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This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

In recent years, the scaling down that Moore’s Law relies on has been gradually slowing down, and the traditional von Neumann architecture has been limiting the improvement of computing power. Thus, neuromorphic in-memory computing hardware has been proposed and is becoming a promising alternative. However, there is still a long way to make it possible, and one of the problems is to provide an efficient, reliable, and achievable neural network for hardware implementation. In this paper, we proposed a two-layer fully connected spiking neural network based on binary MRAM (Magneto-resistive Random Access Memory) synapses with low hardware cost. First, the network used an array of multiple binary MRAM cells to store multi-bit fixed-point weight values. This helps to simplify the read/write circuit. Second, we used different kinds of spike encoders that ensure the sparsity of input spikes, to reduce the complexity of peripheral circuits, such as sense amplifiers. Third, we designed a single-step learning rule, which fit well with the fixed-point binary weights. Fourth, we replaced the traditional exponential Leak-Integrate-Fire (LIF) neuron model to avoid the massive cost of exponential circuits. The simulation results showed that, compared to other similar works, our SNN with 1184 neurons and 313,600 synapses achieved an accuracy of up to 90.6% in the MNIST recognition task with full-resolution (28 × 28) and full-bit-depth (8-bit) images. In the case of low-resolution (16 × 16) and black-white (1-bit) images, the smaller version of our network with 384 neurons and 32,768 synapses still maintained an accuracy of about 77%, extending its application to ultra-low-cost situations. Both versions need less than 30,000 samples to reach convergence, which is a &gt, 50% reduction compared to other similar networks. As for robustness, it is immune to the fluctuation of MRAM cell resistance.

Aerobic denitrifying bacteria have the potential to remove the co-pollutants Ni(II) and nitrate in industrial wastewater. In this study, aerobic denitrifying bacteria with significant Ni(II) removal efficiency was isolated from the biological reaction tank and named as Pseudomonas hibiscicola L1 strain after 16 S rRNA identification analysis. The removal of ever-increasing Ni(II) and NO3--N wastewater under aerobic conditions by strain L1 was discussed. The experimental results showed that strain L1 removed 84% of Ni(II) and 81% of COD, with the use of 34.8 mg L-1 of nitrogen source and without nitrite accumulation yet. Strain L1 had remarkable activity (OD600 = 0.51-0.56 (p 0.05), and the removal ratios of Ni(II) was enhanced (from 42% to 83% (p

A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products.

In recent years, the demand for high throughput signal processing is increasing very fast. Traditional von Neumann processors are unable to handle high throughput data efficiently because of the well-known memory wall and power wall challenges. As an emerging technology, in-memory-computing has become a hot spot because it can alleviate the burden of power wall and memory wall at the same time, suitable for performing efficient operations on high throughput signals. The existing work on in-memory-computing mainly targets at artificial neural networks acceleration, with an implementation of low precision fixed-point operations, because neural networks can tolerate low-precision calculations to some extent. However, in the field of high throughput signal processing, low precision operations are insufficient, and it needs floating-point high precision operations. Therefore this paper proposed a floating-point in-memory-computing architecture based on Resistive Random Access Memory (RRAM) for high throughput signal processing. The architecture has the advantages of both precision and performance. The simulation results show that the throughput performance is 0.819 Gflops with 2 compute units. Each of the computing units is a 128×128 memory array. The energy efficiency is 3.19 Tflops/W. Apart from efficient high throughput signal processing, it can be promoted to the high-performance, high-precision general scientific computing field.

Resistive-switching Random Access Memory (RRAM) has emerged as a promising candidate for the artificial synaptic in neuromorphic computation circuits due to its similar electronic characteristics with the synaptic and features such as high integration density, non-volatile retention and supporting matrix-vector multiplication. In this paper, a digitalized RRAM-based fully-connected Spiking Neuron Network (SNN) system with 3-bit weight and unsupervised online learning scheme is proposed. It consists of 64 pre-neurons and 10 post-neurons, all the neurons are realized by digital circuits for low area overhead, low power consumption and high accuracy. An unsupervised online learning scheme based on binary STDP protocol is applied to train the synaptic weights. Experiments show that the system can be used to recognize the learned ten handwritten digits efficiently.

In recent years, the development of bispecific antibody (bsAb) has become a major trend in the biopharmaceutical industry. By simultaneously engaging 2 molcular targets, bsAbs show unique mechanisms of action that could lead to clinical benefits unattainable by conventional monoclonal antibodies. Various bsAb generation formats have been described, and several are being investigated in clinical development. However, some bsAb constructs have proven to be problematic due to their unfavorable physicochemical and pharmacokinetic properties, as well as poor manufacturing efficiencies. We describe here a new bispecific design, Fabs-in-tandem immunoglobulin (FIT-Ig), in which 2 antigen-binding fragments are fused directly in a crisscross orientation without any mutations or use of peptide linkers. This unique design provides a symmetric IgG-like bispecific molecule with correct association of 2 sets of VH/VL pairs. We show that FIT-Ig molecules exhibit favorable drug-like properties, in vitro and in vivo functions, as well as manufacturing efficiency for commercial development.

In order to quantitatively analyze the variance contributions by correlated input variables to the model output, variance based global sensitivity analysis (GSA) is analytically derived for models with correlated variables. The derivation is based on the input-output relationship of tensor product basis functions and the orthogonal decorrelation of the correlated variables. Since the tensor product basis function based simulator is widely used to approximate the input-output relationship of complicated structure, the analytical solution of the variance based global sensitivity is especially applicable to engineering practice problems. The polynomial regression model is employed as an example to derive the analytical GSA in detail. The accuracy and efficiency of the analytical solution of GSA are validated by three numerical examples, and engineering application of the derived solution is demonstrated by carrying out the GSA of the riveting and two dimension fracture problem.

Mathematical and computational models with correlated multivariate output are commonly used for risk assessment and decision support in engineering. Traditional methods for sensitivity analysis of the model with scalar output fail to provide satisfactory results for this multivariate case. In this work, we introduce a new sensitivity index which looks at the influence of input uncertainty on the entire distribution of the multivariate output without reference to a specific moment of the output. The definition of the new index is based on the multivariate probability integral transformation (PIT), which can take into account both of the uncertainties and the correlations among multivariate output. The mathematical properties of the proposed sensitivity index are discussed and its differences with the sensitivity indices previously introduced in the literature are highlighted. Two numerical examples and a rotating shaft model of an aircraft wing are employed to illustrate the validity and potential benefits of the new sensitivity index.

Interruption of EGFR signaling, either by blocking EGFR binding sites on the extracellular domain of the receptor or by inhibiting intracellular tyrosine kinase activity, can prevent the growth of EGFR-expressing tumors. EGFR tyrosine kinase inhibitors (TKIs) provide a favorable treatment outcome in EGFR mutation positive NSCLC patients. However, many patients eventually develop progressive disease after treatment. Such acquired resistance limits the long-term efficacy of these EGFR TKIs in the clinic. The mechanisms of acquired resistance include a variety of mutations of the EGFR and crosstalk with the adjacent cMet receptors that allow the tumor to partially compensate the EGFR activity. EMB-01 is an innovative bispecific antibody developed based on EpimAb's proprietary FIT-Ig® platform to target EGFR and cMET on tumor cells simultaneously. The anti-EGFR and anti-cMet Fab-domains in each EMB-01 arm are fused directly in-tandem in a unique crisscross orientation without any mutations or use of peptide linkers to form a final tetravalent binding complex with the corresponding receptors on cell surface. We have demonstrated that EMB-01 can maintain all the biological activity from parental monoclonal antibody (mAb) to block ligand binding and the downstream signal pathway. In addition, such tetravalent binding feature is found to induce significant co-degradation of EGFR and cMET in various tumor cells, and such effect is unattainable by either mAb alone or in combination, demonstrating a unique synergistic function of EMB-01. At equivalent doses, EMB-01 induces significantly stronger anti-tumor activity in PDX and CDX tumor models than that of anti-EGFR mAb or anti-cMet mAb alone. Furthermore, EMB-01 shows significant anti-tumor activity in anti-EGFR mAb resistant tumor model. These results demonstrate the potential benefit of EMB-01 in treating EGFR and/or cMET driven cancers, particularly in cases where patients develop acquired resistance due to secondary EGFR mutations in the kinase domain or cMET activation. Currently, EMB-01 has initiated a Phase I/II clinical trial with advanced/metastatic solid tumors. Citation Format: Fang Ren, Xuan Wu, Dandan Yang, Danqing Wu, Shiyong Gong, Yingxi Zhang, Stephan Lensky, Chengbin Wu. EMB-01: An innovative bispecific antibody targeting EGFR and cMet on tumor cells mediates a novel mechanism to improve anti-tumor efficacy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 528.

