Spatial Mapping of Myeloid Cells and Macrophages by Multiplexed Tissue Staining

An array of phenotypically diverse myeloid cells and macrophages (MC&M) resides in the tumor microenvironment, requiring multiplexed detection systems for visualization. Here we report an automated, multiplexed staining approach, named PLEXODY, that consists of five MC&M-related fluorescently-tagged antibodies (anti - CD68, - CD163, - CD206, - CD11b, and - CD11c), and three chromogenic antibodies, reactive with high- and low-molecular weight cytokeratins and CD3, highlighting tumor regions, benign glands and T cells. The staining prototype and image analysis methods which include a pixel/area-based quantification were developed using tissues from inflamed colon and tonsil and revealed a unique tissue-specific composition of 14 MC&M-associated pixel classes. As a proof-of-principle, PLEXODY was applied to three cases of pancreatic, prostate and renal cancers. Across digital images from these cancer types we observed 10 MC&M-associated pixel classes at frequencies greater than 3%. Cases revealed higher frequencies of single positive compared to multi-color pixels and a high abundance of CD68+/CD163+ and CD68+/CD163+/CD206+ pixels. Significantly more CD68+ and CD163+ vs. CD11b+ and CD11c+ pixels were in direct contact with tumor cells and T cells. While the greatest percentage (~70%) of CD68+ and CD163+ pixels was 0–20 microns away from tumor and T cell borders, CD11b+ and CD11c+ pixels were detected up to 240 microns away from tumor/T cell masks. Together, these data demonstrate significant differences in densities and spatial organization of MC&M-associated pixel classes, but surprising similarities between the three cancer types

A Mixed-Precision Implementation of the Density Matrix Renormalization Group

Using the mixed precision strategy to optimize quantum chemistry codes has been proved promising in saving computational cost and maintaining chemical accuracy. Here, an efficient mixed-precision density matrix renormalization group (DMRG) scheme, containing a two-level mixed-precision hierarchy, is developed and demonstrated. At the coarse-grained level, based on the discovery that the single-precision orthogonalization may cause the DMRG generate a totally wrong answer, a feasible single-precision-sweep DMRG method with double-precision orthogonalization process is implemented. At the fine-grained level, a mixed-precision diagonalization algorithm is developed. This algorithm runs specific operations in the single-precision while preserving double-precision accuracy. Combining these two method, a hybrid mixed-precision scheme is presented. By applying this scheme, the DMRG single-point energy calculations are accelerated up to 131%. Mixed-precision DMRG yielded energies are accurate and deviate less than 0.01 kcal/mol compared with standard DMRG calculations

Temperature-Dependence of Rubber Hyperelasticity Based on the Eight-Chain Model

Rubber-based materials are widely used in a variety of industrial applications. In these applications, rubber components withstand various loading conditions over a range of temperatures. It is of great significance to study the mechanical behavior of vulcanized rubber at different temperatures, especially in a range of high temperatures. The temperature dependence of the constitutive behavior of filled rubber is important for the performance of the rubber. However, only a few constitutive models have been reported that investigate the stress–temperature relationship. In this paper, based on an analysis of experimental data, the effects of temperature on the hyperelastic behaviors of both natural rubber and filled rubber, with different mass fractions of carbon black, were studied. The regulation of stress and strain of natural rubber and filled rubber with temperature was revealed. In addition, an eight-chain model that can reasonably characterize the experimental data at different temperatures was proved. An explicit temperature-dependent constitutive model was developed based on the Arruda-Boyce model to describe the stress–strain response of filled rubber in a relatively large temperature range. Meanwhile, it was proved that the model can predict the effect of temperature on the hyperelastic behavior of filled rubber. Finally, the improved Arruda-Boyce model was used to obtain the material parameters and was then successfully applied to finite element analysis (FEA), which showed that the model has high application value. In addition, the model had a simple form and could be conveniently applied in related performance test of actual production or finite element analysis

Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research

Kylin 1.0: An Ab-Initio Density Matrix Renormalization Group Quantum Chemistry Program

The accurate evaluation of electron correlations is highly necessary for the proper descriptions of the electronic structures in strongly correlated molecules, ranging from bond-dissociating molecules, polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, in this paper, a new ab-initio quantum chemistry program Kylin 1.0 for electron correlation calculations at various quantum many-body levels, including configuration interaction (CI), perturbation theory (PT), and density matrix renormalization group (DMRG), is presented. In addtion, fundamental quantum chemical methods such as Hartree-Fock self-consistent field (HF-SCF) and the complete active space SCF (CASSCF) are aslo implemented. The Kylin 1.0 program possesses these features: (1) efficient DMRG implementation based on the matrix product operator (MPO) formulation for describing static electron correlation within a large active space composed of more than 100 orbitals, supporting both $\rm U(1)_{n} \times U(1)_{S_z}$ and $\rm U(1)_{n} \times SU(2)_{S}$ symmetries; (2) efficient second-order DMRG-self-consistent field (SCF) implementation; (3) externally-contracted multi-reference CI (MRCI) and Epstein-Nesbet PT with DMRG reference wave functions for including the remaining dynamic electron correlation outside the large active spaces. In this paper, we introduce the capabilities and numerical benchmark examples of the Kylin 1.0 program

A New Socio-Hydrology System Based on System Dynamics and a SWAT-MODFLOW Coupling Model for Solving Water Resource Management in Nanchang City, China

To address the issue of seasonal water resource shortages in Nanchang City, a multi-system coupling socio-hydrology simulation method was proposed. This approach involves dynamically integrating a centralized socio-economic model with a distributed surface water groundwater numerical model to explore the intricate relationships between the socio-economic system, the surface water–groundwater integrated system, and the outcomes related to seasonal water resource shortages. Taking Nanchang City as an example, this study conducted research on the water resource supply and demand balance, as well as the groundwater emergency supply, using the multi-system coupling model. Three scenarios were established: status quo, developing, and water-saving. The results show that with the increasing total water demand of social and economic development, the severity of the water resource shortage will be most pronounced in 2030. The minimum water resources supply and demand ratios for the status quo, developing, and water-saving scenarios are projected to be 0.68, 0.52, and 0.77, respectively. To meet residents’ water needs during drought conditions, emergency groundwater supply efforts are investigated. According to the simulation results, groundwater emergency supply would increase the total population by 24.0 thousand, 49.4 thousand, and 11.2 thousand people, respectively, in the status quo, developing, and water-saving scenarios. In the water-saving scenario, the Youkou and Xiebu water sources can serve as suitable emergency water sources. In the status quo scenario, the Youkou water source is the most viable emergency water source. However, in the developing scenario, relying solely on any single water source for emergency supply could have an irreversible impact on the aquifer. Therefore, considering the simultaneous use of multiple water sources is recommended, as it can fulfill water demands while ensuring the sustainable utilization of groundwater resources

Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate

Abstract Background Immune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC). Method We developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture. Results The overlap between manual and transferred tumor masks ranged from 20%–90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10−6). Analyzing data from a single slide, the Pearson correlation coefficients of cell type densities outside and inside tumor regions were highest for CD4 + T-cells (r = 0.8), CD8 + T-cells (r = 0.68) or CD68+ macrophages (r = 0.79). The correlation coefficient for CD45+ T- and B-cells was only 0.45. The transfer of the mask generated an error in the measurement of intra- and extra- tumoral CD68+, CD8+ or CD4+ counts (p < 10−10). Conclusions In summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies