LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup

On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts. To alleviate that, we propose LUT-NN, the first system to empower inference by table lookup, to reduce inference cost. LUT-NN learns the typical features for each operator, named centroid, and precompute the results for these centroids to save in lookup tables. During inference, the results of the closest centroids with the inputs can be read directly from the table, as the approximated outputs without computations. LUT-NN integrates two major novel techniques: (1) differentiable centroid learning through backpropagation, which adapts three levels of approximation to minimize the accuracy impact by centroids; (2) table lookup inference execution, which comprehensively considers different levels of parallelism, memory access reduction, and dedicated hardware units for optimal performance. LUT-NN is evaluated on multiple real tasks, covering image and speech recognition, and nature language processing. Compared to related work, LUT-NN improves accuracy by 66% to 92%, achieving similar level with the original models. LUT-NN reduces the cost at all dimensions, including FLOPs ($\leq$ 16x), model size ($\leq$ 7x), latency ($\leq$ 6.8x), memory ($\leq$ 6.5x), and power ($\leq$ 41.7%)

Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p <; 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p <; 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid

CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortunately, collecting large-scale training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions. Our CoSinGAN is able to capture the conditional distribution of the single radiological image, and further synthesize high-resolution (512 × 512) and diverse radiological images that match the input conditions precisely. We evaluate the efficacy of CoSinGAN in learning lung and infection segmentation from very few radiological images by performing 5-fold cross validation on COVID-19-CT-Seg dataset (20 CT cases) and an independent testing on the MosMed dataset (50 CT cases). Both 2D U-Net and 3D U-Net, learned from four CT slices by using our CoSinGAN, have achieved notable infection segmentation performance, surpassing the COVID-19-CT-Seg-Benchmark, i.e., the counterparts trained on an average of 704 CT slices, by a large margin. Such results strongly confirm that our method has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of COVID-19 pandemic

Real-Time Stylized Humanoid Behavior Control through Interaction and Synchronization

Restricted by the diversity and complexity of human behaviors, simulating a character to achieve human-level perception and motion control is still an active as well as a challenging area. We present a style-based teleoperation framework with the help of human perceptions and analyses to understand the tasks being handled and the unknown environment to control the character. In this framework, the motion optimization and body controller with center-of-mass and root virtual control (CR-VC) method are designed to achieve motion synchronization and style mimicking while maintaining the balance of the character. The motion optimization synthesizes the human high-level style features with the balance strategy to create a feasible, stylized, and stable pose for the character. The CR-VC method including the model-based torque compensation synchronizes the motion rhythm of the human and character. Without any inverse dynamics knowledge or offline preprocessing, our framework is generalized to various scenarios and robust to human behavior changes in real-time. We demonstrate the effectiveness of this framework through the teleoperation experiments with different tasks, motion styles, and operators. This study is a step toward building a human-robot interaction that uses humans to help characters understand and achieve the tasks

CPT1A promotes anoikis resistance in esophageal squamous cell carcinoma via redox homeostasis

Anoikis resistance was a prominent hallmark of cancer metastasis, and lipo-genic characteristics have been identified as another metabolic alteration during tumorigenesis. However, their crosstalk has not been fully elucidated, especially in advanced esophageal squamous cell carcinoma (ESCC). In this study, we showed, for the first time, that the key enzyme carnitine O-palmitoyl transferase 1 (CPT1A), which is involved in fatty acid oxidation (FAO), was markedly upregulated in ESCC cells upon detached culture via a metabolism PCR array. Overexpression of CPT1A was associated with poor survival of ESCC patients and could protect ESCC cells from apoptosis via maintaining redox homeostasis through supply of GSH and NADPH. Mechanistically, detached culture conditions enhanced the expression of the transcription factor ETV4 and suppressed the expression of the ubiquitin enzyme RNF2, which were responsible for the elevated expression of CPT1A at the mRNA and protein levels, respectively. Moreover, genetic or pharmacologic disruption of CPT1A switched off the NADPH supply and therefore prevented the anchorage-independent growth of ESCC cells in vitro and lung metastases of xenografted tumor models in vivo. Collectively, our results provide novel insights into how ESCC cancer cells exploit metabolic switching to form distant metastases and some evidence for the link between anoikis and FAO

Corticosteroid treatment in severe patients with SARS-CoV-2 and chronic HBV co-infection: a retrospective multicenter study