Compared with the global analysis, the regional analysis can offer more comprehensive and more detail information with an input variable fixed in an interval. Existing validation metrics mostly focus on the global discrepancy between the observations and the computer models. To further measure the difference in detail, the concept of regional validation was proposed in this work. To make regional validation specific and easily understandable, two existing validation metrics-the area metric and the G metric were applied. The contribution to whole validation (CWV) metric was proposed to depict the relationship between the proposed regional validation and the initial whole scale validation. At last, an example was employed to demonstrate the rationality and necessity of the regional validation.

The target in this work is to validate the component functions of model output between physical observation and computational model with the area metric. Based on the theory of high dimensional model representations (HDMR) of independent input variables, conditional expectations are component functions of model output, and the conditional expectations reflect partial information of model output. Therefore, the model validation of conditional expectations tells the discrepancy between the partial information of the computational model output and that of the observations. Then a calibration of the conditional expectations is carried out to reduce the value of model validation metric. After that, a recalculation of the model validation metric of model output is taken with the calibrated model parameters, and the result shows that a reduction of the discrepancy in the conditional expectations can help decrease the difference in model output. At last, several examples are employed to demonstrate the rationality and necessity of the methodology in case of both single validation site and multiple validation sites.

For structure systems with fuzzy input uncertainty as well as random one, the effects of the two kinds of uncertainties on the failure probability of the structure are studied, and an importance analysis model is established to quantitatively evaluate these effects. Based on the fact that the fuzziness of the output is determined by that of the input, the importance measure is defined to evaluate the effect of the fuzzy input variable. For the random input variable, the established model analyzes its importance from two aspects: (1) its effect on the most plausible value of the failure probability, (2) its effect on the imprecision of the failure probability. In the process of computing the importance measures, the conditional failure probability and unconditional one need to be evaluated, which is time demanding for practical engineering problems. For efficiently performing importance analysis in the presence of the fuzzy input variables and the random ones, the importance sampling (IS) method combined with the state dependent parameter (SDP) method is presented. Several examples show that the established importance analysis model can reflect the effects of the two kinds of input variables on the safety of the structure comprehensively and reasonably, and the presented solution can improve computational efficiency considerably with acceptable precision.

Through several decades of development, global sensitivity analysis has been developed as a very useful guide tool for assessing scientific models and has gained pronounced attention in environmental science. However, standard global sensitivity analysis aims at measuring the contribution of input variables to model output uncertainty on average by investigating their full distribution ranges, but does not investigate the contribution of specific ranges. To deal with this problem, researchers have developed several regional sensitivity analysis techniques such as the contribution to sample mean and variance (CSM and CSV) plots. In this paper, a moment-independent regional sensitivity analysis technique called contribution to delta indices (CDI) plot is developed for assessing the effect of a specific range of an individual input to the uncertainty of model output. The CDI plot can be obtained with the same set of samples used for computing the CSM and CSV. Compared with the CSM and CSV, the CDI plot uses the probability density function shift of model output to describe the uncertainty instead of the mean and variance, thus it is moment-independent. An analytical linear model, the Ishigami function and an environmental model are employed to test the proposed RSA technique.

Quadriaxial non-crimp fabric (QNCF) composites are increasingly being used as primary structural materials in aircraft and automotive applications. Predicting the mechanical properties of QNCF lamina is more complicated compared with that of unidirectional (UD) composites, because of the knitting connection of different plies. In this study, to analyze the stiffness and strength of the QNCF composites, a novel modeling strategy for the meso-scale features is presented based on the semi-laminar assumption. Following the view of the mechanical properties of single composite lamina, the complex QNCF layer is decomposed into individual plies. Three different representative unit cells along fiber direction are selected to predict the mechanical performance of QNCF, including in-plane stiffness, damage initiation, and stiffness degradation. To validate the developed modeling strategy, the predictions are compared with existing experimental results, where a good agreement is presented on the prediction of in-plane stiffness and strength. Furthermore, the effect of in-plane fiber distortion, induced by the stitching yarn on the mechanical properties, is studied.

To develop a method for determination of mandelic acid (MA) and phenylglyoxylic acid (PGA) in urine by reagent-free ion chromatography.Ion chromatography was performed on an AS19 column with a gradient elution solution containing 10-35 mmoL/L KOH at a flow rate of 1.00 ml/min, and MA and PGA were detected at ultraviolet wavelengths of 225 nm and 254 nm, respectively. The samples were diluted 10 times with purified water, then purified on a silver column to remove high concentrations of chloride ion, and injected after being filtered through a 0.2-µm m filter membrane.The recoveries of standard addition of MA and PGA were 96.5% and 99.3%, respectively, with both relative standard deviations less than 5.0%. Good linear relationships were noted in the range of 1.0-100.0 mg/L for both MA and PGA (r0.9995). The detection limits of MA and PGA were 0.02 mg/L and 0.05 mg/L, respectively; the minimum detectable concentrations of MA and PGA were 0.2 mg/L and 0.5 mg/L (when the sampling amount was 5.0 ml and diluted to 50.0 ml with water, and the injection volume was 300 µL).This method is fast, convenient, and highly sensitive and selective. It can be used for the analysis of MA and PGA in the urine of styrene-exposed workers.

