and deletions in chromosome 10 demonstrated by chromosome microarray analysis(CMA). Results: Through the high resolution of GTG-banding, a cryptical translocation in chromosome 10 was found and the karyotype of the carrier was revised as 46,XY,t(3;10;18) (p26.3;q26.1;q21.1).In the cycle of preimplantation genetic diagnosis (PGD),21 oocytes were retrieved, and 15 were fertilized. At last 7 embryos were biospied and sent to diagnosis by next generation sequencing(NGS).Unfortunately, none of the NGS results from the 7 biopsy embryos were normal. Combining previous literature and our results, we assessed the odds of a balanced embryo in a CCR carrier to be about 9.3%(28/302).The transferable embryo rate was approximately 71.4%(20/28) and healthy live born delivery rate was 55%(11/20).Conclusions: NGS and CMA featured high automation, relatively low cost, high throughput, and high repeatability, which made them commonly used during prenatal diagnosis and PGD. The multiple technology combination can provide more accurate diagnosis and better fertility services for CCR patients.

Objective To determine the best method for chromosome detection of mosaicism by comparing the results of karyotype and SNP-array in amniotic fluid,chorionic villi and cord blood. Methods A total of 14,805 pregnant women underwent invasive prenatal diagnosis.SNP-array and karyotype analysis were used to detect chromosomal abnormalities. Results A total of 169 cases of mosaicism were detected in this study.Mosaicism was found in both karyotype and SNP-array in 99(0.66%,99/14,805) cases of prenatal samples. In the remaining 70 cases of mosaicism, the results of karyotype and SNP-array were discrepant with ten cases(1.04%,10/959),forty-five cases (0.5%,45/9034) and fifteen cases(0.31%,15/4812) dectected from CV,AF and CB respectively. The mosaic positive rate of karyotype analysis only was 1.11%(164/14,805), which was significantly higher than that of SNP-array (0.7%,104/14,805), and the difference was statistically significant (P

In medicine, chromosome karyotype analysis is vital for genetic disease diagnoses, such as Edward syndrome, Patau syndrome, and Down syndrome. However, the chromosome karyotype analysis is usually manually done by extensive experienced clinical analysts, which is tedious and time-consuming. Consequently, automatic or partial automatic chromosome karyotyping is essential to alleviate clinical analysts’ jobs. This paper proposes a chromosome cluster identification framework based on geometric chromosome features and machine learning algorithms to identify chromosome clusters needed to further process in the automated instance segmentation task. In the proposed framework, we first collect multiple dimensions of chromosome geometric features into a feature tuple, including object area, bounding box area, convex area, extent, solidity, perimeter, equivalent diameter, eccentricity, major axis length, minor axis length, and minor-major axis ratio. Second, we classify these chromosome feature tuples utilizing different machine learning classification algorithms. The experiment results show that our proposed method yields 96.21 ± 0.91% classification accuracy and 0.9832 ± 0.0111AUC (Area Under The Curve) value in the clinical dataset. The highlight of this paper is that the performance of the proposed approach has exceeded the existing geometric features threshold-based methods and multiple end-to-end deep learning-based baselines. Moreover, our proposed method can be deployed on any devices and platforms with a Python running environment, which significantly improves application flexibility. To facilitate peers in reproducing and employing our work, we release the code and the corresponding clinical dataset on Github.

Although anomaly detection is an urgent and significant task in various applications, there are few good enough methods to meet the requirements. The challenge of anomaly detection lies in how to distinguish the diverse or even unknown anomalies. Therefore, previous studies have focused on imagelevel anomaly detection tasks, and mostly based on unsupervised methods. These methods only consider the characteristics of normal samples and are confined to the “normal” category, ignoring information between samples. In particular, the limitations of unsupervised methods are more obvious for pixellevel abnormalities such as the chromosomal structural anomalies investigated in this paper, because the similarity between normal and abnormal samples is relatively high. In this work, we propose a supervised anomaly detection approach Classification - Enhanced GANomaly (abbreviated as CS-GANomaly). By fusing the category information with the sample, the spatial distribution of the sample has regional characteristics and then combining the anomaly detection strategies to carry out multidimension anomaly detection. The CS-GANomaly adopts the GAN framework for adversarial training and adapts the GAN output to anomaly detection. The experimental results prove the superiority of our approach, whether it is a pixel-level anomaly in abnormal chromosome dataset or image-level anomaly in the benchmark dataset.

Background: To investigate the correlation between Y chromosome microdeletion and cytogenetic analysis in patients with azoospermia.Methods: A total of 516 patients with azoospermia were enrolled from March 2015 to December 2019. Karyotype analysis(G-banding) was performed with peripheral blood.Y-chromosome azoospermia factor (AZF) were detected by quantitative fluorescent polymerase chain reaction (QF-PCR).Results: Chromosome abnormality was detected in 66 of the 516 azoospermia patients, with an abnormal rate of 12.8% In addition, 7.8% cases(40/516) presented with Y-chromosome AZF microdeletions, in which 27 cases patients with karyotype of 46,XY(67.5%,27/40) and 13 cases of chromosome abnormalities (32.5%,13/40).Conclusion: The incidence of Y chromosome microdeletion was higher in the patients with karyotype of 46,XY. Conventional cytogenetics cannot directly reflect the microdeletions of Y chromosome. Therefore, the combination of this two detection methods can help to clarify the genetic causes of patients with azoospermia and provide a theoretical basis for clinical assisted reproductive technology.

