A hybrid prognostics approach for motorized spindle-tool holder remaining useful life prediction

The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e. AI-model with power regression i.e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer

PhageTailFinder:A tool for phage tail module detection and annotation

Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (&gt;80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder).</p

Reactive uptake coefficients for multiphase reactions determined by a dynamic chamber system

Dynamic flow-through chambers are frequently used to measure gas exchange rates between the atmosphere and biosphere on the Earth's surface such as vegetation and soils. Here, we explore the performance of a dynamic chamber system in determining the uptake coefficient γ of exemplary gases (O3 and SO2) on bulk solid-phase samples. After characterization of the dynamic chamber system, the derived γ is compared with that determined from a coated-wall flow tube system. Our results show that the dynamic chamber system and the flow tube method show a good agreement for γin the range of 10−8 to 10−3. The dynamic chamber technique can be used for liquid samples and real atmospheric aerosol samples without complicated coating procedures, which complements the existing techniques in atmospheric kinetic studies.</p

PhageTailFinder:A tool for phage tail module detection and annotation

Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (&gt;80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder).</p

PhageTailFinder: A tool for phage tail module detection and annotation

Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (&gt;80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder)

VCAM-1+ placenta chorionic villi-derived mesenchymal stem cells display potent pro-angiogenic activity

Abstract Introduction Mesenchymal stem cells (MSCs) represent a heterogeneous cell population that is promising for regenerative medicine. The present study was designed to assess whether VCAM-1 can be used as a marker of MSC subpopulation with superior angiogenic potential. Methods MSCs were isolated from placenta chorionic villi (CV). The VCAM-1+/− CV-MSCs population were separated by Flow Cytometry and subjected to a comparative analysis for their angiogenic properties including angiogenic genes expression, vasculo-angiogenic abilities on Matrigel in vitro and in vivo, angiogenic paracrine activities, cytokine array, and therapeutic angiogenesis in vascular ischemic diseases. Results Angiogenic genes, including HGF, ANG, IL8, IL6, VEGF-A, TGFβ, MMP2 and bFGF, were up-regulated in VCAM-1+CV-MSCs. Consistently, angiogenic cytokines especially HGF, IL8, angiogenin, angiopoitin-2, μPAR, CXCL1, IL-1β, IL-1α, CSF2, CSF3, MCP-3, CTACK, and OPG were found to be significantly increased in VCAM-1+ CV-MSCs. Moreover, VCAM-1+CV-MSCs showed remarkable vasculo-angiogenic abilities by angiogenesis analysis with Matrigel in vitro and in vivo and the conditioned medium of VCAM-1+ CV-MSCs exerted markedly pro-proliferative and pro-migratory effects on endothelial cells compared to VCAM-1−CV-MSCs. Finally, transplantation of VCAM-1+CV-MSCs into the ischemic hind limb of BALB/c nude mice resulted in a significantly functional improvement in comparison with VCAM-1−CV-MSCs transplantation. Conclusions VCAM-1+CV-MSCs possessed a favorable angiogenic paracrine activity and displayed therapeutic efficacy on hindlimb ischemia. Our results suggested that VCAM-1+CV-MSCs may represent an important subpopulation of MSC for efficient therapeutic angiogenesis. </jats:sec