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Spermatogenesis is a differentiation process during which diploid spermatogonial stem cells (SSCs) produce haploid spermatozoa. This highly specialized process is precisely controlled at the transcriptional, posttranscriptional, and translational levels. Here we report that N-methyladenosine (mA), an epitranscriptomic mark regulating gene expression, plays essential roles during spermatogenesis. We present comprehensive mA mRNA methylomes of mouse spermatogenic cells from five developmental stages: undifferentiated spermatogonia, type A spermatogonia, preleptotene spermatocytes, pachytene/diplotene spermatocytes, and round spermatids. Germ cell-specific inactivation of the mA RNA methyltransferase Mettl3 or Mettl14 with Vasa-Cre causes loss of mA and depletion of SSCs. mA depletion dysregulates translation of transcripts that are required for SSC proliferation/differentiation. Combined deletion of Mettl3 and Mettl14 in advanced germ cells with Stra8-GFPCre disrupts spermiogenesis, whereas mice with single deletion of either Mettl3 or Mettl14 in advanced germ cells show normal spermatogenesis. The spermatids from double-mutant mice exhibit impaired translation of haploid-specific genes that are essential for spermiogenesis. This study highlights crucial roles of mRNA mA modification in germline development, potentially ensuring coordinated translation at different stages of spermatogenesis.

The closely regulated process of mRNA translation is crucial for precise control of protein abundance and quality. Ribosome profiling, a combination of ribosome foot-printing and RNA deep sequencing, has been used in a large variety of studies to quantify genome-wide mRNA translation. Here, we developed Xtail, an analysis pipeline tailored for ribosome profiling data that comprehensively and accurately identifies differentially translated genes in pairwise comparisons. Applied on simulated and real datasets, Xtail exhibits high sensitivity with minimal false-positive rates, outperforming existing methods in the accuracy of quantifying differential translations. With published ribosome profiling datasets, Xtail does not only reveal differentially translated genes that make biological sense, but also uncovers new events of differential translation in human cancer cells on mTOR signalling perturbation and in human primary macrophages on interferon gamma (IFN-γ) treatment. This demonstrates the value of Xtail in providing novel insights into the molecular mechanisms that involve translational dysregulations.

Elevated expression of RNA binding protein HNRNPC has been reported in cancer cells, while the essentialness and functions of HNRNPC in tumors were not clear. We showed that repression of HNRNPC in the breast cancer cells MCF7 and T47D inhibited cell proliferation and tumor growth. Our computational inference of the key pathways and extensive experimental investigations revealed that the cascade of interferon responses mediated by RIG‐I was responsible for such tumor‐inhibitory effect. Interestingly, repression of HNRNPC resulted in accumulation of endogenous double‐stranded RNA (dsRNA), the binding ligand of RIG‐I. These up‐regulated dsRNA species were highly enriched by Alu sequences and mostly originated from pre‐mRNA introns that harbor the known HNRNPC binding sites. Such source of dsRNA is different than the recently well‐characterized endogenous retroviruses that encode dsRNA. In summary, essentialness of HNRNPC in the breast cancer cells was attributed to its function in controlling the endogenous dsRNA and the down‐stream interferon response. This is a novel extension from the previous understandings about HNRNPC in binding with introns and regulating RNA splicing.

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

Comparing with other biometrics, gait has advantages of being unobtrusive and difficult to conceal. Inertial sensors such as accelerometer and gyroscope are often used to capture gait dynamics. Nowadays, these inertial sensors have commonly been integrated in smartphones and widely used by average person, which makes it very convenient and inexpensive to collect gait data. In this paper, we study gait recognition using smartphones in the wild. Unlike traditional methods that often require the person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under a condition of unconstraint without knowing when, where, and how the user walks. To obtain a high performance of person identification and authentication, deep-learning techniques are presented to learn and model the gait biometrics from the walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space domain and in the time domain are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones on a total of 118 subjects are used for evaluations. Experiments show that the proposed method achieves over 93.5% and 93.7% accuracy in person identification and authentication, respectively.Index Terms-Gait recognition, inertial sensor, person identification, convolutional neural network, recurrent neural network.