Mutations in mitochondrial DNA (mtDNA) are maternally inherited and can cause fatal or debilitating mitochondrial disorders. The severity of clinical symptoms is often associated with the level of mtDNA mutation load or degree of heteroplasmy. Current clinical options to prevent transmission of mtDNA mutations to offspring are limited. Experimental spindle transfer in metaphase II oocytes, also called mitochondrial replacement therapy, is a novel technology for preventing mtDNA transmission from oocytes to pre-implantation embryos. Here, we report a female carrier of Leigh syndrome (mtDNA mutation 8993T > G), with a long history of multiple undiagnosed pregnancy losses and deaths of offspring as a result of this disease, who underwent IVF after reconstitution of her oocytes by spindle transfer into the cytoplasm of enucleated donor oocytes. A male euploid blastocyst wasobtained from the reconstituted oocytes, which had only a 5.7% mtDNA mutation load. Transfer of the embryo resulted in a pregnancy with delivery of a boy with neonatal mtDNA mutation load of 2.36-9.23% in his tested tissues. The boy is currently healthy at 7 months of age, although long-term follow-up of the child's longitudinal development remains crucial.

Cytotrophoblasts of the anchoring villi convert during human placentation from a transporting epithelium to an invasive, extravillous phenotype that expresses a distinct repertoire of adhesion molecules. Developing extravillous trophoblasts accumulate heparin-binding EGF-like growth factor (HB-EGF), a multifunctional cytokine, which binds HER1 and HER4 of the human EGF receptor (HER/ErbB) family. HB-EGF is downregulated in placentae of women with preeclampsia, a disorder associated with deficient trophoblast invasion, raising important questions about its physiological impact on cytotrophoblasts. Addition of HB-EGF during explant culture of first-trimester chorionic villi enhanced extravillous trophoblast differentiation and invasive activity. Using a first-trimester human cytotrophoblast line, the potential for autocrine and paracrine regulation of the developing trophoblast was established based on the expression of all four HER isoforms, as well as HB-EGF and related growth factors. HB-EGF did not alter proliferation, but initiated extravillous differentiation, with decreased alpha6 integrin expression, increased alpha1, and elevated cell migration. Function-blocking antibodies against EGF family members reduced basal cell motility and antibody inhibition of either HER1 or HER4 ligation prevented HB-EGF-induced integrin switching. We conclude that HER-mediated autocrine and paracrine signaling by HB-EGF or other EGF family members induces cytotrophoblast differentiation to an invasive phenotype.

In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Besides, the proposed method could timely compute the cluster assignment for each individual, even when the data is presented in streams. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19% (39%) performance improvement compared with the best baseline. The code is available at https://github.com/XLearning-SCU/2021-AAAI-CC.

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Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a largescale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, joband task-level workload behavior, and how the overall cluster resources are utilized.

Background: Relevant serum tumor markers have been indicated to be associated with peritoneal dissemination (PD) of gastric cancer (GC). Fibrinogen has been shown to play an important role in the systemic inflammatory response (SIR) and in tumor progression. However, the clinical significance of the fibrinogen-to-lymphocyte ratio (FLR) in GC with PD has not been studied.Methods: The clinical data of 391 patients with GC were collected, including 86 cases of PD. Then, 1:3 matching was performed by propensity score matching (PSM), and the clinical data of the matched 344 patients were analyzed by univariate and multivariate conditional logistic regression. Classification tree analysis was used to obtain the decision rules and a random forest algorithm to extract the important risk factors of PD in GC. A nomogram model for risk assessment of PD in GC was established by using the rms package of R software.Results: Univariate analysis showed that the factors related to PD in GC were: carbohydrate antigen (CA) 125 (P < 0.0001), CA19-9 (P < 0.0001), CA72-4 (P < 0.0001), FLR (P < 0.0001), neutrophil-to-lymphocyte ratio (NLR) (P < 0.0001), albumin-to- lymphocyte ratio (ALR) (P < 0.0001), platelet-to-lymphocyte ratio (PLR) (P = 0.013), and carcinoembryonic antigen (CEA) (P = 0.031). Conditional logistic regression found that CA125 (OR: 1.046; P < 0.0001), CA19-9 (OR: 1.002; P < 0.0001), and FLR (OR: 1.266; P = 0.024) were independent risk factors for GC with PD. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the decision rules for detecting PD of GC were 89.5, 77.4, 94.0, 82.8, and 91.8%, respectively. According to the important variables identified by the classification tree and random forest algorithm, the risk assessment model of PD in GC was established. The accuracy, sensitivity, and specificity of the model were 91, 89.5, and 79.5%, respectively.Conclusion: CA125 > 17.3 U/ml, CA19-9 > 27.315 U/ml, and FLR > 2.555 were the risk factors for GC with PD. The decision rules and nomogram model constructed by CA125, CA19-9, CA72-4, and FLR can correctly predict the risk of PD in GC.

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In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and an image collection (untrained). An unsupervised model will avoid the intensive labor of collecting hazy-clean image pairs, and an untrained model is a "real" single image dehazing approach which could remove haze based on the observed hazy image only and no extra images are used. Motivated by the layer disentanglement, we propose a novel method, called you only look yourself (YOLY) which could be one of the first unsupervised and untrained neural networks for image dehazing. In brief, YOLY employs three joint subnetworks to separate the observed hazy image into several latent layers, i.e., scene radiance layer, transmission map layer, and atmospheric light layer. After that, three layers are further composed to the hazy image in a selfsupervised manner. Thanks to the unsupervised and untrained characteristics of YOLY, our method bypasses the conventional training paradigm of deep models on hazy-clean pairs or a large scale dataset, thus avoids the labor-intensive data collection and the domain shift issue. Besides, our method also provides an effective learning-based haze transfer solution thanks to its layer disentanglement mechanism. Extensive experiments show the promising performance of our method in image dehazing compared with 14 methods on six databases. The code could be accessed at www.pengxi.me. Keywords Single image dehazing • Unsupervised learning • Untrained neural network Communicated by Vishal Patel.