The Pseudomonas Quinolone Signal (PQS): Not Just for Quorum Sensing Anymore

The Pseudomonas quinolone signal (PQS) has been studied primarily in the context of its role as a quorum-sensing signaling molecule. Recent data suggest, however, that this molecule may also function to mediate iron acquisition, cytotoxicity, outer-membrane vesicle biogenesis, or to exert host immune modulatory activities

Functional Coordination of BET Family Proteins Underlies Altered Transcription Associated With Memory Impairment in Fragile X Syndrome

Bromodomain and extraterminal proteins (BET) are epigenetic readers that play critical roles in gene regulation. Pharmacologic inhibition of the bromodomain present in all BET family members is a promising therapeutic strategy for various diseases, but its impact on individual family members has not been well understood. Using a transcriptional induction paradigm in neurons, we have systematically demonstrated that three major BET family proteins (BRD2/3/4) participated in transcription with different recruitment kinetics, interdependency, and sensitivity to a bromodomain inhibitor, JQ1. In a mouse model of fragile X syndrome (FXS), BRD2/3 and BRD4 showed oppositely altered expression and chromatin binding, correlating with transcriptional dysregulation. Acute inhibition of CBP/p300 histone acetyltransferase (HAT) activity restored the altered binding patterns of BRD2 and BRD4 and rescued memory impairment in FXS. Our study emphasizes the importance of understanding the BET coordination controlled by a balanced action between HATs with different substrate specificity

Pathogenic TNF-α drives peripheral nerve inflammation in an Aire-deficient model of autoimmunity

Immune cells infiltrate the peripheral nervous system (PNS) after injury and with autoimmunity, but their net effect is divergent. After injury, immune cells are reparative, while in inflammatory neuropathies (e.g., Guillain Barré Syndrome and chronic inflammatory demyelinating polyneuropathy), immune cells are proinflammatory and promote autoimmune demyelination. An understanding of immune cell phenotypes that distinguish these conditions may, therefore, reveal new therapeutic targets for switching immune cells from an inflammatory role to a reparative state. In an autoimmune regulator (Aire)-deficient mouse model of inflammatory neuropathy, we used single-cell RNA sequencing of sciatic nerves to discover a transcriptionally heterogeneous cellular landscape, including multiple myeloid, innate lymphoid, and lymphoid cell types. Analysis of cell-cell ligand-receptor interactions uncovered a macrophage-mediated tumor necrosis factor-α (TNF-α) signaling axis that is induced by interferon-γ and required for initiation of autoimmune demyelination. Developmental trajectory visualization suggested that TNF-α signaling is associated with metabolic reprogramming of macrophages and polarization of macrophages from a reparative state in injury to a pathogenic, inflammatory state in autoimmunity. Autocrine TNF-α signaling induced macrophage expression of multiple genes (Clec4e, Marcksl1, Cxcl1, and Cxcl10) important in immune cell activation and recruitment. Genetic and antibody-based blockade of TNF-α/TNF-α signaling ameliorated clinical neuropathy, peripheral nerve infiltration, and demyelination, which provides preclinical evidence that the TNF-α axis may be effectively targeted to resolve inflammatory neuropathies

Research on coal-rock recognition based on sound signal analysis

The recognition of the cutting state of shearer is the key technology to realize variable speed cutting and mining automation. It is of great significance for improving shearer reliability, ensuring personal safety and improving coal quality. This paper proposed a coal-rock recognition method based on sound signal analysis. The original sound signal produced during the cutting process of shearer is decomposed by variational mode decomposition (VMD), and the obtained IMFs can construct a signal matrix. The signal matrix is processed by singular value decomposition (SVD), and a series of singular values can be obtained and defined as the signal features. Finally, the coal-rock recognition is realized by extreme learning machine (ELM) based on the extracted signal features. The experiment results show that the overall recognition accuracy is 91.7% under the actual cutting condition, which verifies the effectiveness of the proposed method in coal-rock recognition, and lays a theoretical foundation for the automation and intellectualization of shearer mining

Monopulse Feature Extraction and Fault Diagnosis Method of Rolling Bearing under Low-Speed and Heavy-Load Conditions

