Deep Learning Face Attributes in the Wild

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.Comment: To appear in International Conference on Computer Vision (ICCV) 201

Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201

Isolation and characterization of human spermatogonial stem cells

<p>Abstract</p> <p>Background</p> <p>To isolate and characterization of human spermatogonial stem cells from stem spermatogonium.</p> <p>Methods</p> <p>The disassociation of spermatogonial stem cells (SSCs) were performed using enzymatic digestion of type I collagenase and trypsin. The SSCs were isolated by using Percoll density gradient centrifugation, followed by differential surface-attachment method. Octamer-4(OCT4)-positive SSC cells were further identified using immunofluorescence staining and flow cytometry technques. The purity of the human SSCs was also determined, and a co-culture system for SSCs and Sertoli cells was established.</p> <p>Results</p> <p>The cell viability was 91.07% for the suspension of human spermatogonial stem cells dissociated using a two-step enzymatic digestion process. The cells isolated from Percoll density gradient coupled with differential surface-attachement purification were OCT4 positive, indicating the cells were human spermatogonial stem cells. The purity of isolated human spermatogonial stem cells was 86.7% as assessed by flow cytometry. The isolated SSCs were shown to form stable human spermatogonial stem cell colonies on the feeder layer of the Sertoli cells.</p> <p>Conclusions</p> <p>The two-step enzyme digestion (by type I collagenase and trypsin) process is an economical, simple and reproducible technique for isolating human spermatogonial stem cells. With little contamination and less cell damage, this method facilitates isolated human spermatogonial stem cells to form a stable cell colony on the supporting cell layer.</p

Molecular identification and characterization of Heat shock protein 70 family proteins essential for Turnip mosaic virus infection in Arabidopsis thaliana

Turnip mosaic virus (TuMV) belongs to the RNA virus family of Potyviridae and genus Potyvirus. TuMV incurs agricultural losses by causing diseases in vegetable, oilseed, forage, and biofuel crops globally. Viruses are obligate intracellular parasites depending on the host cellular machinery to proliferate. Thus, molecular identification and functional characterization of host factors essential in the viral infection process may open up a new avenue towards developing genetic virus resistance. Eukaryotic translation initiation factor 4E (eIF4E) or its isoform (eIF(iso)4E) is a critical host factor for many potyviruses including TuMV. Heat shock protein 70 family proteins (HSP70) have been identified in the eIF(iso)4E protein complex isolated from Arabidopsis thaliana infected with TuMV. I hypothesized that at least some A. thaliana HSP70s are host factors for TuMV infection since they are associated with the most essential and multiple functional TuMV host factor eIF(iso)4E. To explore the roles of HSP70s in TuMV infection, TuMV infection assay in A. thaliana HSP70 mutants were performed. Combining the HSP70s identified from eIF(iso)4E protein complex, five HSP70s: cytoplasmic HSP70-1, HSP70-2, and HSP70-8, as well as endoplasmic reticulum (ER)- located HSP70-11 and HSP70-12 were selected for further analysis of their involvement in TuMV infection using different systems. I confirmed interaction of all five HSP70s with eIF(iso)4E and discovered their interactions with TuMV viral proteins: replicase NIb and coat protein CP. I found that the HSP70s colocalized with TuMV replication complex in infected plant leaf cells. HSP70-1 and HSP70-2 have mostly inhibitory effect on TuMV infection by accelerating the degradation of NIb via the ubiquitin-proteosome pathway. Consistent to my hypothesis, HSP70-11 and HSP70-12 facilitate TuMV infection, probably by inhibiting the degradation of TuMV protein NIb and CP as well as the host factor eIF(iso)4E. The proviral effect of HSP70-11 and HSP70-12 possibly depends on their ER-localization. Finally, the effect of HSP70-8 on TuMV infection was ambiguous. While HSP70-8 accelerated the degradation of TuMV NIb and CP, TuMV infection was reduced in HSP70-8 knockout A. thaliana plants. Together, these data suggest that HSP70s play complex roles in TuMV, possibly associated with their chaperone activities on different viral proteins and host factors