The fundamental goal of computational neuroscience is to discover anatomical features that reflect the functional organization of the brain. Investigations of the physical connections between neuronal structures and measurements of brain activity in vivo have given rise to the concepts of anatomical and functional connectivity, which have been useful for our understanding of brain mechanisms and their plasticity. However, at present there is no generally accepted computational framework for the quantitative assessment of cortical connectivity. In this paper, we present accurate analytical and modeling tools that can reveal anatomical connectivity pattern and facilitate the interpretation of high-level knowledge regarding brain functions are strongly demanded. We also present a coclustering algorithm, called Business model based Coclustering Algorithm (BCA), which allows an automated and reproducible assessment of the connectivity pattern between different cortical areas based on Diffusion Tensor Imaging (DTI) data. The proposed BCA algorithm not only partitions the cortical mantel into well-defined clusters, but at the same timemaximizes the connection strength between these clusters. Moreover, the BCA algorithm is computationally robust and allows both outlier detection as well as operator-independent determination of the number of clusters. We applied the BCA algorithm to human DTI datasets and show good performance in detecting anatomical connectivity patterns in the human brain.

Contour mapping of surgical resection of cortex is very important in neurosurgery outcome evaluation. Based on advanced MR and PET imaging technologies and our landmark-constrained brain conformal mapping, we present a practical and accurate approach to map the resection contour on the cortical surface from post-surgery brain images to presurgery ones. The approach can accommodate and combat the possible changes of the brain in shape and size over time. To free the user from manually defining the resection contours, we propose an automatic identification algorithm based on dynamic region growing on the cortical surface. We also present an effective method to calculate the area of the region enclosed by the resection contour on the cortical surface. The overall framework provides surgeons an accurate assessment of the agreement between functional PET abnormalities and the extent of surgical resection

Opposite to the established view that collagen is an extracellular substratum for only dispersed hepatocyte culture, hepatocyte spheroids were directly formed within hollow fibers by addition of moderate concentrations of soluble collagen. Morphologically, these spheroids indicated a close relationship with their in vivo structure of liver. The albumin and urea synthetic profiles confirmed that those spheroids maintained liver-specific functions for at least 8 days. Spheroid formation by addition of collagen not only presents a potential methodology for clinical use of spheroids in bioartificial liver device but also indicates a likely function of collagen for self-assembly of primary cells in tissue engineering.

AIM: To investigate diffusion in mammalian cell culture by gel entrapment within hollow fibers. METHODS: Freshly isolated rat hepatocytes or human oral epidermoid carcinoma (KB) cells were entrapped in type I collagen solutions and statically cultured inside microporous and ultrafiltration hollow fibers. During the culture time collagen gel contraction, cell viability and specific function were assessed. Effective diffusion coefficients of glucose in cell-matrix gels were determined by lag time analysis in a diffusion cell. RESULTS: Significant gel contractions occurred in the collagen gels by entrapment of either viable hepatocytes or KB cells. And the gel contraction caused a significant reduction on effective diffusion coefficient of glucose. The cell viability assay of both hepatocytes and KB cells statically cultured in hollow fibers by collagen entrapment further confirmed the existence of the inhibited mass transfer by diffusion. Urea was secreted about 50% more by hepatocytes entrapped in hollow fibers with pore size of 0.1 µm than that in hollow fibers with MWCO of 100 ku. CONCLUSION: Cell-matrix gel and membrane pore size are the two factors relevant to the limited mass transfer by diffusion in such gel entrapment of mammalian cell culture.

Rat hepatocytes were cultured in three polysulfone, hollow-fiber cartridges, characterized by two membrane variables: pore size and inner diameter (ID). Hepatocytes entrapped in a micro-filtration (MF) cartridge with the membrane pore size 0.1 microm had twice the production of urea and 4-fold the amount of albumin in comparison to the control cartridge, a ultra-filtration (UF) cartridge with a molecular weight cut-off (MWCO) of 100 kDa. Hepatocytes entrapped in a UF cartridge with ID of 0.5 mm secreted twice the amount of urea and 10-fold the amount of albumin compared with the control UF cartridge.