According to the rolling bearing local fault vibration mechanism, a monopulse feature extraction and fault diagnosis method of rolling bearing under low-speed and heavy-load conditions based on phase scan and CNN is proposed. The synchronous collected speed signal is used to calculate bearing phase function and divide fault monopulse periods. The monopulse waveforms of multiple fault periods are scanned and ensemble averaged to suppress noise interference and detail feature loss at the same time of feature extraction. By iteratively calibrating phase function, the feature matrix containing bearing fault information can be obtained. Finally, CNN is used to recognize and classify different bearing states. The experimental and analysis results show that bearing fault diagnosis can be achieved. The total recognition rates of constant and variable speed samples are 99.67% and 99.89%, respectively. The trained network has fast convergence speed and good generalization ability for different fault sizes and working conditions. Further experiments show that the method can also accurately identify different bearing degradation states. The total recognition rates of constant and variable speed samples are 96.67% and 95.56%, respectively. The limited errors are concentrated between the degradation states with the same type weak fault. The experimental results using Case Western Reserve University bearing data show that feature extraction and network training are better, and the recognition rates of 5 bearing states are all 100%. Therefore, the proposed method is an effective rolling bearing feature extraction and fault diagnosis technology

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears

Optimization technology of gas extraction pipe network based on “pipe network solving-genetic algorithm optimization”

In order to realize the intelligent decision-making of the pipe diameter optimization scheme of the mine gas extraction pipe network, the gas extraction pipe network diagram theory model is constructed based on the principle of graph theory, and the specific pipe diameter pipe network is solved based on the gas-air hybrid flow model of the pipe network. Taking the minimum pipe network investment as the target function and the pipe diameter as the decision-making variable, the pipe diameter optimization model of the gas extraction pipe network is established. Based on the genetic algorithm of integer coding, the pipe diameter optimization scheme of the extraction area on the west side of Liuzhuang Coal Mine was obtained. The results show that the combination of pipe diameter optimized by genetic algorithm is better than the flow rate method in terms of economy and extraction effect, and the negative pressure of extraction at the extractive end after optimization meets the extraction requirements, and the cost of pipe network is reduced by 483,000 yuan

Carbonic fluids in the Hamadi gold deposit, Sudan: Origin and contribution to gold mineralization

The Hamadi gold deposit is located in North Sudan, and occurs in the Neoproterozoic metamorphic strata of the Arabian–Nubian Shield. Two types of gold mineralization can be discerned: gold-bearing quartz veins and altered rock ores near ductile shear zones. The gold-bearing quartz veins are composed of white to gray quartz associated with small amounts of pyrite and other polymetallic sulfide minerals. Wall-rock alterations include mainly beresitization, epidotization, chloritization, and carbonatization. CO2-rich inclusions are commonly seen in gold-bearing quartz veins and quartz veinlets from gold-bearing altered rocks; these include mainly one-phase carbonic (CO2 ± CH4 ± N2) inclusions and CO2–H2O inclusions with CO2/H2O volumetric ratios of 30% to ∼80%. Laser Raman analysis does not show the H2O peak in carbonic inclusions. In quartz veins, the melting temperature of solid CO2 (Tm,CO2) of carbonic inclusions has a narrow range of −59.6 to −56.8 °C. Carbonic inclusions also have CO2 partial homogenization temperatures (Th,CO2) of −28.3 to +23.7 °C, with most of the values clustering between +4.0 and +20 °C; all of these inclusions are homogenized into the liquid CO2 state. The densities range from 0.73 to 1.03 g/cm3. XCH4 of carbonic fluid inclusions ranges from 0.004 to 0.14, with most XCH4 around 0.05. In CO2–H2O fluid inclusions, Tm,CO2 values are recorded mostly at around −57.5 °C. The melting temperature of clathrate is 3.8–8.9 °C. It is suggested that the lowest trapping pressures of CO2 fluids would be 100 to ∼400 MPa, on the basis of the Th,CO2 of CO2-bearing one-phase (LCO2) inclusions and the total homogenization temperatures (Th,tot) of paragenetic CO2-bearing two-phase (LCO2–LH2O) inclusions. For altered rocks, the Tm,CO2 of the carbonic inclusions has a narrow range of −58.4 to ∼−57.0 °C, whereas the Th,CO2 varies widely (−19 to ∼+29 °C). Most carbonic inclusions and the carbonic phases in the CO2–H2O inclusions are homogenized to liquid CO2 phases, which correspond to densities of 0.70 to ∼1.00 g/cm3. Fluid inclusions in a single fluid inclusion assemblage (FIA) have narrow Tm,CO2 and Th,CO2 values, but they vary widely in different FIAs and non-FIAs, which indicates that there was a wide range of trapping pressure and temperature (P–T) conditions during the ore-forming process in late retrograde metamorphism after the metamorphism peak period. The carbonic inclusions in the Hamadi gold deposit are interpreted to have resulted from unmixing of an originally homogeneous aqueous–carbonic mixture during retrogress metamorphism caused by decreasing P–T conditions. CO2 contributed to gold mineralization by buffering the pH range and increasing the gold concentration in the fluids.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author