An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

The Coronavirus Disease 2019 (COVID-19) has a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose great challenges for effective COVID-19 case prediction. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two component models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE on average, outperforming the composing AR (5.629%) and LSTM (4.934%). In country-level datasets, our hybrid model outperforms the widely-used predictive models - AR, LSTM, SVM, Gradient Boosting, and Random Forest - in predicting COVID-19 cases in 8 countries around the world. In addition, we illustrate the interpretability of our proposed hybrid model, a key feature not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models, which could have significant implications for public health policy making and control of the current and potential future pandemics

General synthesis of 2D rare-earth oxide single crystals with tailorable facets

Two-dimensional (2D) rare-earth oxides (REOs) are a large family of materials with various intriguing applications and precise facet control is essential for investigating new properties in the 2D limit. However, a bottleneck remains with regard to obtaining their 2D single crystals with specific facets because of the intrinsic non-layered structure and disparate thermodynamic stability of different facets. Herein, for the first time, we achieve the synthesis of a wide variety of high-quality 2D REO single crystals with tailorable facets via designing a hard-soft-acid-base couple for controlling the 2D nucleation of the predetermined facets and adjusting the growth mode and direction of crystals. Also, the facet-related magnetic properties of 2D REO single crystals were revealed. Our approach provides a foundation for further exploring other facet-dependent properties and various applications of 2D REO, as well as inspiration for the precise growth of other non-layered 2D materials

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator

Both X-ray imaging and neutron imaging are essential methods in non-destructive testing. In this work, a bimodal imaging method combining neutron and X-ray imaging is introduced. The experiment is based on a small electron accelerator-based photoneutron source that can simultaneously generate the following two kinds of radiations: X-ray and neutron. This identification method utilizes the attenuation difference of the two rays’ incidence on the same material to determine the material’s properties based on dual-imaging fusion. It can enhance the identification of the materials from single ray imaging and has the potential for widespread use in on-site, non-destructive testing where metallic materials and non-metallic materials are mixed

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics

The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator

Both X-ray imaging and neutron imaging are essential methods in non-destructive testing. In this work, a bimodal imaging method combining neutron and X-ray imaging is introduced. The experiment is based on a small electron accelerator-based photoneutron source that can simultaneously generate the following two kinds of radiations: X-ray and neutron. This identification method utilizes the attenuation difference of the two rays’ incidence on the same material to determine the material’s properties based on dual-imaging fusion. It can enhance the identification of the materials from single ray imaging and has the potential for widespread use in on-site, non-destructive testing where metallic materials and non-metallic materials are mixed

Chemical Characteristics and Sources Analysis of PM_2.5 in Shaoxing in Winter

By analyzing the mass concentrations and compositions of atmospheric PM2.5 in Shaoxing from December 2019 to February 2020, the characteristics of carbon-containing components, water-soluble ions and metal elements were obtained. NO3−, OC, SO42− and NH4+ were the main components of PM2.5 in winter. The OC/EC ratio was 3.27, which proved the existence of SOC. The proportion of SOC in OC was 47.3%, which showed that secondary sources made a significant contribution. The values of OC/EC and NO3−/SO42− indicated that vehicle exhaust emissions also made a significant contribution to PM2.5. Trace elements of Na, Ca, K and Cd had higher enrichment factor values and were enriched due to human activities. Finally, PM2.5 sources analysis was performed by the positive matrix factorization model. The results showed that secondary inorganic salts (49.3%), motor vehicles and industrial sources (21.3%) and dust sources (17.0%) were the important sources of PM2.5 pollution

Isolation of Mycobacterium arupense from pleural effusion: culprit or not?

Abstract Background Mycobacterium arupense, first identified in 2006, is a slow-growing nontuberculous mycobacterium (NTM) and an emerging cause of tenosynovitis, potentially associated with immunosuppression. However, unlike the diagnostic value of its isolation from osteoarticular specimens, the significance of detecting M. arupense in respiratory specimens is not yet clear. Case presentation To our knowledge, we, for the first time, described the identification of M. arupense from the pleural effusion of an immunocompetent patient, who presented with fever and chylothorax. The symptoms resolved with doxycycline treatment for 45 days and a low-fat, high-protein diet. Follow-up at 14 months showed no relapse. Conclusions Because the patient fully recovered without combined anti-NTM treatment, we did not consider M. arupense the etiological cause in this case. This indicates that M. arupense detected in pleural effusion is not necessarily a causative agent and careful interpretation is needed in terms of its clinical relevance