Abstract Background The impact of corticosteroids on patients with severe coronavirus disease 2019 (COVID-19)/chronic hepatitis B virus (HBV) co-infection is currently unknown. We aimed to investigate the association of corticosteroids on these patients. Methods This retrospective multicenter study screened 5447 confirmed COVID-19 patients hospitalized between Jan 1, 2020 to Apr 18, 2020 in seven centers in China, where the prevalence of chronic HBV infection is moderate to high. Severe patients who had chronic HBV and acute SARS-cov-2 infection were potentially eligible. The diagnosis of chronic HBV infection was based on positive testing for hepatitis B surface antigen (HBsAg) or HBV DNA during hospitalization and a medical history of chronic HBV infection. Severe patients (meeting one of following criteria: respiratory rate > 30 breaths/min; severe respiratory distress; or SpO2 ≤ 93% on room air; or oxygen index < 300 mmHg) with COVID-19/HBV co-infection were identified. The bias of confounding variables on corticosteroids effects was minimized using multivariable logistic regression model and inverse probability of treatment weighting (IPTW) based on propensity score. Results The prevalence of HBV co-infection in COVID-19 patients was 4.1%. There were 105 patients with severe COVID-19/HBV co-infections (median age 62 years, 57.1% male). Fifty-five patients received corticosteroid treatment and 50 patients did not. In the multivariable analysis, corticosteroid therapy (OR, 6.32, 95% CI 1.17–34.24, P = 0.033) was identified as an independent risk factor for 28-day mortality. With IPTW analysis, corticosteroid treatment was associated with delayed SARS-CoV-2 viral RNA clearance (OR, 2.95, 95% CI 1.63–5.32, P < 0.001), increased risk of 28-day and in-hospital mortality (OR, 4.90, 95% CI 1.68–14.28, P = 0.004; OR, 5.64, 95% CI 1.95–16.30, P = 0.001, respectively), and acute liver injury (OR, 4.50, 95% CI 2.57–7.85, P < 0.001). Methylprednisolone dose per day and cumulative dose in non-survivors were significantly higher than in survivors. Conclusions In patients with severe COVID-19/HBV co-infection, corticosteroid treatment may be associated with increased risk of 28-day and in-hospital mortality

ATP-citrate lyase controls endothelial gluco-lipogenic metabolism and vascular inflammation in sepsis-associated organ injury

International audienceSepsis involves endothelial cell (EC) dysfunction, which contributes to multiple organ failure. To improve therapeutic prospects, elucidating molecular mechanisms of vascular dysfunction is of the essence. ATP-citrate lyase (ACLY) directs glucose metabolic fluxes to de novo lipogenesis by generating acetyl-Co-enzyme A (acetyl-CoA), which facilitates transcriptional priming via protein acetylation. It is well illustrated that ACLY participates in promoting cancer metastasis and fatty liver diseases. Its biological functions in ECs during sepsis remain unclear. We found that plasma levels of ACLY were increased in septic patients and were positively correlated with interleukin (IL)-6, soluble E-selectin (sE-selectin), soluble vascular cell adhesion molecule 1 (sVCAM-1), and lactate levels. ACLY inhibition significantly ameliorated lipopolysaccharide challenge-induced EC proinflammatory response in vitro and organ injury in vivo. The metabolomic analysis revealed that ACLY blockade fostered ECs a quiescent status by reducing the levels of glycolytic and lipogenic metabolites. Mechanistically, ACLY promoted forkhead box O1 (FoxO1) and histone H3 acetylation, thereby increasing the transcription of c-Myc (MYC) to facilitate the expression of proinflammatory and gluco-lipogenic genes. Our findings revealed that ACLY promoted EC gluco-lipogenic metabolism and proinflammatory response through acetylation-mediated MYC transcription, suggesting ACLY as the potential therapeutic target for treating sepsis-associated EC dysfunction and organ injury

Risk factors for secondary hemophagocytic lymphohistiocytosis in severe coronavirus disease 2019 adult patients

International audienceBackground: Secondary hemophagocytic lymphohistiocytosis (sHLH) is a life-threatening hyperinflammatory event and a fatal complication of viral infections. Whether sHLH may also be observed in patients with a cytokine storm induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is still uncertain. We aimed to determine the incidence of sHLH in severe COVID-19 patients and evaluate the underlying risk factors.Method: Four hundred fifteen severe COVID-19 adult patients were retrospectively assessed for hemophagocytosis score (HScore). A subset of 7 patients were unable to be conclusively scored due to insufficient patient data.Results: In 408 patients, 41 (10.04%) had an HScore ≥169 and were characterized as "suspected sHLH positive". Compared with patients below a HScore threshold of 98, the suspected sHLH positive group had higher D-dimer, total bilirubin, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, serum creatinine, triglycerides, ferritin, interleukin-6, C-reactive protein, procalcitonin, lactate dehydrogenase, creatine kinase isoenzyme, troponin, Sequential Organ Failure Assessment (SOFA) score, while leukocyte, hemoglobin, platelets, lymphocyte, fibrinogen, pre-albumin, albumin levels were significantly lower (all P 1922.58 ng/mL), low platelets (2.28 mmol/L) were independent risk factors for suspected sHLH in COVID-19 patients. Importantly, COVID-19 patients that were suspected sHLH positive had significantly more multi-organ failure. Additionally, a high HScore (>98) was an independent predictor for mortality in COVID-19.Conclusions: HScore should be measured as a prognostic biomarker in COVID-19 patients. In particular, it is important that HScore is assessed in patients with high ferritin, triglycerides and low platelets to improve the detection of suspected sHLH