Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures

Breast cancer is characterized by some types of heterogeneity, high aggressive behaviour, and low immunotherapeutic efficiency. Detailed immune stratification is a prerequisite for interpreting resistance to treatment and escape from immune control. Hence, the immune landscape of breast cancer needs further understanding. We systematically clustered breast cancer into six immune subtypes based on the mRNA expression patterns of immune signatures and comprehensively depicted their characteristics. The immunotherapeutic benefit score (ITBscore) was validated to be a superior predictor of the response to immunotherapy in cohorts from various datasets. Six distinct immune subtypes related to divergences in biological functions, signatures of immune or stromal cells, extent of the adaptive immune response, genomic events, and clinical prognostication were identified. These six subtypes were characterized as immunologically quiet, chemokine dominant, lymphocyte depleted, wounding dominant, innate immune dominant, and IFN-γ dominant and exhibited features of the tumor microenvironment (TME). The high ITBscore subgroup, characterized by a high proportion of M1 macrophages:M2 macrophages, an activated inflammatory response, and increased mutational burden (such as mutations in TP53, CDH1 and CENPE), indicated better immunotherapeutic benefits. A low proportion of tumor-infiltrating lymphocytes (TILs) and an inadequate response to immune treatment were associated with the low ITBscore subgroup, which was also associated with poor survival. Analyses of four cohorts treated with immune checkpoint inhibitors (ICIs) suggested that patients with a high ITBscore received significant therapeutic advantages and clinical benefits. Our work may facilitate the understanding of immune phenotypes in shaping different TME landscapes and guide precision immuno-oncology and immunotherapy strategies

Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach

Diet Shift May Trigger LuxS/AI-2 Quorum Sensing in Rumen Bacteria

Recent studies have revealed that LuxS/AI-2 quorum sensing (QS) is the most universal cell-to-cell communication in rumen bacteria; however, it remains unknown how they respond to nutritional stress from a diet shift. This study aimed to explore whether a diet shift could trigger rumen bacterial LuxS/AI-2 QS and its influences on rumen fermentation characteristics and bacterial community diversity and composition. A total of fifteen Hu sheep were selected to undergo a pre-shift diet (Pre, concentrate to forage ratio 75:25) for one month and then abruptly switch to a post-shift diet (Post, concentrate to forage ratio 49:51). Results showed that the serum cortisol and immunoglobulin G concentrations were higher in Post than in Pre (p < 0.05). The microbial density, AI-2 concentration, biofilm formation, and the gene expression of ftsH were higher in Post when compared with Pre (p < 0.05), whilst the gene expression of luxS tended to be lower in Post (p = 0.054). The molar concentration of valerate and fermentation efficiency decreased after the diet shift, while the acetate to propionate ratio and the molar proportion of butyrate were higher in Post compared to Pre (p < 0.05). Moreover, the diet shift increased the richness of ruminal bacteria and the relative abundances of Roseburia, Prevotellaceae UCG-001, and Lachnospira, and decreased the relative abundances of Prevotella, Megasphaera, and Dialister (p < 0.05). A difference in trends was also observed in an analysis of similarity (R = 0.1208 and p = 0.064). This study suggests that a diet shift could trigger rumen bacterial LuxS/AI-2 QS by altering microbial density, AI-2 concentration, biofilm formation, and related gene expression, as well as affect the rumen fermentation pattern and bacterial community diversity and composition. This study may provide insight into a potential strategy for relieving nutritional stress via regulating bacterial communication

Layer-controlled synthesis of graphene-like MoS \u3c inf\u3e 2 from single source organometallic precursor for Li-ion batteries

Herein, we report an new approach to synthesis of graphene-like MoS 2 flakes and tunable layers (from mono- to multi-layer) easily controlled by the thermal decomposition temperature of a single source (Mo(Et2NCS2)4). The approach opens a new way to controlled large-scalable synthesis of graphene-like transition metal sulfides for energy storage, nanoelectronics and optoelectronics. © 2014 the Partner Organisations

Bone loss is ameliorated by fecal microbiota transplantation through SCFA/GPR41/ IGF1 pathway in sickle cell disease mice

Abstract Bone loss is common in sickle cell disease (SCD), but the molecular mechanisms is unclear. Serum insulin-like growth factor 1 (IGF1) was low in SCD subjects and SCD mice. To determine if decreased IGF1 associated with low bone mass in SCD is due to reduced SCFA production by gut microbiota, we performed reciprocal fecal microbiota transplantation (FMT) between healthy control (Ctrl) and SCD mice. uCT and histomorphometry analysis of femur showed decreased bone volume/total volume (BV/TV), trabecular number (Tb.N), osteoblast surface/bone surface (Ob.S/BS), mineralizing surface/ bone surface (MS/BS), inter-label thickness (Ir.L.Th) in SCD mice were significantly improved after receiving Ctrl feces. Bone formation genes Alp, Col1, Runx2, and Dmp1 from SCD mice were significantly decreased and were rescued after FMT from Ctrl feces. Transplantation of Ctrl feces increased the butyrate, valerate, and propionate levels in cecal content of SCD mice. Decreased G-coupled protein receptors 41 and 43 (GPR41 and GPR43) mRNA in tibia and lower IGF1 in bone and serum of SCD mice were partially restored after FMT from Ctrl feces. These data indicate that the healthy gut microbiota of Ctrl mice is protective for SCD bone loss through regulating IGF1 in response to impaired bacterial metabolites SCFAs

Study on Microstructure and Properties of Tailored Hot-Stamped U-shaped Parts Based on Temperature Field Control

In order to meet the needs of the automotive industry, it is necessary to produce “tailored” parts. The U-shaped die equipped with a high-speed airflow device was designed to conduct the hot stamping experiments. The microstructure, micro-hardness, tensile properties, and fracture behavior of the parts were analyzed. The experimental results showed that the quenched phase of the hardened section was mainly martensite, and the micro-hardness and tensile strength could reach 445 HV and 1454 MPa, respectively. The fracture mechanism was brittle fracture. For the toughness section, as the tool temperature increased from 300 to 600 °C, both micro-hardness and tensile strength decreased. Meanwhile, the area fractions of bainite and ferrite increased, and the area fraction of martensite reduced. The fracture behavior was plastic fracture

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation