Simulating multiple faceted variability in single cell RNA sequencing.

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios

Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis

In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resulting in performance drop due to the significant quantization loss. On the contrary, we search the optimal subset of convolution operation that activates the sparse convolution at various locations for quantization error alleviation, and the performance gap between real-valued and binary sparse convolutional networks is closed without complexity overhead. Specifically, we first present the shifted sparse convolution that fuses the information in the receptive field for the active sites that match the pre-defined positions. Then we employ the differentiable search strategies to discover the optimal opsitions for active site matching in the shifted sparse convolution, and the quantization errors are significantly alleviated for efficient point cloud analysis. For fair evaluation of the proposed method, we empirically select the recently advances that are beneficial for sparse convolution network binarization to construct a strong baseline. The experimental results on Scan-Net and NYU Depth v2 show that our BSC-Net achieves significant improvement upon our srtong baseline and outperforms the state-of-the-art network binarization methods by a remarkable margin without additional computation overhead for binarizing sparse convolutional networks.Comment: Accepted to CVPR202

Towards Accurate Data-free Quantization for Diffusion Models

In this paper, we propose an accurate data-free post-training quantization framework of diffusion models (ADP-DM) for efficient image generation. Conventional data-free quantization methods learn shared quantization functions for tensor discretization regardless of the generation timesteps, while the activation distribution differs significantly across various timesteps. The calibration images are acquired in random timesteps which fail to provide sufficient information for generalizable quantization function learning. Both issues cause sizable quantization errors with obvious image generation performance degradation. On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead. Specifically, we partition the timesteps according to the importance weights of quantization functions in different groups, which are optimized by differentiable search algorithms. We also select the optimal timestep for calibration image generation by structural risk minimizing principle in order to enhance the generalization ability in the deployment of quantized diffusion model. Extensive experimental results show that our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost

Chronic Ethanol Exposure Enhances the Aggressiveness of Breast Cancer: The Role of p38γ

Both epidemiological and experimental studies suggest that ethanol may enhance aggressiveness of breast cancer. We have previously demonstrated that short term exposure to ethanol (12–48 hours) increased migration/invasion in breast cancer cells overexpressing ErbB2, but not in breast cancer cells with low expression of ErbB2, such as MCF7, BT20 and T47D breast cancer cells. In this study, we showed that chronic ethanol exposure transformed breast cancer cells that were not responsive to short term ethanol treatment to a more aggressive phenotype. Chronic ethanol exposure (10 days - 2 months) at 100 (22 mM) or 200 mg/dl (44 mM) caused the scattering of MCF7, BT20 and T47D cell colonies in a 3-dimension culture system. Chronic ethanol exposure also increased colony formation in an anchorage-independent condition and stimulated cell invasion/migration. Chronic ethanol exposure increased cancer stem-like cell (CSC) population by more than 20 folds. Breast cancer cells exposed to ethanol in vitro displayed a much higher growth rate and metastasis in mice. Ethanol selectively activated p38γ MAPK and RhoC but not p38α/β in a concentration-dependent manner. SP-MCF7 cells, a derivative of MCF7 cells which compose mainly CSC expressed high levels of phosphorylated p38γ MAPK. Knocking-down p38γ MAPK blocked ethanol-induced RhoC activation, cell scattering, invasion/migration and ethanol-increased CSC population. Furthermore, knocking-down p38γ MAPK mitigated ethanol-induced tumor growth and metastasis in mice. These results suggest that chronic ethanol exposure can enhance the aggressiveness of breast cancer by activating p38γ MAPK/RhoC pathway

Isobavachalcone Sensitizes Cells to E2-Induced Paclitaxel Resistance by Down-Regulating CD44 Expression in ER+ Breast Cancer Cells

Oestrogen receptor (ER) is expressed in approximately 60%‐70% of human breast cancer. Clinical trials and retrospective analyses have shown that ER‐positive (ER+) tumours are more tolerant to chemotherapeutic drug resistance than ER‐negative (ER−) tumours. In addition, isobavachalcone (IBC) is known as a kind of phytoestrogen with antitumour effect. However, the underlying mechanism of IBC in ER+ breast cancer needs to be elucidated further. Our in vitro experiments showed that IBC could attenuate 17β‐estradiol (E2)‐induced paclitaxel resistance and that E2 could stimulate CD44 expression in ER+ breast cancer cells but not in ER− cells. Meanwhile, E2 could promote ERα expression to render ER+ breast cancer cells resistant to paclitaxel. Furthermore, we established paclitaxel‐resistant breast cancer cell lines and determined the function of ERα in the enhancement of paclitaxel resistance via the regulation of CD44 transcription. IBC down‐regulated ERα and CD44 expression and thus inhibited tumour growth in paclitaxel‐resistant xenograft models. Overall, our data demonstrated for the first time that IBC could decrease CD44 expression level via the ERα pathway and make ER+ breast cancer cells sensitive to paclitaxel treatment