Chromosome classification is an important way of clinical genetic diagnosis. However, it is usually done manually by skilled cytologists, which requires extensive experience, domain knowledge, and tedious manual work. In this paper, we tackle the task of chromosome classification utilizing deep learning techniques based on aggregated residual architecture. We proposed a novel chromosome classification method named MixNet. Firstly, we design a deep learning-based chromosome classification framework that consists of a chromosome encoding backbone and an adaptive header. Secondly, we design the chromosome backbone utilizing the aggregated residual architecture and propose the adaptive header by aggregating pooling layers to classify chromosome latent features. Lastly, we propose a training algorithm to fine-tune MixNet. The experimental results demonstrate that our proposed method yields a mean classification accuracy of 98.73% with 0.81 standard deviations on our G-band clinical dataset and a mean classification accuracy of 96.50% with 1.87 standard deviations on public Q-band dataset. To our best knowledge, it is the state-of-the-art result of the public Q-band dataset.

Method: This paper focuses on chromosome classification because it is critical for chromosome karyotyping. In recent years, deep learning-based methods are the most promising methods for solving the tasks of chromosome classification. Although the deep learning-based Inception architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge, it has not been used in chromosome classification tasks so far. Therefore, we develop an automatic chromosome classification approach named CIR-Net based on Inception-ResNet which is an optimized version of Inception. However, the classification performance of origin Inception-ResNet on the insufficient chromosome dataset still has a lot of capacity for improvement. Further, we propose a simple but effective augmentation method called CDA for improving the performance of CIR-Net. Results: The experimental results show that our proposed method achieves 95.98% classification accuracy on the clinical G-band chromosome dataset whose training dataset is insufficient. Moreover, the proposed augmentation method CDA improves more than 8.5% (from 87.46% to 95.9%) classification accuracy compared to other methods. In this paper, the experimental results demonstrate that our proposed method is recent the most effective solution for solving clinical chromosome classification problems in chromosome auto-karyotyping on the condition of the insufficient training dataset. Code and Dataset are available at https://github.com/CloudDataLab/CIR-Net. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, due to the complicated morphological characteristics of various types of chromosome clusters, chromosome instance segmentation is the most challenging stage of chromosome karyotyping analysis, leading chromosome karyotyping analysis to highly dependent on skilled clinical analysts. Since most of the chromosome instance segmentation efforts are currently devoted to segmenting chromosome instances from different types of chromosome clusters, type identification of chromosome clusters is a vital anterior task for chromosome instance segmentation. Firstly, this paper proposes an automatic approach for chromosome cluster identification using recent transfer learning techniques. The proposed framework is based on ResNeXt weakly-supervised learning (WSL) pre-trained backbone and a task-specific network header. Secondly, this paper proposes a fast training methodology that tunes our framework from coarse-to-fine gradually. Extensive evaluations on a clinical dataset consisting of 6592 clinical chromosome samples show that the proposed framework achieves 94.09% accuracy, 92.79% sensitivity, and 98.03% specificity. Such performance is superior to the best baseline model that we obtain 92.17% accuracy, 89.1% sensitivity, and 97.42% specificity. To foster research and application in the chromosome cluster type identification, we make our clinical dataset and code available via GitHub.

In this paper, we consider multi-user mobile-edge computation offloading (MECO) in edge computing systems, where a practical constraint that only finite number of mobile devices (MDs) are allowed to offload tasks to the base station (BS) due to the limited computational capacity of the edge server, referred to as admission control. In particular, we aim to minimize the weighted sum of the energy consumption and the execution latency of all the MDs by jointly optimizing the selection of offloading-permitted MDs, the offloading data size, the transmission power and the CPU frequency of all the MDs. To tackle the non-convex problem, we decompose it into a master-and-slave problem and propose a distributed algorithm to solve it. Simulation results validate the superiority of the proposed scheme compared to the benchmark schemes.

To carry out prenatal diagnosis for two cases of Pallister-Killian syndrome (PKS) using combined chromosomal microarray analysis (CMA) and fluorescence in situ hybridization (FISH).Umbilical cord blood was sampled from the two fetuses and subjected to G-banding chromosomal karyotyping, CMA and FISH assay.Chromosomal karyotyping showed that the two fetuses were mos 47,XX,+i(12)(p10)[3]/46,XX[197] and mos 47,XY,+i(12)(p10)[5]/46,XY[95], respectively. CMA showed that both had carried duplication of 12p. The results of interphase FISH confirmed mosaicism of 12p tetrasomy. Combined with ultrasonographic findings, both fetuses were diagnosed as PKS.Prenatal ultrasound examination, karyotype analysis of umbilical cord blood, G-banded chromosomal analysis, CMA and FISH may be used in conjunct for the prenatal diagnosis of PKS.

To assess the value of chromosomal karyotyping and array-based comparative genomic hybridization for the diagnosis of fetus with abnormalities detected by ultrasonography.Umbilical cord blood samples were derived from 1 603 pregnant women. The samples were cultured for routine G-banding karyotype analysis. Among these, 792 samples have further subjected to array CGH analysis.Among the 1 603 fetuses, 117 (7.30%) were found with chromosomal abnormalities. These included 72 numerical aberrations and 45 structural abnormalities, which respectively accounted for 4.49% and 2.81% of all cases. For those35 years and ≥ 35 years, a significant difference has been found in terms of fetal chromosomal abnormalities (chi-square is 30.687, P0.01). And there was also a significant difference between those with isolated, two or multiple ultrasonographic markers (chi-square is 85.50, P0.01). Among 736 fetuses with a normal karyotype, array CGH has detected 17 (2.31%) with a microdeletion or microduplication.Karyotype analysis and array CGH should be offered to all fetuses with ultrasonography detected anomalies regardless the number of markers.