Tetraspanin CD151 plays a key role in skin squamous cell carcinoma

Here we provide the first evidence that tetraspanin CD151 can support de novo carcinogenesis. During two-stage mouse skin chemical carcinogenesis, CD151 reduces tumor lag time and increases incidence, multiplicity, size, and progression to malignant squamous cell carcinoma (SCC), while supporting both cell survival during tumor initiation and cell proliferation during the promotion phase. In human skin SCC, CD151 expression is selectively elevated compared to other skin cancer types. CD151 support of keratinocyte survival and proliferation may depend on activation of transcription factor STAT3, a regulator of cell proliferation and apoptosis. CD151 also supports PKCα-α6β4 integrin association and PKC-dependent β4 S1424 phosphorylation, while regulating α6β4 distribution. CD151-PKCα effects on integrin β4 phosphorylation and subcellular localization are consistent with epithelial disruption to a less polarized, more invasive state. CD151 ablation, while minimally affecting normal cell and normal mouse functions, markedly sensitized mouse skin and epidermoid cells to chemicals/drugs including DMBA (mutagen) and camptothecin (topoisomerase inhibitor), as well as to agents targeting EGFR, PKC, Jak2/Tyk2, and STAT3. Hence, CD151 ‘co-targeting’ may be therapeutically beneficial. These findings not only support CD151 as a potential tumor target, but also should apply to other cancers utilizing CD151-laminin-binding integrin complexes

CD151-α3β1 Integrin Complexes are Prognostic Markers of Glioblastoma and Cooperate with EGFR to Drive Tumor Cell Motility and Invasion

Glioblastoma, one of the most aggressive forms of brain cancer, is featured by high tumor cell motility and invasiveness, which not only fuel tumor infiltration, but also enable escape from surgical or other clinical interventions. Thus, better understanding of how these malignant traits are controlled will be key to the discovery of novel biomarkers and therapies against this deadly disease. Tetraspanin CD151 and its associated α3β1 integrin have been implicated in facilitating tumor progression across multiple cancer types. How these adhesion molecules are involved in the progression of glioblastoma, however, remains largely unclear. Here, we examined an in-house tissue microarray-based cohort of 96 patient biopsies and TCGA dataset to evaluate the clinical significance of CD151 and α3β1 integrin. Functional and signaling analyses were also conducted to understand how these molecules promote the aggressiveness of glioblastoma at molecular and cellular levels. Results from our analyses showed that CD151 and α3 integrin were significantly elevated in glioblastomas at both protein and mRNA levels, and exhibited strong inverse correlation with patient survival (p \u3c 0.006). These adhesion molecules also formed tight protein complexes and synergized with EGF/EGFR to accelerate tumor cell motility and invasion. Furthermore, disruption of such complexes enhanced the survival of tumor-bearing mice in a xenograft model, and impaired activation of FAK and small GTPases. Also, knockdown- or pharmacological agent-based attenuation of EGFR, FAK or Graf (ARHGAP26)/small GTPase-mediated pathways markedly mitigated the aggressiveness of glioblastoma cells. Collectively, our findings provide clinical, molecular and cellular evidence of CD151-α3β1 integrin complexes as promising prognostic biomarkers and therapeutic targets for glioblastoma

Loss of Hairless Confers Susceptibility to UVB-Induced Tumorigenesis via Disruption of NF-kappaB Signaling

In order to model squamous cell carcinoma development in vivo, researchers have long preferred hairless mouse models such as SKH-1 mice that have traditionally been classified as ‘wild-type’ mice irrespective of the genetic factors underlying their hairless phenotype. The work presented here shows that mutations in the Hairless (Hr) gene not only result in the hairless phenotype of the SKH-1 and Hr−/− mouse lines but also cause aberrant activation of NFκB and its downstream effectors. We show that in the epidermis, Hr is an early UVB response gene that regulates NFκB activation and thereby controls cellular responses to irradiation. Therefore, when Hr expression is decreased in Hr mutant animals there is a corresponding increase in NFκB activity that is augmented by UVB irradiation. This constitutive activation of NFκB in the Hr mutant epidermis leads to the stimulation a large variety of downstream effectors including the cell cycle regulators cyclin D1 and cyclin E, the anti-apoptosis protein Bcl-2, and the pro-inflammatory protein Cox-2. Therefore, Hr loss results in a state of uncontrolled epidermal proliferation that promotes tumor development, and Hr mutant mice should no longer be considered merely hairless 'wild-type' mice. Instead, Hr is a crucial UVB response gene and its loss creates a permissive environment that potentiates increased tumorigenesis

Simulating multiple faceted variability in single cell RNA sequencing.

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios